A method for treating microplastics for soil remediation

Abstract

The present invention discloses a method for treating microplastics for soil remediation, specifically relating to the field of microplastic treatment, including: collecting soil samples in the target treatment area, separating microplastics and identifying the types and contents of plastics, and constructing a soil microplastic pollution distribution model. Conducting a mixed degradation test on the microplastic samples by bacteria, establishing a time-series degradation sequence and screening the stage experiment items with the highest degradation efficiency, detecting the metabolic intermediate products and integrating the experimental environmental parameters and the data of the bacterial community ratio. Using the deep neural network algorithm to label the degradation stages of the degradation experiment data set, establishing a degradation stage recognition model and optimizing the parameters. By constructing a degradation stage recognition model, accurately screening the optimal bacterial community ratio at each stage, enabling different enzyme systems to cooperate and play roles in the degradation of the plastic main chain, the conversion of intermediate products and the final mineralization, as well as other key degradation stages, and effectively reducing the degradation time.

Description

Technical Field

[0001] The present invention relates to the technical field of microplastic treatment, and more specifically, to a microplastic treatment method for soil remediation. Background Art

[0002] Microplastic pollution has become an important threat in the soil environment, widely existing in areas such as farmland, wetlands, and industrial sites, significantly affecting soil ecological functions and the structure of microbial communities, and simultaneously endangering human health through the food chain. Traditional physical and chemical remediation methods have problems such as high cost, low efficiency, and secondary pollution. Therefore, bioremediation technology with microbial degradation as the core has gradually become the research focus. Microplastic degradation is a complex multi-stage process that requires the synergistic action of multiple microbial communities. Different plastic types (such as PET, PE, PP, etc.) have different degradation paths and intermediate products due to differences in chemical structure and molecular weight, and specific microbial communities need to secrete enzyme systems for cleavage and transformation. Degradation generally consists of at least three (or more) stages: main chain breakage, intermediate product transformation, and final mineralization. The optimal microbial community ratio in different stages is crucial for improving degradation efficiency.

[0003] Therefore, how to establish a soil microplastic treatment plan that can identify different degradation stages and output an optimal microbial community ratio plan through experimental data combined with a deep learning model, dynamically adjust the microbial community ratio according to time to adapt to actual soil conditions and microplastic characteristics, thereby improving treatment efficiency and ecological friendliness, is an urgent problem to be solved.

[0004] To solve the above problems, a technical solution is provided as follows. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a microplastic treatment method for soil remediation to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1: Collect soil samples in a set target treatment area, separate the microplastic samples in the soil samples and calculate the microplastic sample content, and determine the chemical composition of the microplastic samples to identify the plastic types in the microplastic samples;

[0008] S2: Integrate the coordinate information of the sampling points, the plastic types of the microplastics, and the microplastic content of the sampling points, and construct a soil microplastic pollution distribution model in combination with geographic information system technology;

[0009] S3: Divide the microplastic samples into a set number of test samples, conduct mixed degradation tests on the test samples with selected microbial flora for degradation treatment, and establish a time-series degradation sequence of different mixed ratios of microbial flora.

[0010] S4: Convert the time-series degradation sequence into an alignment sequence based on the degradation stage, screen out the experimental items with the least number of time steps used in each degradation stage process, and detect the metabolic intermediates of the experimental items in the degradation stage.

[0011] S5: Conduct synchronous replication experiments on each experimental culture system, and integrate the environmental parameters of all experimental culture systems, as well as the corresponding degradation stage, optimal mixed ratio of microbial flora, type and concentration of metabolic intermediates into a degradation experiment data set.

[0012] S6: Perform data preprocessing on the degradation experiment data set, label the degradation stage for the preprocessed data, use the deep neural network algorithm to establish a degradation stage recognition model and adjust the model parameters.

[0013] S7: Based on the microplastic pollution areas in the soil microplastic pollution distribution model, collect the soil environmental parameters and information on metabolic intermediates produced by microorganisms in the polluted areas, input them into the degradation stage recognition model, adjust the ratio of the microbial flora to be put in according to the optimal mixed ratio of microbial flora corresponding to the degradation stage identified by the model, and put the microbial flora with adjusted ratio into the corresponding polluted areas.

