Micro-plastic aging degree evaluation method, device, equipment and storage medium

Abstract

The invention discloses a micro-plastic aging degree evaluation method, device and equipment and a storage medium, and relates to the field of micro-plastics. The method comprises the steps that initial data of target micro-plastic and aging data of the target micro-plastic after aging treatment in a target environment are obtained, and the initial data and the aging data both comprise a plurality of data dimensions; determining target representation data of each data dimension according to the initial data and the aging data; determining an aging evaluation index of the target micro-plastic according to the target characterization data and the weight corresponding to each data dimension; and determining the aging degree of the target micro-plastic in the target environment according to the aging evaluation index and a preset evaluation threshold. The problem that in the prior art, the aging degrees of different types of micro-plastics in different environments cannot be quantitatively evaluated can be solved, and accurate comparison of the aging degrees of different types of micro-plastics in different environments is achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of microplastics, and particularly relates to a microplastic aging degree evaluation method, device, equipment and storage medium. BACKGROUND

[0002] Microplastics (MPs) refer to plastic particles or fibers with a diameter of less than 5 mm. Due to the small size, wide distribution and hidden hazards, microplastics have become a focus in the environmental field. The aging of microplastics can change its physical and chemical properties and may release plastic additives, resulting in new environmental behaviors. Therefore, the aging degree evaluation of microplastics has become a research direction.

[0003] At present, there is no corresponding method to quantitatively evaluate the aging degree of different types of microplastics in different environments, which limits the comparability of the aging results of different types (such as polyethylene, polypropylene and polystyrene) of microplastics in different environments (such as specific temperature, light, humidity and medium conditions), and seriously hinders the aging research of microplastics. Therefore, there is an urgent need for a method that can accurately compare the aging degrees of different types of microplastics in different environments. SUMMARY

[0004] The microplastic aging degree evaluation method, device, equipment and storage medium provided by the embodiments of the application solve the problem that the aging degree of different types of microplastics in different environments cannot be quantitatively evaluated in the prior art, and realize accurate comparison of the aging degrees of different types of microplastics in different environments.

[0005] In a first aspect, the embodiments of the application provide a microplastic aging degree evaluation method, which comprises the following steps:

[0006] obtaining initial data of target microplastics and aging data of the target microplastics after aging treatment in a target environment, wherein the initial data and the aging data each comprise a plurality of data dimensions; determining target characteristic data of each data dimension according to the initial data and the aging data; determining an aging evaluation index of the target microplastics according to the target characteristic data and a weight corresponding to each data dimension; and determining an aging degree of the target microplastics in the target environment according to the aging evaluation index and a preset evaluation threshold.

[0007] Further, the target characteristic data corresponding to each data dimension is determined according to the initial data and the aging data, which comprises the following steps: determining the target characteristic data of each data dimension through a characteristic model according to the initial data and the aging data; and the characteristic model is as shown in the following formula:

[0008]

[0009] In the formula, Q idenotes the target characterization data corresponding to the i-th data dimension, P i denotes the average value of the aging data of the i-th data dimension, P i,max denotes the maximum value of the aging data of the i-th data dimension, P i,min denotes the minimum value of the initial data of the i-th data dimension.

[0010] Further, according to the target characterization data and the weight corresponding to each data dimension, the aging evaluation index of the target microplastic is determined, including: according to the target characterization data and the weight corresponding to each data dimension, combining the aging index comprehensive evaluation model, the aging evaluation index of the target microplastic is determined; the aging index comprehensive evaluation model is as follows:

[0011]

[0012] In the above formula, CAI denotes the aging evaluation index, Q i denotes the target characterization data corresponding to the i-th data dimension, w i denotes the weight corresponding to the i-th data dimension, and n denotes the total number of data dimensions corresponding to the target environment.

[0013] Further, the weight corresponding to each data dimension is determined according to the aging research theory combined with verified evidence.

[0014] Further, the method further includes: curve fitting the aging evaluation index under different aging conditions to establish an aging kinetics model; and determining the relationship curve between the aging evaluation index of the target microplastic and time under the target environment according to the aging kinetics model.

