Soil resource sustainable utilization evaluation and prediction system

Abstract

The invention provides a soil resource sustainable utilization evaluation and prediction system, relates to the technical field of sustainable utilization evaluation and prediction of soil resources, and aims to accurately capture dynamic characteristics of soil carbon circulation by monitoring soil respiration rate, carbon content and other environmental data in real time and combining a multilevel carbon distribution-respiration rate mapping model. The system integrates MLP and LSTM technologies, and dynamically updates a multilevel carbon distribution-respiration rate mapping weight to reflect a nonlinear mapping relationship between soil carbon storage and respiration rate. By constructing a soil resource optimization map and a multi-factor comprehensive evaluation index, the system can visually display potential influences of different soil management measures, and provides scientific decision support for soil resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of sustainable utilization assessment and prediction of soil resources, and in particular to a soil resource sustainable utilization assessment and prediction system. Background Art

[0002] Soil resources, as one of the most important natural resources on the earth, play a vital role in agricultural production, ecosystem balance and global carbon cycle; it is not only the basis for plant growth, but also directly affects food security and biodiversity; however, with the acceleration of industrialization and urbanization and irrational land use, soil degradation is becoming increasingly serious, posing a severe challenge to the sustainable use of soil resources.

[0003] Existing technologies have significant defects in the assessment and prediction of sustainable utilization of soil resources. Traditional assessment methods rely on periodic soil sample collection and analysis, which is unable to monitor the dynamic changes of soil status in real time, resulting in a lack of timeliness in the assessment results. Existing assessment models often ignore the complex nonlinear relationship between soil respiration rate and carbon distribution, and fail to fully capture the dynamic characteristics of soil carbon cycle. In addition, existing prediction models usually fail to effectively integrate the influence of multiple environmental factors and soil management measures, which limits the accuracy and practicality of the prediction results. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies of the prior art, the present invention provides a soil resource sustainable utilization assessment and prediction system, which solves the problem of how to achieve soil resource sustainable utilization assessment through data processing.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a soil resource sustainable utilization assessment and prediction system, comprising: The data collection and preprocessing module collects data on soil respiration rate, carbon distribution and other environmental variables, and preprocesses the collected data; A multi-level carbon distribution-respiration rate mapping model constructs a nonlinear mapping relationship between carbon content changes and soil respiration based on preprocessed data, and updates carbon distribution in real time according to changes in respiration rate; Soil resource optimization map, based on the carbon distribution in the multi-level carbon distribution-respiration rate mapping model, generates a soil resource optimization map through a dynamic optimization algorithm, analyzes future soil changes under different soil management measures, and represents soil resource utilization parameters through nodes in the soil resource optimization map; The multi-factor comprehensive evaluation index is used to quantify the sustainability of soil resource utilization. It integrates data on carbon distribution, respiration rate and other environmental variables in the soil, quantifies the multi-factor comprehensive evaluation index into a score based on a multi-level carbon distribution-respiration rate mapping model, and provides a comprehensive sustainability evaluation result.

[0006] The data acquisition and preprocessing module uses multiple data acquisition devices to comprehensively monitor the soil and obtain accurate data on soil respiration, carbon storage and the environment. Soil respiration rate sensors are installed in soil layers at different depths to capture carbon dioxide released during soil microbial activity. Infrared sensors are used to detect and record changes in carbon dioxide concentration in real time to reflect the soil respiration rate. Carbon content sensors monitor the carbon storage of each layer of the soil, such as the accumulation and distribution of organic carbon. By analyzing soil samples, changes in their carbon content are calculated. The temperature, humidity and moisture content of the soil are monitored in real time to provide data for soil respiration and carbon storage. After data acquisition, data preprocessing is performed. Since soil data is easily affected by changes in the external environment, the data collected by the sensor contains noise. Filtering algorithms are used to remove irrelevant high-frequency interference signals, such as electrical noise and wind speed changes. Outlier detection is used to reduce outliers and errors in the data, and to remove mutation points and unreasonable extreme values. Missing values ​​are filled by interpolation methods, including linear interpolation or Lagrange interpolation, so that the data maintains continuity and integrity on the time axis. The data after standardized preprocessing will be input into the multi-level carbon distribution-respiration rate mapping model.