[0014] In a preferred embodiment, in S1, when collecting soil samples in the set target governance area, separating the microplastic samples from the soil samples and calculating the content of microplastic samples, and determining the plastic types in the microplastic samples by measuring the chemical composition of the microplastic samples specifically includes:

[0015] According to the geographical information of the set target governance area, collect soil samples containing microplastics through a grid distribution with a set gradient, and record the sampling position coordinates;

[0016] Use the saturated salt solution flotation method to separate microplastics from the soil samples, use a low-pore-size filter membrane to separate the microplastic samples in the flotation layer, and calculate the mass ratio of the microplastic samples to the soil samples as the microplastic content of this soil sample;

[0017] Use Fourier transform infrared spectroscopy scanning to analyze the microplastic molecular structure and characteristic groups, qualitatively label the main chemical components, and determine the plastic types in the microplastic samples based on the chemical composition.

[0018] In a preferred embodiment, in S2, the coordinate information of the sampling points, the types of plastics of the microplastics, and the microplastic content of the sampling points are integrated, and a soil microplastic pollution distribution model is constructed by combining geographic information system technology.

[0019] In a preferred embodiment, in S3, the microplastic samples are divided into a set number of test samples, and a mixed degradation test is carried out on the test samples by selecting microbial communities for degradation treatment. Establishing a time-sequential degradation sequence of different microbial community mixing ratios specifically includes:

[0020] The microplastic samples separated from all sampling points are collected, uniformly mixed and then divided into a set number of test samples. Based on the types of plastics in the microplastic samples, microbial communities for degradation treatment are selected for mixed degradation tests;

[0021] Construct multiple experimental culture systems, each experimental culture system having different environmental parameters, where the environmental parameters include the temperature, humidity and pH value of the soil;

[0022] The microplastic test samples are mixed with different proportions of microbial communities for grouped degradation experiments, and each mixing ratio corresponds to an experimental item;

[0023] The microplastic test samples in the grouped degradation experiments are measured for quality according to a set reaction time step, the mass change values of the microplastic samples in chronological order are obtained, the degradation amount of the microplastics is calculated according to the mass change, and a time-sequential degradation sequence of different microbial community mixing ratios is established.

[0024] In a preferred embodiment, in S4, the time-sequential degradation sequence is converted into an alignment sequence based on the degradation stage, and the experimental item with the least number of time steps used in each degradation stage process is screened out, and the metabolic intermediate products of the experimental item in the degradation stage are detected. Specifically includes:

[0025] The time-sequential degradation sequence is aligned from the set reaction time step to the degradation stage alignment based on the set degradation amount gradient;

[0026] Statistically, in the grouped degradation experiments of the experimental culture system, the number of time steps spanned by each experimental item in the same degradation stage is counted, and the experimental item with the least number of time steps used in each degradation stage process is screened out, and the mixing ratio of the experimental microbial community corresponding to the experimental item is marked as the stage-optimal ratio;

[0027] The information of the metabolic intermediate products produced by the microorganisms in each degradation stage of each experimental item in each degradation stage is analyzed by liquid chromatography-mass spectrometry, and the types and concentrations of the metabolic intermediate products are recorded.

[0028] In a preferred embodiment, in S5, synchronous replication experiments are conducted on each experimental culture system, and the environmental parameters of all experimental culture systems, as well as the corresponding degradation stages, optimal microbial flora mixing ratios, types and concentrations of metabolic intermediates, are integrated into a degradation experiment dataset, which specifically includes:

[0029] Synchronous replication experiments are conducted on each experimental culture system, and the experimental items with the shortest number of time steps used in each degradation stage in the degradation experiment grouping are extracted;

[0030] The stage-optimal ratio corresponding to each degradation stage of the experimental item is obtained, and combined with the type and concentration data of the generated metabolic intermediates, a joint data of degradation stage - optimal microbial flora mixing ratio - metabolic intermediate for different experimental culture systems is established and marked as the degradation experiment dataset.

[0031] In a preferred embodiment, in S6, data preprocessing is performed on the degradation experiment dataset, and the preprocessed data is marked for the degradation stage. A degradation stage recognition model is established using a deep neural network algorithm and the model parameters are adjusted, which specifically includes:

[0032] Data preprocessing is performed on the data of the degradation experiment dataset. The outlier detection method is applied to remove noise from the multi-dimensional data in the degradation experiment dataset, and the value range of all data is adjusted to a unified scale through the data standardization method;

[0033] The preprocessed degradation experiment data is marked for the stage according to its belonging degradation stage. The environmental parameters and metabolic intermediate parameters are used as inputs, and the marked degradation stage and the corresponding optimal microbial flora mixing ratio at this stage are used as outputs to establish a training dataset and a validation dataset;

