Method and device for quickly identifying degradable material

Abstract

The invention discloses a method and device for rapidly identifying a degradable material, and the method comprises the steps: carrying out the multi-modal data collection of a multilayer-structure degradable material, and obtaining the physical structure data, chemical composition data and preliminary degradation characteristic data; performing interlayer interface characteristic analysis according to the data to obtain interface characteristic parameters; evaluating the degradation synergistic effect to obtain interlayer interaction quantitative description data; an optimized random forest model is adopted to predict the overall degradation potential; analyzing the dynamic degradation path to obtain a degradation path diagram, a key intermediate product timeline and a potential degradation bottleneck point; and finally calculating the comprehensive degradation index. According to the technical scheme, the overall degradation potential of the multilayer structure degradable material can be rapidly, accurately and nondestructively evaluated, the interaction between layers of the material and the comprehensive influence of the layers on the degradation process are considered, a scientific basis is provided for efficient waste classification and treatment, the resource recovery efficiency is improved, and the environmental risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of degradable material identification, and in particular to a method and a device for quickly identifying degradable materials. Background Art

[0002] Degradable materials are materials that can be decomposed into harmless substances through biological, chemical or physical effects under specific environmental conditions. Such materials can be composed of natural polymers (such as starch, cellulose), synthetic polymers (such as polylactic acid, polycaprolactone) or their composites. Multilayer degradable materials are a complex material form that can be composed of layers with different properties, such as an outer waterproof coating, an intermediate fiber layer and an inner sealing coating, and the degradation characteristics of each layer may be very different. This multilayer structural design gives the material excellent functionality, such as waterproofing, preservation, and heat insulation, and is widely used in packaging, agriculture, medical and other fields. However, this complex structure also brings great challenges to the degradation process and degradation performance evaluation of the material.

[0003] Existing methods for identifying degradable materials mainly rely on simple chemical tests or surface analysis techniques. These methods work well when dealing with single materials, but they are often inadequate when dealing with multilayered materials. For example, traditional surface analysis methods may misjudge materials that degrade quickly on the surface but are difficult to degrade internally as fully degradable. Another common method is to decompose multilayer materials into single components for analysis, but this process is not only time-consuming and costly, but may also destroy the interactions between materials, resulting in inaccurate assessments of overall degradation characteristics. The limitations of these methods are even more obvious in high-throughput waste treatment facilities. They cannot quickly, accurately, and non-destructively assess the overall degradation potential of complex materials, nor can they take into account the impact of the actual use environment on the material degradation process. This leads to inaccurate waste classification and treatment decisions, affects resource recovery efficiency, and may also bring potential environmental risks. Therefore, the development of a rapid identification method that can comprehensively assess the degradation potential of multilayered degradable materials has become a key issue that needs to be urgently addressed in the current waste management system. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing degradable material identification method cannot quickly, accurately and non-destructively evaluate the overall degradation potential of multi-layered degradable materials.

[0005] A first aspect of the present invention provides a method for quickly identifying degradable materials, the method for quickly identifying degradable materials comprising: Performing multimodal data collection on the multi-layered degradable material to obtain physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; According to the physical structure data, chemical composition data and preliminary degradation characteristic data, characteristic analysis is performed on the interlayer interface of the multilayer structure degradable material to obtain interface characteristic parameters; According to the interface characteristic parameters, the degradation synergistic effect of the multilayer structured degradable material is evaluated to obtain quantitative description data of the interlayer interaction; Using a preset optimized random forest model, the overall degradation potential of the multi-layered degradable material is predicted based on the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain a prediction result; According to the prediction results, the dynamic degradation path of the multilayered degradable material is analyzed to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks; According to the prediction results, degradation pathway diagram, key intermediate product timeline and potential degradation bottleneck points, the comprehensive degradation index of the multi-layer structure degradable material is calculated.

[0006] Optionally, the multi-modal data collection of the multi-layered degradable material to obtain the physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material includes: Performing spectral imaging scanning on the multi-layered degradable material to obtain surface and shallow spectral information data of the multi-layered degradable material; Performing ultrasonic scanning on the multi-layered degradable material to obtain internal structure data of the multi-layered degradable material; Performing terahertz wave scanning on the multi-layered degradable material to obtain molecular vibration and rotation information data of the multi-layered degradable material; Generating a three-dimensional characteristic image of the multi-layered degradable material according to the surface and shallow layer spectral information data, internal structure data, and molecular vibration and rotation information data; The physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material are extracted according to the three-dimensional characteristic image.

[0007] Optionally, the interlayer interface of the multilayer degradable material is analyzed according to the physical structure data, chemical composition data and preliminary degradation characteristic data to obtain interface characteristic parameters, including: According to the physical structure data, chemical bond strength analysis is performed on the interlayer interface of the multilayer structured degradable material to obtain interface chemical bond strength data; According to the chemical composition data, a molecular diffusion behavior analysis is performed on the interlayer interface of the multilayer structured degradable material to obtain interface molecular diffusion coefficient data; According to the preliminary degradation characteristic data, a local microenvironment change analysis is performed on the interlayer interface of the multilayer structure degradable material to obtain local pH value change data of the interface; The interface characteristic parameters of the multilayer structured degradable material are calculated based on the interface chemical bond strength data, the interface molecular diffusion coefficient data and the interface local pH value change data.

[0008] Optionally, the degradation synergistic effect of the multilayer degradable material is evaluated according to the interface characteristic parameters to obtain quantitative description data of the interlayer interaction, including: Calculating the degradation rate of each layer of the multi-layered degradable material according to the interface characteristic parameters to obtain the degradation rate data of each layer; According to the interface characteristic parameters and the individual degradation rate data of each layer, the interlayer material exchange of the multilayer structured degradable material is simulated to obtain the interlayer material exchange flux data; Analyzing the diffusion behavior of degradation products of the multilayer degradable material according to the interlayer material exchange flux data to obtain a diffusion influencing factor of the degradation products; According to the individual degradation rate data of each layer, the interlayer material exchange flux data and the degradation product diffusion influence factor, the interlayer interaction quantitative description data of the multi-layer structured degradable material is calculated.

[0009] Optionally, the step of analyzing the diffusion behavior of the degradation products of the multilayer degradable material according to the interlayer material exchange flux data to obtain the degradation product diffusion influencing factor comprises: Calculating the concentration gradient between the layers of the multilayer degradable material according to the interlayer material exchange flux data to obtain interlayer concentration gradient data; Calculating the diffusion rate of degradation products in the multilayer degradable material according to the interlayer concentration gradient data to obtain the diffusion rate data of degradation products; Analyzing the accumulation of degradation products in each layer of the multi-layered degradable material according to the degradation product diffusion rate data to obtain the degradation product cumulative distribution data; According to the cumulative distribution data of the degradation products, the influence degree of the degradation products on the degradation process of each layer is calculated to obtain the diffusion influence factor of the degradation products.

[0010] Optionally, the preset optimized random forest model is used to predict the overall degradation potential of the multilayered degradable material according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain a prediction result, including: Performing feature vector quantization processing on the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain an input feature vector; Inputting the input feature vector into the preset optimized random forest model, wherein the preset optimized random forest model includes a plurality of decision trees, each decision tree includes a root node, an internal node and a leaf node, and obtaining a prediction output of each decision tree; Integrating the prediction outputs of each decision tree to obtain a predicted value of the degradation rate of each layer of the multi-layered degradable material; Calculating the predicted value of the overall degradation time of the multi-layered degradable material according to the predicted values ​​of the degradation rates of the layers; Calculating the inter-layer degradation rate difference coefficient of the multi-layer structured degradable material according to the predicted degradation rate of each layer and the predicted degradation time of the whole layer; The predicted values ​​of the degradation rates of the various layers, the predicted value of the overall degradation time and the coefficient of difference in degradation rates between the layers are combined to obtain the predicted result.