[0015] Further, the plurality of data dimensions include at least two of the particle size distribution dimension, the micro-morphology dimension, the specific surface area dimension, the crystallinity dimension, the contact angle dimension, the Zeta potential dimension, the carbonyl index dimension, the oxygen-carbon ratio dimension, the chlorine ion release dimension, the key additive release dimension, the mass fraction dimension, and the NPs release dimension.

[0016] Further, according to the aging evaluation index and the preset evaluation threshold, the aging degree of the target microplastic under the target environment is determined, including: when the aging evaluation index is less than 0.3, the aging degree of the target microplastic under the target environment is determined to be mild aging; when the aging evaluation index is greater than or equal to 0.3 and less than 0.6, the aging degree of the target microplastic under the target environment is determined to be moderate aging; and when the aging evaluation index is greater than 0.6, the aging degree of the target microplastic under the target environment is determined to be severe aging.

[0017] In a second aspect, an embodiment of the present application provides a microplastic aging degree evaluation device, including:

[0018] The acquisition module is configured to acquire initial data of the target microplastic and aging data of the target microplastic after the target microplastic is subjected to aging treatment in a target environment, and the initial data and the aging data each include a plurality of data dimensions; the processing module is configured to determine target characterization data of each data dimension according to the initial data and the aging data; determine an aging evaluation index of the target microplastic according to the target characterization data and a weight corresponding to each data dimension; and the evaluation module is configured to determine an aging degree of the target microplastic in the target environment according to the aging evaluation index and a preset evaluation threshold.

[0019] In a third aspect, an apparatus is provided. The apparatus includes a processor, a memory storing processor-executable instructions, and the processor implements the method of the first aspect or any possible implementation of the first aspect when executing the processor-executable instructions.

[0020] In a fourth aspect, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium includes a computer program or instructions stored thereon, and the computer program or instructions, when executed, cause the method of the first aspect or any possible implementation of the first aspect to be implemented.

[0021] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] In the embodiments of the present application, the initial data of the target microplastic and the aging data of the target microplastic after the target microplastic is subjected to aging treatment in a target environment are acquired, target characterization data reflecting the aging of the target microplastic in each data dimension is determined according to the initial data and the aging data, an aging evaluation index of the target microplastic under the comprehensive effect of a plurality of data dimensions is determined according to the target characterization data and a weight corresponding to each data dimension, and an aging degree of the target microplastic in the target environment is determined according to the aging evaluation index and a preset evaluation threshold. Thus, the problem that the aging degree of different types of microplastics in different environments cannot be quantitatively evaluated in the prior art is solved, and the aging degree of different types of microplastics in different environments can be accurately compared through the quantitative aging evaluation index. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0024] Figure 1 A flowchart of a microplastic aging degree evaluation method provided by the embodiments of the present application is shown in the figure.

[0025] Figure 2 A schematic diagram of the composition of the microplastic aging degree evaluation device provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0027] The following describes some technologies related to the embodiments of the present application to help understanding, which should be considered only as exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted in the following description.

[0028] Microplastics (MPs) refer to plastic particles or fibers with a diameter of less than 5 millimeters. Due to its small size, wide distribution and hidden harm, microplastics have become a key concern in the environmental field. The aging of microplastics can change its physical and chemical properties, and may also release plastic additives, leading to new environmental behaviors. Therefore, the aging degree evaluation of microplastics has become a research direction.

[0029] Currently, there is no corresponding method to quantitatively evaluate the aging degree of different types of microplastics in different environments, which limits the comparability of the aging results of different types of microplastics (such as polyethylene, polypropylene, polystyrene, etc.) in different environments (such as specific temperature, light, humidity, medium conditions, etc.), and seriously hinders the aging research of microplastics. Therefore, there is an urgent need for a method that can accurately compare the aging degrees of different types of microplastics in different environments.

[0030] Under this background, the present disclosure provides a microplastic aging degree evaluation method, which can accurately compare the aging degrees of different types of microplastics in different environments.

