Glass transition temperature testing method, device, equipment, medium and program product

Abstract

The embodiment of the invention discloses a glass transition temperature testing method, device and equipment, a medium and a program product. The method comprises the following steps: carrying out a near infrared spectrum test on a to-be-tested object in a target environment to obtain a near infrared spectrum signal of the to-be-tested object; inputting the near infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature measurement model; and determining the measured glass-transition temperature corresponding to the near infrared spectrum signal of the object to be measured according to the corresponding relation between the near infrared spectrum signal and the glass-transition temperature through the trained glass-transition temperature model. According to the embodiment of the invention, rapid and accurate determination of the glass transition temperature can be realized in an industrial production field.

Description

[0001] This application is a divisional application based on the application number 202411612384.2, the application date is November 12, 2024, the applicant is China National Materials Technology Wind Power Blade Co., Ltd., and the application name is "Glass transition temperature test method, device, equipment, medium and program product". Technical Field

[0002] The present application belongs to the technical field of glass transition temperature testing, and in particular relates to a glass transition temperature testing method, device, equipment, computer storage medium, and computer program product. Background Art

[0003] The glass transition temperature (Tg) is a key parameter that characterizes the unique physical behavior of polymer materials. It marks the critical point at which polymer chains gain sufficient energy from the frozen glassy state to begin random rotation. At this specific temperature, Tg, the material undergoes a fundamental transformation from a hard and brittle glassy state to a soft and fluid rubbery state (or viscoelastic state), with significant changes in its mechanical properties, such as elastic modulus and toughness. Therefore, the glass transition temperature is not only an important basis for determining the operating temperature range of polymer materials, but also a core indicator for evaluating material processing performance and finished product quality. It is of great significance in the field of materials science and engineering, directly affecting product design, material selection, and the optimization and control of the manufacturing process.

[0004] Currently, the commonly used methods for measuring glass transition temperature in industrial production are differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Although DSC and DMA can provide relatively accurate results, they require laboratory operation, are time-consuming, and require specialized technicians for data analysis. In modern factories that pursue efficient production, rapid quality inspection, and cost control, traditional testing methods are difficult to meet production needs. Therefore, there is an urgent need to provide an accurate glass transition temperature test method that can be used in industrial production sites. Summary of the Invention

[0005] The embodiments of the present application provide a glass transition temperature testing method, apparatus, device, computer storage medium, and computer program product, which can achieve rapid and accurate determination of the glass transition temperature.

[0006] In a first aspect, the present invention provides a method for testing glass transition temperature, comprising:

[0007] Performing near-infrared spectroscopy testing on an object to be tested in a target environment to obtain a near-infrared spectral signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer;

[0008] Inputting the near-infrared spectral signal of the object to be measured into a pre-trained glass transition temperature measurement model, wherein the glass transition temperature measurement model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal;

[0009] Through the trained glass transition temperature model, according to the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, the measured glass transition temperature corresponding to the near-infrared spectral signal of the object to be measured is determined.

[0010] In an optional embodiment, before inputting the near-infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature measurement model, the method further includes:

[0011] Performing near-infrared spectroscopy tests on a plurality of standard samples under a plurality of preset conditions in a target environment, respectively, to obtain a near-infrared spectral signal of each of the plurality of standard samples under each of the preset conditions;

[0012] Obtain the calibrated glass transition temperature of the target polymer in each standard sample;

[0013] The near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample are respectively used to create training samples to obtain a training sample set;

[0014] Inputting the training sample set into the glass transition temperature determination model;

[0015] Through the glass transition temperature measurement model, according to the preset correspondence between the near-infrared spectral signal and the glass transition temperature, the measured glass transition temperature corresponding to each training sample is obtained;

[0016] According to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the glass transition temperature measurement model is iteratively trained to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, thereby obtaining a trained glass transition temperature measurement model.

[0017] In an optional embodiment, before performing near-infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method further comprises:

[0018] Obtain the boundary values of the uncontrollable parameters corresponding to the target environment;

[0019] Determine the value range of the uncontrollable parameter according to the boundary value of the uncontrollable parameter;

[0020] According to the value range of the uncontrollable parameter, a plurality of preset conditions are designed, wherein the plurality of preset conditions include test conditions corresponding to boundary values of the uncontrollable parameter and test conditions corresponding to an intermediate value of at least one uncontrollable parameter.

[0021] In an optional embodiment, before performing near-infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method further comprises:

[0022] Obtain the boundary values of uncontrollable product parameters corresponding to the standard samples in the target production line;

[0023] Determine the value range of the uncontrollable product parameters according to the boundary values of the uncontrollable product parameters;

[0024] According to the value range of the uncontrollable product parameter, a plurality of standard samples are prepared, wherein the plurality of standard samples include standard samples corresponding to the boundary values of the uncontrollable product parameter and a standard sample corresponding to the intermediate value of at least one uncontrollable product parameter.

[0025] In an optional embodiment, obtaining the calibrated glass transition temperature of the target polymer in each standard sample includes:

[0026] Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0027] In an optional embodiment, the wavelength of the near-infrared spectroscopy test is 780 nm to 2526 nm.

[0028] In an optional embodiment, the target polymer includes a thermosetting polymer material and / or a thermoplastic polymer material.

[0029] In a second aspect, an embodiment of the present application provides a glass transition temperature testing device, comprising:

[0030] A testing module, configured to perform near-infrared spectroscopy testing on an object to be tested in a target environment to obtain a near-infrared spectral signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer;

[0031] An input module is used to input the near-infrared spectral signal of the object to be measured into a pre-trained glass transition temperature measurement model, wherein the glass transition temperature measurement model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. At least one of the glass transition temperature labels are calibrated glass transition temperatures of the target polymer in the standard samples corresponding to the near-infrared spectral signals;

[0032] The determination module is used to determine the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model.

[0033] In a third aspect, an embodiment of the present application provides a glass transition temperature testing device, the device comprising:

[0034] A processor and a memory storing computer program instructions; when the processor executes the computer program instructions, any one of the above glass transition temperature test methods is implemented.