[0011] Optionally, the dynamic degradation path of the multilayered degradable material is analyzed according to the prediction result to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks, including: According to the predicted values ​​of degradation rate of each layer and the predicted value of overall degradation time in the predicted results, a degradation process simulation is performed on each layer of the multi-layered degradable material to obtain degradation intermediate product data of each layer; Dynamically evaluating the interlayer interaction of the multilayer degradable material according to the degradation intermediate product data of each layer to obtain an interlayer degradation influencing factor; According to the degradation intermediate product data of each layer and the interlayer degradation influencing factor, a dynamic degradation network diagram of the multi-layer structured degradable material is constructed, wherein nodes represent degradation intermediate products and edges represent transformation relationships; Performing a time series analysis on the dynamic degradation network diagram, identifying key degradation pathways and bottleneck reactions, and obtaining a degradation pathway diagram; According to the degradation pathway diagram, time series prediction is performed on key degradation intermediates to obtain a timeline of key intermediates; According to the degradation pathway diagram and key intermediate product timeline, the rate-limiting steps in the degradation process are identified to obtain potential degradation bottlenecks.

[0012] Optionally, the comprehensive degradation index of the multilayered degradable material is calculated according to the prediction results, the degradation pathway diagram, the key intermediate product timeline and the potential degradation bottleneck point, including: Calculating the basic degradation score of the multi-layered degradable material according to the predicted value of the overall degradation time and the coefficient of difference in degradation rate between layers in the prediction result; According to the number and complexity of key degradation pathways in the degradation pathway diagram, the basic degradation score is corrected to obtain a pathway complexity correction coefficient; According to the key intermediate product timeline, the average generation rate and duration of the key intermediate product are calculated to obtain the intermediate product impact factor; Calculating the bottleneck influence coefficient of the degradation process according to the number and severity of the potential degradation bottleneck points; Evaluate the actual application scenarios of the multi-layered degradable material to obtain an application scenario adaptability coefficient; According to the basic degradation score, the path complexity correction coefficient, the intermediate product impact factor, the bottleneck impact coefficient and the application scenario adaptability coefficient, the comprehensive degradation index of the multi-layer structure degradable material is calculated by weighted average.

[0013] Optionally, the actual application scenario of the multilayered degradable material is evaluated to obtain an application scenario adaptability coefficient, including: Analyzing the expected use environment of the multi-layered degradable material to obtain environmental factor data, wherein the environmental factor data includes temperature, humidity, pH value and microbial activity; According to the environmental factor data, the degradation behavior of the multi-layered degradable material in the expected use environment is simulated to obtain an environmental adaptability score; Analyzing the expected use cycle of the multi-layered degradable material to obtain use cycle data; According to the environmental adaptability score and the usage cycle data, the application scenario adaptability coefficient of the multi-layer structured degradable material is calculated.

[0014] A second aspect of the present invention provides a device for quickly identifying degradable materials, comprising: A multimodal data acquisition module is used to perform multimodal data acquisition on the multi-layered degradable material to obtain physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; An interface characteristic analysis module, used to perform characteristic analysis on the interlayer interface of the multi-layered degradable material according to the physical structure data, chemical composition data and preliminary degradation characteristic data, to obtain interface characteristic parameters; A degradation synergistic effect evaluation module, used to evaluate the degradation synergistic effect of the multilayer structure degradable material according to the interface characteristic parameters, and obtain quantitative description data of the interlayer interaction; A degradation potential prediction module, for predicting the overall degradation potential of the multi-layered degradable material using a preset optimized random forest model according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data, to obtain a prediction result; A dynamic degradation path analysis module, used to analyze the dynamic degradation path of the multilayer structure degradable material according to the prediction results, and obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks; The comprehensive degradation index calculation module is used to calculate the comprehensive degradation index of the multi-layer structure degradable material according to the prediction results, degradation path diagram, key intermediate product timeline and potential degradation bottleneck points.

[0015] The technical solution provided by the embodiments of the present application has at least the following advantages: This method uses multimodal data acquisition technology, combined with spectral imaging, ultrasonic and terahertz wave scanning, to comprehensively obtain the physical structure, chemical composition and preliminary degradation characteristics of the material. This multi-dimensional data acquisition method overcomes the limitations of traditional single detection methods. For example, spectral imaging can provide chemical composition information on the surface and shallow layer of the material, ultrasonic scanning can non-destructively detect the internal structure, and terahertz wave scanning can reveal vibration and rotation information at the molecular level. The combination of these three technologies enables us to fully understand the microstructure and macroscopic properties of the material, laying a solid data foundation for subsequent analysis.

[0016] Secondly, this method introduces the analysis of interlayer interface characteristics and the evaluation of degradation synergy. These two steps directly target the particularities of multilayered degradable materials. By analyzing the chemical bond strength, molecular diffusion behavior, and local microenvironmental changes at the interlayer interface, we are able to gain a deep understanding of the interactions between the layers. This analysis is based on the principles of interface science and takes into account the effects of chemical bond energy, molecular motion, and local environmental changes at the interface on the overall degradation behavior of the material. The subsequent degradation synergy evaluation further quantifies this interaction and reveals how the degradation of one layer affects the degradation process of other layers. This in-depth analysis makes up for the shortcomings of traditional methods that only focus on the degradation characteristics of a single layer, and provides key information for the accurate evaluation of the overall degradation potential.

[0017] Furthermore, this method uses an optimized random forest model to predict the overall degradation potential. As a powerful machine learning algorithm, the random forest model can handle high-dimensional, nonlinear and complex data relationships. By inputting the multidimensional data obtained in the previous steps into the model, we can comprehensively consider the impact of various factors on the degradation process, thereby obtaining more accurate prediction results. This data-driven prediction method overcomes the limitations of traditional empirical formulas or simple statistical models and can better capture the degradation behavior of complex multilayer structural materials.

[0018] In addition, this method also includes dynamic degradation path analysis and comprehensive degradation index calculation. Dynamic degradation path analysis provides an intuitive visual representation of the material degradation process by constructing a degradation network diagram to identify key degradation paths and potential bottlenecks. This analysis is based on graph theory and network science principles and can reveal the complex dynamic changes in the degradation process. The calculation of the comprehensive degradation index takes into account multiple aspects, including basic degradation scores, path complexity, the impact of intermediate products, etc., providing a comprehensive evaluation index. This multi-dimensional evaluation method ensures the comprehensiveness and reliability of the evaluation results.

[0019] In general, this method constructs a complete multi-layered degradable material evaluation system through multimodal data collection, in-depth interface analysis, machine learning prediction, dynamic path analysis and comprehensive index evaluation. This method can not only quickly and non-destructively obtain material information, but also comprehensively consider the complex structure of the material and the dynamic changes during the degradation process, thereby providing more accurate and reliable degradation potential evaluation results. This is of great significance for solving the problem of inaccurate and incomplete evaluation of multi-layered degradable materials in the existing technology, and also provides strong scientific support for material design, optimization and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of a method for quickly identifying degradable materials in an embodiment of the present invention; Figure 2 This is a schematic diagram of an embodiment of a device for quickly identifying degradable materials in an embodiment of the present invention.

[0022] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0025] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0026] An embodiment of the present application provides a method for quickly identifying degradable materials. Figure 1 A flow chart of a method for quickly identifying degradable materials provided in an embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , performing multimodal data collection on the multi-layered degradable material to obtain physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; In one embodiment of the present invention, the multimodal data collection of the multilayer structured degradable material to obtain the physical structure data, chemical composition data and preliminary degradation characteristic data of the multilayer structured degradable material includes: performing spectral imaging scanning on the multilayer structured degradable material to obtain the surface and shallow spectral information data of the multilayer structured degradable material; performing ultrasonic scanning on the multilayer structured degradable material to obtain the internal structure data of the multilayer structured degradable material; performing terahertz wave scanning on the multilayer structured degradable material to obtain the molecular vibration and rotation information data of the multilayer structured degradable material; generating a three-dimensional characteristic image of the multilayer structured degradable material according to the surface and shallow spectral information data, internal structure data, molecular vibration and rotation information data; and extracting the physical structure data, chemical composition data and preliminary degradation characteristic data of the multilayer structured degradable material according to the three-dimensional characteristic image.

[0027] Specifically, spectral imaging scanning is performed on the multi-layered degradable material. This step can use equipment such as a near-infrared spectrometer or a Raman spectrometer. Spectral imaging scanning can obtain spectral information data on the surface and shallow layer of the material, which reflects the chemical composition and molecular structure of the material. For example, spectral imaging scanning of a degradable packaging material composed of a polylactic acid outer layer and a starch inner layer can clearly distinguish the characteristic peaks of the two materials, thereby determining the surface composition and preliminary hierarchical structure of the material. The advantage of this step is that it can obtain material surface information non-destructively, providing basic data for subsequent analysis.

