A microplastic particle size measurement method and system based on data analysis

Abstract

The application discloses a microplastic particle size measurement method and system based on data analysis, and relates to the technical field of microplastic particle size measurement. The microplastic particle size measurement method based on data analysis acquires optical detection data and kinetic detection data of microplastic particles; the optical detection data and the kinetic detection data of the microplastic particles are preprocessed, and light scattering characteristics and sedimentation characteristics of the microplastic particles are extracted respectively; based on the light scattering characteristics and the sedimentation characteristics of the microplastic particles, light scattering indexes and sedimentation indexes of the microplastic particles are analyzed respectively; and based on the light scattering indexes and the sedimentation indexes of the microplastic particles, particle size values of the microplastic particles are estimated. The application can comprehensively analyze the light scattering characteristics and the sedimentation characteristics of the microplastic particles by a multi-data fusion method, so that more reliable light scattering indexes and sedimentation indexes are obtained, and the accuracy of particle size estimation can be effectively improved through calculation of the indexes.

Description

Technical Field

[0001] This invention relates to the field of microplastic particle size measurement technology, specifically to a microplastic particle size measurement method and system based on data analysis. Background Technology

[0002] Microplastic pollution, a growing global environmental problem, has attracted widespread attention. Microplastic particles are widely present in oceans, rivers, lakes, and soil, seriously impacting ecosystems and human health. The particle size of microplastics is a crucial parameter for assessing their environmental impact, monitoring pollution levels, and implementing appropriate remediation measures. Therefore, accurate and rapid measurement of microplastic particle size is essential for microplastic pollution monitoring and environmental remediation.

[0003] Currently, methods for measuring microplastic particle size mainly include physical sieving, microscopic observation, and laser diffraction. Each method has its advantages but also limitations. For example, sieving often relies on manual operation and is prone to errors when measuring fine particles; microscopic observation requires high-precision equipment and is cumbersome to operate, making it difficult to perform large-scale sample testing; while laser diffraction can provide rapid measurements, it may be affected by particle morphology and dispersion in complex environments.

[0004] With technological advancements, data analysis-based methods for measuring microplastic particle size have gradually become a research hotspot. By employing optical and kinetic detection techniques, combined with advanced data processing technologies, accurate estimation of microplastic particle size can be achieved. This method not only improves measurement accuracy and reduces experimental time but also maintains high reliability and stability under complex environmental conditions.

[0005] Therefore, developing a data analysis-based method and system for measuring microplastic particle size can solve the particle size measurement problem in microplastic pollution monitoring more efficiently and accurately, which is of great significance for promoting the research and control of microplastic pollution.

[0006] The limitations of existing technologies include at least the following problems. First, existing technologies for measuring microplastic particle size mostly rely on traditional physical methods, such as sieving or sedimentation. These methods typically depend on a single detection method, making it difficult to comprehensively consider the different physical characteristics of microplastic particles in the same experiment. This results in low accuracy of measurement results and often requires a long experimental cycle and high cost, making it difficult to meet the needs for efficient and accurate microplastic particle size detection.

[0007] Secondly, existing technologies lack particle size estimation methods based on comprehensive analysis of multi-dimensional data (such as optical detection data and kinetic detection data). As a result, in complex environments, particle size estimation is often affected by insufficient or biased data, which reduces the accuracy and reliability of microplastic particle size measurement. This makes it difficult to effectively cope with diverse and complex environmental conditions and limits its widespread application in environmental protection and industrial fields. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a data analysis-based method and system for measuring microplastic particle size, which solves the problem of low particle size measurement accuracy caused by limited data in existing technologies.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a microplastic particle size measurement method based on data analysis, comprising the following steps: acquiring optical detection data and kinetic detection data of microplastic particles; preprocessing the optical detection data and kinetic detection data of microplastic particles, and extracting the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; analyzing the light scattering index and sedimentation index of microplastic particles based on the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; and estimating the particle size value of microplastic particles based on the light scattering index and sedimentation index of microplastic particles.

[0010] Further, the specific steps for estimating the particle size of microplastic particles are as follows: read the light scattering index and sedimentation index of the microplastic particles; input the light scattering index and sedimentation index of the microplastic particles into the particle size estimation model to estimate the particle size of the microplastic particles.

[0011] Furthermore, the particle size estimation model is as follows: ;in, , , The values, in order, are the particle size, light scattering index, and sedimentation index of the microplastic particles. , , The components are, in order, the light scattering regression coefficient, the sedimentation regression coefficient, and the error factor stored in the database.