[0034] A degradation stage recognition model is established based on the deep neural network algorithm and the initial parameters of the model are configured;

[0035] The labeled degradation experiment dataset is input into the initialized model. Through the supervised learning method, the association pattern between the environmental parameters, metabolic intermediate parameters and the degradation stage recognition result is initially trained, and the optimization objective function is set to adjust the internal parameters of the model to fit the non-linear relationship between the metabolic intermediate and the degradation stage under different environmental parameters;

[0036] The validation set data is input into the preliminarily trained model, and the accuracy, recall rate and F1 score metrics of the model on the validation data split are calculated using the cross-validation method. The hyperparameters of the model are adjusted in combination with grid search until the model recognition accuracy reaches the set metrics.

[0037] In a preferred embodiment, in S7, based on the microplastic pollution areas in the soil microplastic pollution distribution model, the soil environmental parameters and the information of the metabolic intermediates produced by microorganisms in the polluted areas are collected and input into the degradation stage identification model. Based on the degradation stage corresponding to the optimal microbial flora mixture ratio identified by the model, the microbial flora to be put in is adjusted in proportion, and the adjusted microbial flora is put into the corresponding polluted area, which specifically includes:

[0038] The areas in the soil microplastic pollution distribution model where the microplastic content exceeds the set pollution content threshold are marked as microplastic pollution areas, and the soil environmental parameters and the information of the metabolic intermediates produced by microorganisms are collected at time intervals corresponding to the set reaction time steps for each polluted area;

[0039] The environmental parameters and the information of the metabolic intermediates are input into the degradation stage identification model to identify the degradation stage of the microplastic pollution in each polluted area and the corresponding optimal microbial flora mixture ratio;

[0040] The microbial flora to be put in is adjusted in proportion according to the optimal microbial flora mixture ratio, and the adjusted microbial flora is put into the corresponding microplastic pollution area.

[0041] The technical effects and advantages of a microplastic treatment method for soil remediation according to the present invention:

[0042] Soil samples are collected in the target treatment area, microplastics are separated and the types and contents of plastics are identified to construct a soil microplastic pollution distribution model. Microbial flora mixed degradation tests are carried out on the microplastic samples, a time-sequential degradation sequence is established and the stage experiment items with the highest degradation efficiency are screened, the metabolic intermediates are detected and the experimental environmental parameters and the microbial flora ratio data are integrated. The degradation stage of the degradation experiment data set is labeled by a deep neural network algorithm, a degradation stage identification model is established and the parameters are optimized. By constructing a degradation stage identification model, the optimal microbial flora ratio of each stage is accurately screened, so that different enzyme systems cooperate to play a role in the degradation of the plastic main chain, the conversion of intermediate products and the final mineralization, as well as other key degradation stages, effectively reducing the degradation time. In addition, based on the soil pollution distribution model and the deep learning model, the microbial flora ratio is adjusted in real time according to the soil environmental parameters to ensure that the activity of the microbial flora can adapt to complex and changeable soil conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the process of a microplastic treatment method for soil remediation according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment 1, Figure 1 A method for treating microplastics for soil remediation according to the present invention is provided, which includes the following steps:

[0046] S1: Collect soil samples in a set target treatment area, separate the microplastic samples in the soil samples and calculate the content of the microplastic samples, and determine the chemical components of the microplastic samples to identify the types of plastics in the microplastic samples;

[0047] S2: Integrate the coordinate information of the sampling points, the types of plastics of the microplastics, and the microplastic content of the sampling points, and combine with geographic information system technology to construct a soil microplastic pollution distribution model;

[0048] S3: Divide the microplastic samples into a set number of test samples, select microbial communities for degradation treatment for the test samples for mixed degradation testing, and establish a time-series degradation sequence of different microbial community mixing ratios;

[0049] S4: Convert the time-series degradation sequence into an alignment sequence based on the degradation stage, screen out the experimental items with the least number of time steps used in each degradation stage process, and detect the metabolic intermediates of the experimental items in the degradation stage;

[0050] S5: Conduct synchronous replication experiments on each experimental culture system, and integrate the environmental parameters of all experimental culture systems, as well as the corresponding degradation stages, optimal microbial community mixing ratios, types and concentrations of metabolic intermediates into a degradation experiment data set;

[0051] S6: Perform data preprocessing on the degradation experiment data set, label the degradation stages of the preprocessed data, and use a deep neural network algorithm to establish a degradation stage recognition model and adjust the model parameters;

[0052] S7: Based on the microplastic pollution areas in the soil microplastic pollution distribution model, collect the soil environmental parameters and information on metabolic intermediates produced by microorganisms in the pollution areas, input them into the degradation stage recognition model, and adjust the ratio of the microbial communities to be put in according to the optimal microbial community mixing ratio corresponding to the degradation stage identified by the model output. Then, put the microbial communities with adjusted ratios into the corresponding pollution areas.