[0035] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, any one of the above-mentioned glass transition temperature testing methods is implemented.

[0036] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device can perform any of the above-mentioned glass transition temperature testing methods.

[0037] The glass transition temperature testing method, apparatus, device, computer storage medium, and computer program product of the embodiments of the present application can perform near-infrared spectroscopy testing on an object to be tested in a target environment to obtain a near-infrared spectral signal of the object to be tested. The object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer. Then, the near-infrared spectral signal of the object to be tested is input into a pre-trained glass transition temperature measurement model. The glass transition temperature measurement model is trained using a training sample set. The training sample set includes multiple training samples. The multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectroscopy testing on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals. The near-infrared spectral signals of the training sample set are all measured in the target environment. In this way, a corresponding target environment can be designed for a relatively fixed and controllable production site environment, so that the glass transition temperature measurement model can be applied to a specific production site environment. The multiple preset conditions include test conditions designed based on uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. The multiple standard samples contain a target polymer, each comprising multiple standard samples having different uncontrollable product parameters, the uncontrollable product parameters including at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. This provides a more comprehensive training sample set, maximizing coverage of potential results expected from field glass transition temperature testing in industrial environments, facing a variety of uncontrollable test environment factors and uncontrollable product factors. The calibrated glass transition temperature of the target polymer in the standard sample serves as the glass transition temperature label for the training sample. The trained glass transition temperature determination model is thus capable of accurately determining the glass transition temperature of the target polymer in the test object based on near-infrared spectral signals collected at the production site. This allows the use of portable near-infrared spectrometers as measurement tools for nondestructive testing at industrial production sites, thereby enabling glass transition temperature testing with both high convenience and accuracy. The present embodiment uses a trained glass transition temperature model to determine the measured glass transition temperature corresponding to the near-infrared spectral signal of the object under test based on the corresponding relationship between the near-infrared spectral signal and the glass transition temperature. This allows for rapid and accurate measurement of the glass transition temperature in a variety of industrial applications, thereby improving production efficiency and product quality management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 1 is a flow chart of a glass transition temperature testing method provided in one embodiment of the present application;

[0040] Figure 2 1 is a flow chart of a glass transition temperature testing method provided in another embodiment of the present application;

[0041] Figure 3 1 is a schematic structural diagram of a glass transition temperature testing device provided in yet another embodiment of the present application;

[0042] Figure 4 This is a schematic structural diagram of a glass transition temperature testing device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0043] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0044] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0045] Currently, the commonly used methods for measuring glass transition temperature in industrial production are differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Although DSC and DMA provide relatively accurate results, they require laboratory operation, are time-consuming, and require specialized technicians for data analysis. In modern factories, which strive for efficient production, rapid quality inspection, and cost control, traditional testing methods are difficult to meet production needs.

[0046] Near infrared spectroscopy (NIR) technology is currently widely used as an efficient and non-destructive quality inspection tool in the food, chemical and pharmaceutical industries. NIR is a region between visible light and mid-infrared light, with a wavelength range of 780nm to 2526nm and a wavenumber range of 12500cm -1 ~4000cm -1 Near-infrared spectroscopy is a type of molecular vibrational spectroscopy, arising from the anharmonic vibrations of covalent chemical bonds and representing the harmonics and combination frequencies of molecular vibrations. The near-infrared spectral signal of polymers encompasses the vibrational harmonics and combination frequency bands of various molecular groups, such as CH, OH, and NH. Subtle changes in these frequency bands directly reflect microscopic changes in the internal structure and physical state of the polymer. When the sample's component content changes, its near-infrared spectral signal will also change accordingly.

[0047] Related technologies explore the feasibility of using NIR to measure the glass transition temperature of epoxy resins. Specifically, they employ near-infrared light with a wavelength range of 830nm to 2630nm to measure the glass transition temperature of epoxy resins with a glass transition temperature range of 45°C to 98°C. However, this approach suffers from low accuracy and is not yet sufficient to meet industrial-grade requirements.

[0048] Related technologies also involve near-infrared spectroscopy using near-infrared light in the wavelength range of 2000nm to 2450nm to measure the glass transition temperature of epoxy prepregs with a glass transition temperature between 25°C and 42°C. However, this approach is limited in its applicability, being applicable only to epoxy resin prepregs within a narrow temperature range.

[0049] Furthermore, both of these solutions share common limitations. Firstly, they only consider laboratory conditions, and neither approach can achieve the accuracy required for industrial applications in the complex real-world factory scenarios. Secondly, both solutions utilize near-infrared spectrometers designed for laboratory environments, making the complete set of equipment unsuitable for industrial production sites.

[0050] In summary, despite preliminary laboratory research into NIR technology for measuring glass transition temperature, it is not suitable for industrial applications. The accuracy, applicability, and convenience of NIR technology for measuring glass transition temperature in industrial applications need to be improved.

[0051] In order to solve the above problems, the inventors, after in-depth thinking, cleverly proposed a glass transition temperature testing method, device, equipment, computer storage medium and computer program product.

[0052] The glass transition temperature test method provided in the embodiment of the present application is described below with reference to specific embodiments and their application scenarios in conjunction with the accompanying drawings. The glass transition temperature test method provided in the embodiment of the present application, the device for executing the method can be a glass transition temperature test device, or a partial module of the glass transition temperature test device for executing the glass transition temperature test method. In the embodiment of the present application, the glass transition temperature test method provided in the embodiment of the present application is described in detail by taking the glass transition temperature test device executing the glass transition temperature test method as an example.

[0053] In addition, it should be noted that the glass transition temperature testing method provided in the embodiments of the present application, after performing a near-infrared spectroscopy test on the object to be tested in the target environment and obtaining the near-infrared spectral signal of the object to be tested, needs to determine the measured glass transition temperature corresponding to the near-infrared spectral signal using a glass transition temperature measurement model. Therefore, before determining the measured glass transition temperature corresponding to the near-infrared spectral signal using the glass transition temperature measurement model, it is necessary to first train the glass transition temperature measurement model. The following describes a specific embodiment of the training method for the glass transition temperature measurement model used in the glass transition temperature testing method provided in the embodiments of the present application.

