Method and system for testing photo-thermal evaporation performance of biomass char-based hydrogel

Abstract

The application provides a method and system for testing the photothermal evaporation performance of biomass charcoal-based hydrogel. The method performs photothermal irradiation on a biomass charcoal-based hydrogel sample and collects a corresponding thermal imaging image sequence of the biomass charcoal-based hydrogel sample during the irradiation process. Surface temperature information in the thermal imaging image sequence is extracted to construct a thermal diffusion mask, and the thermal imaging image sequence is corrected for thermal diffusion based on the thermal diffusion mask to obtain a corrected thermal field image sequence. Based on the corrected thermal field image sequence, the diffuse reflection warming characteristics of the biomass charcoal-based hydrogel sample are extracted, and the light trapping score of the biomass charcoal-based hydrogel sample during the photothermal evaporation process is determined according to the diffuse reflection warming characteristics. The light trapping score is used to determine the photothermal evaporation stability grade label of the biomass charcoal-based hydrogel sample. The diffuse reflection warming characteristics are used to determine the light trapping score of the hydrogel sample during the photothermal evaporation process, improving the reliability of the photothermal evaporation performance test results.

Description

Technical Field

[0001] This application relates to the field of materials testing technology, and more specifically, to a method and system for testing the photothermal evaporation performance of biomass char-based hydrogels. Background Technology

[0002] The photothermal evaporation process of biomass-based hydrogels can be considered as the result of the combined effects of direct light absorption and internal light trapping. The direct light absorption contribution originates from the absorption of incident light by the material surface, while the internal light trapping contribution originates from the secondary absorption process that occurs after multiple scattering and diffuse reflection of light within the porous structure of the material.

[0003] The multiple scattering and diffuse reflection light-trapping mechanism formed inside the material helps to extend the propagation path of light inside the material, improve the light energy utilization efficiency, and reduce the impact of incident light changes on the evaporation process. In addition, the contribution of internal light trapping can not only improve the uniformity of the thermal field, but also reduce the impact of local hot spots on phenomena such as salting out, thereby enhancing the photothermal evaporation stability of the material.

[0004] However, existing technologies lack a testing method that can effectively characterize the diffuse reflectance photothermal utilization capacity within biomass carbon-based hydrogels. Summary of the Invention

[0005] This application provides a method and system for testing the photothermal evaporation performance of biomass carbon-based hydrogels. The method can determine the light trapping score of hydrogel samples during the photothermal evaporation process by utilizing diffuse reflection heating characteristics, thereby improving the reliability of the photothermal evaporation performance test results.

[0006] In a first aspect, this application provides a method for testing the photothermal evaporation performance of biomass char-based hydrogels. This method can be executed by a network device, or by a chip configured in the network device, and this application does not limit the method.

[0007] Specifically, the method includes:

[0008] Obtain the biomass carbon-based hydrogel sample to be tested and perform pre-water absorption treatment;

[0009] The biomass carbon-based hydrogel sample was subjected to photothermal irradiation, and a thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample was acquired during the irradiation process.

[0010] Surface temperature information is extracted from the thermal imaging image sequence to construct a thermal diffusion mask, and thermal diffusion correction is performed on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence;

[0011] Based on the corrected thermal field image sequence, the diffuse reflection heating characteristics of the biomass carbon-based hydrogel sample are extracted, and the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process is determined according to the diffuse reflection heating characteristics.

[0012] The photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample is determined based on the light trapping score.

[0013] In conjunction with the first aspect, in certain implementations of the first aspect, extracting surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask includes:

[0014] Obtain the surface temperature matrix corresponding to each time moment in the thermal imaging image sequence, and calculate the temperature gradient distribution based on the temperature difference between adjacent pixels;

[0015] The temperature rise rate of each pixel is calculated based on the surface temperature matrix at consecutive time intervals; the heat diffusion region is identified based on the temperature gradient distribution and the temperature rise rate; the heat diffusion region is marked to generate a heat diffusion mask.

[0016] In conjunction with the first aspect, in certain implementations of the first aspect, performing thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence specifically includes:

[0017] Obtain the spatial mapping relationship between the coordinates of the thermal imaging image and the heat diffusion mask;

[0018] For any thermal imaging image in the thermal imaging image sequence, the thermal diffusion region of the thermal imaging image is identified according to the spatial mapping relationship, and the thermal diffusion correction coefficient corresponding to each pixel in the thermal diffusion region is calculated according to the thermal diffusion intensity of the pixel in the corresponding consecutive time frame.

[0019] Based on the thermal diffusion correction coefficient, the thermal diffusion region of the thermal imaging image is corrected to obtain a corrected thermal field image; the same method is used to perform thermal diffusion correction on other thermal imaging image sequences, and a corrected thermal field image sequence is formed according to the time sequence.