[0053] In S1, soil samples are collected from the set target governance area, the microplastic samples in the soil samples are separated and the content of the microplastic samples is calculated, and the chemical composition of the microplastic samples is determined to identify the types of plastics in the microplastic samples.

[0054] Within the set target governance area, a spatial gradient grid is set through the Geographic Information System (GIS), and multiple sampling areas are selected. Soil samples are collected from the surface layer of 0 - 20 cm and the deep layer of 20 - 50 cm using standard sampling tools (such as soil drills). The mass of the soil samples collected at each sampling point is 500 g, and the geographical coordinates (latitude and longitude) and environmental parameters of each sampling point are recorded, including soil moisture, pH value, temperature, etc.

[0055] The collected soil samples are air-dried (for about 48 hours), and large particle impurities are sieved out using a sieve with a 2 mm pore size. Weigh 100 g of the dried soil sample and place it in a beaker, add 300 mL of saturated sodium chloride solution, stir with a magnetic stirrer at 400 rpm for 10 minutes, and then let it stand for 30 minutes. The microplastic particles in the flotation layer are separated from the sediment by density difference. The upper flotation liquid is filtered through a 0.22 μm low-pore-size filter membrane, the microplastic particles on the filter membrane are collected, and the filter membrane is rinsed with deionized water to remove salts. After drying the filter membrane, it is weighed to obtain the mass of the separated microplastic samples, and the ratio of the microplastic mass to the initial soil mass is calculated as the microplastic content of the soil sample.

[0056] The separated microplastic particles are transferred to the Fourier Transform Infrared Spectroscopy (FTIR) sample stage with tweezers and scanned in the ATR (Attenuated Total Reflection) mode. The wavelength range is set to 4000 - 600 cm⁻¹, the scanning resolution is 4 cm⁻¹, and the number of scans is 64 times. The absorption peaks unique to plastics in the obtained spectrogram are analyzed, the bands corresponding to characteristic groups (such as C-H, C=O, C-O, etc.) are calibrated, and the chemical composition of the microplastics is qualitatively identified by combining with the reference spectral library. According to the identification results, the microplastic samples are classified into main types such as polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), or polystyrene (PS).

[0057] In S2, the coordinate information of the sampling points, the types of plastics of the microplastics, and the microplastic content of the sampling points are integrated, and a soil microplastic pollution distribution model is constructed by combining geographic information system technology.

[0058] According to the microplastic type data of each sampling point, the sampling points are classified by plastic type (such as PE, PP, PET), and multiple type distribution layers are generated. The spatial interpolation algorithm is applied to each layer to generate the distribution heat maps of different plastic types respectively, showing the spatial distribution characteristics of various microplastics.

[0059] Integrate the distribution layers of various plastic types and the pollution gradient grid to construct a comprehensive pollution distribution model for the target area. Intuitively display the scope and degree of the polluted area through classification (such as low-pollution, medium-pollution, and high-pollution areas). Overlay soil environmental parameters (such as humidity, pH value) onto the pollution distribution model to analyze the impact of environmental conditions on the distribution of microplastic pollution.

[0060] In S3, divide the microplastic samples into a set number of test samples, select microbial communities for degradation treatment for the test samples for mixed degradation testing, and establish a time-sequential degradation sequence for different mixed ratios of microbial communities.

[0061] Collect and centralize the microplastic samples separated from all sampling points in the target treatment area, and use a homogenizer to mix them evenly to ensure the uniformity of the sample source. After weighing the mixed samples, divide them into 1000 groups of test samples with equal mass, each group with a mass of 1 g, to ensure that the plastic types and contents in each test sample are consistent with the original sample distribution.