[0054] Figure 1 A flow chart of a glass transition temperature testing method provided in one embodiment of the present application is shown, specifically a flow chart of a method for training a glass transition temperature determination model used in the glass transition temperature testing method provided in an embodiment of the present application.

[0055] like Figure 1 As shown, the training method of the glass transition temperature measurement module used in the glass transition temperature testing method provided in the embodiment of the present application may include steps S110 to S160.

[0056] S110, performing near-infrared spectroscopy tests on multiple standard samples under multiple preset conditions and in a target environment, respectively, to obtain near-infrared spectral signals of each standard sample in the multiple standard samples under each preset condition; wherein the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and at least one of composite material structure parameters.

[0057] Step S110 can distinguish between controllable and uncontrollable parameters that affect the accuracy of glass transition temperature measurements in an actual factory environment, fix the controllable parameters, and design comprehensive and representative measurement samples and test environments for the uncontrollable parameters. Specifically, in step S110, the target environment can be a fixed test environment designed based on the controllable parameters of the industrial production site. For example, the target environment can be a factory environment with a fixed background for measurement. The multiple preset conditions can be multiple measurement environments designed based on uncontrollable environmental parameters of the industrial production site. Exemplarily, the uncontrollable parameters corresponding to the target environment can include uncontrollable environmental parameters in the factory environment, such as, but not limited to, ambient temperature, relative humidity, and one or more environmental pollutants that may be present in the factory environment. The multiple standard samples can be measurement samples designed based on the controllable structural parameters and uncontrollable product parameters of the material and / or product, with a fixed product structure but different uncontrollable product parameters. For example, they can be specially designed measurement samples based on the surface structure of the product. Among the uncontrollable product parameters, composite material parameters of the target polymer may include, for example, the types of other materials compounded with the target polymer, including but not limited to the type of fabric and core material. Composite material structural parameters may include but are not limited to the relative orientation of the fabrics and the surface morphology of the core material. It should be understood that these controllable and uncontrollable parameters are not fixed and can be adjusted based on actual needs. For example, some industrial production environments have strict requirements on ambient temperature and relative humidity, which can also be considered as controllable parameters.

[0058] It is understood that the target environment can include a fixed test environment designed for the same industrial production site, and the multiple standard samples can include measurement samples designed for the same material or product with different uncontrollable product parameters. When training a glass transition temperature measurement model applicable to multiple industrial production sites and / or multiple materials and / or products, it is possible to distinguish between controllable and uncontrollable parameters for each material and / or product at each industrial production site, fix the controllable parameters, and design comprehensive and representative measurement samples and test environments for the uncontrollable parameters, and collect the corresponding near-infrared spectral signals.

[0059] S120, obtaining the calibrated glass transition temperature of the target polymer in each standard sample.

[0060] In step S120, the calibrated glass transition temperature may include a glass transition temperature measured by a glass transition temperature test method with high accuracy. The calibrated glass transition temperature has high accuracy and can be considered as the actual glass transition temperature of the target polymer in the standard sample.

[0061] S130 , creating training samples based on the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set.

[0062] In step S130 , the calibrated glass transition temperature corresponding to the standard sample can be used as a glass transition temperature label to measure the effect of model training.

[0063] S140: Input the training sample set into the glass transition temperature measurement model.

[0064] In step S140, the glass transition temperature measurement model may include a correction prediction model constructed by a mathematical algorithm, for example, a correction prediction model constructed based on a partial least squares regression method, a principal component regression method, a multivariate linear regression method, or a neural network algorithm, etc. This embodiment of the present application is not particularly limited to this.

[0065] S150 , obtaining the measured glass transition temperature corresponding to each training sample through a glass transition temperature measurement model according to a preset correspondence between near-infrared spectral signals and glass transition temperatures.

[0066] S160, iteratively training the glass transition temperature measurement model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, so as to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, thereby obtaining a trained glass transition temperature measurement model.

[0067] In step S160, the glass transition temperature measurement model can be iteratively trained based on the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample until a preset training stop condition is met, thereby obtaining a trained glass transition temperature measurement model. The training stop condition can be, for example, reaching a preset number of iterations or the performance of the glass transition temperature measurement model meeting preset requirements, which are not limited herein. For example, the measured glass transition temperature and the calibrated glass transition temperature of each training sample can be calculated to determine a deviation function value of the glass transition temperature measurement model. If the deviation function value is less than a preset threshold, the trained glass transition temperature measurement model is obtained.

[0068] According to the above embodiment, near-infrared spectroscopy testing can be performed on multiple standard samples under multiple preset conditions and in a target environment, thereby obtaining a near-infrared spectral signal for each of the multiple standard samples under each preset condition. The multiple preset conditions include test conditions designed based on uncontrollable parameters corresponding to the target environment, where the uncontrollable parameters include environmental parameters. The multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, where the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters. In this way, a corresponding target environment can be designed for a relatively fixed and controllable production site environment; multiple standard samples with different uncontrollable product parameters can be designed for uncontrollable product parameters; and multiple preset conditions can be designed for uncontrollable test environment factors. In this way, the near-infrared spectral signals collected for each standard sample under each preset condition are relatively comprehensive, and can cover as much of the possible results that may occur in a field test of glass transition temperature in an industrial environment as possible.

[0069] Subsequently, the calibrated glass transition temperature of the target polymer in each standard sample can be obtained. This calibrated glass transition temperature has a high degree of accuracy and can be considered the actual glass transition temperature of the target polymer in the standard sample. Next, training samples are created by combining the near-infrared spectral signal corresponding to each standard sample under each preset condition with the calibrated glass transition temperature corresponding to the standard sample, thereby obtaining a training sample set. This training sample set thus created includes a rich set of training samples and is highly representative. The training sample set is then input into a glass transition temperature determination model. The glass transition temperature determination model uses a preset correspondence between the near-infrared spectral signal and the glass transition temperature to obtain the measured glass transition temperature corresponding to each training sample. Based on the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the glass transition temperature determination model is iteratively trained to adjust the correspondence between the near-infrared spectral signal and the glass transition temperature, thereby obtaining a trained glass transition temperature determination model. This trained glass transition temperature determination model is then used to determine the glass transition temperature of the target polymer in the test object. In this way, the trained glass transition temperature determination model can accurately determine the glass transition temperature of the corresponding target polymer based on the near-infrared spectral signals collected at the production site.