[0007] The multi-level carbon distribution-respiration rate mapping model constructs a dynamic model that is updated in real time by deeply analyzing the nonlinear relationship between soil carbon distribution and respiration rate. The nonlinear relationship is established by using the carbon distribution data at different depths in the soil and the real-time measured soil respiration rate. The nonlinear relationship is expressed as , where R(t) represents soil respiration rate, which is affected by soil carbon distribution C(z) at different depths, β is the weight parameter of the model, T and W are environmental variables, namely soil temperature and moisture, respectively. is the noise term; , where sin(C(z)) indicates that carbon fluctuates at different depths in the soil. It indicates that the metabolic rate of microorganisms has an exponential growth response to temperature. The parameter γ adjusts the sensitivity of temperature to the respiration rate and is set at 0.01-0.1. ln(W+1) indicates that the effect of water on soil respiration is decreasing. is a noise term, which indicates random errors that cannot be captured by the model. The model reveals the nonlinear mapping relationship between soil carbon storage, respiration rate and environment. To establish the nonlinear mapping relationship, the model first standardizes the data of each measurement. The model is trained through a neural network to capture the nonlinear mapping between these variables through the C(z), R(t), T and W data obtained after processing. The soil respiration rate R(t) is not only affected by carbon distribution, but also by the combined effects of environmental factors such as temperature and moisture. This relationship is comprehensively considered in the model through the weight parameter β. After each measurement of soil respiration rate, the carbon distribution C(z) is adjusted to update the model in real time. Using C(z) new =C(z) old +ΔC(z,R(t)) updates the carbon distribution, where ΔC(z, R(t)) represents the carbon change caused by the respiration rate R(t); ΔC(z, R(t)) is calculated by Get, where R avg is the historical average respiration rate, Δt is the time interval, and α is the response coefficient, which is used to adjust the impact of changes in respiration rate on carbon distribution. Through regression analysis of historical data on soil respiration and carbon dynamics, the response coefficient is determined to be between 0.01 and 0.1. The model dynamically adjusts the carbon distribution according to each new respiration rate measurement to reflect soil changes in real time.

[0008] The model combines MLP and LSTM to dynamically update the weight parameter β. MLP is used to capture the basic nonlinear relationship between carbon distribution and respiration rate, while LSTM is used to process long-term dependencies in time series data. After each model prediction, the weight parameter β is adjusted according to the error feedback. Optimize, where η is the learning rate, L LSTM is the loss function of LSTM, which represents the error between the model prediction and the actual observation; by continuously optimizing these weights, the model can gradually capture the impact of changes in soil respiration rate on carbon distribution more accurately, enabling more accurate soil assessment and management.

[0009] The update of the weight parameter β combines MLP and LSTM. Starting from the input layer, MLP receives static feature data, including soil carbon distribution, temperature and moisture. After the static feature data is transformed by multiple hidden layers, a preliminary respiratory rate prediction value is generated. LSTM receives the respiratory rate prediction value from MLP and the historical respiratory rate data to form an input sequence. Because LSTM can capture the time dependency of the input sequence and process dynamic features, at each time step, LSTM manages the data through a gating mechanism to generate the respiratory rate prediction value R at the current moment. LSTM After each model run, LSTM calculates the predicted value and the actual measured value R observedThe loss function between .

[0010] The construction of the soil resource optimization map uses a multi-level carbon distribution-respiration rate mapping model to extract real-time carbon distribution data and respiration rate prediction values ​​to provide support for different soil management. The system first combines various soil management measures such as tillage depth, irrigation method and fertilization strategy, and defines each soil management measure as a node, representing the potential impact of different soil management measures on soil resource utilization efficiency. In the process of map construction, a multi-level carbon distribution-respiration rate mapping model is used to simulate the effects of different soil management measures under specific soil conditions. The model receives data on various soil management measures including tillage depth, irrigation method and fertilization strategy as input parameters. For example, considering the tillage depth, the model receives data on two different management measures, shallow tillage and deep tillage. These data reflect different degrees of soil disturbance, which in turn affects the carbon distribution and respiration rate of the soil. The model uses these input data, combined with the soil environment such as temperature and humidity, to derive the carbon distribution under each management measure. For example, deep tillage will increase soil disturbance, thereby affecting the distribution of carbon in the soil and the respiration rate of microorganisms, while shallow tillage will affect soil structure and carbon cycle. The impact of the cycle is small; the results of the model output, such as changes in carbon storage and respiration rate, provide basic data for evaluating the effects of different management measures; a series of evaluation indicators are defined, which are based on the simulation results of the model to quantify the impact of each management measure on soil resource utilization efficiency; for example, one evaluation indicator is defined as carbon storage efficiency, which measures the distribution of carbon per unit area of ​​soil under a management measure; another evaluation indicator is the respiration rate change rate, which is used to measure the change in soil respiration rate and reflect the change in soil microbial activity; by quantifying these evaluation indicators, the resource utilization efficiency of each management measure is evaluated; for example, if the carbon storage efficiency under deep tillage is higher, but the respiration rate change rate is also higher, it means that although deep tillage increases soil carbon storage, it also leads to more carbon emissions; by comparing the evaluation indicators under different management measures, it is possible to identify which measures have a positive impact on carbon storage and respiration rate, and which measures need to be adjusted to reduce negative impacts; in the map, each soil management measure is represented as a node, and the lines between the nodes represent the interactions and impacts between the soil management measures.