[0028] Next, ultrasonic scanning of the multi-layered biodegradable material can be performed using an ultrasonic detector. Ultrasonic waves can penetrate the interior of the material and obtain the internal structure data of the material by analyzing the time and intensity of the reflected wave. These data include the location and thickness of the interface between layers, as well as possible internal defects. Taking a multi-layer biodegradable medical packaging material as an example, ultrasonic scanning can show the precise thickness and distribution of the waterproof layer, antibacterial layer and water-absorbing layer, which is crucial for evaluating the overall performance and potential degradation behavior of the material. The advantage of ultrasonic scanning is that it can obtain internal information of the material non-destructively, filling the deficiency that spectral imaging can only analyze the surface.

[0029] The third step is to perform terahertz wave scanning on the multi-layered degradable material, which can be done using a terahertz time-domain spectrometer. Terahertz waves have the unique advantage of being able to detect the molecular vibration and rotation information of materials. This information is crucial for understanding the microstructure and intermolecular interactions of materials. For example, by performing terahertz wave scanning on a multi-layered degradable film containing nanocellulose, the vibration mode and arrangement of the cellulose molecular chains can be obtained, which directly affects the degradation characteristics of the material. The advantage of terahertz wave scanning is that it can provide microstructural information that is difficult to obtain with other technologies, providing key data for a comprehensive assessment of the degradation potential of materials.

[0030] After obtaining the surface and shallow spectral information data, internal structure data, and molecular vibration and rotation information data, the next step is to integrate these data to generate a three-dimensional characteristic image of the multi-layered degradable material. This step can use a special data fusion algorithm and three-dimensional image reconstruction technology. For example, for a multi-layer degradable mulch used in agriculture, the three-dimensional characteristic image can intuitively show the spatial distribution and relationship between the waterproof layer, nutrient release layer, and biodegradable layer. The advantage of this three-dimensional characteristic image is that it provides a comprehensive view of the material structure and composition, making subsequent analysis more intuitive and accurate.

[0031] Finally, based on the generated three-dimensional characteristic images, the physical structure data, chemical composition data and preliminary degradation characteristic data of the multilayered degradable material are extracted. This step involves image analysis and data mining technology. The physical structure data includes the thickness, density and porosity of each layer; the chemical composition data includes the main chemical components of each layer and their distribution; the preliminary degradation characteristic data includes the hydrophilicity, crystallinity and other factors that affect the degradation rate of the material. For example, for a multilayered degradable material used for food packaging, by analyzing its three-dimensional characteristic images, key data such as the thickness and uniformity of the outer moisture-proof coating, the crystallinity distribution of the middle layer of polylactic acid, and the porosity of the inner starch substrate can be obtained. The extraction of these data provides comprehensive and detailed basic information for the subsequent degradation potential assessment.

[0032] The significant advantage of this multimodal data acquisition and analysis method is that it can comprehensively and non-destructively obtain the various characteristics of multi-layered degradable materials. By combining spectral imaging, ultrasound and terahertz wave scanning technology, the limitations of a single technology are overcome, providing a rich and accurate data basis for the overall evaluation of the material. This method is particularly suitable for complex multi-layered degradable materials, and can reveal the internal microstructure of the material and the interaction between the layers, which is crucial for accurately predicting the degradation behavior of the material in the actual environment. At the same time, the non-invasive characteristics of this method ensure that the analysis process does not change the original properties of the material, thereby ensuring the reliability of the evaluation results.

[0033] Please continue reading Figure 1 , performing characteristic analysis on the interlayer interface of the multilayer degradable material according to the physical structure data, chemical composition data and preliminary degradation characteristic data to obtain interface characteristic parameters; In one embodiment of the present invention, the interlayer interface of the multilayer structured degradable material is subjected to characteristic analysis according to the physical structure data, chemical composition data and preliminary degradation characteristic data to obtain interface characteristic parameters, including: performing chemical bond strength analysis on the interlayer interface of the multilayer structured degradable material according to the physical structure data to obtain interface chemical bond strength data; performing molecular diffusion behavior analysis on the interlayer interface of the multilayer structured degradable material according to the chemical composition data to obtain interface molecular diffusion coefficient data; performing local microenvironment change analysis on the interlayer interface of the multilayer structured degradable material according to the preliminary degradation characteristic data to obtain interface local pH value change data; and calculating the interface characteristic parameters of the multilayer structured degradable material according to the interface chemical bond strength data, interface molecular diffusion coefficient data and interface local pH value change data.

[0034] Specifically, the chemical bond strength analysis of the interlayer interface of the multilayer degradable material is performed based on the physical structure data. This step involves molecular dynamics simulation or quantum chemical calculation. By analyzing the atomic arrangement and electron distribution in the physical structure data, the chemical bond energy and bond length at the interlayer interface can be calculated. For example, for a degradable packaging material composed of polylactic acid and starch, the hydrogen bond strength and van der Waals force between the polylactic acid molecular chain and the starch molecule can be calculated. This analysis can provide interfacial chemical bond strength data, reflecting the degree of bonding between the layers of the material, which is crucial for predicting the interlayer separation behavior of the material during the degradation process.

[0035] Next, the molecular diffusion behavior of the interlayer interface of the multilayered biodegradable material is analyzed based on the chemical composition data. This step uses Monte Carlo simulation or molecular dynamics simulation methods. By simulating the motion trajectories and energy changes of different molecules at the interface, the interface molecular diffusion coefficient data can be obtained. Taking a multilayer biodegradable medical dressing as an example, the diffusion behavior of water molecules or drug molecules between the hydrophilic layer and the hydrophobic layer can be simulated to calculate the diffusion coefficient at the interface. These data reflect the ease of material exchange between the layers of the material during the degradation process, which is of great significance for predicting the migration and accumulation of substances during the degradation process.

[0036] The third step is to analyze the local microenvironment changes at the interlayer interface of the multilayered degradable material based on the preliminary degradation characteristic data. This step involves chemical kinetics simulation and solution of the reaction diffusion equation. By analyzing the chemical reaction rate and product distribution in the preliminary degradation characteristic data, the local pH changes at the interface can be predicted. For example, for a degradable agricultural mulch film containing acidic degradation products, the dynamic changes in pH at the interface during the degradation process can be simulated. This analysis can obtain data on local pH changes at the interface, reflecting the dynamic changes in the local microenvironment of the material during the degradation process, which is very important for evaluating the autocatalytic effect and potential toxicity risks of the degradation process.

[0037] Finally, the interface characteristic parameters of the multilayered degradable material are calculated based on the interface chemical bond strength data, interface molecular diffusion coefficient data, and interface local pH value change data. This step uses multivariate statistical analysis or machine learning algorithms. By comprehensively considering the chemical bond strength, molecular diffusion behavior, and local environmental changes, a series of comprehensive parameters describing the interface characteristics can be obtained. For example, for a complex multilayer degradable food packaging material, parameters such as the interface stability index, material exchange efficiency, and sensitivity to microenvironmental changes can be calculated. These interface characteristic parameters provide comprehensive and accurate input data for the subsequent evaluation of degradation synergy effects.

[0038] This multi-level, multi-angle interface analysis method has significant advantages. First, it can comprehensively capture the complex characteristics of the interface of multilayered degradable materials, taking into account not only static structural characteristics, but also dynamic diffusion behavior and environmental changes. This comprehensiveness is crucial for accurately predicting the behavior of materials during actual degradation processes. Secondly, by combining theoretical calculations and numerical simulations, this method overcomes the limitations of traditional experimental methods in studying interface microscopic processes and can provide interface property data that is difficult to measure directly through experiments. Furthermore, the non-invasive nature of this method ensures that the analysis process does not destroy the original structure of the material, thereby ensuring the reliability of the evaluation results.

[0039] In addition, another important advantage of this analysis method is its adaptability and scalability. By adjusting the simulation parameters and calculation methods, it can be applied to various types of multilayer degradable materials, from simple double-layer structures to complex multi-component composite materials. For example, for a new type of multilayer degradable electronic packaging material, this method can easily incorporate the analysis of the interface characteristics between the conductive layer and the insulating layer, providing key information for evaluating the degradation behavior and potential environmental impact of the material.

