Method for evaluating and analyzing influence of bio-based degradable material on arid region field soil environment

Abstract

The invention provides a method for evaluating and analyzing the influence of a bio-based degradable material on an arid region field soil environment. The method for evaluating and analyzing the influence of the bio-based degradable material on the field soil environment in the arid region comprises the following steps: a, providing a multi-factor environment simulation model which is combined with different drought degrees, seasonal changes and long-term climate fluctuation factors to describe the dynamic process of soil changes; according to the method for evaluating and analyzing the influence of the bio-based degradable material on the field soil environment in the arid region, a multi-factor environment simulation model is introduced, various variables such as the drought degree, the seasonal change and the climate fluctuation are fused into experimental design, and the problem that the previous experiment is carried out only based on a single environment condition is avoided. By means of the innovative design, the method can verify the influence of the bio-based degradable material on the soil environment under wider conditions, and extrapolation and accuracy of experimental results are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of environmental impact of bio-based degradable materials, in particular to an environmental impact assessment and analysis method of bio-based degradable materials on field soil in arid areas. Background Art

[0002] The "Analysis Method for the Environmental Impact Assessment of Field Soil in Arid Areas by Biodegradable Materials" is usually divided into four main steps. The first is experimental design and material selection. This part requires the selection of suitable biodegradable materials, usually natural polymers or degradable plant fiber materials, based on the climate characteristics and soil properties of arid areas. The second is the setting of evaluation indicators. Researchers establish an evaluation system by setting indicators related to the soil environment, such as soil organic matter content, pH value, water retention capacity, microbial activity, etc. The next step is data collection and analysis. Through field experiments or simulation experiments, data on soil changes after the use of the degradable material are collected. The last step is result analysis and evaluation. Statistical analysis is performed on the experimental data to compare soil environmental changes under different conditions and evaluate the improvement effect of biodegradable materials on soil in arid areas and their long-term sustainability.

[0003] Although this evaluation and analysis method provides a systematic framework for studying the application of biodegradable materials in arid areas, it still has some obvious defects. The limitations of experimental conditions lead to the lack of universality of the experimental results. Because the experiment is mainly based on a single environmental condition, it is impossible to fully consider the influence of complex factors such as different degrees of drought, seasonal changes and long-term climate fluctuations. The extrapolation of the conclusions is problematic. The data collection and analysis process is relatively complicated, 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 of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for evaluating and analyzing the environmental impact of bio-based degradable materials on field soil in arid areas, which solves the problem of being unable to fully consider the impact of complex factors such as different degrees of drought, seasonal changes and long-term climate fluctuations; it involves the interaction of multiple environmental factors, and the impact 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 objectives, the present invention is implemented through the following technical solutions: A method for evaluating and analyzing the environmental impact of biodegradable materials on field soil in arid areas, comprising:

[0006] a. Provide a multi-factor environmental simulation model, which combines different drought levels, seasonal changes, and long-term climate fluctuations 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)+∈

[0008] Among them, S(t) is the value of soil state at time t, D(t) is the time change of drought degree, T(t) is the temperature change, C(t) is the climate change parameter, M(t) is the microbial activity, ∈ is the error term, and a, b, c, d are the coefficients to be determined, which are obtained by fitting the experimental data;

[0009] b. According to the multi-factor environmental 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;

[0010] c. Apply biodegradable materials under different experimental conditions and monitor multiple indicators such as soil physical and chemical properties, microbial activity, and water retention capacity. The monitoring process collects data according to the following formula:

[0011] M(t)=M0·e -λt

[0012] 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 at which the microbial activity changes with time;

[0013] d. For each experimental group, soil samples were 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 indicator was calculated according to the following formula:

[0014]

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

[0016] e. Evaluate the soil environment changes of each experimental group at different time periods through statistical analysis methods, calculate the comprehensive impact factors of bio-based degradable materials, and conduct data analysis;

[0017] f. Using machine learning algorithms, by integrating multiple data sources such as meteorological data, soil characteristics, and crop growth status, comprehensive monitoring and prediction of the experimental environment can be achieved. The formula of the multivariate data fusion algorithm is:

[0018]

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

[0020] g. When collecting soil samples, we combine the Internet of Things technology and use real-time sensors to monitor the temperature, humidity, pH value, etc. of the soil in real time to ensure the timeliness and accuracy of the experimental data.