[0011] During the map analysis process, the system uses a multi-objective optimization algorithm to analyze the effect of each management measure; by optimizing the combination of different management parameters, the system quantifies the impact of each soil management on soil carbon storage, respiration rate and soil fertility in the short and long term, helping users understand the interactions and trade-offs between different measures. For example, tillage depth may affect soil carbon storage, but may lead to an increase in respiration rate; irrigation helps to improve soil moisture retention, but has less impact on carbon storage; the system integrates these impacts through optimization algorithms and visualizes them through paths in the map, allowing users to intuitively understand the long-term and short-term benefits of various soil management; the map simulates the management effects under different scenarios to help users predict future trends in soil resources and provide support for agricultural decision-making.

[0012] The multi-factor comprehensive evaluation index obtains the latest soil data through real-time monitoring sensors, including carbon distribution, soil respiration rate and environmental variables; in order to comprehensively evaluate the health and sustainability of the soil, the system constructs a multi-factor comprehensive evaluation index that integrates various soil data; each data is calculated by weight, and the determination of the weight depends not only on the analysis of historical data, but also on the knowledge and experience of experts in the field; for example, the balance weight of soil carbon input and output is set according to the dynamic characteristics of the carbon cycle, while the weights of soil respiration rate and carbon fixation capacity vary according to their impact on the long-term health of the soil; each data is weighted and quantified into a comprehensive sustainability score based on a nonlinear mapping relationship, which reflects the soil resources. The system uses the data from the GIS to analyze the overall health of the soil and its potential for sustainable use now and in the future. The calculation of the comprehensive sustainability score takes into account current soil conditions and also simulates the long-term effects of different management measures, such as tillage methods, irrigation strategies, and fertilization methods on soil resources. By sorting the comprehensive sustainability scores obtained under different soil management measures, the scores are obtained through sorting. By comparing the scores of different soil management measures, the system identifies which measures have high scores, and soil management measures with high scores can improve the sustainability of soil resources. For example, there are three soil management measures with comprehensive sustainability scores of 60, 85, and 95, respectively. Through the comprehensive sustainability score, the system can understand the best one among these measures, thereby providing users with data-supported decision-making basis. Beneficial Effects

[0013] The present invention provides a soil resource sustainable utilization assessment and prediction system, which has the following beneficial effects: The present invention can more accurately capture the dynamic characteristics of soil carbon cycle by real-time monitoring of soil respiration rate and carbon content and combining a multi-level carbon distribution-respiration rate mapping model, thereby improving the accuracy of assessment and prediction of sustainable utilization of soil resources.

[0014] Through dynamic soil resource optimization maps and multi-factor comprehensive evaluation indicators, the present invention can intuitively display the potential impacts of different soil management measures, provide strong decision-making support for soil resource management, and help formulate more effective soil protection and utilization measures.

[0015] The present invention provides a comprehensive sustainability assessment result by integrating multiple data such as soil carbon distribution, respiration rate, environmental variables, etc., providing a more scientific decision-making basis for soil resource management.

[0016] The present invention helps monitor and mitigate climate change by accurately assessing soil carbon storage and respiration rate, while promoting the function of soil as a carbon sink, providing a scientific basis and technical support for global environmental protection and climate change response. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] The data acquisition and preprocessing module conducts comprehensive soil monitoring through multiple data acquisition devices to obtain accurate data on soil respiration, carbon storage and the environment; soil respiration rate sensors are installed in soil layers at different depths to capture carbon dioxide released during soil microbial activities; infrared sensors are used to detect and record changes in carbon dioxide concentration in real time to reflect the respiration rate of the soil; carbon content sensors monitor the carbon storage of each layer of the soil, such as the accumulation and distribution of organic carbon; soil samples are analyzed to calculate changes in their carbon content; soil temperature, humidity and moisture content are monitored in real time to provide data for soil respiration and carbon storage.