[0040] Please continue reading Figure 1 , evaluating the degradation synergistic effect of the multilayer degradable material according to the interface characteristic parameters, and obtaining quantitative description data of the interlayer interaction; In one embodiment of the present invention, the degradation synergistic effect of the multilayer structured degradable material is evaluated according to the interface characteristic parameters to obtain quantitative description data of interlayer interaction, including: calculating the degradation rate of each layer of the multilayer structured degradable material according to the interface characteristic parameters to obtain the individual degradation rate data of each layer; simulating the interlayer material exchange of the multilayer structured degradable material according to the interface characteristic parameters and the individual degradation rate data of each layer to obtain interlayer material exchange flux data; analyzing the diffusion behavior of the degradation products of the multilayer structured degradable material according to the interlayer material exchange flux data to obtain the degradation product diffusion influence factor; calculating the quantitative description data of the interlayer interaction of the multilayer structured degradable material according to the individual degradation rate data of each layer, the interlayer material exchange flux data and the degradation product diffusion influence factor.

[0041] Specifically, a comprehensive evaluation of the degradation synergistic effect of multilayered degradable materials is a key step in accurately predicting their overall degradation potential. First, the degradation rate of each layer of the multilayered degradable material is calculated individually based on the interface characteristic parameters. This step specifically involves establishing the degradation reaction equation for each layer of material, such as hydrolysis reaction or oxidation reaction, and using a differential equation solver to calculate the degradation rate over time. For example, for a degradable food packaging material composed of an outer layer of polylactic acid and an inner layer of starch, the ester bond hydrolysis equation of polylactic acid and the glycosidic bond cleavage equation of starch can be established respectively, and then the theoretical degradation rate of each layer under different temperature and humidity conditions can be calculated by numerical integration method. This analysis can obtain the degradation rate data of each layer individually, providing a benchmark value for the subsequent synergistic effect evaluation.

[0042] Next, based on the interface characteristic parameters and the degradation rate data of each layer, the interlayer material exchange of the multilayer structured biodegradable material is simulated. This step specifically uses the finite element analysis method to divide the material into tiny units, and applies the laws of mass conservation and diffusion in each unit. By solving the material transfer equations between these tiny units, the interlayer material exchange flux data can be obtained. Taking a multilayer biodegradable medical dressing as an example, the diffusion process of drug molecules from the sustained-release layer to the absorption layer can be simulated, and the drug concentration gradient and diffusion rate between different layers at each time point can be calculated. These data reflect the degree of mutual influence between the layers of the material during the degradation process, which is crucial to understanding the overall degradation behavior of the material.

[0043] The third step is to analyze the diffusion behavior of degradation products of multilayer degradable materials based on the interlayer material exchange flux data. This step specifically involves establishing a two-dimensional or three-dimensional diffusion model, taking into account the changes in the concentration distribution and diffusion coefficient of the degradation products over time and space. Through numerical simulation methods, such as the finite difference method, the diffusion influencing factors of the degradation products can be calculated. For example, for a degradable agricultural mulch film containing acidic degradation products, the diffusion path of the acidic substance in the material can be simulated, and its effect on the pH value of each layer can be calculated, thereby quantifying its acceleration or inhibition of the overall degradation rate. This analysis can specifically quantify the impact of degradation products on the overall degradation process, providing an important basis for evaluating the long-term environmental impact of materials.

[0044] Finally, according to the individual degradation rate data of each layer, the interlayer material exchange flux data and the degradation product diffusion influencing factor, the interlayer interaction quantitative description data of the multilayer structure degradable material is calculated. This step specifically uses multivariate regression analysis or neural network model. By inputting the various data obtained in the previous steps, a mathematical model that can predict the overall degradation behavior is constructed. For example, for a complex multilayer degradable electronic packaging material, this model can be used to calculate specific values, such as the interlayer degradation rate influence coefficient (indicating how the degradation of one layer accelerates or slows down the degradation of the adjacent layer), the overall degradation synergy factor (quantifying the difference in degradation rate of the multilayer structure compared to the single-layer material), etc. These interlayer interaction quantitative description data fully reflect the complex dynamic behavior of the multilayer structure during the degradation process in a specific numerical form.

[0045] The advantage of this method is that it can provide specific, quantifiable data rather than vague qualitative descriptions. For example, it can accurately calculate the expected life of a multi-layer degradable material under specific environmental conditions, as well as the degradation order and rate of each layer of material. This precision is extremely useful for both material designers and waste managers, enabling them to make more informed and scientific decisions. At the same time, the systematic and comprehensive nature of this method makes it possible and objective to compare the performance of different materials, providing a solid scientific basis for material selection.

[0046] In one embodiment of the present invention, analyzing the diffusion behavior of the degradation products of the multilayer degradable material according to the interlayer material exchange flux data to obtain the degradation product diffusion influencing factor includes: calculating the concentration gradient between the layers of the multilayer degradable material according to the interlayer material exchange flux data to obtain interlayer concentration gradient data; calculating the diffusion rate of the degradation products in the multilayer degradable material according to the interlayer concentration gradient data to obtain degradation product diffusion rate data; analyzing the accumulation of the degradation products in each layer of the multilayer degradable material according to the degradation product diffusion rate data to obtain degradation product cumulative distribution data; calculating the influence of the degradation products on the degradation process of each layer according to the degradation product cumulative distribution data to obtain the degradation product diffusion influencing factor.

[0047] Specifically, first, the concentration gradient between the layers of the multilayer structured degradable material is calculated based on the interlayer material exchange flux data to obtain the interlayer concentration gradient data. This step uses Fick's law to determine the concentration gradient by analyzing the amount of material passing through a unit area per unit time. Specifically, the interlayer concentration gradient can be obtained by dividing the interlayer material exchange flux data by the diffusion coefficient and interface area of ​​the material. For example, for a double-layer degradable packaging material composed of polylactic acid and starch, the concentration gradient of lactic acid monomers when diffusing from the polylactic acid layer to the starch layer can be calculated. The importance of this step lies in that it provides basic data for subsequent diffusion rate calculations, and also reflects the strength of interaction between the layers of the material.

[0048] Next, based on the interlayer concentration gradient data, the diffusion rate of the degradation products in the multilayer structured degradable material is calculated to obtain the diffusion rate data of the degradation products. This step uses the modified Fick's second law to take into account the effect of the structural changes of the material during the degradation process on the diffusion coefficient. By numerically solving the partial differential equation, the diffusion rates of the degradation products at different positions and time points can be obtained. Taking a multilayer degradable mulch film used in agriculture as an example, the diffusion rates of fertilizer molecules between the hydrophobic layer and the hydrophilic layer can be calculated, as well as how these rates change as the material degrades. The advantage of this step is that it can accurately capture the dynamic effects of material structural changes on diffusion behavior during the degradation process, providing an important basis for evaluating the long-term performance of the material.

[0049] The third step is to analyze the accumulation of degradation products in each layer of the multilayer biodegradable material based on the diffusion rate data of the degradation products to obtain the cumulative distribution data of the degradation products. This step uses the principle of conservation of mass, combined with the diffusion rate calculated previously, to obtain the cumulative amount of degradation products in each layer at different time points through integral calculation. For example, for a multilayer biodegradable medical dressing, the accumulation of antibiotic degradation products in each functional layer can be analyzed to evaluate its impact on material performance and potential biological activity. The importance of this step lies in that it reveals the distribution dynamics of degradation products in the material, which is crucial for predicting the degradation behavior and potential environmental impact of the material.

[0050] Finally, based on the cumulative distribution data of degradation products, the degree of influence of degradation products on the degradation process of each layer is calculated to obtain the diffusion influence factor of degradation products. This step uses multivariate regression analysis to correlate the cumulative amount of degradation products with the changes in the degradation rate of each layer. For example, for a complex multi-layer degradable electronic packaging material, it is possible to calculate how the degradation products of the conductive layer affect the degradation rate of the insulating layer, thereby obtaining a comprehensive diffusion influence factor. The advantage of this step is that it can comprehensively evaluate the role of degradation products in the entire material system, providing a quantitative basis for optimizing material design and predicting long-term performance.