[0070] The glass transition temperature determination model obtained by training according to the above-mentioned embodiment can have higher accuracy at the industrial production site. In this way, it is possible to use a portable near-infrared spectrometer (such as a handheld near-infrared spectrometer) as a measuring tool to non-destructively, quickly and accurately measure the glass transition temperature of raw materials and products in industrial applications. Thus, the glass transition temperature test method combined with the glass transition temperature determination model has higher applicability and can be applied to the production and maintenance of parts and products in the fields of aerospace, wind power, energy storage, ocean and automobile. Its applicable raw materials may include thermosetting and thermoplastic polymer materials, as well as composite materials based on these materials. Its applicable products may include but are not limited to composite components used on aircraft, wind turbine blades, energy storage tanks, automotive parts, coatings, adhesives, and structural adhesives.

[0071] In one embodiment, before performing near-infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method may further include:

[0072] Get the boundary values of the uncontrollable parameters corresponding to the target environment.

[0073] According to the boundary value of the uncontrollable parameter, the value range of the uncontrollable parameter is determined.

[0074] According to the value range of the uncontrollable parameter, a plurality of preset conditions are designed, wherein the plurality of preset conditions include test conditions corresponding to boundary values of the uncontrollable parameter and test conditions corresponding to an intermediate value of at least one uncontrollable parameter.

[0075] In the above embodiment, the boundary value may include the extreme value that the uncontrollable parameter can reach under the target environment, or the extreme value that may be reached. The value range of the uncontrollable parameter may represent the optional range of the uncontrollable parameter. According to the value range of the uncontrollable parameter, multiple preset conditions are designed, which may include designing test conditions corresponding to the boundary value of the uncontrollable parameter and test conditions corresponding to the intermediate value of at least one uncontrollable parameter. In some examples, the uncontrollable parameter may include multiple environmental parameters, and the multiple environmental parameters may be combined based on the value range of each environmental parameter so that the multiple preset conditions designed can cover various changes in the actual production environment as much as possible.

[0076] According to the above embodiment, by obtaining the boundary values of the uncontrollable parameters corresponding to the target environment, the value range of the uncontrollable parameters is determined, and then multiple preset conditions are designed based on the value range of the uncontrollable parameters. In this way, the multiple preset conditions can cover as many variations as possible in the actual production environment, thereby improving the comprehensiveness and representativeness of the training samples. In this way, the glass transition temperature measurement model can maintain a high level of accuracy when facing the variable uncontrollable environmental parameters in the target environment, thereby improving the accuracy and flexibility of glass transition temperature testing.

[0077] In one embodiment, before performing near-infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method may further include:

[0078] Obtain the boundary values of the uncontrollable product parameters corresponding to the standard samples in the target production line.

[0079] According to the boundary value of the uncontrollable product parameter, the value range of the uncontrollable product parameter is determined.

[0080] According to the value range of the uncontrollable product parameter, a plurality of standard samples are prepared, wherein the plurality of standard samples include standard samples corresponding to the boundary values of the uncontrollable product parameter and a standard sample corresponding to the intermediate value of at least one uncontrollable product parameter.

[0081] In the above-described embodiment, the boundary value may include the extreme value that the uncontrollable product parameter corresponding to the standard sample in the target production line can reach, or the extreme value that may be reached. The value range of the uncontrollable product parameter can represent the optional range of the uncontrollable parameter. Based on the value range of the uncontrollable product parameter, multiple standard samples are prepared, which may include preparing standard samples corresponding to the boundary values of the uncontrollable product parameter and standard samples corresponding to at least one intermediate value of the uncontrollable product parameter. For example, taking the target polymer color as an example, to account for the natural color differences between different batches, it is necessary to design a sample set containing a color gradient, from lightest to darkest, whose span needs to exceed the color range used in actual production. In this way, the impact of these uncontrollable variables can be effectively corrected for minor differences between raw materials or product batches using chemical quantitative analysis methods. In some examples, the number of uncontrollable product parameters can be multiple, and multiple uncontrollable product parameters can be combined based on the value range of each uncontrollable product parameter so that the prepared multiple standard samples can cover as many variations as possible in the actual production environment.

[0082] According to the above embodiment, by obtaining the boundary values of the uncontrollable product parameters corresponding to the target environment, the value range of the uncontrollable product parameters is determined, and then multiple standard samples are prepared based on the value range of the uncontrollable product parameters. This allows the multiple standard samples to cover as many variations as possible in the actual production environment, thereby improving the comprehensiveness and representativeness of the training samples. In this way, the glass transition temperature measurement model can maintain a high level of accuracy when faced with the variable uncontrollable product parameters in industrial production, thereby improving the accuracy and flexibility of glass transition temperature testing.

[0083] In one embodiment, obtaining the calibrated glass transition temperature of the target polymer in each standard sample may specifically include:

[0084] Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0085] According to the above embodiment, the calibrated glass transition temperature of the target polymer in each standard sample is measured using differential scanning calorimetry and / or dynamic mechanical analysis. Both differential scanning calorimetry and dynamic mechanical analysis have high accuracy, and the calibrated glass transition temperatures measured using these two methods are close to the actual glass transition temperature of the target polymer in the standard sample. Therefore, using the calibrated glass transition temperature as a label for training samples can improve the accuracy of the glass transition temperature measurement model.

[0086] In one embodiment, performing near-infrared spectroscopy tests on a plurality of standard samples under a plurality of preset conditions in a target environment to obtain a near-infrared spectral signal of each of the plurality of standard samples under each preset condition may specifically include:

[0087] Performing near-infrared spectroscopy tests on a plurality of standard samples under a plurality of preset conditions in a target environment, respectively, to obtain an original near-infrared spectral signal of each of the plurality of standard samples under each of the preset conditions;

[0088] The original near-infrared spectral signal of each standard sample under each preset condition is preprocessed to obtain the near-infrared spectral signal of each standard sample in the plurality of standard samples under each preset condition.

