Method for evaluating and analyzing influence of biobased degradable material on soil environment in arid region field

Abstract

The application provides a kind of bio-based degradable material to arid region field soil environment influence evaluation analysis method.The bio-based degradable material to arid region field soil environment influence evaluation analysis method includes a. Provide a multi-factor environment simulation model, the model combines different aridity, seasonal variation, long-term climate fluctuation factor, describes the dynamic process of soil change.The bio-based degradable material to arid region field soil environment influence evaluation analysis method, by introducing multi-factor environment simulation model, it is avoided that the problem that only single environmental condition is based on in the previous experiment by integrating a variety of variables such as aridity, seasonal variation, climate fluctuation into experimental design.This innovative design enables the method to verify the influence of bio-based degradable material on soil environment under a wider range of conditions, significantly improving the extrapolation and accuracy of experimental results.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental impact of biobased degradable materials, specifically a method for evaluating and analyzing the environmental impact of biobased degradable materials on farmland soil in arid regions. BACKGROUND

[0002] The "method for evaluating and analyzing the environmental impact of biobased degradable materials on farmland soil in arid regions" generally consists of four main steps. First is experimental design and material selection, which involves selecting appropriate biobased degradable materials based on the climate characteristics and soil properties of arid regions, typically natural polymers or biodegradable plant fiber materials. Second is the setting of evaluation indicators, where researchers establish an evaluation system by setting indicators related to the soil environment, such as soil organic matter content, pH value, water retention capacity, and microbial activity. Third is data collection and analysis, which involves collecting data on soil changes after using the degradable materials through field experiments or simulation experiments. Finally, results analysis and evaluation, which involves statistical analysis of experimental data to compare soil environmental changes under different conditions and evaluate the improvement of biobased degradable materials on arid region soil and its long-term sustainability.

[0003] Although this evaluation and analysis method provides a systematic framework for studying the application of biobased degradable materials in arid regions, it still has some obvious defects. The limitations of experimental conditions result in insufficient universality of experimental results, as the experiments are mainly based on a single environmental condition and cannot fully consider the influence of complex factors such as different aridity levels, seasonal changes, and long-term climate fluctuations, which may pose problems in extrapolating the results. The data collection and analysis process is complex, involving the interaction of multiple environmental factors, and the influence of each factor may overlap, which may challenge the accuracy of the data and the reliability of the analysis in practice. SUMMARY

[0004] To address the shortcomings of the prior art, the present application provides a method for evaluating and analyzing the environmental impact of biobased degradable materials on farmland soil in arid regions, which solves the problem of not being able to fully consider the influence of complex factors such as different aridity levels, seasonal changes, and long-term climate fluctuations. It involves the interaction of multiple environmental factors, and the influence of each factor may overlap, which may challenge the accuracy of the data and the reliability of the analysis in practice.

[0005] To achieve the above purpose, the present application is implemented by the following technical solution: a method for evaluating and analyzing the environmental impact of biobased degradable materials on farmland soil in arid regions, comprising:

[0006] a. providing a multi-factor environmental simulation model, which combines different aridity levels, seasonal changes, and long-term climate fluctuation factors to describe the dynamic process of soil changes; the model formula is:

[0007] S(t) = a · D(t) + b · T(t) + c · C(t) + d · M(t) + ε

[0008] where S(t) is the value of soil state at time t, D(t) is the time variation of drought degree, T(t) is the temperature variation, C(t) is the climate change parameter, M(t) is the microbial activity, ε is the error term, and a, b, c, d are undetermined coefficients obtained by fitting experimental data;

[0009] b. According to the multi-factor environment simulation model, at least three different soil types, drought degrees, seasonal variations and climate conditions are selected to design experimental groups, and initial experimental conditions are set: drought degree, soil pH, temperature range;

[0010] c. Apply bio-based degradable materials under different experimental conditions, and monitor the physical and chemical properties of the soil, microbial activity, and water retention capacity of the soil. The monitoring process is based on the following formula for data collection:

[0011] M(t) = M0·e -λt

[0012] where M(t) represents the microbial activity at time t, M0 is the initial microbial activity, λ is the decay constant, which represents the rate of change of microbial activity over time;