[0051] This multi-step degradation product diffusion behavior analysis method has significant advantages. First, it provides a systematic framework that can comprehensively capture the complex diffusion process in multilayered degradable materials. Second, by gradually refining the analysis, from macroscopic material exchange to microscopic molecular diffusion, and then to system-level impact assessment, this method can reveal the key mechanisms in the material degradation process. In addition, the quantitative and precise nature of this method makes it possible to compare the performance of different materials, providing a scientific basis for material design and selection.

[0052] In practical applications, this analytical method can help solve many key problems. For example, when designing controlled-release agricultural mulch, this method can be used to precisely control the release rate of fertilizers, ensuring that crops get enough nutrients while minimizing the impact on the environment. When developing biodegradable medical implants, this method can help predict the degradation behavior of materials in the body and ensure that the degradation products do not have adverse effects on surrounding tissues. For the food packaging industry, this method can help develop new packaging materials that can extend the shelf life of food and degrade quickly after disposal.

[0053] Please continue reading Figure 1 , using a preset optimized random forest model, according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data, predicting the overall degradation potential of the multi-layer structured degradable material to obtain a prediction result; In one embodiment of the present invention, the overall degradation potential of the multilayer structured degradable material is predicted according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data by using a preset optimized random forest model to obtain a prediction result, including: performing feature vector quantization processing on the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain an input feature vector; inputting the input feature vector into the preset optimized random forest model, wherein the preset optimized random forest model includes a plurality of decision trees, each decision tree includes a root node, an internal node and a leaf node, and obtaining a prediction output of each decision tree; integrating the prediction output of each decision tree to obtain a predicted value of the degradation rate of each layer of the multilayer structured degradable material; calculating a predicted value of the overall degradation time of the multilayer structured degradable material according to the predicted values ​​of the degradation rates of each layer; calculating a difference coefficient of the degradation rates between layers of the multilayer structured degradable material according to the predicted values ​​of the degradation rates of each layer and the predicted value of the overall degradation time; combining the predicted values ​​of the degradation rates of each layer, the predicted value of the overall degradation time and the difference coefficient of the degradation rates between layers to obtain the prediction result.

[0054] Specifically, first, the physical structure data, chemical composition data, preliminary degradation characteristics data, interface characteristic parameters and interlayer interaction quantitative description data are eigenvectorized to obtain the input eigenvector. This step uses the principal component analysis (PCA) method to convert the high-dimensional raw data into a low-dimensional eigenvector while retaining the main information of the data. For example, for a degradable food packaging material composed of a polylactic acid outer layer and a starch inner layer, the physical structure data may include the thickness and density of each layer, the chemical composition data includes the main component ratio of each layer, the preliminary degradation characteristics data includes the hydrolysis rate of each layer, the interface characteristic parameters include the interlayer bonding strength, and the interlayer interaction quantitative description data includes the material exchange rate. Through PCA processing, these multidimensional data are converted into a compact eigenvector, which greatly improves the efficiency and accuracy of subsequent modeling.

[0055] Next, the input feature vector is input into the preset optimized random forest model to obtain the predicted output of each decision tree. The random forest model is an integrated learning method consisting of multiple decision trees, each of which includes a root node, internal nodes, and leaf nodes. In this step, each decision tree makes a prediction based on the input feature vector and outputs a degradation rate prediction value. The advantage of the optimized random forest model is that it can handle high-dimensional data, is insensitive to outliers, and can evaluate the importance of features. For example, for the degradable food packaging material mentioned above, each decision tree may predict the degradation rate of the material based on different feature combinations (such as the thickness of the polylactic acid layer, the hydrolysis rate of the starch layer, the interlayer bonding strength, etc.).

[0056] Then, the prediction output of each decision tree is integrated to obtain the predicted degradation rate of each layer of the multi-layered biodegradable material. This step uses a weighted average method to give different weights according to the performance of each decision tree, so as to obtain more accurate prediction results. This integration method can effectively reduce the bias and variance of a single model and improve the stability and accuracy of the prediction. For complex multi-layered biodegradable materials, such as a medical dressing containing a waterproof layer, a sustained-release layer, and a biodegradable layer, this method can accurately predict the degradation rate of each layer, taking into account the complex effects of the interactions between the layers.

[0057] Next, based on the predicted degradation rate of each layer, the predicted value of the overall degradation time of the multilayered degradable material is calculated. This step uses the numerical integration method, treating the degradation process of each layer as a dynamic system, and obtaining the overall degradation time by solving a set of differential equations. The advantage of this method is that it takes into account the nonlinear changes and mutual influence of the degradation rates of each layer, and can more accurately predict the overall life of the material. For example, for a multilayered degradable material used for electronic product packaging, this method can accurately predict the time from the beginning of use to complete degradation of the material, providing an important basis for product life cycle management.

[0058] Subsequently, the coefficient of difference in degradation rate between layers of the multilayered degradable material is calculated based on the predicted values ​​of the degradation rate of each layer and the predicted value of the overall degradation time. This step uses the coefficient of variation calculation method to quantify the degree of difference in degradation rate between different layers. The coefficient of difference in degradation rate between layers reflects the uniformity of the material degradation process and is of great significance for predicting the degradation behavior and potential environmental impact of the material. For example, for a multilayer degradable agricultural mulch film, if the difference in degradation rate between layers is too large, it may cause the material to fragment during the degradation process, increasing the risk of environmental pollution.

[0059] Finally, the predicted values ​​of the degradation rate of each layer, the predicted value of the overall degradation time, and the coefficient of difference in degradation rate between layers are combined to obtain the prediction result. This step integrates all the parameters calculated previously into a comprehensive prediction result, which fully reflects the degradation potential of multilayered degradable materials. The prediction result of this multi-parameter combination not only provides a quantitative description of material degradation, but also includes the quality characteristics of the degradation process, providing comprehensive guidance for material design and application.

[0060] This method for predicting the degradation potential of multilayered degradable materials based on an optimized random forest model has significant advantages. First, it can handle complex nonlinear relationships and adapt to the complexity of multilayered degradable materials. Secondly, through feature vectorization and ensemble learning, this method improves the accuracy and robustness of the prediction. Furthermore, this method not only predicts the overall degradation time, but also provides the degradation rate of each layer and the difference between layers, providing multi-dimensional guidance for the optimal design of materials. In addition, this method is highly interpretable and can evaluate the importance of different features to the degradation process, which helps to deeply understand the degradation mechanism of the material. In practical applications, this prediction method can help designers quickly evaluate and optimize new multilayered degradable materials, accelerate the research and development process of materials, and provide accurate degradation predictions for waste management, promoting the development of a circular economy.

[0061] Please continue reading Figure 1 , according to the prediction results, analyzing the dynamic degradation path of the multilayered degradable material to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks; In one embodiment of the present invention, the dynamic degradation path of the multilayer structure degradable material is analyzed according to the prediction result to obtain a degradation path diagram, a key intermediate product timeline and a potential degradation bottleneck point, including: simulating the degradation process of each layer of the multilayer structure degradable material according to the predicted degradation rate of each layer and the predicted overall degradation time in the prediction result to obtain degradation intermediate product data of each layer; dynamically evaluating the interlayer interaction of the multilayer structure degradable material according to the degradation intermediate product data of each layer to obtain an interlayer degradation influencing factor; constructing a dynamic degradation network diagram of the multilayer structure degradable material according to the degradation intermediate product data of each layer and the interlayer degradation influencing factor, wherein nodes represent degradation intermediate products and edges represent transformation relationships; performing time series analysis on the dynamic degradation network diagram to identify key degradation paths and bottleneck reactions to obtain a degradation path diagram; performing time series prediction on key degradation intermediates according to the degradation path diagram to obtain a key intermediate product timeline; identifying the rate limiting steps in the degradation process according to the degradation path diagram and the key intermediate product timeline to obtain a potential degradation bottleneck point.

[0062] Specifically, first, based on the predicted values ​​of the degradation rate of each layer and the predicted value of the overall degradation time in the prediction results, the degradation process of each layer of the multilayered degradable material is simulated to obtain the data of the degradation intermediate products of each layer. This step uses the molecular dynamics simulation method to simulate the degradation process of the material under different environmental conditions by establishing an atomic-level model. For example, for a degradable food packaging material composed of a polylactic acid outer layer and a starch inner layer, the simulation process will consider the hydrolysis of water molecules on polylactic acid bonds and the breaking process of starch molecular chains, thereby obtaining degradation intermediates at different time points, such as lactic acid monomers, oligomers and glucose. This microscopic-scale simulation can provide detailed information on the degradation mechanism and lay the foundation for subsequent analysis.