[0089] The above-mentioned preprocessing can be achieved by preprocessing methods known in the art, for example, it may include but is not limited to applying smoothing techniques to remove random noise, performing normalization operations to standardize data distribution, and revealing at least one of the potential spectral features through first-order derivative and second-order derivative transformations.

[0090] According to the above embodiment, preprocessing the raw near-infrared spectral signal facilitates the extraction of the spectral characteristics exhibited by the target polymer system as the glass transition temperature changes under production conditions. This facilitates the glass transition temperature measurement model to accurately identify the potential relationship between the glass transition temperature and the near-infrared spectral signal, thereby improving the accuracy of the glass transition temperature measurement.

[0091] The embodiment of the present application does not limit the wavelength of the near-infrared spectroscopy test. In the embodiment of the present application, the glass transition temperature determination model used in the glass transition temperature test method has high accuracy and applicability and has a wide range of application. Thus, depending on the chemical structure of the target polymer, the wavelength of the near-infrared spectroscopy test can be any part of the wavelength between 780nm and 2526nm. For example, it can be 780nm to 2526nm, 780nm to 2400nm, 780nm to 2200nm, 780nm to 2000nm, 780nm to 1800nm, 780nm to 1600nm, 780nm to 1200nm, 780nm to 1000nm, 800nm to 2526nm, 800nm to 2300nm, 800nm to 2100nm, 800nm to 1900nm, 800nm to 1500nm, and so on.

[0092] In this way, different test wavelengths can be selected for different target polymers, so that the measurement results of the glass transition temperatures of different target polymers have higher accuracy.

[0093] In one embodiment, after iteratively training the glass transition temperature determination model based on the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the method may further include:

[0094] The performance of the glass transition temperature measurement model is verified using actual industrial materials and / or actual products corresponding to the standard samples, so as to adjust the model parameters of the glass transition temperature measurement model.

[0095] In this way, the glass transition temperature determination model can be optimized and calibrated using actual industrial materials and / or actual products, thereby further improving the accuracy of the model.

[0096] In one embodiment, the wavelength of the near-infrared spectrum test may be 950 nm to 1650 nm, which is advantageous in achieving both higher accuracy, wider applicability, and lower instrument production costs.

[0097] In one embodiment, the target polymer may include a thermosetting polymer material and / or a thermoplastic polymer material.

[0098] Exemplarily, the thermosetting polymer material may include epoxy resin, unsaturated polyester, polyurethane, and their modified, block, hybrid or blended materials and systems. Exemplarily, the thermoplastic polymer material may include free radical polymerized polyolefin, and their modified, block, hybrid or blended polyolefin materials and systems.

[0099] The following is combined with Figure 2 The glass transition temperature testing method provided in the examples of the present application is described in detail.

[0100] Figure 2 FIG. 1 is a flow chart of a method for testing glass transition temperature according to an embodiment of the present application. Figure 2 As shown, the glass transition temperature testing method may specifically include the following steps S210 to S230.

[0101] S210 , performing a near infrared spectroscopy test on an object to be tested in a target environment to obtain a near infrared spectroscopy signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer.

[0102] In step S210 , the object to be tested may include the surface structure or raw materials of the product.

[0103] S220, inputting the near-infrared spectral signal of the object to be measured into a pre-trained glass transition temperature measurement model, wherein the glass transition temperature measurement model is obtained by training a training sample set. The training sample set includes multiple training samples. The multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals. The multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. The multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and at least one of composite material structure parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal.

[0104] S230 , determining the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured by using the trained glass transition temperature model and according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature.

[0105] The glass transition temperature testing method of the embodiment of the present application can perform near-infrared spectroscopy testing on an object to be tested in a target environment to obtain a near-infrared spectral signal of the object to be tested. The object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer. Then, the near-infrared spectral signal of the object to be tested is input into a pre-trained glass transition temperature measurement model. The glass transition temperature measurement model is trained using a training sample set. The training sample set includes multiple training samples. The above-mentioned multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectroscopy testing on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals. The near-infrared spectral signals of the above-mentioned training sample set are all measured in the target environment. In this way, a corresponding target environment can be designed for a relatively fixed and controllable production site environment, so that the glass transition temperature measurement model can be applied to a specific production site environment. The above-mentioned multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. The multiple standard samples contain a target polymer, each comprising multiple standard samples having different uncontrollable product parameters, the uncontrollable product parameters including at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. This provides a more comprehensive training sample set, maximizing coverage of potential results expected from field glass transition temperature testing in industrial environments, facing a variety of uncontrollable test environment factors and uncontrollable product factors. The calibrated glass transition temperature of the target polymer in the standard sample serves as the glass transition temperature label for the training sample. The trained glass transition temperature determination model is thus capable of accurately determining the glass transition temperature of the target polymer in the test object based on near-infrared spectral signals collected at the production site. This allows the use of portable near-infrared spectrometers as measurement tools for nondestructive testing at industrial production sites, thereby enabling glass transition temperature testing with both high convenience and accuracy. The present embodiment uses a trained glass transition temperature model to determine the measured glass transition temperature corresponding to the near-infrared spectral signal of the object under test based on the corresponding relationship between the near-infrared spectral signal and the glass transition temperature. This allows for rapid and accurate measurement of the glass transition temperature in a variety of industrial applications, thereby improving production efficiency and product quality management capabilities.

[0106] To better illustrate the entire solution, a specific example is given based on the above embodiments to illustrate the glass transition temperature test method of the embodiment of the present application. This is explained in detail below. It should be noted that the following example is only for the purpose of explaining the embodiment of the present application and does not constitute a limitation of the embodiment of the present application.

[0107] As an example, the glass transition temperature testing method may include the following steps S310 to S360 .

[0108] S310, determining parameters that affect the accuracy of glass transition temperature testing in a factory environment.