[0012] Furthermore, the optical detection data includes the scattering intensity value and the scattering light deflection angle under illumination of different wavelengths of light, while the dynamic detection data includes the settling velocity, settling distance, and settling duration at several time points.

[0013] Furthermore, the light scattering characteristics are the average scattering intensity, the standard deviation of the scattering angle, and the rate of change of scattering intensity; the sedimentation characteristics are the average sedimentation velocity, the rate of change of sedimentation distance, and the standard deviation of sedimentation duration.

[0014] Further, the specific steps for analyzing the light scattering index of microplastic particles are as follows: read the average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity of the microplastic particles, and perform standardization processing; perform comprehensive analysis on the standardized average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity to obtain the light scattering index of the microplastic particles.

[0015] Furthermore, the specific formula for calculating the light scattering index of microplastic particles is as follows: ;in, , , , The following are the light scattering index, average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity of the standardized microplastic particles, in that order. , , The values ​​are, in order, the average scattering coefficient, scattering angle coefficient, and scattering variation coefficient stored in the database.

[0016] Further, the specific steps for analyzing the sedimentation index of microplastic particles are as follows: read the average sedimentation velocity, sedimentation distance change rate, and sedimentation time standard deviation of the microplastic particles, and perform standardization processing; conduct a comprehensive analysis of the standardized average sedimentation velocity, sedimentation distance change rate, and sedimentation time standard deviation to obtain the light scattering index of the microplastic particles.

[0017] Furthermore, the specific formula for calculating the sedimentation index of microplastic particles is as follows: ;in, , , , The following are the sedimentation index, average sedimentation velocity, rate of change of sedimentation distance, and standard deviation of sedimentation time for the standardized microplastic particles, in that order. , , The values ​​are, in order, the average settlement coefficient, the settlement variation coefficient, and the settlement duration coefficient stored in the database.

[0018] A data analysis-based microplastic particle size measurement system includes: a detection data acquisition unit for acquiring optical detection data and kinetic detection data of microplastic particles; a feature extraction unit for preprocessing the optical detection data and kinetic detection data of microplastic particles and extracting the light scattering characteristics and sedimentation characteristics of microplastic particles, respectively; an index analysis unit for analyzing the light scattering index and sedimentation index of microplastic particles based on the light scattering characteristics and sedimentation characteristics of microplastic particles, respectively; and a particle size estimation unit for estimating the particle size value of microplastic particles based on the light scattering index and sedimentation index of microplastic particles.

[0019] The present invention has the following beneficial effects:

[0020] (1) The microplastic particle size measurement method based on data analysis achieves accurate estimation of microplastic particle size by combining optical detection and kinetic detection data. Traditional methods usually rely on a single type of detection data, such as optical methods or sedimentation methods, which are often limited by their respective technologies, resulting in large particle size measurement errors. However, the multi-data fusion method of this invention can comprehensively analyze the light scattering and sedimentation characteristics of microplastic particles, such as scattering intensity, scattering angle and sedimentation velocity, thereby obtaining more reliable light scattering index and sedimentation index. By calculating these indices, the accuracy of particle size estimation can be effectively improved, while reducing experimental time and cost. The combination of optical detection data and kinetic detection data can maximize the advantages of both and avoid the limitations of a single detection method under specific conditions. For example, optical detection is suitable for smaller particles, while kinetic detection is more suitable for larger particles. The combined use of the two allows for a comprehensive estimation of the particle size of microplastic particles. This method not only improves the measurement accuracy but also greatly improves the measurement efficiency, solving the problems caused by insufficient detection accuracy or excessive detection time in traditional methods.

[0021] (2) This data analysis-based microplastic particle size measurement method, by employing multi-dimensional data analysis and feature extraction methods, makes the measurement method applicable to different types of microplastic particles, whether they are relatively regular microplastics or irregular particles. Traditional particle size measurement methods can usually only measure particles of a certain type or under certain environmental conditions, which limits their application scope. However, by combining optical detection data and kinetic detection data, this method can adapt to the characteristics of microplastic particles under different environments. In specific operation, the method extracts the light scattering characteristics and sedimentation characteristics of microplastic particles respectively, and combines specific standardization processing and regression coefficients to achieve accurate particle size estimation. This multi-feature analysis method can adapt to microplastic particles with different particle size ranges, different shapes and different environmental conditions, thus having stronger versatility and adaptability. Especially in complex environmental monitoring and pollution assessment, this method can provide more accurate microplastic particle size data, providing strong support for various environmental protection, industrial and scientific research projects.