[0020] In conjunction with the first aspect, in certain implementations of the first aspect, extracting the diffuse reflectance heating features corresponding to the biomass-based hydrogel sample based on the corrected thermal field image sequence specifically includes:

[0021] Extract the temperature distribution matrix corresponding to each time frame in the corrected thermal field image sequence;

[0022] Statistical analysis was performed based on the temperature distribution matrix corresponding to each time frame to determine the diffuse reflection heating characteristics composed of multiple diffuse reflection evaporation parameters.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, the process of acquiring the thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample during irradiation specifically includes: acquiring continuous thermal imaging images of the biomass carbon-based hydrogel sample using a thermal imaging camera according to a preset acquisition frequency, and assembling a thermal imaging image sequence according to the acquisition time sequence.

[0024] In conjunction with the first aspect, in some implementations of the first aspect, the pre-water absorption treatment may further include: measuring the mass of the biomass carbon-based hydrogel sample and recording the initial mass parameters after pre-water absorption.

[0025] In conjunction with the first aspect, in some implementations of the first aspect, the pre-water absorption treatment includes: immersing the biomass char-based hydrogel sample in the liquid to be evaporated until the biomass char-based hydrogel sample reaches water saturation.

[0026] Secondly, this application provides a testing system for the photothermal evaporation performance of biomass char-based hydrogels, which includes a stability testing unit, the stability testing unit comprising:

[0027] The pretreatment module is used to acquire the biomass carbon-based hydrogel sample to be tested and to perform pre-water absorption treatment;

[0028] The testing module is used to irradiate the biomass carbon-based hydrogel sample with photothermal radiation and to acquire the corresponding thermal imaging image sequence of the biomass carbon-based hydrogel sample during the irradiation process.

[0029] The testing module is also used to extract surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask, and to perform thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence.

[0030] The testing module also extracts the diffuse reflection heating characteristics of the biomass carbon-based hydrogel sample based on the corrected thermal field image sequence, and determines the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process based on the diffuse reflection heating characteristics.

[0031] The grading module is used to determine the photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample based on the light trapping score.

[0032] Thirdly, this application provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for testing the photothermal evaporation performance of biomass carbon-based hydrogels.

[0033] Fourthly, this application provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to perform the operations described above in the test method for the photothermal evaporation performance of a biomass carbon-based hydrogel.

[0034] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0035] This application provides a method and system for testing the photothermal evaporation performance of biomass char-based hydrogels. First, a biomass char-based hydrogel sample to be tested is acquired and pre-absorbed with water. The sample is then subjected to photothermal irradiation, and a corresponding thermal imaging image sequence is acquired during the irradiation process. Surface temperature information is extracted from the thermal imaging image sequence to construct a thermal diffusion mask, and thermal diffusion correction is performed on the thermal imaging image sequence based on the mask to obtain a corrected thermal field image sequence. Diffuse reflection heating characteristics of the biomass char-based hydrogel sample are extracted based on the corrected thermal field image sequence, and a light trapping score is determined for the sample during photothermal evaporation based on these characteristics. Finally, a photothermal evaporation stability level label is determined for the biomass char-based hydrogel sample based on the light trapping score.

[0036] Therefore, this application utilizes the surface temperature information of the hydrogel sample under test to construct a thermal diffusion mask, corrects the influence of thermal diffusion in the thermal imaging image sequence, weakens the interference of heat conduction and diffusion on the thermal field distribution, and uses diffuse reflection heating characteristics to determine the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process, thereby realizing the quantitative characterization of the material's internal light trapping ability. At the same time, by determining the photothermal evaporation stability level label based on the light trapping score, it can reflect the ability of biomass carbon-based hydrogels to maintain stable evaporation performance under different evaporation scenarios, realizing a comprehensive evaluation of the material's internal light trapping ability and photothermal evaporation stability, improving the guiding value of the test results for practical application scenarios, and also enhancing the material stability of biomass carbon-based hydrogels in practical applications.

[0037] In summary, this application can utilize diffuse reflection heating characteristics to determine the light trapping score of hydrogel samples during photothermal evaporation, thereby improving the reliability of photothermal evaporation performance test results. Attached Figure Description

[0038] Figure 1 This is an exemplary flowchart of a method for testing the photothermal evaporation performance of a biomass-based hydrogel according to some embodiments of this application;

[0039] Figure 2 This is a schematic diagram of the structure of a stability testing unit according to some embodiments of this application;

[0040] Figure 3 This is a schematic diagram of the structure of a computer terminal device for implementing a method for testing the photothermal evaporation performance of a biomass char-based hydrogel, according to some embodiments of this application. Detailed Implementation

[0041] This application involves photothermal irradiation of biomass char-based hydrogel samples and the acquisition of corresponding thermal imaging image sequences during the irradiation process. Surface temperature information is extracted from the thermal imaging image sequences to construct a thermal diffusion mask. Thermal diffusion correction is then applied to the thermal imaging image sequences based on the thermal diffusion mask to obtain a corrected thermal field image sequence. Diffuse reflection temperature rise characteristics of the biomass char-based hydrogel samples are extracted based on the corrected thermal field image sequence, and a light trapping score for the biomass char-based hydrogel samples during photothermal evaporation is determined based on these characteristics. Finally, a photothermal evaporation stability level label is determined based on the light trapping score of the biomass char-based hydrogel samples. This method utilizes diffuse reflection temperature rise characteristics to determine the light trapping score of hydrogel samples during photothermal evaporation, thus improving the reliability of photothermal evaporation performance test results.