[0109] In step S310, parameters affecting the accuracy of the glass transition temperature test may include parameters affecting near-infrared spectral signal acquisition, particularly factors that may affect the absorption characteristics and signal intensity feedback of the near-infrared spectrum. For example, these parameters may include environmental parameters, polymer-related parameters, polymer composite material parameters, and composite material structural parameters. Polymer composite material parameters may include, for example, the types of materials within the depth range that near-infrared light can penetrate during measurement, while composite material structural parameters may include, for example, the layup sequence and interface structure of the composite material. Exemplarily, environmental factors may include ambient temperature, relative humidity, and environmental pollutants. Polymer-related parameters may include color, polymer raw material ratio (e.g., the resin and curing agent mixture ratio), moisture content, and the like. Polymer composite material parameters and composite material structural parameters may be determined by the actual product. Exemplarily, polymer composite material parameters may include, in addition to the polymer matrix material, the types of composite materials within the depth range that near-infrared light can penetrate, for example, including but not limited to reinforcing materials compounded with the polymer matrix material, such as glass fiber, carbon fiber, aramid fiber, boron fiber, silicon carbide fiber, and natural fiber. For the same type of fiber, it can also be divided into different types of fabrics, such as normal modulus uniaxial fabric, normal modulus biaxial fabric, normal modulus triaxial fabric, high modulus uniaxial fabric, high modulus biaxial fabric, high modulus triaxial fabric, ultra-high modulus uniaxial fabric, ultra-high modulus biaxial fabric, ultra-high modulus triaxial fabric, etc. For example, the types of materials within the depth range that near-infrared light can penetrate can also include foams for reducing weight and increasing stiffness, such as polyethylene terephthalate (PET), polyvinyl chloride (PVC), polyurethane (PU), polystyrene (PS), polymethacrylimide (PMI), acrylonitrile-styrene foam, polyethylene foam, balsa wood, engineering foam, etc. Composite material structural parameters can include the layup structure of different combinations of various composite materials, the number of layers of fabric used, and the angle between fabrics. Composite material structural parameters can also include the surface results of the composite material, such as the surface structure of foam and balsa wood (including but not limited to the structure of grooves, the structure and distribution of engineering foam reinforcement materials, etc.).

[0110] S320, fixing the controllable parameters and designing a training sample set for the uncontrollable parameters.

[0111] In step S320, fixed controllable parameters may include a fixed test environment and a fixed test object. For example, a fixed test environment may include fixing the test environment to a specific factory environment and controlling the measurement background to a fixed background. A fixed test object may include fixing the test object to a specific type of product or raw material, and fixing the specifications of the test object. Fixed controllable parameters specifically refer to simplifying the influencing factor system, where the production process allows. For example, in the case of reinforced composite materials, the outer surface material that contacts the near-infrared spectrometer can be used as a design prototype to create a sample that can be peeled from the product or component without causing damage. When the product's outer surface is composed of reinforced composite materials, the sample thickness can be adjusted based on the number of fabric layers, and can be single, double, or multi-layer, depending on the impact of fabric thickness on the accuracy of glass transition temperature measurements and the balance between glass transition temperature measurement accuracy and ease of operation. When the sample is a multi-layer fabric structure, the fabric fibers can be designed to have the same orientation or maintain a certain angle. Strict control of the relative orientation of the fabric fibers in each layer is not necessary without affecting the accuracy of the glass transition temperature measurement. After the production process of a product or component is completed, these samples can be safely removed from the production line and their glass transition temperature can be measured using near-infrared spectroscopy without affecting the spectral quality.

[0112] Designing a training sample set for uncontrollable parameters involves designing and constructing a comprehensive training sample set for uncontrollable parameters that are difficult to predict or adjust, ensuring that the training sample set fully represents various scenarios in actual production environments. When designing a training sample set, in addition to covering all variations under standard production conditions, it is also necessary to expand the scope to ensure that the training samples remain representative and effective even under extreme or boundary conditions. For example, in the case of polymer color, to account for the natural color differences between different batches, a sample set with a color gradient, from lightest to darkest, should be designed, spanning a range that exceeds the color range used in actual production. In this way, the effects of these uncontrollable variables can be effectively corrected for minor differences between raw material batches using chemical quantitative analysis. Exemplarily, designing a training sample set for uncontrollable parameters can include designing multiple test conditions corresponding to different environmental parameters, and designing multiple standard samples corresponding to different uncontrollable product parameters. The detailed implementation of constructing a training sample set has been described above and will not be repeated here.

[0113] S330 , collecting near-infrared spectral signals and calibrated glass transition temperatures of training samples in the training sample set.

[0114] In step S330, near-infrared spectral signals of the training samples in the training sample set are collected, which may include collecting the corresponding raw near-infrared spectral signals of each training sample in the training sample set under designed test conditions, and preprocessing the raw spectral signals, including but not limited to applying smoothing techniques to remove random noise, performing normalization operations to standardize data distribution, and revealing potential spectral features through first-order and second-order derivative transformations. Collecting and calibrating the glass transition temperature may include measuring the glass transition temperature of each sample using differential scanning calorimetry or dynamic mechanical analysis to serve as the glass transition temperature label for each training sample.

[0115] S340: Training a glass transition temperature determination model using the training sample set.

[0116] S350, validation using actual industrial raw materials and / or actual products from the factory to optimize the glass transition temperature determination model.

[0117] S360 , collecting near-infrared spectrum signals of the object to be measured, inputting the signals into a glass transition temperature measurement model, and obtaining the glass transition temperature of the target polymer in the object to be measured.

[0118] The above example uses a systematic method to identify and control the parameters that affect the accuracy of measuring the glass transition temperature, distinguish between controllable parameters and uncontrollable parameters, design and construct a training sample set, improve the accuracy of measuring the glass transition temperature with near-infrared spectroscopy in industrial applications, solve the problem of large measurement deviation caused by differences in environment, materials and product structure in actual production applications of near-infrared spectroscopy technology, and ensure the reliability of measurement data. This glass transition temperature test method is not only applicable to a variety of thermosetting and thermoplastic polymer materials and their composite materials, but can also be widely used in quality inspection of products in multiple industrial fields including aerospace, wind power, energy storage, ocean, automobile and sports equipment, with high flexibility and universal applicability. The glass transition temperature test method of the embodiment of the present application effectively solves the limitations of laboratory exploration and research on near-infrared spectroscopy technology for measuring glass transition temperature in industrial applications, such as low measurement accuracy, narrow scope of application and inconvenient on-site operation of instruments, and provides a fast and accurate non-destructive measurement technology for glass transition temperature for industrial-related materials and products.