[0031] The execution subject of the microplastic aging degree evaluation method provided by the embodiments of the present disclosure can be a computer or a server, or can also be other electronic devices with data processing capability; or the execution subject of the method can also be a processor (for example, a central processing unit (CPU)) in the above-mentioned electronic devices; or the execution subject of the method can also be an application (APP) installed in the above-mentioned electronic devices and capable of realizing the function of the method; or the execution subject of the method can also be a functional module or unit with the function of the method in the above-mentioned electronic devices, and the like. The execution subject of the method is not limited herein.

[0032] The microplastic aging degree evaluation method will be exemplarily described below with reference to the accompanying drawings.

[0033] Figure 1 is a flowchart of the microplastic aging degree evaluation method provided by the embodiments of the present disclosure. Wherein, Figure 1 This is only one execution order shown by the embodiments of the present disclosure, and does not represent the only execution order of the microplastic aging degree evaluation method, and as long as the final result can be achieved, Figure 1 The steps shown can be executed in parallel or in reverse. As Figure 1 shown, the method can include:

[0034] S101, obtaining initial data of target microplastics and aging data of the target microplastics after aging treatment in a target environment.

[0035] Wherein, the initial data and the aging data each include a plurality of data dimensions.

[0036] It can be understood that the target microplastics refer to microplastics that need to be evaluated for aging degree, which can be polyethylene, polypropylene, polystyrene, etc., and the type of microplastics is not limited. The target environment can refer to the environment in which the target microplastics are subjected to aging treatment, such as specific temperature, light, humidity, medium conditions, etc., and the target environment is not limited.

[0037] Exemplarily, the plurality of data dimensions include at least two of a particle size distribution dimension, a micro-morphology dimension, a specific surface area dimension, a crystallinity dimension, a contact angle dimension, a Zeta potential dimension, a carbonyl index dimension, an oxygen-carbon ratio dimension, a chlorine ion release dimension, a key additive release dimension, a mass fraction dimension, and a NPs (Nanoparticles) release dimension.

[0038] Exemplarily, the initial data can be the data corresponding to the target microplastics without aging treatment, which can be an actual value or a theoretical value, and the aging data can be the data corresponding to the target microplastics after aging treatment in the target environment, which is an actual value.

[0039] The initial data and / or aging data corresponding to the plurality of data dimensions can be obtained in the following manner.

[0040] The particle size distribution dimension data is obtained by using image analysis software (image J) to measure the particle size of MPs particles in the image after obtaining a high-resolution image by SEM (Scanning Electron Microscope). It is important to ensure that enough particles (>100) are selected for measurement, and the particle size data of each particle is recorded.

[0041] The micro-morphology dimension data is obtained by preprocessing the SEM image of microplastics to ensure the accuracy and reliability of subsequent analysis. From the obtained SEM image, clear and representative areas are selected to avoid image blur or impurity interference affecting the analysis results. Image processing software (Image J) is used to remove background noise and appropriately adjust the image contrast and brightness to make the MP profile clearer for subsequent measurement and analysis. By observing the surface of MPs through the scanning electron microscope image, features such as cracks, holes, increased surface roughness, fragmentation, etc. are observed, and the micro-morphology of microplastics is valued according to the surface condition of microplastics to obtain the micro-morphology dimension data. For example, slight roughness, no obvious cracks are valued as 0 to 0.3, surface with obvious cracks or holes are valued as 0.4 to 0.7, and surface with severe fragmentation or large-area cracking are valued as 0.8 to 1.

[0042] The specific surface area dimension data is obtained by measuring the adsorption amount of nitrogen at different pressures through a BET specific surface area analyzer to obtain an adsorption isotherm. The data points of the adsorption isotherm within the relative pressure (P / P0) range of 0.05-0.35 are linearly fitted to calculate the monolayer adsorption volume (V m ), and the specific surface area is obtained by combining the cross-sectional area of the gas molecules.

[0043] The crystallinity dimension data is obtained by obtaining the XRD (X-ray Diffraction) pattern of the microplastics, in which sharp diffraction peaks (corresponding to crystalline regions) and broadened diffuse peaks (corresponding to amorphous regions) appear. In order to separate the crystalline peaks from the amorphous peaks, the diffraction pattern is fitted to the peaks using XRD analysis software (such as Jade). The crystal peaks are usually suitable for Gaussian or Lorentz functions, while the amorphous diffuse peaks are suitable for broadened background peaks. After fitting, the total area (A crystalline ) of the crystalline peaks and the total area (A amorphous), and then the crystallinity was calculated.