[0042] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a method for testing the photothermal evaporation performance of a biomass char-based hydrogel according to some embodiments of this application. The method 100 for testing the photothermal evaporation performance of the biomass char-based hydrogel mainly includes the following steps:

[0043] In step S101, the biomass carbon-based hydrogel sample to be tested is obtained and pre-water absorption treatment is performed.

[0044] Optionally, in some embodiments, the biomass char-based hydrogel sample is a three-dimensional porous composite hydrogel material formed by dispersing biomass char particles in a hydrogel precursor solution and then cross-linking and curing it. The biomass char particles are obtained by pyrolysis and carbonization of biomass raw materials. The hydrogel precursor solution includes at least one of polyvinyl alcohol, polyacrylamide, sodium alginate, carboxymethyl cellulose, and chitosan.

[0045] Optionally, in some embodiments, the pre-water absorption treatment includes immersing the biomass char-based hydrogel sample in the liquid to be evaporated until the biomass char-based hydrogel sample reaches water saturation.

[0046] Optionally, in some embodiments, the process after pre-water absorption may further include: measuring the mass of the biomass-based hydrogel sample and recording the initial mass parameters after pre-water absorption.

[0047] In specific implementation, the pre-water absorption process includes: placing the biomass char-based hydrogel sample in a constant temperature environment for pre-equilibrium treatment, and immersing the pre-equilibrium biomass char-based hydrogel sample in the liquid to be evaporated. The liquid to be evaporated permeates into the interior through the pore structure of the biomass char-based hydrogel, continuously absorbing water until it reaches a water saturation state. The biomass char-based hydrogel sample is then removed from the liquid to be evaporated, and the free droplets attached to the sample surface are removed. In this application, the liquid to be evaporated is a liquid medium that evaporates under photothermal action. Deionized water, which is commonly used in photothermal evaporation, can be selected as the liquid to be evaporated. This application does not make any specific limitation on this.

[0048] In step S102, the biomass carbon-based hydrogel sample is subjected to photothermal irradiation, and a thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample is acquired during the irradiation process.

[0049] Optionally, in some embodiments, a xenon lamp device can be used as a simulated solar light source to irradiate the biomass carbon-based hydrogel sample with photothermal radiation. In some other embodiments, a composite light source of white LED array and near-infrared LED array can also be used for photothermal radiation.

[0050] It should be noted that after incident light irradiates the surface of the biomass char-based hydrogel sample, the biomass char in the biomass char-based hydrogel absorbs the light energy and converts it into heat energy, which creates a temperature field inside the biomass char-based hydrogel sample and drives the internal liquid to migrate to the evaporation surface. As the photothermal irradiation continues, the liquid on the surface of the biomass char-based hydrogel sample evaporates, which will create a dynamic thermal field inside the sample where the heat transfer process and the evaporation cooling process are coupled.

[0051] Optionally, in some embodiments, the process of acquiring the thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample during irradiation specifically includes: acquiring continuous thermal imaging images of the biomass carbon-based hydrogel sample using a thermal imaging camera according to a preset acquisition frequency, and assembling a thermal imaging image sequence according to the acquisition time sequence.

[0052] In step S103, surface temperature information is extracted from the thermal imaging image sequence to construct a thermal diffusion mask, and thermal diffusion correction is performed on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence.

[0053] Optionally, in some embodiments, extracting surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask includes:

[0054] Obtain the surface temperature matrix corresponding to each time moment in the thermal imaging image sequence, and calculate the temperature gradient distribution based on the temperature difference between adjacent pixels;

[0055] The surface temperature matrix at consecutive time intervals is used to calculate the temperature rise rate corresponding to each pixel; the heat diffusion region is identified based on the temperature gradient distribution and the temperature rise rate; the heat diffusion region is marked to generate a heat diffusion mask.

[0056] In practice, the temperature calibration parameters output by the thermal imager are obtained, and the grayscale information in the thermal imaging image sequence is converted into corresponding temperature values ​​based on the temperature calibration parameters. The thermal field analysis area is segmented according to the position of the biomass carbon-based hydrogel sample in the image to obtain thermal field temperature data containing only the biomass carbon-based hydrogel sample.