[0062] To test the impact of different environmental conditions on the degradation of microplastics, establish 10 experimental culture systems. The environmental parameters (temperature, humidity, pH value) of each culture system are set at different gradients in turn, where the temperature range is 15°C, 25°C, 35°C, the humidity range is 30%, 50%, 70%, and the pH range is 5, 7, 9. Introduce a simulated soil matrix into each system to ensure consistency with the physical and chemical properties of the actual soil environment.

[0063] Select microbial communities A, B, and C that are known to be able to degrade microplastics, mix them in different ratios (such as 3:1:1, 1:2:2, etc.), design 100 groups of different mixed ratios of microbial communities, and inoculate each group of microbial communities and test samples at a solid-liquid ratio of 1:10 (w / v). Add 100 groups of test samples and 100 groups of microbial community mixtures to each culture system respectively to ensure that each microbial community ratio corresponds to one experimental item.

[0064] Set the reaction time step to 48 hours (flexibly set according to the microplastic type, up to 30 days at most), sample at 48 hours, 96 hours... 48N hours (N is the set number of monitoring times), take out the microplastic samples and separate them using the filtration method. After drying the separated microplastic samples, weigh them and record the residual mass values of the samples.

[0065] Based on the degradation amount data of each experimental culture system and the mixed ratio of microbial communities, organize the degradation trends of microplastic samples at different time steps. Plot the curve of the degradation amount changing with time for each mixed ratio of microbial communities, establish a time-sequential degradation sequence, and mark the degradation efficiency, microbial community activity, and corresponding environmental parameters at each stage to form a complete experimental data set.

[0066] In S4, the temporal degradation sequence is converted into an alignment sequence based on the degradation stage, the experimental items with the least number of time steps used in each degradation stage process are screened out, and the metabolic intermediates of the experimental items in the degradation stage are detected.

[0067] The temporal degradation sequences of the mixed ratios of each group of bacteria in the experimental culture system are divided into five degradation stages according to the microplastic degradation amount gradient (such as 10%, 30%, 50%, 70%, 90% of the initial mass). The time step data of each group of experiments are aligned with the corresponding degradation amount gradient to determine the number of time steps spanned by each experimental item in each degradation stage. A phased degradation sequence based on the degradation amount gradient is formed to replace the original time step sequence.

[0068] The number of time steps of all experimental items in the same degradation stage is statistically analyzed, and the experimental item with the least time used to complete this stage is screened out. Record the mixed ratio of the bacteria in this experimental item and mark it as the optimal ratio for this degradation stage. Organize the screening results into a corresponding table of degradation stage and optimal ratio to ensure that each stage corresponds to a unique bacterial proportion.

[0069] The culture solution of the stage-optimal experimental items screened out is centrifuged (4000 rpm, 10 minutes), and the supernatant is taken for liquid chromatography-mass spectrometry (LC-MS) analysis. Record the types and peak areas of the metabolic intermediates in each degradation stage, calculate the relative concentration, and after normalization, associate it with the bacterial mixture ratio. Mark the main metabolites (such as phenols, aldehydes or organic acids) and statistically analyze the trend of their concentration changes.

[0070] In S5, a synchronous replication experiment is carried out on each experimental culture system, and the environmental parameters of all experimental culture systems, as well as the corresponding degradation stage, optimal microbial flora mixed ratio, types and concentrations of metabolic intermediates, are integrated into a degradation experiment data set.

[0071] A synchronous replication experiment is carried out on each experimental culture system (that is, 100 groups of test samples and 100 groups of bacterial mixture experiments are carried out synchronously), and the experimental items with the shortest number of time steps used in each degradation stage in the degradation experiment grouping are extracted.

[0072] Obtain the stage-optimal ratio corresponding to each experimental item in each degradation stage, combine the types and concentration data of the generated metabolic intermediates, establish a joint data of degradation stage - optimal microbial flora mixed ratio - metabolic intermediate for different experimental culture systems, and mark it as the degradation experiment data set. The data fields of the degradation experiment data set include: degradation stage, optimal flora ratio, metabolic intermediate information, experimental culture conditions.

[0073] In S6, data preprocessing is performed on the degradation experiment dataset, and the preprocessed data is labeled for the degradation stage. A degradation stage recognition model is established using a deep neural network algorithm and the model parameters are adjusted.

[0074] Multidimensional data cleaning and standardization processing are performed on the degradation experiment dataset. First, outlier detection methods (such as the three - standard - deviation method or the IQR method) are used to identify and remove abnormal data in environmental parameters (such as temperature, humidity, pH value) and metabolite intermediate product parameters (such as concentration, type) to eliminate noise interference. Subsequently, a normalization method is used to adjust all data to the range of [0, 1] to ensure the consistency of the data value range and improve the convergence speed and accuracy of model training.