[0044] The contact angle dimension data was obtained by capturing the droplet image deposited on the surface of the MP sample using a contact angle goniometer camera, analyzing the droplet profile using the instrument, and calculating the contact angle by commonly used calculation methods (such as the tangent method and the Young-Laplace fitting method).

[0045] The zeta potential dimension data was obtained by direct measurement.

[0046] The carbonyl index dimension data was obtained by obtaining the FTIR spectrum, measuring the peak intensity of the carbonyl peak and the reference peak (the key absorption peak of the carbonyl (C=O) representing the oxidation degree of the MPs has a stretching vibration, which usually occurs in the range of 1700-1750 cm -1 . In order to eliminate the influence of sample thickness and measurement conditions, an absorption peak that is stable in chemical structure and not affected by aging needs to be selected as the reference peak) using spectral analysis software (Origin), and the carbonyl index was calculated.

[0047] The oxygen-carbon ratio dimension data was obtained by splitting and fitting the obtained carbon (C 1s ) and oxygen (O 1s ) peak spectra, calculating the total peak area of C 1s and O 1s spectra (A C 1s and A O 1s ) after fitting, and combining the corresponding sensitivity factors to calculate the oxygen-carbon ratio.

[0048] The chlorine ion release dimension data was obtained by converting the peak area of chlorine ions in the target microplastics into the solution concentration according to the chlorine ion standard curve (obtained by measuring the known concentration of the chlorine ion standard solution), and then calculating the chlorine ion release degree according to the solution volume and the mass of the target microplastics.

[0049] The key additive release dimension data was obtained by recording the content of total organic carbon (TOC) in the test results. According to the standard curve, the TOC value of the target microplastics was converted into the organic carbon solution concentration, and then the key additive release degree was calculated according to the solution volume and the mass of the target microplastics.

[0050] The mass fraction dimension data was obtained by recording the retention time of each peak after gas chromatography separation of the pyrolysis products, introducing the separated compounds into the mass spectrometer detector, analyzing the molecular structure by mass spectrum, and comparing with the standard mass spectrum library to identify the chemical composition of each peak, and then calculating the mass fraction by using the peak area of the characteristic peak.

[0051] The NPs release dimension data is calculated by a chemical imaging mode to calculate the comprehensive intensity of the characteristic infrared absorption peak of the NPs, and a chemical distribution map is generated to locate the NPs aggregation area, and then the particle number of the NPs is counted according to the morphological scanning image (S i ), and the NPs release amount is calculated.

[0052] S102, determining the target characterization data of each data dimension according to the initial data and the aging data.

[0053] Specifically, the target characterization data corresponding to each data dimension is determined according to the initial data and the aging data, including:

[0054] According to the initial data and the aging data, the target characterization data of each data dimension is determined by a characterization model; the characterization model is as follows:

[0055]

[0056] In the formula, Q i represents the target characterization data corresponding to the i-th data dimension, P i represents the average value of the aging data of the i-th data dimension, P i,max represents the maximum value of the aging data of the i-th data dimension, P i,min represents the minimum value of the initial data of the i-th data dimension.

[0057] Exemplarily, for a certain data dimension, multiple aging data under the data dimension can be obtained by multiple analysis and calculation, and the average value of the multiple aging data can be used as the value of P i of the data dimension; when a certain data dimension only has one aging data obtained by one analysis and calculation, the aging data can be used as the value of P i of the data dimension.

[0058] In this way, the target characterization data reflecting the aging of the target microplastics under each data dimension can be accurately determined.

[0059] S103, determining the aging evaluation index of the target microplastics according to the target characterization data and the weight corresponding to each data dimension.

[0060] Specifically, the aging evaluation index of the target microplastics is determined according to the target characterization data and the weight corresponding to each data dimension, including:

[0061] According to the target characterization data and the weight corresponding to each data dimension, the aging evaluation index of the target microplastics is determined by combining an aging index comprehensive evaluation model;

[0062] The aging index comprehensive evaluation model is as follows:

[0063]

[0064] In the above formula, CAI represents an aging evaluation index, Q i represents the target characterization data corresponding to the i-th data dimension, w i represents the weight corresponding to the i-th data dimension, and n represents the total number of data dimensions corresponding to the target environment.