[0013] d. For each experimental group, soil samples are collected at different time points to measure their organic matter content, total nitrogen content and soil water retention capacity, and the change rate of each index is calculated according to the following formula:

[0014]

[0015] where △OM is the change rate of organic matter content, OM t and OM t+1 are the organic matter contents at times t and t+1, respectively;

[0016] e. Through statistical analysis methods, the changes in the soil environment of each experimental group at different time periods are evaluated, and the comprehensive influence factor of the bio-based degradable material is calculated, and data analysis is performed;

[0017] f. Machine learning algorithms are used to integrate meteorological data, soil characteristics, and crop growth status data sources to achieve comprehensive monitoring and prediction of the experimental environment. The formula for the multi-data fusion algorithm is:

[0018]

[0019] where Z(t) is the comprehensive monitoring index, X i (t) is the i-th monitoring index, wi corresponding to the weight, and

[0020] g. During soil sample collection, the combination of Internet of Things technology and real-time sensors is used to monitor the temperature, humidity, and pH value of the soil in real time, ensuring the timeliness and accuracy of experimental data.

[0021] Preferably, the simulation model adjusts the experimental conditions dynamically and self-adaptively, and adjusts the values of the model parameters a, b, c, and d in real time according to the environmental monitoring data, so as to accurately simulate the changes of the soil under different environmental conditions. The adjustment formula is:

[0022] θ(t) = θ(t-1) + Δθ·δ(t)

[0023] where θ(t) is the value of the model parameter at time t, Δθ is the parameter adjustment amount at the last time, and δ(t) is the feedback adjustment coefficient automatically calculated according to the current experimental environment.

[0024] Preferably, the machine learning algorithm is a support vector machine or a random forest, which is used to predict the long-term impact of biobased degradable materials on soil changes under different environmental conditions. The objective function of the machine learning algorithm is:

[0025]

[0026] where α is the Lagrange multiplier in the support vector machine, K is the kernel function matrix, and y is the label of the experimental data.

[0027] Preferably, the experimental group design step includes setting the application amount of biobased degradable materials according to different seasonal changes and dynamically adjusting the application period of the degradable materials to simulate the influence of different seasons on the degradation process.

[0028] Preferably, the experimental data are stored and analyzed through a cloud platform, and the experimental parameters can be optimized in real time according to the experimental results to realize dynamic self-adaptive adjustment of the experimental process.

[0029] Preferably, the data analysis platform uses multiple algorithms to perform comprehensive analysis and evaluate the effect of biobased degradable materials from multiple dimensions to obtain more accurate and reliable analysis results.

[0030] Preferably, the monitoring data of microbial activity are integrated through multi-sensor fusion technology to improve the accuracy and precision of data acquisition and reduce the influence of external interference on experimental results.

[0031] Preferably, the platform of the data analysis adopts a genetic algorithm for optimization to screen the optimal degradation material application scheme from multiple experimental conditions to achieve the optimal configuration of the degradation material, and the method is suitable for different types of arid regions, including but not limited to semi-arid, arid and extremely arid regions, and the method can adjust the experimental parameters according to the differences of specific environments to achieve strong universal application.

[0032] The application provides a bio-based degradation material impact evaluation and analysis method for arid region field soil environment.

[0033] The bio-based degradation material impact evaluation and analysis method for arid region field soil environment introduces a multi-factor environment simulation model, integrates various variables such as aridity, seasonal changes and climate fluctuations into the experimental design, and avoids the problem of previous experiments based on only a single environmental condition. This innovative design enables the method to verify the impact of bio-based degradation materials on soil environment under a wider range of conditions, significantly improving the extrapolation and accuracy of experimental results. By dynamically adjusting the experimental conditions, the model can adapt to different environmental changes, thus more realistically simulating the soil ecosystem in arid regions and providing reliable basis for scientific decision-making.