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

[0022] θ(t)=θ(t-1)+Δθ·δ(t)

[0023] Among them, θ(t) is the value of the model parameter at time t, Δθ is the parameter adjustment amount at the previous moment, 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 biodegradable materials on soil changes under different environmental conditions. The objective function of the machine learning algorithm is:

[0025]

[0026] Among them, 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 the bio-based degradation material according to different seasonal changes, and dynamically adjusting the application cycle of the degradation material to simulate the influence of different seasons on the degradation process.

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

[0029] Preferably, the data analysis platform uses integrated multiple algorithms for comprehensive analysis to evaluate the effects of bio-based degradable materials from multiple dimensions to obtain more accurate and reliable analysis results.

[0030] Preferably, the monitoring data of the microbial activity is integrated through multi-sensor fusion technology, thereby improving the accuracy and precision of data collection and reducing the impact of external interference on the experimental results.

[0031] Preferably, the data analysis platform is optimized using a genetic algorithm to screen out the optimal application scheme of the degradation material from multiple experimental conditions to achieve the optimal configuration of the degradation material. The method is applicable to different types of arid areas, including but not limited to semi-arid, arid and extremely arid areas, and the method can adjust the experimental parameters according to the specific environment to achieve a highly universal application.

[0032] The present invention provides a method for evaluating and analyzing the environmental impact of biodegradable materials on field soil in arid areas. It has the following beneficial effects:

[0033] This method for evaluating and analyzing the environmental impact of bio-based degradable materials on field soil in arid areas introduces a multi-factor environmental simulation model, and incorporates multiple variables such as drought degree, seasonal changes, and climate fluctuations into the experimental design, avoiding the problem that previous experiments were based only on a single environmental condition. This innovative design enables the method to verify the impact of bio-based degradable materials on the soil environment under a wider range of conditions, significantly improving the extrapolation and accuracy of the experimental results. By dynamically adjusting the experimental conditions, the model can adapt to different environmental changes, thereby more realistically simulating the soil ecosystem in arid areas and providing a reliable basis for scientific decision-making.

[0034] By adopting advanced real-time monitoring technology, combined with long-term data collection of indicators such as microbial activity, soil physical and chemical properties, and soil water retention capacity, comprehensive and accurate soil change monitoring is ensured. At the same time, the method introduces machine learning algorithms and multivariate regression analysis to effectively deal with the interaction effects between multiple environmental factors and reduce the deviation caused by the overlap of dependent variables in traditional data analysis. This innovative method not only improves the accuracy of data analysis, but also provides accurate quantitative analysis results for complex soil environmental changes, greatly enhancing the reliability of experimental design. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] like Figure 1As shown, the embodiment of the present invention provides a method for evaluating and analyzing the environmental impact of biodegradable materials on field soil in arid areas, including: a. providing a multi-factor environmental simulation model, which combines different drought levels, seasonal changes, and long-term climate fluctuations 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] Among them, S(t) is the value of the soil state at time t, D(t) is the time change of drought degree, T(t) is the temperature change, C(t) is the climate change parameter, M(t) is the microbial activity, ∈ is the error term, and a, b, c, d are unknown coefficients obtained by fitting experimental data.

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

[0042] θ(t)=θ(t-1)+Δθ·δ(t)

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

[0044] c. Apply biodegradable materials under different experimental conditions and monitor multiple indicators such as soil physical and chemical properties, microbial activity, and water retention capacity. The monitoring process collects data according to the following formula:

[0045] M(t)=M0·e -λt

[0046] Among them, 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 at which the microbial activity changes over time. 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 impact of external interference on the experimental results.