[0063] Next, based on the data of the degradation intermediates of each layer, the interlayer interactions of the multilayered degradable material are dynamically evaluated to obtain the interlayer degradation influencing factors. This step uses the cross-correlation analysis method to calculate the degree of mutual influence of the degradation intermediates between different layers. The interlayer degradation influencing factor reflects how the degradation products of one layer accelerate or inhibit the degradation process of the adjacent layer. For example, the degradation products of the polylactic acid layer (such as lactic acid) may change the local pH value of the starch layer, thereby affecting the hydrolysis rate of starch. This dynamic evaluation helps to reveal the complex interactions of the multilayer structure during the degradation process and improves the accuracy of the prediction of the overall degradation behavior.

[0064] Then, based on the degradation intermediate product data of each layer and the inter-layer degradation influencing factors, a dynamic degradation network diagram of the multi-layered biodegradable material is constructed, where the nodes represent the degradation intermediates and the edges represent the transformation relationships. This step uses graph theory and network analysis methods to visualize the complex degradation process. For example, for a multi-layer biodegradable medical dressing, the network diagram may show how antibiotic molecules are gradually degraded into small molecules and how these small molecules affect the degradation process of other layers. The construction of the dynamic degradation network diagram makes the complex degradation process intuitive and understandable, and helps to identify key degradation paths and potential problem points.

[0065] Next, the dynamic degradation network diagram is subjected to time-series analysis to identify key degradation paths and bottleneck reactions, and obtain a degradation path diagram. This step uses graph algorithms, such as shortest path analysis and centrality analysis, to find the paths and reactions that have the greatest impact on the overall degradation process. The degradation path diagram reveals the main pathways of a material from its initial state to complete degradation, which is essential for understanding and optimizing the degradation behavior of the material. For example, for a degradable agricultural mulch film, the degradation path diagram may show how certain additives hinder the degradation of the main material, thereby guiding the direction of material improvement.

[0066] Subsequently, according to the degradation pathway diagram, the key intermediates are predicted in time series to obtain the key intermediate timeline. This step uses time series analysis methods, such as the autoregressive integrated moving average model (ARIMA), to predict the generation and consumption dynamics of key intermediates. The key intermediate timeline provides detailed time information on the material degradation process, which helps to assess the environmental impact of the material at different stages. For example, for a degradable electronic product packaging material, the key intermediate timeline can predict the release time of potentially harmful substances and provide guidance for the safe use and handling of the material.

[0067] Finally, based on the degradation pathway diagram and the timeline of key intermediates, the rate-limiting steps in the degradation process are identified to obtain potential degradation bottlenecks. Potential degradation bottlenecks refer to steps or reactions that significantly slow down the overall degradation rate during the degradation process. This step uses a sensitivity analysis method to change the rates of different reaction steps and observe the impact on the overall degradation time, thereby identifying the key rate-limiting steps. For example, for a complex multi-layer degradable packaging material, it may be found that the cross-linked structure of a certain layer significantly slows down the overall degradation process, becoming a potential degradation bottleneck. Identifying these bottlenecks is of great significance for optimizing material design and improving degradation performance.

[0068] This multi-step dynamic degradation path analysis method has significant advantages. First, it provides a systematic framework that can comprehensively capture the complex dynamic behavior of multilayered degradable materials during the degradation process. Second, by combining microscopic simulation and macroscopic analysis, this method can reveal the intrinsic mechanism of material degradation and provide a scientific basis for material design and optimization. Furthermore, the introduction of dynamic degradation network diagrams and time series predictions makes the complex degradation process visual and predictable, greatly improving the intuitiveness and practicality of the analysis. In addition, by identifying critical paths and potential bottlenecks, this method provides a clear direction for improving the degradation efficiency and environmental friendliness of materials.

[0069] In practical applications, this analytical method can solve many key problems. For example, when developing new degradable food packaging materials, this method can be used to accurately control the degradation time of the material to ensure that the packaging integrity is maintained during the shelf life of the food, while being able to degrade quickly after disposal. When designing biodegradable medical implants, this method can help predict the degradation behavior of materials in the body and ensure that the degradation products will not cause harm to the human body. For degradable mulch films for agricultural use, this method can help develop products that can meet the needs of crop growth and can be completely degraded after use, reducing white pollution.

[0070] Please continue reading Figure 1 , the comprehensive degradation index of the multilayer structure degradable material is calculated according to the prediction results, degradation pathway diagram, key intermediate product timeline and potential degradation bottleneck points.

[0071] In one embodiment of the present invention, the comprehensive degradation index of the multilayer structured degradable material is calculated according to the prediction results, the degradation pathway diagram, the key intermediate product timeline and the potential degradation bottleneck points, including: calculating the basic degradation score of the multilayer structured degradable material according to the overall degradation time prediction value and the inter-layer degradation rate difference coefficient in the prediction results; correcting the basic degradation score according to the number and complexity of the key degradation pathways in the degradation pathway diagram to obtain a pathway complexity correction coefficient; calculating the average generation rate and duration of the key intermediate products according to the key intermediate product timeline to obtain an intermediate product influence factor; calculating the bottleneck influence coefficient of the degradation process according to the number and severity of the potential degradation bottleneck points; evaluating the actual application scenarios of the multilayer structured degradable material to obtain an application scenario adaptability coefficient; and calculating the comprehensive degradation index of the multilayer structured degradable material by weighted average according to the basic degradation score, the pathway complexity correction coefficient, the intermediate product influence factor, the bottleneck influence coefficient and the application scenario adaptability coefficient.

[0072] Specifically, first, the basic degradation score of the multilayer structured degradable material is calculated based on the predicted value of the overall degradation time and the coefficient of difference in degradation rate between layers in the prediction results. This step uses a normalization method to convert the predicted value of the overall degradation time into a score of 0 to 100, where 100 represents the fastest degradation rate. The coefficient of difference in degradation rate between layers is used to adjust this basic score. The smaller the difference, the higher the score. For example, for a degradable food packaging material consisting of an outer layer of polylactic acid and an inner layer of starch, if the predicted overall degradation time is 6 months and the difference in degradation rate between layers is small, its basic degradation score may be 80 points. This scoring method provides an intuitive benchmark that reflects the overall degradation performance of the material.

[0073] Next, the basic degradation score is corrected according to the number and complexity of the key degradation pathways in the degradation pathway diagram to obtain the path complexity correction factor. This step uses graph theory analysis methods to calculate the average length and number of branches of the degradation pathway. The shorter the path and the fewer the branches, the closer the correction factor is to 1; otherwise, the correction factor will be less than 1. For example, if the degradation pathway diagram of a multi-layer biodegradable medical dressing shows multiple parallel simple degradation pathways, its path complexity correction factor may be 0.95, slightly reducing the basic score. This correction reflects the complexity of the material degradation process. Complex degradation pathways usually mean degradation behaviors that are more difficult to predict and control.

[0074] Then, based on the key intermediate product timeline, the average generation rate and duration of the key intermediate products are calculated to obtain the intermediate product impact factor. This step uses the time series analysis method to calculate the area under the generation rate curve and duration of each key intermediate product. If the intermediate product is generated quickly and lasts for a short time, the impact factor is close to 1; if it is generated slowly and exists for a long time, the impact factor will be greater than 1. For example, for a degradable agricultural mulch film, if the intermediate products produced during its degradation process are quickly converted into final products, its intermediate product impact factor may be 1.05, slightly increasing the overall score. This factor reflects the environmental friendliness of the material degradation process, and rapidly converted intermediates usually mean lower environmental risks.

[0075] Next, the bottleneck impact coefficient of the degradation process is calculated based on the number and severity of potential degradation bottlenecks. This step uses a multi-factor evaluation method to comprehensively consider the number of bottlenecks and the degree of influence of each bottleneck on the overall degradation rate. The fewer the bottlenecks and the smaller the impact, the closer the coefficient is to 1; conversely, the coefficient will be less than 1. For example, if a complex multi-layer degradable electronic product packaging material has only a slight bottleneck in the degradation process, its bottleneck impact coefficient may be 0.98, which only slightly reduces the score. This coefficient reflects the uniformity and efficiency of the material degradation process and helps identify links that need to be optimized.