[0119] Targeting the measurement of the glass transition temperature of the shell and web of wind turbine blades, a near-infrared spectroscopy measurement technique was developed for the infused epoxy resin system A, based on the glass transition temperature testing method described in the present embodiment. This technique is used to non-destructively measure the glass transition temperature of glass fiber-reinforced composite materials during the curing process. Using the method described in the present embodiment, a glass transition temperature testing model was established, which was used to test the glass transition temperature of factory shells and webs. A statistical analysis was performed on the deviations between the over 2,000 glass transition temperature data output by the model and those measured by differential scanning calorimetry. The standard deviation between the glass transition temperatures measured by near-infrared spectroscopy and differential scanning calorimetry was 3.0517°C. A measurement system analysis (MSA) was performed on the glass transition temperature measurement method for epoxy resin A using near-infrared spectroscopy, and the results are shown in Table 1. As can be seen from Table 1, the coefficient of variation (SV) of this method is 1.67%, meeting the requirements of the quality control system.

[0120] Table 1

[0121]

[0122] Based on the same inventive concept, an embodiment of the present application also provides a glass transition temperature testing device.

[0123] like Figure 3 As shown, the glass transition temperature testing device 300 may include a first testing module 301 , a first input module 302 and a first determining module 303 .

[0124] The first testing module 301 is used to perform a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested. The object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer.

[0125] The first input module 302 is used to input the near-infrared spectral signal of the object to be measured into a pre-trained glass transition temperature measurement model, wherein the glass transition temperature measurement model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and at least one of composite material structure parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal.

[0126] The first determination module 303 is configured to determine the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured by using the trained glass transition temperature model and according to the correspondence between the near-infrared spectrum signal and the glass transition temperature.

[0127] The glass transition temperature testing device of an embodiment of the present application can perform near-infrared spectroscopy testing on an object to be tested in a target environment to obtain a near-infrared spectral signal of the object to be tested. The object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer. Then, the near-infrared spectral signal of the object to be tested is input into a pre-trained glass transition temperature measurement model. The glass transition temperature measurement model is trained using a training sample set. The training sample set includes multiple training samples. The multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectroscopy testing on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals. The near-infrared spectral signals of the training sample set are all measured in the target environment. In this way, a corresponding target environment can be designed for a relatively fixed and controllable production site environment, so that the glass transition temperature measurement model can be applied to a specific production site environment. The multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. The multiple standard samples contain a target polymer, each comprising multiple standard samples having different uncontrollable product parameters, the uncontrollable product parameters including at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. This provides a more comprehensive training sample set, maximizing coverage of potential results expected from field glass transition temperature testing in industrial environments, facing a variety of uncontrollable test environment factors and uncontrollable product factors. The calibrated glass transition temperature of the target polymer in the standard sample serves as the glass transition temperature label for the training sample. The trained glass transition temperature determination model is thus capable of accurately determining the glass transition temperature of the target polymer in the test object based on near-infrared spectral signals collected at the production site. This allows the use of portable near-infrared spectrometers as measurement tools for nondestructive testing at industrial production sites, thereby enabling glass transition temperature testing with both high convenience and accuracy. The present embodiment uses a trained glass transition temperature model to determine the measured glass transition temperature corresponding to the near-infrared spectral signal of the object under test based on the corresponding relationship between the near-infrared spectral signal and the glass transition temperature. This allows for rapid and accurate measurement of the glass transition temperature in a variety of industrial applications, thereby improving production efficiency and product quality management capabilities.

[0128] In one embodiment, the apparatus may further include:

[0129] The second testing module is used to perform near-infrared spectral tests on multiple standard samples under multiple preset conditions and in a target environment before inputting the near-infrared spectral signal of the object to be tested into a pre-trained glass transition temperature measurement model, thereby obtaining the near-infrared spectral signal of each standard sample in the multiple standard samples under each preset condition.

[0130] The first acquisition module is used to obtain the calibrated glass transition temperature of the target polymer in each standard sample.

[0131] The creation module is used to create training samples by respectively combining the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set.

[0132] The second input module is used to input the training sample set into the glass transition temperature determination model.

[0133] The processing module is used to obtain the measured glass transition temperature corresponding to each training sample according to the preset correspondence between the near-infrared spectrum signal and the glass transition temperature through the glass transition temperature measurement model.

[0134] The training module is used to iteratively train the glass transition temperature measurement model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, so as to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature to obtain a trained glass transition temperature measurement model.

[0135] In one embodiment, the apparatus may further include:

[0136] The second acquisition module is used to obtain the boundary value of the uncontrollable parameter corresponding to the target environment before performing near-infrared spectrum tests on multiple standard samples in the target environment under multiple preset conditions.

[0137] The second determining module is used to determine the value range of the uncontrollable parameter according to the boundary value of the uncontrollable parameter.

[0138] The design module is used to design multiple preset conditions according to the value range of the uncontrollable parameter. The multiple preset conditions include test conditions corresponding to the boundary values of the uncontrollable parameter and test conditions corresponding to the intermediate value of at least one uncontrollable parameter.

[0139] In one embodiment, the apparatus may further include:

[0140] The third acquisition module is used to obtain the boundary values of the uncontrollable product parameters corresponding to the standard samples in the target production line before performing near-infrared spectroscopy tests on multiple standard samples in the target environment under multiple preset conditions.

[0141] The third determining module is used to determine the value range of the uncontrollable product parameter according to the boundary value of the uncontrollable product parameter.

[0142] The preparation module is used to prepare multiple standard samples according to the value range of the uncontrollable product parameter. The multiple standard samples include standard samples corresponding to the boundary values of the uncontrollable product parameter and standard samples corresponding to the intermediate value of at least one uncontrollable product parameter.