[0020] After the data collection is completed, the data is preprocessed. Since soil data is easily affected by changes in the external environment, the data collected by the sensor often contains noise. A filtering algorithm is used to remove irrelevant high-frequency interference signals, such as electrical noise and wind speed changes. The filtering algorithm smoothes the data, reduces high-frequency fluctuations, and retains low-frequency trends, making the data more stable and reliable. Next, outlier detection is performed to reduce outliers and errors in the data, such as using standard deviation or interquartile range (IQR) to identify data points that deviate significantly from the normal range. Specifically, the mean and standard deviation of the data set are calculated, and then those data points that exceed the mean ±3 times the standard deviation are considered outliers. Alternatively, the IQR method is used, that is, the first quartile Q1 and the third quartile Q3 of the data are calculated, and then the IQR is defined as Q3-Q1; any data below Q1-1.5IQR or above Q3+1.5IQR Data points that are outliers are considered as outliers and are removed from the data set. After removing outliers, missing values ​​may be encountered in the data. These missing values ​​are filled by interpolation methods, which include linear interpolation and Lagrange interpolation. Linear interpolation estimates the middle value by connecting two known data points with a straight line, which is suitable for situations where the data changes are relatively linear. Lagrange interpolation is a polynomial interpolation method that fits the known data points by constructing a polynomial function and uses this polynomial function to estimate missing values. It is used in situations where the data changes are more complex and can provide more accurate interpolation results. The above interpolation methods are used to fill the missing values ​​in the data so that the data maintains continuity and integrity on the time axis. The data after standardized preprocessing will be input into the multi-level carbon distribution-respiration rate mapping model. Standardization is to scale the data to a uniform scale, such as scaling all data to between 0 and 1.

[0021] The multi-level carbon distribution-respiration rate mapping model constructs a real-time updated dynamic model by deeply analyzing the nonlinear relationship between soil carbon distribution and respiration rate. The nonlinear relationship is established by using the carbon distribution data C(z) at different depths in the soil and the real-time measured soil respiration rate R(t). The nonlinear relationship is expressed as , where R(t) represents soil respiration rate, which is affected by soil carbon distribution C(z) at different depths, β is the weight parameter of the model, T and W are environmental variables, namely soil temperature and moisture, respectively. is the noise term; , where sin(C(z)) indicates that carbon fluctuates at different depths in the soil. It indicates that the metabolic rate of microorganisms has an exponential growth response to temperature. The parameter γ adjusts the sensitivity of temperature to the respiration rate and is set at 0.01-0.1. ln(W+1) indicates that the effect of water on soil respiration is decreasing. is a noise term, which represents the random error that the model cannot capture. The model reveals the nonlinear mapping relationship between soil carbon storage and respiration rate and environment. To establish the nonlinear mapping relationship, the model first standardizes the data of each measurement. The model is trained through a neural network using the C(z), R(t), T and W data obtained after processing to capture the nonlinear mapping between these variables. For example, under drought conditions, even if the soil carbon content is high, the respiration rate may decrease due to insufficient water. The soil respiration rate R(t) is not only affected by carbon distribution, but also by the combined effects of environmental factors such as temperature and moisture. This relationship is comprehensively considered in the model through the weight parameter β. When β is optimized, the multi-level carbon distribution-respiration rate mapping model not only improves the accuracy of respiration rate prediction, but also more accurately estimates the changes in soil carbon distribution. For example, changes in respiration rate may cause dynamic changes in soil carbon content. The optimized β makes the multi-level carbon distribution-respiration rate mapping model more sensitive to such changes. After each new data input, the value of β can be dynamically adjusted to allow the carbon distribution to be accurately updated with the real-time changes in soil respiration rate, thereby more realistically reflecting the carbon cycle process in the soil ecosystem.

[0022] The model is updated in real time by adjusting the carbon distribution C(z) after each measurement of soil respiration rate; using C(z) new =C(z)old+ΔC(z,R(t)) updates the carbon distribution, where ΔC(z,R(t)) represents the carbon change caused by the respiration rate R(t); ΔC(z, R(t)) is calculated by Get, where R avg is the historical average respiration rate, Δt is the time interval, and α is the response coefficient, which is used to adjust the effect of changes in respiration rate on carbon distribution; for example, if the current respiration rate R(t) is significantly higher than the historical average R avg , then ΔC(z,R(t)) will be a large positive value, indicating that the soil carbon content is decreasing due to the higher respiration rate; allowing the model to dynamically adjust the carbon distribution according to each new respiration rate measurement to reflect soil changes in real time.