[0065] It can be understood that different target environments can correspond to different data dimensions and quantities.

[0066] In some possible embodiments, the weight corresponding to each data dimension is determined according to aging research theory combined with verified evidence.

[0067] The weight corresponding to the carbonyl index dimension, the oxygen-carbon ratio dimension, the chloride ion release dimension, and the key additive release dimension is 0.10 to 0.12. They are directly related to chemical degradation (oxidation, dechlorination), are of high importance, and are closely related to environmental impact (such as ecological toxicity). For example, the carbonyl index and the oxygen-carbon ratio can be used to reflect the degree of rupture of the oxidation chain, which is the main pathway of MP aging; chloride ion release is a specific indicator of PVC microplastic aging, indicating the instability of the polymer matrix and the increase in toxicity.

[0068] The weight corresponding to the particle size distribution dimension, the micro-morphology dimension, the specific surface area dimension, and the mass fraction dimension is 0.08 to 0.10. They are used to capture the physical fragmentation and surface area changes in the aging process, which affect the bioavailability of MPs, but are secondary to chemical conversion, and are of medium importance.

[0069] The weight corresponding to the crystallinity dimension, the contact angle dimension, the Zeta potential dimension, and the NPs release dimension is 0.05 to 0.07. They are indirect representatives of aging, for example, changes in crystallinity can lag behind surface oxidation, and are of low importance.

[0070] For example, the weight corresponding to the particle size distribution dimension can be 0.1, the weight corresponding to the micro-morphology dimension can be 0.1, the weight corresponding to the specific surface area dimension can be 0.08, the weight corresponding to the crystallinity dimension can be 0.05, the weight corresponding to the contact angle dimension can be 0.05, the weight corresponding to the Zeta potential dimension can be 0.05, the weight corresponding to the carbonyl index dimension can be 0.12, the weight corresponding to the oxygen-carbon ratio dimension can be 0.1, the weight corresponding to the chloride ion release dimension can be 0.1, the weight corresponding to the key additive release dimension can be 0.1, the weight corresponding to the mass fraction dimension can be 0.08, and the weight corresponding to the NPs release dimension can be 0.07.

[0071] S104, determine the aging degree of the target microplastic in the target environment according to the aging evaluation index and the preset evaluation threshold.

[0072] Specifically, the aging degree of the target microplastic in the target environment is determined according to the aging evaluation index and the preset evaluation threshold, including:

[0073] When the aging evaluation index is less than 0.3, the aging degree of the target microplastic in the target environment is determined to be mild aging; when the aging evaluation index is greater than or equal to 0.3 and less than 0.6, the aging degree of the target microplastic in the target environment is determined to be moderate aging; and when the aging evaluation index is greater than 0.6, the aging degree of the target microplastic in the target environment is determined to be severe aging.

[0074] It can be understood that the larger the aging evaluation index is, the higher the aging degree is.

[0075] The embodiments of the present application obtain the initial data of the target microplastic and the aging data of the target microplastic after aging treatment in the target environment, determine the target characterization data that can reflect the aging of the target microplastic in each data dimension according to the initial data and the aging data, determine the aging evaluation index of the target microplastic in multiple data dimensions according to the target characterization data and the weight corresponding to each data dimension, and determine the aging degree of the target microplastic in the target environment according to the aging evaluation index and the preset evaluation threshold, thereby solving the problem that the aging degree of different types of microplastics in different environments cannot be quantitatively evaluated in the prior art, and enabling accurate comparison of the aging degree of different types of microplastics in different environments through the quantitative aging evaluation index.

[0076] In some possible embodiments, the method further includes:

[0077] The aging evaluation index of the target microplastic in the target environment is curve-fitted to establish an aging kinetics model, and the relationship curve between the aging evaluation index of the target microplastic and time in the target environment is determined according to the aging kinetics model.