[0143] In one embodiment, the first acquisition module is used to obtain the calibrated glass transition temperature of the target polymer in each standard sample, which may specifically include:

[0144] The testing submodule is used to test each standard sample using a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0145] In one embodiment, the wavelength of the near infrared spectrum test may be 780 nm to 2526 nm.

[0146] In one embodiment, the target polymer may include a thermosetting polymer material and / or a thermoplastic polymer material.

[0147] The glass transition temperature testing device provided in the embodiment of the present application can achieve Figure 2 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0148] Figure 4 The figure shows a hardware structure diagram of the glass transition temperature testing device provided in the embodiment of the present application.

[0149] The glass transition temperature testing device may include a processor 401 and a memory 402 storing computer program instructions.

[0150] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0151] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0152] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0153] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any one of the glass transition temperature testing methods in the above embodiments.

[0154] As an example, the glass transition temperature testing device may further include a communication interface 404 and a bus 410. Figure 4 As shown, the processor 401 , the memory 402 , and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0155] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0156] Bus 410 includes hardware, software or both, and the parts of glass transition temperature equipment are coupled to each other.For example, but not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus 410 can include one or more buses.Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0157] The glass transition temperature test device can perform the glass transition temperature test method in the embodiment of the present application, thereby achieving the combination of Figure 2 and Figure 3 Described is the glass transition temperature test method and apparatus.

[0158] In addition, in conjunction with the data processing methods in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the data processing methods in the above embodiments is implemented.

[0159] An embodiment of the present application further provides a computer program product, including a computer program, which implements any one of the glass transition temperature testing methods in the above embodiments when the computer program is processed and executed.

[0160] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0161] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0162] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0163] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0164] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A glass transition temperature test method, characterized in that: include: Performing near-infrared spectroscopy tests on a plurality of standard samples under a plurality of preset conditions and in a target environment, respectively, to obtain a near-infrared spectral signal of each of the plurality of standard samples under each preset condition; the plurality of preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, wherein the uncontrollable parameters include environmental parameters; the target environment is a fixed test environment designed according to controllable parameters of an industrial production site; the plurality of standard samples include a target polymer, and the plurality of standard samples include a plurality of standard samples having a fixed product structure and different uncontrollable product parameters designed according to controllable structural parameters and uncontrollable product parameters of materials and / or products, wherein the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters; Obtain the calibrated glass transition temperature of the target polymer in each standard sample; Creating training samples based on the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set; inputting the training sample set into a glass transition temperature determination model; By using the glass transition temperature measurement model, according to the preset correspondence between the near-infrared spectrum signal and the glass transition temperature, the measured glass transition temperature corresponding to each training sample is obtained; The glass transition temperature measurement model is iteratively trained according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, thereby obtaining a trained glass transition temperature measurement model.

2. The method according to claim 1, characterized in that Before performing near-infrared spectroscopy tests on the plurality of standard samples in the target environment under a plurality of preset conditions, the method further includes: Obtaining a boundary value of the uncontrollable parameter corresponding to the target environment; Determining a value range of the uncontrollable parameter according to a boundary value of the uncontrollable parameter; According to the value range of the uncontrollable parameter, the multiple preset conditions are designed, and the multiple preset conditions include test conditions corresponding to the boundary values of the uncontrollable parameter and test conditions corresponding to at least one intermediate value of the uncontrollable parameter.

3. The method according to claim 1, characterized in that Before performing near-infrared spectroscopy tests on the plurality of standard samples in the target environment under a plurality of preset conditions, the method further includes: Obtaining boundary values of uncontrollable product parameters corresponding to the standard sample in the target production line; Determining a value range of the uncontrollable product parameter according to a boundary value of the uncontrollable product parameter; According to the value range of the uncontrollable product parameter, the multiple standard samples are prepared, and the multiple standard samples include standard samples corresponding to the boundary values of the uncontrollable product parameter and at least one standard sample corresponding to the intermediate value of the uncontrollable product parameter.

4. The method according to claim 1, wherein The step of obtaining the calibrated glass transition temperature of the target polymer in each standard sample comprises: Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

5. The method according to claim 1, wherein After iteratively training the glass transition temperature measurement model based on the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature and obtaining the trained glass transition temperature measurement model, the method further includes: Performing a near-infrared spectroscopy test on an object to be tested in a target environment to obtain a near-infrared spectroscopy signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes the target polymer; Inputting the near-infrared spectrum signal of the object to be measured into the trained glass transition temperature measurement model; The measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured is determined by using the trained glass transition temperature model and according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature.

6. The method according to claim 1, characterized in that The wavelength of the near infrared spectrum test is 780nm to 2526nm.

7. The method according to any one of claims 1 to 6, characterized in that The target polymer includes thermosetting polymer materials and / or thermoplastic polymer materials.

8. A glass transition temperature testing device, characterized in that: include: A testing module for performing near-infrared spectroscopy tests on a plurality of standard samples under a plurality of preset conditions and in a target environment, respectively, to obtain a near-infrared spectral signal of each of the plurality of standard samples under the respective preset conditions; the plurality of preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, wherein the uncontrollable parameters include environmental parameters; the target environment is a fixed test environment designed according to controllable parameters of an industrial production site; the plurality of standard samples include a target polymer, and the plurality of standard samples include a plurality of standard samples having a fixed product structure and different uncontrollable product parameters designed according to controllable structural parameters and uncontrollable product parameters of materials and / or products, wherein the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structural parameters; an acquisition module, for acquiring the calibrated glass transition temperature of the target polymer in each standard sample; A creation module is used to create training samples by respectively using the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set; An input module, configured to input the training sample set into a glass transition temperature determination model; A processing module, configured to obtain the measured glass transition temperature corresponding to each training sample according to a preset correspondence between the near-infrared spectrum signal and the glass transition temperature through the glass transition temperature measurement model; The training module is used to iteratively train the glass transition temperature measurement model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, so as to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature to obtain a trained glass transition temperature measurement model.

9. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the glass transition temperature testing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the glass transition temperature testing method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the glass transition temperature testing method according to any one of claims 1 to 7.

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