[0022] (3) The data analysis-based microplastic particle size measurement method significantly reduces the cost and resource consumption of experiments by integrating optical and dynamic detection data. Traditional microplastic particle size measurement methods often require multiple devices and a lot of time for repetitive experiments, such as using a single sieving method, sedimentation method or electron microscope. These methods are not only time-consuming, but also require a lot of manual intervention and experimental consumption. However, the optical and dynamic data analysis method adopted in this invention greatly simplifies the experimental steps through standardized processing and regression analysis, reducing the dependence on expensive equipment and long-term experiments. Especially in practical applications, the required data can be obtained only through optical sensors and standard sedimentation experiments. The data is processed and analyzed by computer models, which saves equipment investment and reduces human resource input. In addition, the use of standardized data for regression analysis improves the automation and accuracy of data processing, further optimizes the experimental process, and makes the entire measurement process more efficient and less costly, greatly improving the economy and operability of microplastic particle size measurement.

[0023] (4) This data analysis-based microplastic particle size measurement system significantly improves the automation level and real-time data processing capability of the system by integrating optical detection data and kinetic detection data, and combining feature extraction unit, index analysis unit and particle size estimation unit. Traditional particle size measurement methods usually rely on manual intervention and cumbersome experimental steps, resulting in low data processing efficiency and difficulty in meeting the needs of large-scale microplastic monitoring. However, this system enables the entire measurement process to be completed in a short time through automated data acquisition, automated feature extraction and automated index analysis, thereby meeting the needs of environmental monitoring, scientific research and industrial fields for rapid detection of microplastic particle size. The system achieves efficient data flow and real-time feedback during data processing, avoiding errors in manual calculation and traditional methods, and improving measurement accuracy and efficiency. Due to its highly integrated processing unit, this system can quickly respond to changes in microplastic particle size, providing strong technical support for real-time monitoring and more effectively addressing the complexity and variability of microplastic pollution in the environment.

[0024] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0025] Figure 1 This is a flowchart of a microplastic particle size measurement method based on data analysis according to the present invention.

[0026] Figure 2 This is a flowchart illustrating the specific steps involved in estimating the particle size of microplastic particles in a data analysis-based microplastic particle size measurement method according to the present invention.

[0027] Figure 3This is a block diagram of a microplastic particle size measurement system based on data analysis according to the present invention. Detailed Implementation

[0028] Please see Figure 1 This invention provides a technical solution: a data analysis-based method for measuring the particle size of microplastics, comprising the following steps: acquiring optical detection data and kinetic detection data of microplastic particles; preprocessing the optical detection data and kinetic detection data of microplastic particles, and extracting the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; analyzing the light scattering index and sedimentation index of microplastic particles based on the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; and estimating the particle size value of microplastic particles based on the light scattering index and sedimentation index of microplastic particles.

[0029] Optical detection data includes the scattering intensity value and the scattering light deflection angle under illumination of different wavelengths of light, while dynamic detection data includes the settling velocity, settling distance, and settling duration at several time points.

[0030] The scattering intensity value (the scattering intensity value under illumination of different wavelengths of light) is usually obtained through light scattering experiments. In the experiment, microplastic particles are placed under a controlled light source and irradiated with a laser or other light source of a specific wavelength. By measuring the intensity of the light scattered after the light source illuminates the microplastic particles, the scattering intensity values ​​at different wavelengths can be obtained. These data can be acquired using a photodetector or a spectrometer. Experimental equipment includes: spectrometer, photodetector, laser source, etc.

[0031] The scattered light deflection angle (scattering angle) refers to the angle between the direction in which light is scattered by microplastic particles after a light source illuminates the particles and the direction of the incident light source. The scattering angle can be calculated by measuring the intensity of scattered light at different angles using detectors placed in different directions. Typically, scattering angle data is used for further analysis of particle morphology and size. Experimental equipment: laser light source, photodetector, rotation angle sensor.

[0032] Settling velocity refers to the rate at which microplastic particles sink in a liquid. It is calculated by recording the time required for particles to settle from the liquid surface to a certain depth. Typically, the particles are placed in a transparent container, and the settling process is recorded in real time using video monitoring or other methods. By measuring the time and distance of particle settling, its settling velocity can be calculated. Experimental equipment includes: a transparent container, a camera, video analysis software, and an electronic scale (for precise control of sample mass and volume).