[0047] d. For each experimental group, soil samples were 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 indicator was calculated according to the following formula:

[0048]

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

[0050] e. Through statistical analysis methods, the soil environment changes of each experimental group in different time periods are evaluated, and the comprehensive influencing factors of bio-based degradable materials are calculated, and data analysis is performed. The data analysis platform uses integrated multiple algorithms for comprehensive analysis, and evaluates the effects of bio-based degradable materials from multiple dimensions to obtain more accurate and reliable analysis results. The data analysis platform uses genetic algorithms for optimization, and selects the optimal degradation material application plan from multiple experimental conditions to achieve the optimal configuration of degradation materials. The method is suitable for different types of arid areas, including but not limited to semi-arid, arid and extremely arid areas, and the method can adjust the experimental parameters according to the specific environment to achieve a highly universal application.

[0051] f. Using machine learning algorithms, by integrating multiple data sources such as meteorological data, soil characteristics, and crop growth status, comprehensive monitoring and prediction of the experimental environment can be achieved. The formula of the multivariate data fusion algorithm is:

[0052]

[0053] Among them, Z(t) is the comprehensive monitoring index, X i (t) is the i-th monitoring indicator, 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 impact of biodegradable materials on soil changes under different environmental conditions. The objective function of the machine learning algorithm is:

[0054]

[0055] Among them, 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. The experimental group design steps include setting the application amount of bio-based degradable materials according to seasonal changes, and dynamically adjusting the application cycle of the degradation materials to simulate the impact of different seasons on the degradation process. 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 achieve dynamic adaptive adjustment of the experimental process.

[0056] g. When collecting soil samples, we combine the Internet of Things technology and use real-time sensors to monitor the temperature, humidity, pH value, etc. of the soil in real time to ensure the timeliness and accuracy of the experimental data.

[0057] Embodiment 2

[0058] Purpose:

[0059] To evaluate the effects of biodegradable materials on soil environment (e.g. organic matter content, microbial activity and water retention capacity) under different soil types and climatic conditions in arid areas.

[0060] Experimental design:

[0061] Experimental site and soil selection:

[0062] The experiment selected three typical soil types in arid areas: sandy soil, clay and loam. Each soil type was collected from three different experimental sites, representing different degrees of drought (mild drought, moderate drought and severe drought).

[0063] Experimental group design:

[0064] Each soil type was set up with three experimental groups under three drought levels, for a total of 9 experimental groups. The experimental conditions of each experimental group included:

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

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

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

[0068] Experimental Materials:

[0069] The biodegradable material used was a mixture of degradable polylactic acid (PLA) and polyhydroxyalkanoate (PHA). In the experiment, the material was applied to the soil surface at a dosage of 50 grams per square meter.

[0070] Experimental process:

[0071] a. Soil pretreatment:

[0072] Soil samples from each experimental group were pre-treated and adjusted to the set drought level (by adjusting soil moisture) and temperature range (by indoor temperature control equipment).

[0073] b. Application of degradation materials:

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

[0075] c. Real-time monitoring:

[0076] Use temperature and humidity sensors, pH probes, and microbial activity sensors to monitor changes in the soil environment in real time. Use multi-sensor fusion technology to upload data to the experimental analysis platform in real time.

[0077] Data collection and analysis:

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

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

[0080] Total Nitrogen Content: The total nitrogen content in the soil is 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 holding capacity: The water holding capacity of the soil is measured by specific gravity and capillary water methods, and the change in water holding capacity is calculated.

[0083] Data Analysis:

[0084] The collected data were statistically analyzed, mainly through regression analysis and multivariate analysis of variance (ANOVA) to evaluate the differences in soil environment among different experimental groups, especially the effects of biodegradable materials on soil organic matter content, microbial activity and water retention capacity. The comprehensive influencing factors of each experimental group were calculated by model fitting, and the impact trend under other drought conditions was predicted using machine learning algorithms.

[0085] Experimental results:

[0086] Soil organic matter content:

[0087] After applying the biodegradable materials, the organic matter content of all experimental groups increased significantly on the 14th and 28th days of the experiment, especially in the sandy soil with mild drought, where the organic matter increased by 25%. Compared with the control group without application of materials (natural drought only), the biodegradable materials significantly improved the accumulation of organic matter in the soil.