[0057] The following is a specific embodiment of this application that marks the heat diffusion region to generate a heat diffusion mask:

[0058] For each frame of the thermal imaging sequence, the surface temperature value corresponding to each pixel within the thermal field analysis area is extracted. Multiple time labels and their corresponding surface temperature matrices are constructed. Temporal analysis is performed on the surface temperature matrices at consecutive time points. For any given thermal imaging image, the temperature change of each pixel is calculated, where the temperature change is defined as the difference between the current temperature and the initial reference temperature. When the temperature change of a pixel exceeds a first preset temperature rise threshold, the pixel is marked as a photothermal response region. When the temperature change of a pixel is less than a second preset temperature rise threshold, the pixel is marked as a thermal diffusion influence region. The temperature rise threshold is greater than the second preset temperature rise threshold. Further, the temperature difference between adjacent pixels is calculated and compared with the preset temperature gradient threshold. When the temperature difference between adjacent pixels is greater than the preset temperature gradient threshold, the corresponding area is marked as a heat diffusion transition area to characterize the spatial diffusion trend of heat. The temperature changes of pixels in continuous time frames are statistically analyzed, and the temperature rise rate of each pixel per unit time is calculated. When the temperature rise rate of a pixel is lower than the preset rate threshold, the pixel is determined to be a heat diffusion dominant area. When the temperature rise rate is higher than the preset rate threshold, the pixel is determined to be a photothermal absorption dominant area.

[0059] Specifically, the fusion judgment process for the heat diffusion affected area is as follows: when a pixel simultaneously satisfies the conditions that the temperature rise rate is lower than a preset threshold and the temperature gradient is higher than a preset threshold, the pixel is finally judged as a heat diffusion area and assigned a value of 1; otherwise, it is judged as a non-heat diffusion area and assigned a value of 0. The judgment results of all pixels are then matrixed to obtain a binary matrix with the same size as the thermal imaging image. The binary matrix is ​​the heat diffusion mask.

[0060] Optionally, in some embodiments, performing thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence specifically includes:

[0061] Obtain the spatial mapping relationship between the coordinates of the thermal imaging image and the heat diffusion mask;

[0062] For any thermal imaging image in the thermal imaging image sequence, the thermal diffusion region of the thermal imaging image is identified according to the spatial mapping relationship, and the thermal diffusion correction coefficient corresponding to each pixel in the thermal diffusion region is calculated according to the thermal diffusion intensity of the pixel in the corresponding consecutive time frame.

[0063] Based on the thermal diffusion correction coefficient, the thermal diffusion region of the thermal imaging image is corrected to obtain a corrected thermal field image; the same method is used to perform thermal diffusion correction on other thermal imaging image sequences, and a corrected thermal field image sequence is formed according to the time sequence.

[0064] In specific implementation, the thermal imaging image sequence is acquired, and the spatial coordinates of any thermal imaging image in the sequence are uniformly processed. The thermal diffusion mask is spatially registered with each thermal imaging image to establish a pixel correspondence, so that the pixel at any spatial position in different time frames corresponds one-to-one with the corresponding position in the thermal diffusion mask. The pixels in any thermal imaging image are divided into regions according to the thermal diffusion mask, and the pixels with a mask value of 1 are determined as the thermal diffusion region.

[0065] Specifically, for any pixel within the heat diffusion region, the temperature change information of its corresponding spatial location in the thermal imaging image sequence across consecutive time frames is obtained to construct a temperature time series sequence for that pixel. Based on the temperature time series sequence, the temperature rise rate feature of that pixel is calculated, which is determined by the ratio of the temperature difference between adjacent time frames to the sampling time interval. Simultaneously, the spatial temperature gradient between that pixel and its neighboring pixels is calculated. The temperature rise rate feature and the spatial temperature gradient feature are normalized and then weighted and fused to obtain the heat diffusion intensity parameter corresponding to that pixel.

[0066] Based on the threshold range of the heat diffusion intensity parameter, a corresponding heat diffusion correction coefficient is assigned to each pixel within the heat diffusion region according to a preset interval mapping table. The heat diffusion correction coefficient is set inversely to the heat diffusion intensity parameter, and further, the value range of the correction coefficient is set within a preset interval. The original temperature values ​​of each pixel within the heat diffusion region are weighted and corrected according to the corresponding correction coefficients to obtain the corrected temperature values; for pixels outside the heat diffusion region, the original temperature values ​​remain unchanged. The same method is used to perform frame-by-frame correction processing on other images in the thermal imaging image sequence, and the corrected thermal field images are reconstructed according to time sequence to obtain the corrected thermal field image sequence.

[0067] In step S104, the diffuse reflection heating characteristics corresponding to the biomass carbon-based hydrogel sample are extracted based on the corrected thermal field image sequence, and the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process is determined based on the diffuse reflection heating characteristics.

[0068] Optionally, in some embodiments, extracting the diffuse reflectance heating features corresponding to the biomass-based hydrogel sample based on the corrected thermal field image sequence specifically includes:

[0069] Extract the temperature distribution matrix corresponding to each time frame in the corrected thermal field image sequence;

[0070] Statistical analysis was performed based on the temperature distribution matrix corresponding to each time frame to determine the diffuse reflection heating characteristics composed of multiple diffuse reflection evaporation parameters.

[0071] It should be noted that the corrected thermal field image sequence in this application has weakened the influence of heat diffusion on temperature distribution. Therefore, the temperature change characteristics in the corrected thermal field image sequence can more realistically reflect the light energy absorption and local heat generation inside the sample. For biomass char-based hydrogels, when incident light enters its internal porous structure, it undergoes multiple scattering and reflections between the pore walls. Some photons are absorbed by the biomass char material and converted into heat energy during multiple propagations, thus forming an additional heat source inside the sample. After eliminating the interference of heat diffusion, the temperature growth characteristics exhibited in the remaining thermal field can serve as a characterization of the internal light-trapping capability.