[0033] Settling distance refers to the total distance that microplastic particles travel in a liquid. During the experiment, settling distance data is obtained by recording the distance the particles travel from their initial settling point to their final position. Typically, the settling height of the particles at different time points is recorded, and the total settling distance is then calculated. Experimental equipment includes: a transparent container, precisely calibrated measuring instruments (such as optical measuring instruments), and a video monitoring system.

[0034] Settling time refers to the time required for particles to settle from the start of settling to a steady state. Settling time can be obtained by recording the start and end times of the particle settling process. It is typically measured by continuously tracking the particle's descent in a liquid using video until the settling rate stabilizes, indicating that the particle has reached its final settling state. Experimental equipment includes: a transparent container, a camera, video analysis software, and an automated data acquisition system.

[0035] The light scattering characteristics are the average scattering intensity, the standard deviation of the scattering angle, and the rate of change of scattering intensity. The sedimentation characteristics are the average sedimentation velocity, the rate of change of sedimentation distance, and the standard deviation of sedimentation duration.

[0036] Specifically, such as Figure 2 As shown, the specific steps for estimating the particle size of microplastic particles are as follows: read the light scattering index and sedimentation index of the microplastic particles; input the light scattering index and sedimentation index of the microplastic particles into the particle size estimation model to estimate the particle size of the microplastic particles.

[0037] The particle size estimation model is as follows: ;in, , , The values, in order, are the particle size, light scattering index, and sedimentation index of the microplastic particles. , , The components are, in order, the light scattering regression coefficient, the sedimentation regression coefficient, and the error factor stored in the database.

[0038] It should be explained that the steps for obtaining the light scattering regression coefficient, sedimentation regression coefficient, and error factor are as follows:

[0039] First, a large amount of experimental data was collected, including the light scattering characteristics (such as scattering intensity and scattering angle) and sedimentation characteristics (such as sedimentation velocity and sedimentation distance) of microplastic particles. The light scattering index and sedimentation index, along with the known particle size, were then calculated. The data were fitted using regression analysis methods (such as least squares) to calculate regression coefficients, reflecting the contribution of each characteristic to particle size. Simultaneously, an error factor was calculated, representing the prediction error of the regression model—the difference between the model's predicted value and the actual particle size. The regression coefficients and error factor were continuously adjusted using optimization algorithms to ultimately obtain a model capable of accurately estimating particle size.

[0040] In this implementation scheme, by combining light scattering and sedimentation characteristics for comprehensive analysis, the particle size of microplastic particles can be estimated more comprehensively and accurately. Traditional particle size measurement methods often rely on a single detection method, which is easily affected by specific environmental factors, resulting in low measurement accuracy. However, this invention uses regression analysis to calculate light scattering regression coefficients, sedimentation regression coefficients, and error factors from a large amount of experimental data, ensuring that the influence of light scattering and sedimentation characteristics on particle size estimation is fully considered. Through optimization and adjustment of these regression coefficients and error factors, the model can continuously improve its accuracy and reliability. The error factor plays a very important role in the regression model; it measures the difference between the predicted value and the actual value, thereby helping the model reduce errors when processing new data. This optimized model obtained through regression analysis can effectively reduce data uncertainty and improve the accuracy and stability of particle size measurement. Therefore, this method can not only provide more accurate particle size data for microplastic pollution monitoring, but also greatly improve the efficiency and operability of microplastic particle size estimation, and has significant technical advantages and application value.

[0041] Specifically, the steps for analyzing the light scattering index of microplastic particles are as follows: read the average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity of the microplastic particles, and perform standardization processing (i.e., unit removal); perform comprehensive analysis on the standardized average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity to obtain the light scattering index of the microplastic particles.

[0042] The specific formula for calculating the light scattering index of microplastic particles is as follows: ;in, , , , The following are the light scattering index, average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity of the standardized microplastic particles, in that order. , , The values ​​are, in order, the average scattering coefficient, scattering angle coefficient, and scattering variation coefficient stored in the database.

[0043] It should be explained that the steps for obtaining the average scattering coefficient, scattering angle coefficient, and scattering variation coefficient stored in the database are as follows:

[0044] Data on light scattering characteristics, including average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity, are obtained through experiments or measurements. These data are typically obtained by conducting multiple measurements on different microplastic particles.