[0076] Subsequently, the actual application scenarios of the multi-layered degradable materials are evaluated to obtain the application scenario adaptability coefficient. This step uses a multi-criteria decision analysis method to consider the degradation behavior of the material under different environmental conditions (such as temperature, humidity, and pH value), as well as the degree of match with the intended use. If the material performs well in the intended application environment, the coefficient is close to or greater than 1; if it is not suitable, the coefficient will be less than 1. For example, a degradable agricultural mulch designed for tropical climates may have an application scenario adaptability coefficient of 1.1 if it has excellent degradation performance under high temperature and high humidity conditions, which improves the final score. This coefficient ensures that the comprehensive degradation index not only reflects the inherent properties of the material, but also takes into account the needs of actual applications.

[0077] Finally, the comprehensive degradation index of multilayer degradable materials is calculated by weighted average based on the basic degradation score, path complexity correction coefficient, intermediate product impact factor, bottleneck impact coefficient and application scenario adaptability coefficient. This step uses an optimization algorithm to determine the weight of each factor to balance the importance of different aspects. For example, for a multilayer degradable packaging material with excellent comprehensive performance, its final comprehensive degradation index may be 85 points (out of 100 points), reflecting the comprehensive performance of the material in terms of degradation speed, environmental friendliness and application adaptability.

[0078] This multi-factor comprehensive evaluation method has significant advantages. First, it comprehensively considers all aspects that affect the degradation performance of the material, from the basic degradation rate to the complex degradation path, and then to the adaptability to practical applications, providing a comprehensive evaluation system. Secondly, by introducing multiple correction factors, this method can flexibly adapt to different types of multilayered degradable materials, improving the accuracy and applicability of the evaluation. Furthermore, the calculation method of the comprehensive degradation index is highly interpretable, and each component has a clear physical or chemical meaning, which helps material designers understand and improve material performance. In addition, the output results of this method are intuitive and easy to understand, which facilitates comparison and selection between different materials.

[0079] In one embodiment of the present invention, the actual application scenario of the multilayer structure degradable material is evaluated to obtain the application scenario adaptability coefficient, including: analyzing the expected use environment of the multilayer structure degradable material to obtain environmental factor data, the environmental factor data including temperature, humidity, pH value and microbial activity; simulating the degradation behavior of the multilayer structure degradable material in the expected use environment according to the environmental factor data to obtain an environmental adaptability score; analyzing the expected use cycle of the multilayer structure degradable material to obtain use cycle data; and calculating the application scenario adaptability coefficient of the multilayer structure degradable material according to the environmental adaptability score and the use cycle data.

[0080] Specifically, first, the expected use environment of the multi-layered degradable material is analyzed to obtain environmental factor data. This step uses environmental monitoring and data analysis technology to collect and process the temperature, humidity, pH value and microbial activity data of the expected use environment. For example, for a degradable agricultural mulch used in tropical areas, environmental factor data may include an annual average temperature of 30°C, a relative humidity of 80%, a soil pH of 6.5 and high microbial activity. These data provide key input parameters for subsequent degradation behavior simulations, ensuring the pertinence and accuracy of the assessment.

[0081] Next, based on the environmental factor data, the degradation behavior of the multilayered degradable material in the expected use environment is simulated to obtain the environmental adaptability score. This step uses multi-physics field coupling simulation software to establish a mathematical model of material degradation and perform numerical solutions. The simulation process takes into account the effect of temperature on reaction rate, the effect of humidity on the hydrolysis process, the effect of pH on enzymatic degradation, and the contribution of microbial activity to biodegradation. By running a large number of simulation experiments, the degradation curve of the material under different environmental conditions can be obtained, and compared with the ideal degradation behavior, the environmental adaptability score is calculated. The advantage of this simulation method is that it can predict the long-term behavior of the material in a short time, greatly shortening the evaluation cycle.

[0082] Then, the expected use cycle of the multi-layered degradable material is analyzed to obtain the use cycle data. This step combines the product life cycle analysis (LCA) method with the actual application requirements to determine the time from the start of use to the functional failure of the material. For example, for a degradable food packaging material, its use cycle may be the entire process from packaging manufacturing, transportation, storage to food consumption, usually 3-6 months. The introduction of the use cycle data ensures the close integration of degradation performance evaluation with actual application requirements.

[0083] Finally, the application scenario adaptability coefficient of the multilayer structured biodegradable material is calculated based on the environmental adaptability score and the use cycle data. This step adopts a weighted scoring method to comprehensively consider the environmental adaptability score and the use cycle matching degree. If the degradation characteristics of the material are highly matched with the use cycle and perform well in the expected environment, the application scenario adaptability coefficient is close to or exceeds 1; conversely, if there is a significant mismatch, the coefficient will be less than 1. For example, a biodegradable material designed for refrigerated food packaging, if it remains stable in a low-temperature environment and degrades rapidly at room temperature, its application scenario adaptability coefficient may be 1.2, indicating that it is very suitable for its intended use.

[0084] This multi-step application scenario adaptability assessment method has significant advantages. First, it closely combines the degradation performance of the material with the actual application environment, improving the practicality and reliability of the assessment results. Secondly, by introducing environmental factor data and use cycle analysis, this method can comprehensively consider various factors that affect the actual performance of the material, avoiding the differences between laboratory evaluation and actual application. Furthermore, the introduction of environmental adaptability scores provides a quantitative basis for the performance of materials in different application scenarios, which helps to optimize material design and selection. In addition, the output results of this method are intuitive and easy to understand, making it easy for material developers and users to understand and apply.

[0085] The above describes the method for quickly identifying degradable materials in the embodiment of the present invention. The following describes the device for quickly identifying degradable materials in the embodiment of the present invention. Figure 2 , an embodiment of the device for quickly identifying degradable materials in the embodiment of the present invention comprises: The multimodal data acquisition module 101 is used to perform multimodal data acquisition on the multi-layered degradable material to obtain the physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; An interface characteristic analysis module 102 is used to perform characteristic analysis on the interlayer interface of the multi-layered degradable material according to the physical structure data, chemical composition data and preliminary degradation characteristic data to obtain interface characteristic parameters; A degradation synergistic effect evaluation module 103 is used to evaluate the degradation synergistic effect of the multi-layered degradable material according to the interface characteristic parameters to obtain quantitative description data of the interlayer interaction; The degradation potential prediction module 104 is used to predict the overall degradation potential of the multi-layered degradable material according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data by using a preset optimized random forest model to obtain a prediction result; The dynamic degradation path analysis module 105 is used to analyze the dynamic degradation path of the multilayer structure degradable material according to the prediction result to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottleneck points; The comprehensive degradation index calculation module 106 is used to calculate the comprehensive degradation index of the multi-layered degradable material according to the prediction results, the degradation path diagram, the key intermediate product timeline and the potential degradation bottleneck point.

[0086] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for quickly identifying degradable materials, characterized in that: include: Performing multimodal data collection on the multi-layered degradable material to obtain physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; According to the physical structure data, chemical composition data and preliminary degradation characteristic data, characteristic analysis is performed on the interlayer interface of the multilayer structure degradable material to obtain interface characteristic parameters; According to the interface characteristic parameters, the degradation synergistic effect of the multilayer structured degradable material is evaluated to obtain quantitative description data of the interlayer interaction; Using a preset optimized random forest model, the overall degradation potential of the multi-layered degradable material is predicted based on the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain a prediction result; According to the prediction results, the dynamic degradation path of the multilayered degradable material is analyzed to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks; According to the prediction results, degradation pathway diagram, key intermediate product timeline and potential degradation bottleneck points, the comprehensive degradation index of the multi-layer structure degradable material is calculated.

2. The method for rapidly identifying degradable materials according to claim 1, characterized in that: The multi-modal data collection of the multi-layered degradable material to obtain the physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material includes: Performing spectral imaging scanning on the multi-layered degradable material to obtain surface and shallow spectral information data of the multi-layered degradable material; Performing ultrasonic scanning on the multi-layered degradable material to obtain internal structure data of the multi-layered degradable material; Performing terahertz wave scanning on the multi-layered degradable material to obtain molecular vibration and rotation information data of the multi-layered degradable material; Generating a three-dimensional characteristic image of the multi-layered degradable material according to the surface and shallow layer spectral information data, internal structure data, and molecular vibration and rotation information data; The physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material are extracted according to the three-dimensional characteristic image.