[0072] In specific implementation, the temperature distribution matrix corresponding to each time frame in the corrected thermal field image sequence is obtained, and the temperature values ​​of all pixels in the region where the biomass char-based hydrogel sample is located are extracted. Using the initial temperature corresponding to the start of photothermal irradiation as the reference temperature, the temperature rise value of each pixel in each time frame relative to the initial temperature is calculated, thereby obtaining the temperature rise distribution sequence. Furthermore, statistical analysis is performed on the temperature rise distribution in each time frame to extract image statistical features reflecting the internal light-trapping behavior. Specifically, this includes:

[0073] The average temperature rise of all pixels within the sample area is obtained; the standard deviation of the temperature rise within the sample area is obtained; the proportion of high-temperature pixels within the sample area is obtained; the cumulative change of temperature rise in each time frame is obtained; and the uniformity index of the temperature rise in spatial distribution is obtained. The uniformity index is obtained by calculating the thermal field entropy after normalizing the temperature values ​​into a probability distribution. Since the light-capturing process formed by internal diffuse reflection usually exhibits a relatively uniform temperature increase rather than the local hot spot aggregation of heat diffusion, when the light-capturing ability inside the sample is strong, its corresponding area often has a high average temperature rise level, a large coverage of high-temperature areas, and a small degree of temperature dispersion. Based on the above rules, the extracted statistical features are normalized to form a diffuse reflection temperature rise feature vector.

[0074] In specific implementation, the different dimensions of the diffuse reflection heating feature vector can include: average temperature rise value, temperature uniformity index, high temperature region proportion, heat accumulation feature value, and thermal field stability feature value. The heat accumulation feature value is based on the corrected thermal field image sequence. The average temperature rise value in the sample area is calculated for each time frame, and multiplied by the time interval to obtain the heat accumulation increment for that time period. The heat accumulation increments in the entire photothermal irradiation process are accumulated over time to obtain the total heat accumulation value. The total heat accumulation value is further normalized and used as the heat accumulation feature value. The thermal field stability feature value is obtained by calculating the difference in average temperature rise values ​​in adjacent time frames using the temperature distribution matrix. A temperature rise change rate sequence is formed according to the time sequence, and the standard deviation of the sequence is calculated as the thermal field stability feature value.

[0075] Optionally, in some embodiments, an internal light trapping performance evaluation model is constructed based on the diffuse reflection heating feature vector, and the average temperature rise feature, the proportion of high temperature region, the heat accumulation feature, and the temperature uniformity feature are used as evaluation factors. Each evaluation factor is weighted and fused according to a preset weight to obtain the corresponding light trapping score.

[0076] Optionally, in some embodiments, during the process of determining the light trapping score corresponding to the biomass carbon-based hydrogel sample based on the diffuse reflection heating characteristics, a BP neural network can also be used as an internal light trapping performance evaluation model. The diffuse reflection heating characteristics are identified by the internal light trapping performance evaluation model to determine the light trapping score corresponding to the biomass carbon-based hydrogel sample.

[0077] In specific implementation: the diffuse reflection heating feature is input into the BP neural network in the form of a feature vector. The BP neural network includes an input layer, a hidden layer and an output layer. The input layer is used to receive the diffuse reflection heating feature data, the hidden layer is used to establish a nonlinear mapping relationship between the diffuse reflection heating feature and the internal light trapping capability, and the output layer is used to output the corresponding light trapping score result.

[0078] The diffuse reflection heating characteristic vector includes the following different dimensions: average temperature rise, temperature uniformity index, high-temperature region proportion, heat accumulation characteristic value, and thermal field stability characteristic value. The average temperature rise reflects the overall temperature rise level after the biomass carbon-based hydrogel sample absorbs light energy; the temperature uniformity index reflects the uniformity of internal heat distribution; the high-temperature region proportion reflects the effective heat absorption area coverage within the sample; the heat accumulation characteristic value reflects the thermal energy storage capacity under continuous illumination; and the thermal field stability characteristic value reflects the stability of the temperature field over time.

[0079] In practice, multiple biomass-based hydrogel samples with different pore structures and carbonization characteristics can be prepared in advance, and the diffuse reflection temperature rise characteristics of the corresponding samples can be obtained. At the same time, optical absorption tests, multi-angle light evaporation tests, and evaporation stability tests are performed on each sample, and the corresponding light trapping artificial scoring results are constructed by combining the test results. The diffuse reflection temperature rise characteristics and the corresponding light trapping artificial scoring results together constitute the training sample set.

[0080] During training, diffuse reflection heating feature vectors from the training sample set are input into the input layer of the BP neural network. After nonlinear feature learning by the hidden layer neurons, the corresponding predicted light trapping score is output by the output layer. Subsequently, the error between the predicted light trapping score and the corresponding human light trapping score is calculated, and the backpropagation algorithm is used to iteratively adjust the connection weights and bias parameters in the BP neural network, so that the network output gradually approaches the human score.