[0034] By using advanced real-time monitoring technology, combined with long-term data collection of microbial activity, soil physical and chemical properties, soil water retention capacity and other indicators, comprehensive and accurate soil change monitoring is ensured. At the same time, the method introduces machine learning algorithms and multiple regression analysis, effectively dealing with the interaction between various environmental factors and reducing the bias caused by overlapping variables in traditional data analysis. This innovative approach not only improves the accuracy of data analysis, but also provides precise quantitative analysis results for complex soil environmental changes, greatly enhancing the reliability of experimental design. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The flowchart of the application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0037] Embodiment one

[0038] As Figure 1As shown, the embodiment of the present application provides a method for evaluating and analyzing the influence of biobased degradable materials on the soil environment in arid regions, which includes: a. providing a multi-factor environmental simulation model that combines different aridity levels, seasonal changes, and long-term climate fluctuation factors to describe the dynamic process of soil changes. The model formula is:

[0039] S(t) = a·D(t) + b·T(t) + c·C(t) + d·M(t) + ∈

[0040] where S(t) is the value of the soil state at time t, D(t) is the time variation of aridity, T(t) is the temperature variation, C(t) is the climate change parameter, M(t) is the microbial activity, ∈ is the error term, and a, b, c, d are undetermined coefficients obtained by fitting experimental data.

[0041] b. According to the multi-factor environmental simulation model, at least three different soil types, aridity levels, seasonal changes and climate conditions are selected to design experimental groups, and initial experimental conditions are set: aridity level, soil pH, temperature range, the simulation model adjusts the experimental conditions dynamically and adaptively, and adjusts the values of model parameters a, b, c, d in real time according to the environmental monitoring data, so as to accurately simulate the soil changes under different environmental conditions. The adjustment formula is:

[0042] θ(t) = θ(t-1) + Δθ·δ(t)

[0043] where θ(t) is the value of the model parameter at time t, Δθ is the parameter adjustment amount at the last time, and δ(t) is the feedback adjustment coefficient automatically calculated according to the current experimental environment.

[0044] c. Apply biobased degradable materials under different experimental conditions, and monitor the physicochemical properties, microbial activity, and water retention capacity of the soil. The monitoring process collects data according to the following formula:

[0045] M(t) = M0·e -λt

[0046] where M(t) represents the microbial activity at time t, M0 is the initial microbial activity, λ is the decay constant, which represents the rate of change of microbial activity with time, and the monitoring data of microbial activity is integrated through multi-sensor fusion technology to improve the accuracy and precision of data acquisition and reduce the influence of external interference on experimental results.

[0047] d. For each experimental group, collect soil samples at different time points, measure the organic matter content, total nitrogen content and soil water retention capacity, and calculate the change rate of each index according to the following formula:

[0048]

[0049] wherein, △OM is the rate of change of organic matter content, OM t and OM t+1 are the organic matter content at time t and t+1 respectively.

[0050] e. Through statistical analysis method, the changes of soil environment in different time periods of each experimental group are evaluated, and the comprehensive influence factor of biobased degradable material is calculated, and data analysis is carried out. The platform of data analysis adopts integrated multiple algorithms for comprehensive analysis, and the effect of biobased degradable material is evaluated from multiple dimensions to obtain more accurate and reliable analysis results. The platform of data analysis adopts genetic algorithm for optimization, and the optimal degradable material application scheme is selected from multiple experimental conditions to realize the optimal configuration of degradable material. The method is suitable for different types of arid regions, including but not limited to semi-arid, arid and extremely arid regions, and the method can adjust experimental parameters according to different specific environments to realize strong universal application.

[0051] f. Machine learning algorithm is adopted to realize comprehensive monitoring and prediction of experimental environment by integrating meteorological data, soil characteristics and crop growth state of various data sources. The formula of multiple data fusion algorithm is:

[0052]

[0053] wherein, Z(t) is the comprehensive monitoring index, X i (t) is the i th monitoring index, w i is the corresponding weight, and The machine learning algorithm is support vector machine or random forest, which is used to predict the long-term influence of biobased degradable material on soil changes under different environmental conditions. The objective function of machine learning algorithm is:

[0054]

[0055] wherein, α is the Lagrange multiplier in support vector machine, K is the kernel function matrix, and y is the label of experimental data. The experimental group design steps include setting the application amount of biobased degradable material according to different seasonal changes, dynamically adjusting the application period of degradable material to simulate the influence of different seasons on the degradation process. Experimental data are stored and analyzed through cloud platform, and experimental parameters can be optimized in real time according to experimental results to realize dynamic self-adaptive adjustment of experimental process.