[0045] To eliminate unit differences between different features, standardization is performed.

[0046] Standardized light scattering characteristic data (i.e., average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity) are input into a regression model for regression analysis. The model is fitted using the least squares method or gradient descent method to obtain the average scattering coefficient, scattering angle coefficient, and scattering variation coefficient. These coefficients represent the contribution of each feature to the light scattering index.

[0047] The regression coefficients are obtained through data fitting and are automatically adjusted based on experimental data to minimize the error between predicted and actual values. By continuously optimizing the regression coefficients, coefficients that can accurately predict the light scattering index are finally obtained.

[0048] In this implementation scheme, standardization and regression analysis effectively address the dimensional differences among different light scattering characteristics, improving the stability and accuracy of the particle size estimation model. Traditional microplastic particle size measurement methods often suffer from low measurement accuracy and limited methodologies. This invention, by integrating light scattering characteristics (such as average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity) and standardizing them, effectively eliminates unit differences among different characteristics, ensuring a balanced consideration of the influence of each characteristic. Through regression analysis (such as least squares or gradient descent), not only can light scattering coefficients (such as average scattering coefficient, scattering angle coefficient, and scattering change coefficient) be calculated from experimental data, but the regression coefficients can also be adjusted during continuous optimization, thereby minimizing prediction errors. These regression coefficients play a crucial role in particle size estimation, ensuring that the contribution of each light scattering characteristic to particle size is reasonably quantified. Therefore, this method provides a more accurate light scattering index, thereby improving the overall accuracy of microplastic particle size measurement and providing reliable data support for microplastic pollution monitoring.

[0049] Specifically, the steps for analyzing the sedimentation index of microplastic particles are as follows: read the average sedimentation velocity, sedimentation distance change rate, and sedimentation time standard deviation of the microplastic particles, and perform standardization processing (i.e., unit removal); perform comprehensive analysis on the standardized average sedimentation velocity, sedimentation distance change rate, and sedimentation time standard deviation to obtain the light scattering index of the microplastic particles.

[0050] The specific formula for calculating the sedimentation index of microplastic particles is as follows: ;in, , , , The following are the sedimentation index, average sedimentation velocity, rate of change of sedimentation distance, and standard deviation of sedimentation time for the standardized microplastic particles, in that order. , , The values ​​are, in order, the average settlement coefficient, the settlement variation coefficient, and the settlement duration coefficient stored in the database.

[0051] It should be explained that the steps for obtaining the average settlement coefficient, settlement variation coefficient, and settlement duration coefficient stored in the database are as follows:

[0052] Data on sedimentation characteristics are obtained through experiments or measurements, including average sedimentation velocity, rate of change of sedimentation distance, and standard deviation of sedimentation time. These data typically come from multiple measurements of the sedimentation process of microplastic particles in a liquid.

[0053] Similarly, standardization is performed to eliminate unit differences between different features. Standardized data will be easier to calculate in the regression model, ensuring that the contributions of each feature are relatively balanced.

[0054] Standardized settlement characteristic data (i.e., average settlement velocity, rate of change of settlement distance, and standard deviation of settlement duration) are input into a regression model, and regression analysis (such as least squares method) is used to fit the data. The average settlement coefficient, settlement variation coefficient, and settlement duration coefficient are obtained through data fitting.

[0055] The sedimentation regression coefficients were obtained through regression analysis; these coefficients represent the contribution of each sedimentation feature to the sedimentation index. By optimizing these coefficients, the model can more accurately predict the sedimentation index of microplastic particles.

[0056] In this implementation scheme, standardization and regression analysis effectively address the unit differences between various sedimentation characteristics, enhancing the accuracy and stability of the particle size measurement model. Traditional microplastic particle size measurement methods typically rely on a single characteristic, making it difficult to comprehensively consider particle sedimentation behavior and resulting in significant measurement errors. By simultaneously analyzing sedimentation velocity, sedimentation distance change rate, and sedimentation time standard deviation, and performing standardization, this method eliminates the dimensional differences of these characteristics, ensuring that each characteristic contributes equally to the sedimentation index. Furthermore, through regression analysis, such as the least squares method, the calculated sedimentation regression coefficients (e.g., average sedimentation coefficient, sedimentation change coefficient, sedimentation time coefficient) accurately reflect the impact of sedimentation characteristics on particle size estimation. The optimized regression coefficients minimize prediction errors, thereby improving the accuracy and reliability of microplastic particle size measurement. In addition, this method can be trained with a large amount of experimental data to automatically optimize the regression coefficients, making the model more adaptable to different environments and effectively applicable to various microplastic monitoring and pollution control tasks. This sedimentation index calculation method based on multi-feature analysis not only improves measurement accuracy but also significantly reduces the time and resource consumption required for experiments.