[0081] When the mean square error between the predicted light trapping score and the manual score is less than a preset error threshold during a continuous training cycle, the BP neural network is determined to have completed training, and a mapping relationship model between diffuse reflection heating characteristics and light trapping score is obtained.

[0082] In the actual testing process, the diffuse reflection heating feature vector of the biomass carbon-based hydrogel sample to be tested is input into a trained BP neural network. Forward calculation is performed through the input layer, hidden layer, and output layer to obtain the corresponding light trapping score. The higher the light trapping score, the more obvious the multiple scattering effect generated by the porous structure inside the sample, the longer the residence time of internal photons, and the stronger the material's ability to utilize incident light. Conversely, it indicates that the light trapping ability inside the sample is weak, and more light energy is lost in the form of reflection or transmission. Its photothermal evaporation process mainly depends on the direct light absorption effect on the surface.

[0083] In step S105, the photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample is determined based on the light trapping score.

[0084] In specific implementation, determining the photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample based on the light trapping score specifically includes: obtaining the light trapping score of the sample to be tested, and determining the corresponding photothermal evaporation stability level label according to the pre-trained stability evaluation model.

[0085] The stability evaluation model is constructed using historical test samples. Multiple biomass-based hydrogel samples with different pore structure characteristics are pre-acquired and subjected to photothermal evaporation tests under different incident angles, light intensities, and evaporation scenarios. The fluctuation of evaporation efficiency for each sample under different test conditions is statistically analyzed. Furthermore, for any sample, its evaporation efficiency sequence under multiple test conditions is obtained, and the evaporation efficiency fluctuation coefficient is calculated. A smaller evaporation efficiency fluctuation coefficient indicates less variation in evaporation efficiency under different test conditions, and a more stable photothermal evaporation process. Light trapping scores for each training sample are obtained, and a correlation between the light trapping score and the evaporation efficiency fluctuation coefficient is established. Since samples with strong internal light-trapping capabilities can enhance multiple scattering and retention of light within the pore structure, improving thermal field uniformity, their evaporation efficiency fluctuation coefficients are usually smaller. After training, a stability level mapping rule is established based on the correlation between the light trapping score and the evaporation efficiency fluctuation coefficient.

[0086] In some embodiments, a monotonic correlation model between the light trapping score and the evaporation efficiency fluctuation coefficient of multiple training samples is constructed by regression fitting. Specifically, a training sample set is constructed, which includes multiple biomass char-based hydrogel samples. Each sample corresponds to a set of feature data, including the light trapping score of the sample and the evaporation efficiency sequence measured under different incident light angles, different light intensities, and different evaporation environmental conditions. For any training sample, the evaporation efficiency fluctuation coefficient is calculated based on its corresponding evaporation efficiency sequence under different test conditions. Specifically, the variance of the evaporation efficiency under each test condition is calculated as the evaporation efficiency fluctuation coefficient. The light trapping score corresponding to each training sample is used as the input variable, and the corresponding evaporation efficiency fluctuation coefficient is used as the input variable. Using the dynamic coefficient as the output variable, a training data set is constructed. A linear regression model is used to fit the training data, resulting in a monotonic function relationship model between the light trapping score and the evaporation efficiency fluctuation coefficient. The constraint condition is set as follows: when the light trapping score increases, the corresponding evaporation efficiency fluctuation coefficient monotonically decreases. The model parameters are solved using the least squares method to minimize the sum of squared errors between the predicted value and the actual evaporation efficiency fluctuation coefficient. After the model training is completed, a monotonic mapping relationship function between the light trapping score and the evaporation efficiency fluctuation coefficient is obtained. The reciprocal of the evaporation efficiency fluctuation coefficient is used as a stability index, and the photothermal evaporation stability level label corresponding to the light trapping score is determined based on the threshold of the stability index. The classification rules for the stability level label include:

[0087] When the stability index is greater than the first preset threshold, it is identified as a high stability level label;

[0088] When the stability index is between the first preset threshold and the second preset threshold, it is determined to be a medium stability level label;

[0089] When the stability index is less than the second preset threshold, it is identified as a low stability level label.

[0090] In the actual testing process, the light trapping score of the biomass carbon-based hydrogel sample to be tested is input into the stability evaluation model to obtain the corresponding stability index, and the photothermal evaporation stability level label is determined based on the stability index.

[0091] For example, samples can be classified into high stability, medium stability, and low stability levels based on their stability index: a high stability level is defined when the stability index is greater than a first preset threshold; a medium stability level is defined when the stability index is between the first and second preset thresholds; and a low stability level is defined when the stability index is lower than the second preset threshold. The final output of the photothermal evaporation stability level label serves as the test result for the photothermal evaporation performance of the biomass carbon-based hydrogel sample.