[0075] According to the degradation stage classification information in the degradation experiment dataset (such as degradation amount gradients of 10%, 30%, 50%, 70%, 90%), the preprocessed data is labeled for the degradation stage. The environmental parameters and metabolite intermediate product parameters are used as the input features of the model, and the degradation stage label and the optimal bacterial community mixing ratio for the corresponding stage are used as the output results. The data is randomly divided into a training set (80%) and a validation set (20%) to ensure the balanced distribution of data for each degradation stage.

[0076] A degradation stage recognition model is constructed based on a deep neural network algorithm. The model includes: an input layer (receiving environmental parameters and metabolite intermediate product parameters), a hidden layer (setting 3 hidden layers, each layer containing 128, 64, and 32 neurons, with the activation function being ReLU), and an output layer (outputting the degradation stage classification results, using the Softmax function to achieve multi - classification).

[0077] The model parameters (such as weights and bias values) are initialized using the Xavier initialization method, the optimization objective function is set as the cross - entropy loss function, and the optimization algorithm selects the Adam optimizer.

[0078] The labeled training set data is input into the initialized model and is preliminarily trained through a supervised learning method. During the training process, the environmental parameters and metabolite intermediate product parameters are used as inputs, and the degradation stage label is used as the target output. The model adjusts its internal parameters by calculating the loss (cross - entropy) between the predicted value and the target value. The learning rate is set to 0.001, the batch size is 64, and the training iterates for 50 epochs. The model fits the non - linear relationship between metabolite intermediates and degradation stages under different environmental parameters.

[0079] Input the validation set data into the preliminarily trained model, and use the cross-validation method to calculate the performance metrics of the model on the validation data, including accuracy, recall, and F1 score. Combine the grid search method to optimize the model hyperparameters (such as learning rate, number of hidden layer nodes, batch size, etc.). Gradually adjust the parameters until the accuracy of the model on the validation set reaches the set target (such as ≥95%), and ensure a high F1 score (such as ≥0.9) to improve the stability and accuracy of the classification results.

[0080] In S7, based on the microplastic pollution area in the soil microplastic pollution distribution model, collect the soil environmental parameters of the polluted area and the information of the metabolic intermediates produced by microorganisms, input them into the degradation stage identification model, and adjust the ratio of the microorganisms to be put in according to the optimal mixed ratio of the microbial flora corresponding to the degradation stage identified by the model output. Put the adjusted microbial flora into the corresponding polluted area.

[0081] According to the soil microplastic pollution distribution model, extract the microplastic content data in the polluted area. Set the pollution content threshold (such as ≥0.5%), and mark the area exceeding the threshold as the microplastic pollution area.

[0082] In each marked polluted area, conduct multiple interval samplings according to the set reaction time step. When collecting samples, use standard soil sampling tools to obtain surface layer (0 - 20 cm) and deep layer (20 - 50 cm) soil samples. Measure and record the soil environmental parameters, including temperature (using a soil temperature probe, accuracy ±0.1°C), humidity (determined by the gravimetric method, accuracy ±1%), and pH value (potentiometric method, accuracy ±0.01). At the same time, use liquid chromatography-mass spectrometry (LC-MS) technology to analyze the information of microbial metabolic intermediates in the supernatant of the samples, and record the types and concentration changes of the main metabolites.

[0083] Input the collected soil environmental parameters and metabolic intermediate information into the degradation stage identification model. According to the input data, the model outputs the microplastic degradation stage corresponding to each polluted area, and extracts the optimal mixed ratio of the microbial flora at this stage. The model output results include the degradation stage classification results, the recommended mixed ratio of the flora (such as flora A:B:C = 2:1:1), and at the same time mark the dynamic degradation stage distribution of the polluted area.