[0056] g. When collecting soil samples, real-time sensors are used to monitor the temperature, humidity and pH value of soil in real time by combining with Internet of Things technology, so as to ensure the timeliness and accuracy of experimental data.

[0057] Example two

[0058] Objective of the experiment:

[0059] To evaluate the impact of bio-based degradable materials on soil environment (such as organic matter content, microbial activity, and water retention capacity) under different soil types and climate conditions in arid regions.

[0060] Experimental design:

[0061] Experimental site and soil selection:

[0062] Three typical soil types in arid regions were selected: sandy soil, clay soil, and loamy soil. Each soil type was collected from three different experimental sites representing different levels of drought (mild drought, moderate drought, and severe drought).

[0063] Experimental group design:

[0064] Each soil type was set up in three experimental groups representing different levels of drought, totaling nine experimental groups. The experimental conditions for each group included:

[0065] Soil type: sandy soil, clay soil, loamy soil

[0066] Drought level: mild drought, moderate drought, severe drought

[0067] Initial conditions: soil pH (6.0-7.5), temperature range (25°C-35°C)

[0068] Experimental materials:

[0069] The bio-based degradable material used was a mixture containing degradable polylactic acid (PLA) and polyhydroxyalkanoate (PHA). The material was applied to the soil surface at a dose of 50 grams per square meter.

[0070] Experimental process:

[0071] a. Soil pretreatment:

[0072] Each soil sample in each experimental group was pretreated to adjust to the set drought level (by adjusting soil moisture) and temperature range (by using indoor temperature control equipment).

[0073] b. Application of degradable material:

[0074] Under the set environmental conditions, the bio-based degradable material was evenly spread on the soil surface, ensuring that each experimental group received the same amount of material application.

[0075] c. Real-time monitoring:

[0076] Temperature and humidity sensors, pH probes, and microbial activity sensors were used to monitor the changes in the soil environment in real-time. Data was uploaded to the experimental analysis platform in real-time through multi-sensor fusion technology.

[0077] Data collection and analysis:

[0078] Soil samples were collected every 7 days after the start of the experiment, and the following indicators were measured:

[0079] Organic matter content: The organic matter content in the soil was measured by gravimetric method, and its rate of change was calculated.

[0080] Total nitrogen content: The total nitrogen content in the soil was measured using the Kjeldahl method.

[0081] Microbial activity: Soil respiration and fluorescence methods were used to assess microbial activity and monitor changes in microbial populations.

[0082] Water retention capacity: The soil's water retention capacity is measured by the gravity method and capillary water method, and the change in water retention is calculated.

[0083] Data Analysis:

[0084] Statistical analysis was performed on the collected data, primarily using regression analysis and multivariate analysis of variance (ANOVA) to assess the differences in soil environment among different experimental groups, particularly the impact of bio-based degradable materials on soil organic matter content, microbial activity, and water retention capacity. The comprehensive impact factor for each experimental group was calculated through model fitting, and machine learning algorithms were used to predict the impact trend under other drought conditions.

[0085] Experimental results:

[0086] Soil organic matter content:

[0087] After application of bio-based degradable materials, the organic matter content in all experimental groups increased significantly on days 14 and 28 of the experiment, especially in the slightly arid sandy soil, where the organic matter increased by 25%. Compared with the control group (natural drought only), the bio-based degradable materials significantly improved soil organic matter accumulation.

[0088] Microbial activity:

[0089] In all experimental groups, microbial activity significantly increased within the first two weeks after material application, particularly in moderately arid loam, where it increased by 18%. Microbial activity was positively correlated with changes in soil organic matter, indicating that the degradation process of the degrading material promoted the growth and reproduction of the microbial community.

[0090] Moisture retention capacity:

[0091] The bio-based degradable material shows good water retention capacity in various soil types, especially in clay and loam soil, the water retention capacity of the soil is increased by 12%-15%. This improvement in water retention helps to alleviate water loss in arid regions and improves the stability of water supply for crop growth.