[0078] For example, a nonlinear least squares fitting method can be used to fit a plurality of preset models (such as a linear model, a power law model, a polynomial model, a logistic model, and an exponential decay model) respectively to obtain a plurality of fitting models; and a determination coefficient R res is calculated according to the residual sum of squares (SS tot ) between the aging evaluation index predicted value and the aging evaluation index actual value of the plurality of fitting models, and the total sum of squares (SS 2 ). res tot 2 ​​The aging evaluation index is used for measuring the degree of explanation of the fitting model to the data variation, as an evaluation index of the fitting model; the aging evaluation index corresponding to different fitting models and time can be obtained by using the ggplot2 visualization software, so that the fitting effects of different fitting models on the microplastic aging process can be intuitively compared, and the determination coefficient R 2 The aging kinetics model is determined from different fitting models, and then the relationship curve between the aging evaluation index of the target microplastic and time in the target environment is obtained. In this way, according to the relationship curve between the aging evaluation index of the target microplastic and time, the change trend of the aging rate and the potential nonlinear behavior characteristics can be analyzed and judged, and further in-depth research on the microplastic aging can be realized.

[0079] Although the present application provides method operation steps such as examples or flowcharts, more or fewer operation steps can be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one of the many execution orders, and does not represent the only execution order. When the device or client product is executed in practice, the method order shown in the embodiments or the drawings can be executed in sequence or in parallel (for example, in a parallel processor or multi-thread processing environment).

[0080] As shown in Figure 2 The present application also provides a microplastic aging degree evaluation device. The device comprises:

[0081] The acquisition module 201 is configured to acquire initial data of the target microplastic and aging data of the target microplastic after aging treatment in a target environment, and the initial data and the aging data each comprise a plurality of data dimensions;

[0082] The processing module 202 is configured to determine target feature data of each data dimension according to the initial data and the aging data, and determine an aging evaluation index of the target microplastic according to the target feature data and weights corresponding to each data dimension.

[0083] The evaluation module 203 is configured to determine the aging degree of the target microplastic in the target environment according to the aging evaluation index and a preset evaluation threshold.

[0084] The beneficial effects and specific implementation manners of the device embodiments can be referred to the foregoing method embodiments, which will not be repeated here.

[0085] Some of the modules in the apparatus described in the present application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, and the like, that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such activities can depend, for example, on the implementation of the computer-implemented methods. The methods and systems described in the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0086] The apparatus or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above apparatus is described as various modules according to functions. In the implementation of the embodiments of the present application, the functions of the modules can be implemented in one or more software and / or hardware. Of course, the modules implementing certain functions can also be implemented by a combination of multiple sub-modules or sub-units.

[0087] The methods, apparatuses or modules described in the present application can be implemented in a computer-readable program code in any appropriate manner, for example, the controller can take the form of, for example, a microprocessor or a processor, and a computer-readable medium storing computer-readable program code (for example, software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers and embedded microcontrollers. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that, in addition to implementing the controller in a pure computer-readable program code manner, the same function can also be implemented by logically programming the method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the means included therein for implementing various functions can also be regarded as structures within the hardware component. Alternatively, the means for implementing various functions can also be regarded as both software modules implementing the method and structures within the hardware component.

[0088] The embodiments of the present application also provide a device, which comprises: a processor; a memory for storing processor-executable instructions; and the processor executes the executable instructions to implement the method described in the embodiments of the present application.

[0089] The embodiments of the present application further provide a nonvolatile computer readable storage medium, which stores computer programs or instructions, and when the computer programs or instructions are executed, the method as described in the embodiments of the present application is realized.

[0090] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist independently, or two or more modules can be integrated in one module.

[0091] The storage medium includes but is not limited to random access memory (English: Random Access Memory; abbreviation: RAM), read-only memory (English: Read-Only Memory; abbreviation: ROM), cache (English: Cache), hard disk (English: Hard Disk Drive; abbreviation: HDD) or memory card (English: Memory Card). The memory can be used to store computer program instructions.

[0092] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary hardware. Based on such understanding, the technical solutions of the present application can be embodied in the form of software products or in the form of data migration in the implementation process. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, mobile terminal, server, or network device, etc.) to execute the method described in each embodiment or some parts of the embodiments of the present application.