[0057] Please see Figure 3This invention provides a technical solution: a microplastic particle size measurement system based on data analysis, comprising: a detection data acquisition unit for acquiring optical detection data and kinetic detection data of microplastic particles; a feature extraction unit for preprocessing the optical detection data and kinetic detection data of microplastic particles and extracting the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; an index analysis unit for analyzing the light scattering index and sedimentation index of microplastic particles based on the light scattering characteristics and sedimentation characteristics of microplastic particles respectively; and a particle size estimation unit for estimating the particle size value of microplastic particles based on the light scattering index and sedimentation index of microplastic particles.

[0058] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0059] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for measuring the particle size of microplastics based on data analysis, characterized in that, Includes the following steps: Optical and kinetic detection data of microplastic particles were obtained. The optical detection data included the scattering intensity and scattering deflection angle under different wavelengths of light, while the kinetic detection data included the settling velocity, settling distance, and settling time at several time points. The optical and kinetic detection data of microplastic particles were preprocessed, and the light scattering and sedimentation characteristics of microplastic particles were extracted respectively. The light scattering characteristics were the average scattering intensity, the standard deviation of the scattering angle, and the rate of change of scattering intensity. The sedimentation characteristics were the average sedimentation velocity, the rate of change of sedimentation distance, and the standard deviation of sedimentation time. Based on the light scattering and sedimentation characteristics of microplastic particles, the light scattering index and sedimentation index of microplastic particles are analyzed respectively, and the specific formulas are as follows; ； ； in, , , , , , , , The following are the light scattering index, average scattering intensity, standard deviation of scattering angle, rate of change of scattering intensity, sedimentation index, average sedimentation velocity, rate of change of sedimentation distance, and standard deviation of sedimentation time for standardized microplastic particles, in that order. , , , , , The following are the average scattering coefficient, scattering angle coefficient, scattering variation coefficient, average settlement coefficient, settlement variation coefficient, and settlement duration coefficient stored in the database, in that order. The particle size of microplastic particles is estimated based on their light scattering index and sedimentation index. The specific steps are as follows: Read the light scattering index and sedimentation index of microplastic particles; The light scattering index and sedimentation index of the microplastic particles are input into the particle size estimation model to estimate the particle size value of the microplastic particles, as follows: The particle size estimation model is as follows: ； in, , , The values, in order, are the particle size, light scattering index, and sedimentation index of the microplastic particles. , , The components are, in order, the light scattering regression coefficient, the sedimentation regression coefficient, and the error factor stored in the database.

2. The microplastic particle size measurement method based on data analysis according to claim 1, characterized in that, The specific steps for analyzing the light scattering index of microplastic particles are as follows: The average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity of microplastic particles are read and standardized. The light scattering index of microplastic particles was obtained by comprehensively analyzing the average scattering intensity, standard deviation of scattering angle, and rate of change of scattering intensity after standardization.

3. The microplastic particle size measurement method based on data analysis according to claim 1, characterized in that, The specific steps for analyzing the sedimentation index of microplastic particles are as follows: The average settling velocity, settling distance variation rate, and settling time standard deviation of microplastic particles were read and standardized. The light scattering index of microplastic particles was obtained by comprehensively analyzing the average settling velocity, the rate of change of settling distance, and the standard deviation of settling time after standardization.

4. A microplastic particle size measurement system based on data analysis, employing the microplastic particle size measurement method based on data analysis as described in any one of claims 1-3, characterized in that, include: The detection data acquisition unit is used to acquire optical detection data and kinetic detection data of microplastic particles; The feature extraction unit is used to preprocess the optical and dynamic detection data of microplastic particles and extract the light scattering and sedimentation features of the microplastic particles, respectively. The index analysis unit is used to analyze the light scattering index and sedimentation index of microplastic particles based on their light scattering and sedimentation characteristics, respectively. The particle size estimation unit is used to estimate the particle size of microplastic particles based on their light scattering index and sedimentation index.

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