[0092] It should be noted that this application determines the photothermal evaporation stability level label based on the light trapping score, which can reflect the ability of biomass char-based hydrogels to maintain stable evaporation performance under different incident light angles, light intensities, and evaporation scenarios, providing an evaluation basis for material screening, structural optimization, and practical applications. For biomass char-based hydrogels with strong internal light-trapping capabilities, because they can enhance the retention and absorption of light in the pore structure, improve the uniformity of the thermal field, and reduce local heat accumulation, they can reduce the impact of environmental changes on evaporation efficiency and improve the stability of the photothermal evaporation process. Therefore, this invention can not only obtain the photothermal evaporation performance parameters of biomass char-based hydrogels, but also achieve a comprehensive evaluation of the material's internal light-trapping capability and photothermal evaporation stability, improving the guiding value of the test results for practical application scenarios and enhancing the material stability of biomass char-based hydrogels in practical applications.

[0093] In another aspect, in some embodiments, this application provides a testing system for the photothermal evaporation performance of biomass char-based hydrogels. This system includes a stability testing unit, referencing... Figure 2 The figure is a schematic diagram of the exemplary hardware and / or software structure of a stability testing unit according to some embodiments of this application. The stability testing unit 200 includes: a preprocessing module 201, a testing module 202, and a grading module 203, which are described below:

[0094] The pretreatment module 201 is used to acquire the biomass carbon-based hydrogel sample to be tested and to perform pre-water absorption treatment;

[0095] Test module 202 is used to irradiate the biomass carbon-based hydrogel sample with photothermal radiation and to acquire the corresponding thermal imaging image sequence of the biomass carbon-based hydrogel sample during the irradiation process.

[0096] The test module 202 is also used to extract surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask, and to perform thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence.

[0097] The test module 202 further extracts the diffuse reflection heating characteristics of the biomass carbon-based hydrogel sample based on the corrected thermal field image sequence, and determines the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process based on the diffuse reflection heating characteristics.

[0098] The grading module 203 is used to determine the photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample based on the light trapping score.

[0099] The foregoing detailed an example of a test method and system for the photothermal evaporation performance of a biomass-based hydrogel provided in the embodiments of this application. It is understood that the corresponding device includes hardware structures and / or software modules for performing each function in order to achieve the above functions.

[0100] Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in a manner that drives hardware or computer software depends on the specific application and design constraints of the technical solution. Therefore, those skilled in the art can use different methods to implement the described function for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In addition, this application also provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for testing the photothermal evaporation performance of biomass carbon-based hydrogels.

[0102] In some embodiments, reference Figure 3 The figure is a schematic diagram of a computer terminal device for implementing a method for testing the photothermal evaporation performance of a biomass char-based hydrogel, according to some embodiments of this application. The method for testing the photothermal evaporation performance of a biomass char-based hydrogel in the above embodiments can be achieved through... Figure 3The computer terminal device 300 shown is used to implement this, and the computer terminal device 300 includes at least one communication bus 301, communication interface 302, processor 303 and memory 304.

[0103] The processor 303 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more test methods for controlling the execution of the photothermal evaporation performance test method of a biomass char-based hydrogel in this application.

[0104] The communication bus 301 may include a path for transmitting information between the aforementioned components.

[0105] Memory 304 may be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 304 may exist independently and be connected to processor 303 via communication bus 301. Memory 304 may also be integrated with processor 303.

[0106] The memory 304 stores program code for executing the scheme of this application, and its execution is controlled by the processor 303. The processor 303 executes the program code stored in the memory 304. The program code may include one or more software modules. In the above embodiments, the determination of the light trapping score can be achieved by the processor 303 and one or more software modules in the program code in the memory 304.

[0107] Communication interface 302 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0108] Optionally, the computer terminal device 300 may also include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0109] In a specific implementation, as one example, a computer terminal device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0110] The aforementioned computer terminal device can be a general-purpose computer terminal device or a dedicated computer terminal device. In specific implementations, the computer terminal device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer terminal device.

[0111] In addition, other aspects of this application provide a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to perform the operations performed by the above-described method for testing the photothermal evaporation performance of a biomass-based hydrogel.

[0112] In summary, the method and system for testing the photothermal evaporation performance of biomass char-based hydrogels disclosed in this application first subject the biomass char-based hydrogel sample to photothermal irradiation and acquire a corresponding thermal imaging image sequence during the irradiation process. Surface temperature information is extracted from the thermal imaging image sequence to construct a thermal diffusion mask, and thermal diffusion correction is performed on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence. Diffuse reflection heating characteristics of the biomass char-based hydrogel sample are extracted based on the corrected thermal field image sequence, and the light trapping score of the biomass char-based hydrogel sample during the photothermal evaporation process is determined based on the diffuse reflection heating characteristics. The photothermal evaporation stability level label of the biomass char-based hydrogel sample is determined based on the light trapping score. This method utilizes diffuse reflection heating characteristics to determine the light trapping score of the hydrogel sample during the photothermal evaporation process, improving the reliability of the photothermal evaporation performance test results.