[0088] Microbial activity:

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

[0090] Moisture Retention Capacity:

[0091] Biodegradable materials show good water retention capacity in all soil types, especially in clay and loam, where the soil water retention capacity is increased by 12%-15%. This improvement in water retention helps alleviate water loss in arid areas and improves the stability of water supply for crop growth.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating and analyzing the environmental impact of biodegradable materials on field soil in arid areas, characterized in that: The following steps are involved: a. Provide a multi-factor environmental simulation model, which combines different drought levels, seasonal changes, and long-term climate fluctuations to describe the dynamic process of soil changes; the model formula is: S(t)=a·D(t)+b·T(t)+c·C(t)+d·M(t)+∈ Among them, S(t) is the value of soil state at time t, D(t) is the time change of drought degree, T(t) is the temperature change, C(t) is the climate change parameter, M(t) is the microbial activity, ∈ is the error term, and a, b, c, d are the coefficients to be determined, which are obtained by fitting the experimental data; b. According to the multi-factor environmental 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 biodegradable materials under different experimental conditions and monitor multiple indicators such as soil physical and chemical properties, microbial activity, and water retention capacity. The monitoring process collects data according to the following formula: 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 at which the microbial activity changes with time; d. For each experimental group, soil samples were 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 indicator was calculated according to the following formula: Among them, △OM is the change rate of organic matter content, OM t and OM t+1 are the organic matter contents at time t and t+1 respectively; e. Evaluate the soil environment changes of each experimental group at different time periods through statistical analysis methods, calculate the comprehensive impact factors of bio-based degradable materials, and conduct data analysis; f. Using machine learning algorithms, by integrating multiple data sources such as meteorological data, soil characteristics, and crop growth status, comprehensive monitoring and prediction of the experimental environment can be achieved. The formula of the multivariate data fusion algorithm is: Among them, Z(t) is the comprehensive monitoring index, X i (t) is the i-th monitoring indicator, w i is the corresponding weight, and g. When collecting soil samples, we combine the Internet of Things technology and use real-time sensors to monitor the temperature, humidity, pH value, etc. of the soil in real time to ensure the timeliness and accuracy of the experimental data.

2. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The simulation model dynamically and adaptively adjusts the experimental conditions and adjusts the values ​​of model parameters a, b, c, and d in real time according to environmental monitoring data, thereby accurately modeling soil changes under different environmental conditions of the dam. The adjustment formula is: θ(t)=θ(t-1)+Δθ·δ(t) Among them, θ(t) is the value of the model parameter at time t, Δθ is the parameter adjustment amount at the previous moment, and δ(t) is the feedback adjustment coefficient automatically calculated according to the current experimental environment.

3. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The machine learning algorithm is a support vector machine or a random forest, which is used to predict the long-term impact of biodegradable materials on soil changes under different environmental conditions. The objective function of the machine learning algorithm is: Among them, 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.

4. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The experimental group design steps include setting the application amount of the bio-based degradation material according to different seasonal changes, and dynamically adjusting the application cycle of the degradation material to simulate the influence of different seasons on the degradation process.

5. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: 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 achieve dynamic adaptive adjustment of the experimental process.

6. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The data analysis platform uses integrated multiple algorithms for comprehensive analysis, and evaluates the effects of bio-based degradable materials from multiple dimensions to obtain more accurate and reliable analysis results.

7. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The monitoring data of the microbial activity is integrated through multi-sensor fusion technology, thereby improving the accuracy and precision of data collection and reducing the impact of external interference on the experimental results.

8. The method for evaluating and analyzing the environmental impact of biodegradable materials on soil in arid areas according to claim 1, characterized in that: The data analysis platform is optimized using a genetic algorithm to screen out the best application scheme for degradation materials from multiple experimental conditions to achieve the optimal configuration of degradation materials. The method is applicable to different types of arid areas, including but not limited to semi-arid, arid and extremely arid areas, and the method can adjust experimental parameters according to different specific environments to achieve highly universal applications.

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