[0084] According to the optimal flora mixed ratio output by the model, prepare the adjusted microbial flora combination in the laboratory. Cultivate the flora to the target concentration respectively (such as the flora density 10 6(CFU / mL), after mixing in a set ratio, transfer to a controllable delivery carrier (such as a slow-release granule or a liquid spraying device). Before delivery, design reasonable delivery points and delivery amounts according to the distribution characteristics (area, soil permeability) of the contaminated area to ensure uniform distribution of the microbial community within the contaminated area. Adopt the slow-release carrier technology to control the active release rate of the microbial community and adapt to the degradation requirements of the contaminated area. After the microbial community is delivered, collect soil samples from the contaminated area again at set time steps, and monitor environmental parameters and metabolite information. Input the monitoring data into the degradation stage identification model to track the degradation effect of the microbial community in real time. If it is found that the degradation efficiency decreases or the stage conversion fails to meet the standard, adjust the microbial community ratio or supplement the microbial community delivery in combination with the model suggestions to ensure the sustainability and high efficiency of the degradation process.

[0085] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0086] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0087] Those of ordinary skill in the art will realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or modules can be electrical, mechanical, or other forms.

[0090] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0092] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0093] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0094] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for treating microplastics for soil remediation, characterized in that, It includes the following steps: S1: Collect soil samples in the set target governance area, separate the microplastic samples in the soil samples, calculate the content of the microplastic samples, and determine the chemical composition of the microplastic samples to identify the types of plastics in the microplastic samples; S2: Integrate the coordinate information of the sampling points, the types of plastics of the microplastics, and the microplastic content of the sampling points, and combine with the geographic information system technology to construct a soil microplastic pollution distribution model; S3: Divide the microplastic samples into a set number of test samples, select the microbial flora for degradation treatment for the test samples for mixed degradation testing, and establish a chronological degradation sequence of different microbial flora mixing ratios; S4: Convert the chronological degradation sequence into an alignment sequence based on the degradation stage, screen out the experimental items with the least number of time steps used in each degradation stage process, and detect the metabolic intermediates of the experimental items in the degradation stage; S5: Conduct synchronous replication experiments on each experimental culture system, and integrate the environmental parameters of all experimental culture systems, as well as the corresponding degradation stage, the optimal microbial flora mixing ratio, and the types and concentrations of metabolic intermediates into a degradation experiment data set; S6: Perform data preprocessing on the degradation experiment data set, label the degradation stage of the preprocessed data, use the deep neural network algorithm to establish a degradation stage recognition model and adjust the model parameters; S7: Based on the microplastic pollution area in the soil microplastic pollution distribution model, collect the soil environmental parameters and the information of the metabolic intermediates produced by microorganisms in the pollution area, input them into the degradation stage recognition model, and adjust the ratio of the microbial flora to be put in according to the optimal microbial flora mixing ratio corresponding to the degradation stage identified by the model. Put the microbial flora with adjusted ratio into the corresponding pollution area.

2. The microplastic treatment method for soil remediation according to claim 1, characterized in that In S1, collecting soil samples in the set target governance area, separating the microplastic samples in the soil samples, calculating the content of the microplastic samples, and determining the chemical composition of the microplastic samples to identify the types of plastics in the microplastic samples specifically include: According to the geographic information of the set target governance area, collect soil samples containing microplastics through grid distribution with a set gradient, and record the sampling position coordinates; Use the saturated salt solution flotation method to separate the microplastics from the soil samples, use a low-pore-size filter membrane to separate the microplastic samples in the flotation layer, and calculate the mass ratio of the microplastic samples to the soil samples as the microplastic content of this soil sample; Use Fourier transform infrared spectroscopy scanning to analyze the microplastic molecular structure and characteristic groups, qualitatively label the main chemical components, and determine the types of plastics in the microplastic samples based on the chemical composition.

3. The microplastic treatment method for soil remediation according to claim 2, wherein, In S2, integrate the coordinate information of the sampling points, the types of plastics of the microplastics, and the microplastic content of the sampling points, and combine with the geographic information system technology to construct a soil microplastic pollution distribution model.

4. A method for treating microplastics for soil remediation according to claim 3, characterized in that, In S3, divide the microplastic samples into a set number of test samples, select the microbial flora for degradation treatment for the test samples for mixed degradation testing, and establish a chronological degradation sequence of different microbial flora mixing ratios specifically include: The microplastic samples separated from all sampling points are concentrated, evenly mixed and divided into a set number of test samples. Based on the types of plastics in the microplastic samples, the microbial flora used for degradation treatment is selected for mixed degradation testing; Construct multiple experimental culture systems, each with different environmental parameters, including soil temperature, humidity and pH value; The microplastic test samples were mixed with microbial flora in different proportions for group degradation experiments, with each mixing ratio corresponding to one experimental item; The mass of the microplastic test samples in the group degradation experiment was measured according to the set reaction time step, the mass change values ​​of the microplastic samples in time sequence were obtained, the degradation amount of the microplastics was calculated based on the mass change, and a time-series degradation sequence of different mixed ratios of microbial flora was established.