[0092] While embodiments of the present application have been shown and described, it is to be understood that the embodiments described are merely divergences, modifications, replacements and variations of the embodiments, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating and analyzing the influence of a bio-based degradable material on the soil environment of a dryland field, characterized by, The method comprises the following steps: a. Providing a multi-factor environment simulation model that combines different drought levels, seasonal changes, long-term climate fluctuation factors, and describes the dynamic process of soil changes; the model formula is: S(t) = a·D(t) + b·T(t) + c·C(t) + d·M(t) + ∈ where S(t) is the value of the soil state at time t, D(t) is the time variation of drought level, T(t) is the temperature variation, C(t) is the climate change parameter, M(t) is the microbial activity, ∈ is the error term, and a, b, c, d are undetermined coefficients obtained by fitting experimental data; b. According to the multi-factor environment simulation model, select at least three different soil types, drought levels, seasonal changes, and climate conditions, design experimental groups, and set initial experimental conditions: drought level, soil pH, temperature range; c. Apply biological-based degradation materials under different experimental conditions, and monitor the physicochemical properties, microbial activity, and water retention capacity of the soil, and the monitoring process is based on the following formula for data collection: M(t) = M0·e -λt where M(t) represents the microbial activity at time t, M0 is the initial microbial activity, and λ is the decay constant, which represents the rate of change of microbial activity over time; d. For each experimental group, collect soil samples at different time points, measure the organic matter content, total nitrogen content, and soil water retention capacity, and calculate the change rate of each index according to the following formula: Wherein, △OM is the organic matter content change rate, OM t and OM t+1 are the organic matter content at time t and t+1 respectively. e. Through statistical analysis methods, evaluate the soil environment changes of each experimental group in different time periods, calculate the comprehensive influence factor of biological-based degradation materials, and perform data analysis. The data analysis platform uses a genetic algorithm for optimization to select the optimal degradation material application scheme from multiple experimental conditions to achieve the optimal configuration of degradation materials. This method is applicable to different types of arid regions, including but not limited to semi-arid, arid, and extremely arid regions, and can adjust experimental parameters according to different specific environments to achieve strong universality; f. Use machine learning algorithms to integrate meteorological data, soil characteristics, and crop growth status data sources to achieve comprehensive monitoring and prediction of the experimental environment. The formula for the multi-element data fusion algorithm is: Wherein, Z(t) is a comprehensive monitoring index, X i (t) is the i th monitoring index, w i is the corresponding weight, and g. During soil sample collection, use real-time sensors to monitor soil temperature, humidity, and pH in real time to ensure the timeliness and accuracy of experimental data.

2. The method according to claim 1, wherein the method is characterized by: The simulation model dynamically adjusts experimental conditions in real time by adjusting the values of model parameters a, b, c, and d based on environmental monitoring data, thereby accurately modeling soil changes under different environmental conditions. The adjustment formula is: θ(t) = θ(t-1) + Δθ·δ(t) where θ(t) is the value of the model parameter at time t, Δθ is the parameter adjustment amount at the previous time, and δ(t) is the feedback adjustment coefficient automatically calculated based on the current experimental environment.

3. The method according to claim 1, wherein the method is characterized by: The machine learning algorithm is a support vector machine or a random forest, which is used to predict the long-term impact of biological-based degradation materials on soil changes under different environmental conditions. The objective function of the machine learning algorithm is: Wherein, α is the Lagrange multiplier in support vector machine, K is the kernel function matrix, and y is the label of experimental data.

4. The method according to claim 1, wherein the method is characterized by: The experimental group design step includes setting the application amount of biobased degradable material according to different seasonal changes, dynamically adjusting the application period of degradable material, and simulating the influence of different seasons on the degradation process.

5. The method according to claim 1, wherein the method is characterized by: The experimental data is centrally stored and analyzed through the cloud platform, and the experimental parameters can be optimized in real time according to the experimental results to realize dynamic self-adaptive adjustment of the experimental process.

6. The method according to claim 1, wherein the method is characterized by: The data analysis platform uses multiple algorithms to perform comprehensive analysis, evaluates the effect of biobased degradable material from multiple dimensions, and obtains more accurate and reliable analysis results.

7. The method according to claim 1, wherein the method is characterized by: The monitoring data of microbial activity is integrated through multi-sensor fusion technology, thereby improving the accuracy and precision of data acquisition and reducing the influence of external interference on experimental results.

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