[0113] The above descriptions are merely embodiments of this application, and common knowledge such as specific technical solutions or characteristics in the solutions are not described in detail here. It should be noted that those skilled in the art can make several modifications and improvements without departing from the technical solutions of this application, and these should also be considered within the scope of protection of this application, without affecting the effectiveness of the implementation of this application or the practicality of the patent.

[0114] The scope of protection claimed in this application shall be determined by the content of its claims. The specific embodiments described in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for testing the photothermal evaporation performance of biomass char-based hydrogels, characterized in that, include: Obtain the biomass carbon-based hydrogel sample to be tested and perform pre-water absorption treatment; The biomass carbon-based hydrogel sample was subjected to photothermal irradiation, and a thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample was acquired during the irradiation process. Surface temperature information is extracted from the thermal imaging image sequence to construct a thermal diffusion mask, and thermal diffusion correction is performed on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence; Based on the corrected thermal field image sequence, the diffuse reflection heating characteristics of the biomass carbon-based hydrogel sample are extracted, and the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process is determined according to the diffuse reflection heating characteristics. The photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample is determined based on the light trapping score.

2. The method as described in claim 1, characterized in that, Extracting surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask includes: Obtain the surface temperature matrix corresponding to each time moment in the thermal imaging image sequence, and calculate the temperature gradient distribution based on the temperature difference between adjacent pixels; The temperature rise rate of each pixel is calculated based on the surface temperature matrix at consecutive time intervals; the heat diffusion region is identified based on the temperature gradient distribution and the temperature rise rate; the heat diffusion region is marked to generate a heat diffusion mask.

3. The method as described in claim 1, characterized in that, Performing thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence specifically includes: Obtain the spatial mapping relationship between the coordinates of the thermal imaging image and the heat diffusion mask; For any thermal imaging image in the thermal imaging image sequence, the thermal diffusion region of the thermal imaging image is identified according to the spatial mapping relationship, and the thermal diffusion correction coefficient corresponding to each pixel in the thermal diffusion region is calculated according to the thermal diffusion intensity of the pixel in the corresponding consecutive time frame. Based on the thermal diffusion correction coefficient, the thermal diffusion region of the thermal imaging image is corrected to obtain a corrected thermal field image; the same method is used to perform thermal diffusion correction on other thermal imaging image sequences, and a corrected thermal field image sequence is formed according to the time sequence.

4. The method as described in claim 1, characterized in that, Extracting the diffuse reflectance heating characteristics of the biomass-based hydrogel sample based on the corrected thermal field image sequence specifically includes: Extract the temperature distribution matrix corresponding to each time frame in the corrected thermal field image sequence; Statistical analysis was performed based on the temperature distribution matrix corresponding to each time frame to determine the diffuse reflection heating characteristics composed of multiple diffuse reflection evaporation parameters.

5. The method as described in claim 1, characterized in that, The process of acquiring the thermal imaging image sequence corresponding to the biomass carbon-based hydrogel sample during irradiation specifically includes: acquiring continuous thermal imaging images of the biomass carbon-based hydrogel sample using a thermal imaging camera according to a preset acquisition frequency, and assembling a thermal imaging image sequence according to the acquisition time sequence.

6. The method as described in claim 1, characterized in that, The process may further include: measuring the mass of the biomass-based hydrogel sample and recording the initial mass parameters after pre-absorption of water.

7. The method as described in claim 1, characterized in that, The pre-water absorption treatment includes immersing the biomass char-based hydrogel sample in the liquid to be evaporated until the biomass char-based hydrogel sample reaches water saturation.

8. A testing system for the photothermal evaporation performance of a biomass char-based hydrogel, comprising a stability testing unit, wherein the stability testing unit is used to perform the testing method for the photothermal evaporation performance of a biomass char-based hydrogel according to any one of claims 1 to 7, characterized in that, The stability testing unit includes: The pretreatment module is used to acquire the biomass carbon-based hydrogel sample to be tested and to perform pre-water absorption treatment; The testing module is used to irradiate the biomass carbon-based hydrogel sample with photothermal radiation and to acquire the corresponding thermal imaging image sequence of the biomass carbon-based hydrogel sample during the irradiation process. The testing module is also used to extract surface temperature information from the thermal imaging image sequence to construct a thermal diffusion mask, and to perform thermal diffusion correction on the thermal imaging image sequence based on the thermal diffusion mask to obtain a corrected thermal field image sequence. The testing module also extracts the diffuse reflection heating characteristics of the biomass carbon-based hydrogel sample based on the corrected thermal field image sequence, and determines the light trapping score of the biomass carbon-based hydrogel sample during the photothermal evaporation process based on the diffuse reflection heating characteristics. The grading module is used to determine the photothermal evaporation stability level label corresponding to the biomass carbon-based hydrogel sample based on the light trapping score.

9. A computer terminal device, characterized in that, The computer terminal device includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute a test method for the photothermal evaporation performance of a biomass carbon-based hydrogel as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that, The computer program is loaded and executed by a processor to perform the operations described in any one of claims 1 to 7 of the method for testing the photothermal evaporation performance of a biomass carbon-based hydrogel.

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