5. A microplastic treatment method for soil remediation according to claim 4, characterized in that, In S4, the time-series degradation sequence is converted into an alignment sequence based on the degradation stage, and the experimental items with the least time steps used in each degradation stage are screened out. The metabolic intermediates of the experimental items in the degradation stage are specifically detected, including: Transform the temporal degradation sequence from alignment with set reaction time steps to alignment with degradation stages based on set degradation amount gradients; In the group degradation experiment of the experimental culture system, the number of time steps spanned by each experimental item in the same degradation stage is counted, and the experimental item with the least time steps used in each degradation stage process is screened out, and the experimental microbial flora mixing ratio corresponding to the experimental item is marked as the optimal ratio of the stage; Liquid chromatography-mass spectrometry was used to analyze the information on metabolic intermediates produced by microorganisms in each degradation stage of the experimental items, and the types and concentrations of metabolic intermediates were recorded.

6. A microplastic treatment method for soil remediation according to claim 5, characterized in that In S5, a synchronous replication experiment was conducted for each experimental culture system, and the environmental parameters of all experimental culture systems, as well as the corresponding degradation stages, optimal microbial flora mixing ratios, and the types and concentrations of metabolic intermediates were integrated into a degradation experimental data set, specifically including: Conduct synchronous replication experiments for each experimental culture system and extract the experimental items with the shortest number of time steps used in each degradation stage in the degradation experiment grouping; The optimal ratio of the experimental items in each degradation stage is obtained, and combined with the type and concentration data of the metabolic intermediates produced, the joint data of degradation stage-optimal microbial flora mixing ratio-metabolic intermediates of different experimental culture systems are established and marked as the degradation experimental data set.

7. The microplastic treatment method for soil remediation according to claim 6, characterized in that, In S6, the degradation experiment data set is preprocessed, and the degradation stage is marked on the preprocessed data. A degradation stage recognition model is established using a deep neural network algorithm and model parameter adjustment is performed, including: The degradation experiment data set was preprocessed, and the outlier detection method was used to remove noise from the multidimensional data in the degradation experiment data set. The value range of all data was adjusted to a unified scale through the data standardization method. The pre-treated degradation experimental data are labeled according to the degradation stage to which they belong, and the environmental parameters and metabolic intermediate product parameters are used as input, and the labeled degradation stage and the optimal microbial flora mixing ratio corresponding to the stage are used as output to establish the training data set and the verification data set; Build a degradation stage recognition model based on the deep neural network algorithm and configure the initial parameters of the model; Input the labeled degradation experiment data set into the initialized model, and conduct preliminary training on the association pattern between environmental parameters, metabolite intermediate parameters and degradation stage recognition results through the supervised learning method. Set the optimization objective function to adjust the internal parameters of the model and fit the non-linear relationship between metabolite intermediates and degradation stages under different environmental parameters; Input the validation set data into the preliminarily trained model, and use the cross-validation method to calculate the accuracy, recall rate and F1 score indicators of the model on the validation data split. Combine grid search to adjust the hyperparameters of the model until the model recognition accuracy reaches the set indicators.

8. A method for treating microplastics for soil remediation according to claim 7, characterized in that, In S7, based on the microplastic pollution area in the soil microplastic pollution distribution model, collect the soil environmental parameters and the information of metabolite intermediates produced by microorganisms in the polluted area, input them into the degradation stage recognition model, and adjust the ratio of the microbial flora to be put in based on the optimal microbial flora mixture ratio corresponding to the degradation stage output by the model recognition. The specific steps of putting the adjusted microbial flora into the corresponding polluted area include: Mark the areas in the soil microplastic pollution distribution model where the microplastic content exceeds the set pollution content threshold as microplastic pollution areas, and collect the soil environmental parameters and the information of metabolite intermediates produced by microorganisms at time intervals corresponding to the set reaction time steps for each polluted area; Input the environmental parameters and metabolite intermediate information into the degradation stage recognition model to identify the degradation stage of microplastic pollution in each polluted area and the corresponding optimal microbial flora mixture ratio; Adjust the ratio of the microbial flora to be put in according to the optimal microbial flora mixture ratio, and put the adjusted microbial flora into the corresponding microplastic pollution area.

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