[0093] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. The whole or part of the present application can be used in many general or special computer system environments or configurations. For example: personal computer, server computer, handheld device or portable device, tablet device, mobile communication terminal, multi-processor system, microprocessor-based system, programmable electronic device, network PC, small computer, large computer, distributed computing environment including any of the above systems or devices, etc.

[0094] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or some or all of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for assessing the degree of aging of microplastics, characterized in that, The method comprises: obtaining initial data of target microplastics and aging data of the target microplastics after aging treatment in a target environment, wherein the initial data and the aging data each comprise a plurality of data dimensions; determining target characterization data of each data dimension according to the initial data and the aging data; determining an aging evaluation index of the target microplastics according to the target characterization data and a weight corresponding to each data dimension; determining an aging degree of the target microplastics in the target environment according to the aging evaluation index and a preset evaluation threshold.

2. The method of claim 1, wherein, The determining of the target characterization data corresponding to each data dimension according to the initial data and the aging data comprises: determining the target characterization data of each data dimension according to the initial data and the aging data through a characterization model; The characterization model is as follows: In the formula, Q i represents the target representation data corresponding to the i-th data dimension, P i represents the average value of the aging data of the i-th data dimension, P i,max represents the maximum value of the aging data of the i-th data dimension, P i,min represents the minimum value of the initial data of the i-th data dimension.

3. The method of claim 1, wherein, The determining of the aging evaluation index of the target microplastics according to the target characterization data and the weight corresponding to each data dimension comprises: determining the aging evaluation index of the target microplastics according to the target characterization data and the weight corresponding to each data dimension in combination with an aging index comprehensive evaluation model; The aging index comprehensive evaluation model is as follows: In the above formula, CAI represents an aging evaluation index, Q i represents the target feature data corresponding to the i-th data dimension, w i represents the weight corresponding to the i-th data dimension, and n represents the total number of corresponding data dimensions under the target environment.

4. The method of claim 1, wherein, The weight corresponding to each data dimension is determined according to an aging research theory in combination with verified evidence.

5. The method of claim 1, wherein, The method further comprises: performing curve fitting on the aging evaluation index under different aging conditions to establish an aging kinetics model; determining a relationship curve between the aging evaluation index of the target microplastics and time in the target environment according to the aging kinetics model.

6. The method of claim 1, wherein, The plurality of data dimensions comprise at least two of a particle size distribution dimension, a micro-morphology dimension, a specific surface area dimension, a crystallinity dimension, a contact angle dimension, a Zeta potential dimension, a carbonyl index dimension, an oxygen-carbon ratio dimension, a chlorine ion release dimension, a key additive release dimension, a mass fraction dimension, and an NPs release dimension.

7. The method of claim 1, wherein, The determining of the aging degree of the target microplastics in the target environment according to the aging evaluation index and the preset evaluation threshold comprises: when the aging evaluation index is less than 0.3, determining that the aging degree of the target microplastics in the target environment is mild aging; when the aging evaluation index is greater than or equal to 0.3 and less than 0.6, determining that the aging degree of the target microplastics in the target environment is moderate aging; and when the aging evaluation index is greater than 0.6, determining that the aging degree of the target microplastics in the target environment is severe aging.

8. A microplastic aging degree evaluation device, characterized by, The method comprises: an obtaining module, configured to obtain initial data of target microplastics and aging data of the target microplastics after aging treatment in a target environment, wherein the initial data and the aging data each comprise a plurality of data dimensions; a processing module, configured to determine target characterization data of each data dimension according to the initial data and the aging data, and determine an aging evaluation index of the target microplastics according to the target characterization data and a weight corresponding to each data dimension; an evaluation module, configured to determine an aging degree of the target microplastics in the target environment according to the aging evaluation index and a preset evaluation threshold.

9. An apparatus for performing a microplastic aging degree assessment method, characterized in that, The method comprises: a processor; a memory for storing processor-executable instructions; The processor, when executing the executable instructions, implements the method as claimed in any one of claims 1 to 7.

10. A non-transitory computer readable storage medium, comprising: comprising computer program or instructions for causing the method as claimed in any one of claims 1 to 7 to be implemented when executed.

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