3. The method for rapid identification of degradable materials according to claim 1, characterized in that: The method of performing characteristic analysis on the interlayer interface of the multilayer degradable material according to the physical structure data, chemical composition data and preliminary degradation characteristic data to obtain interface characteristic parameters includes: According to the physical structure data, chemical bond strength analysis is performed on the interlayer interface of the multilayer structured degradable material to obtain interface chemical bond strength data; According to the chemical composition data, a molecular diffusion behavior analysis is performed on the interlayer interface of the multilayer structured degradable material to obtain interface molecular diffusion coefficient data; According to the preliminary degradation characteristic data, a local microenvironment change analysis is performed on the interlayer interface of the multilayer structure degradable material to obtain local pH value change data of the interface; The interface characteristic parameters of the multilayer structured degradable material are calculated based on the interface chemical bond strength data, the interface molecular diffusion coefficient data and the interface local pH value change data.

4. The method for rapidly identifying degradable materials according to claim 1, characterized in that: The step of evaluating the degradation synergistic effect of the multilayer degradable material according to the interface characteristic parameters to obtain quantitative description data of the interlayer interaction includes: Calculating the degradation rate of each layer of the multi-layered degradable material according to the interface characteristic parameters to obtain the degradation rate data of each layer; According to the interface characteristic parameters and the individual degradation rate data of each layer, the interlayer material exchange of the multilayer structured degradable material is simulated to obtain the interlayer material exchange flux data; Analyzing the diffusion behavior of degradation products of the multilayer degradable material according to the interlayer material exchange flux data to obtain a diffusion influencing factor of the degradation products; According to the individual degradation rate data of each layer, the interlayer material exchange flux data and the degradation product diffusion influence factor, the interlayer interaction quantitative description data of the multi-layer structured degradable material is calculated.

5. The method for rapid identification of degradable materials according to claim 4, characterized in that: The step of analyzing the diffusion behavior of the degradation products of the multilayer degradable material according to the interlayer material exchange flux data to obtain the diffusion influencing factor of the degradation products comprises: Calculating the concentration gradient between the layers of the multilayer degradable material according to the interlayer material exchange flux data to obtain interlayer concentration gradient data; Calculating the diffusion rate of degradation products in the multilayer degradable material according to the interlayer concentration gradient data to obtain the diffusion rate data of degradation products; Analyzing the accumulation of degradation products in each layer of the multi-layered degradable material according to the degradation product diffusion rate data to obtain the degradation product cumulative distribution data; According to the cumulative distribution data of the degradation products, the influence degree of the degradation products on the degradation process of each layer is calculated to obtain the diffusion influence factor of the degradation products.

6. The method for rapidly identifying degradable materials according to claim 1, characterized in that: The preset optimized random forest model is used to predict the overall degradation potential of the multi-layered degradable material according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data, and the prediction results are obtained, including: Performing feature vector quantization processing on the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data to obtain an input feature vector; Inputting the input feature vector into the preset optimized random forest model, wherein the preset optimized random forest model includes a plurality of decision trees, each decision tree includes a root node, an internal node and a leaf node, and obtaining a prediction output of each decision tree; Integrating the prediction outputs of each decision tree to obtain a predicted value of the degradation rate of each layer of the multi-layered degradable material; Calculating the predicted value of the overall degradation time of the multi-layered degradable material according to the predicted values ​​of the degradation rates of the layers; Calculating the inter-layer degradation rate difference coefficient of the multi-layer structured degradable material according to the predicted degradation rate of each layer and the predicted degradation time of the whole layer; The predicted values ​​of the degradation rates of the various layers, the predicted value of the overall degradation time and the coefficient of difference in degradation rates between the layers are combined to obtain the predicted result.

7. The method for rapidly identifying degradable materials according to claim 1, characterized in that: According to the prediction results, the dynamic degradation path of the multilayered degradable material is analyzed to obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks, including: According to the predicted values ​​of degradation rate of each layer and the predicted value of overall degradation time in the predicted results, a degradation process simulation is performed on each layer of the multi-layered degradable material to obtain degradation intermediate product data of each layer; Dynamically evaluating the interlayer interaction of the multilayer degradable material according to the degradation intermediate product data of each layer to obtain an interlayer degradation influencing factor; According to the degradation intermediate product data of each layer and the interlayer degradation influencing factor, a dynamic degradation network diagram of the multi-layer structured degradable material is constructed, wherein nodes represent degradation intermediate products and edges represent transformation relationships; Performing a time series analysis on the dynamic degradation network diagram, identifying key degradation pathways and bottleneck reactions, and obtaining a degradation pathway diagram; According to the degradation pathway diagram, time series prediction is performed on key degradation intermediates to obtain a timeline of key intermediates; According to the degradation pathway diagram and key intermediate product timeline, the rate-limiting steps in the degradation process are identified to obtain potential degradation bottlenecks.

8. The method for rapid identification of degradable materials according to claim 1, characterized in that: The comprehensive degradation index of the multi-layered degradable material is calculated according to the prediction results, the degradation pathway diagram, the key intermediate product timeline and the potential degradation bottleneck point, including: Calculating the basic degradation score of the multi-layered degradable material according to the predicted value of the overall degradation time and the coefficient of difference in degradation rate between layers in the prediction result; According to the number and complexity of key degradation pathways in the degradation pathway diagram, the basic degradation score is corrected to obtain a pathway complexity correction coefficient; According to the key intermediate product timeline, the average generation rate and duration of the key intermediate product are calculated to obtain the intermediate product impact factor; Calculating the bottleneck influence coefficient of the degradation process according to the number and severity of the potential degradation bottleneck points; Evaluate the actual application scenarios of the multi-layered degradable material to obtain an application scenario adaptability coefficient; According to the basic degradation score, the path complexity correction coefficient, the intermediate product impact factor, the bottleneck impact coefficient and the application scenario adaptability coefficient, the comprehensive degradation index of the multi-layer structure degradable material is calculated by weighted average.

9. The method for rapidly identifying degradable materials according to claim 8, characterized in that: The actual application scenario of the multi-layered degradable material is evaluated to obtain the application scenario adaptability coefficient, including: Analyzing the expected use environment of the multi-layered degradable material to obtain environmental factor data, wherein the environmental factor data includes temperature, humidity, pH value and microbial activity; According to the environmental factor data, the degradation behavior of the multi-layered degradable material in the expected use environment is simulated to obtain an environmental adaptability score; Analyzing the expected use cycle of the multi-layered degradable material to obtain use cycle data; According to the environmental adaptability score and the usage cycle data, the application scenario adaptability coefficient of the multi-layer structured degradable material is calculated.

10. A device for quickly identifying degradable materials, characterized in that: The device for quickly identifying degradable materials adopts the method for quickly identifying degradable materials according to any one of claims 1 to 9, and the device for quickly identifying degradable materials comprises: A multimodal data acquisition module is used to perform multimodal data acquisition on the multi-layered degradable material to obtain physical structure data, chemical composition data and preliminary degradation characteristic data of the multi-layered degradable material; An interface characteristic analysis module, used to perform characteristic analysis on the interlayer interface of the multi-layered degradable material according to the physical structure data, chemical composition data and preliminary degradation characteristic data, to obtain interface characteristic parameters; A degradation synergistic effect evaluation module, used to evaluate the degradation synergistic effect of the multilayer structure degradable material according to the interface characteristic parameters, and obtain quantitative description data of the interlayer interaction; A degradation potential prediction module, for predicting the overall degradation potential of the multi-layered degradable material using a preset optimized random forest model according to the physical structure data, chemical composition data, preliminary degradation characteristic data, interface characteristic parameters and interlayer interaction quantitative description data, to obtain a prediction result; A dynamic degradation path analysis module, used to analyze the dynamic degradation path of the multilayer structure degradable material according to the prediction results, and obtain a degradation path diagram, a key intermediate product timeline and potential degradation bottlenecks; The comprehensive degradation index calculation module is used to calculate the comprehensive degradation index of the multi-layer structure degradable material according to the prediction results, degradation path diagram, key intermediate product timeline and potential degradation bottleneck points.

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