[0023] The multi-level carbon distribution-respiration rate mapping model combines MLP and LSTM to dynamically update the weight parameter β. MLP is used to capture the basic nonlinear relationship between carbon distribution and respiration rate, while LSTM is used to process the long-term dependencies in time series data. After each prediction of the multi-level carbon distribution-respiration rate mapping model, the weight parameter β is adjusted according to the error feedback. Optimize, where η is the learning rate, L LSTMis the loss function of LSTM, which represents the error between the model prediction and the actual observation. By continuously optimizing these weights, the multi-level carbon distribution-respiration rate mapping model can gradually capture the impact of changes in soil respiration rate on carbon distribution more accurately, making it more accurate for soil assessment and management. For example, suppose that over a period of time, due to climate change, soil temperature and moisture conditions have changed, which may affect the soil respiration rate. LSTM detects this long-term trend and adjusts the weight parameters together with MLP to reflect the impact of these changes on carbon distribution. If the respiration rate predicted by the multi-level carbon distribution-respiration rate mapping model is lower than the actual observed value, the loss function L of LSTM is LSTM will increase, the gradient ∇L LSTM This will point the direction to reduce the error, thereby adjusting the weight parameters so that the multi-level carbon distribution-respiration rate mapping model can more accurately reflect this change in future predictions.

[0024] The update of the weight parameter β combines MLP and LSTM. Starting from the input layer, MLP receives static feature data, including soil carbon distribution, temperature and moisture; the static feature data is imported through the input layer and processed by the nonlinear activation function of multiple hidden layers, so that it can extract deep features related to soil respiration rate layer by layer. The output of each layer gradually approaches the complex mapping relationship of the actual respiration rate through nonlinear transformation, and finally generates a preliminary respiration rate prediction value. LSTM receives the prediction value from MLP and the historical respiration rate data to form an input sequence; because LSTM can capture the time dependency of the input sequence and process dynamic features, at each time step, LSTM manages the data through the gating mechanism to generate the current moment's respiration rate prediction value R LSTM After each multi-level carbon distribution-respiration rate mapping model is run, LSTM calculates the predicted value R LSTM Compared with the actual measured value R observed The loss function between .

[0025] The soil resource optimization map extracts real-time carbon distribution data and respiration rate prediction values ​​through a multi-level carbon distribution-respiration rate mapping model, thereby providing decision support for different soil management measures; the soil resource optimization map shows the relationship between different soil management measures, reveals the differences in the effects of these measures under different soil conditions, and provides a scientific basis for future soil resource management.

[0026] Nodes are defined when constructing the soil resource optimization map. The nodes include soil management measures such as tillage depth, irrigation method and fertilization strategy. Each node represents the potential impact of different soil management measures on soil resource utilization efficiency. For example, the node of tillage depth is associated with the impact of soil turning on carbon storage and respiration rate, while the node of fertilization strategy is associated with changes in soil fertility and crop yield. In the process of constructing the soil resource optimization map, the system simulates the effects of these soil management measures under specific soil conditions. For example, suppose there is a piece of soil characterized by low carbon content and slow respiration rate. The system simulates the impact of increasing tillage depth, changing irrigation frequency, or applying different types of fertilizers on this soil. These measures optimize carbon storage, increase soil respiration rate, or improve soil health. The system considers the effects of each soil management measure in these aspects, and compares historical data with real-time monitoring data to evaluate the resources of each node. Utilization efficiency; for example, the system found that increasing tillage depth can increase soil carbon storage because it can mix more organic matter into the soil, but it may also lead to an increase in respiration rate because tillage destroys soil structure and increases microbial activity; on the other hand, the system found that increasing irrigation can improve soil moisture retention, which helps crop growth, but the impact on carbon storage may be smaller; in order to more accurately evaluate the impact of these soil management measures on soil resources, the system uses a multi-objective optimization algorithm to analyze the effect of each soil management measure, helping to quantify the impact of each soil management measure on soil carbon storage, respiration rate and soil fertility in the short and long term; by optimizing the combination of different management parameters, the system helps users understand the interactions and trade-offs between different soil management measures; for example, an increase in tillage depth is combined with adjustments to fertilization strategies to balance the relationship between carbon storage and respiration rate.

[0027] The system integrates these influences through a multi-objective optimization algorithm and visualizes them through paths in the soil resource optimization map, allowing users to intuitively understand the long-term and short-term benefits of various soil management; for example, the soil resource optimization map will show that increasing tillage depth and fertilization can increase crop yields in the short term, but in the long run, irrigation strategies may need to be combined to maintain a balance between soil health and carbon storage; by simulating the management effects under different conditions, the soil resource optimization map predicts future trends in soil resources for users and provides support for agricultural decision-making; for example, if forecasts show that future climate change may lead to drought, the soil resource optimization map will recommend that users take water-saving irrigation measures and adjust tillage and fertilization strategies to maintain soil carbon storage and fertility.

[0028] The system obtains the latest soil data through real-time monitoring sensors, including carbon distribution, soil respiration rate and environmental variables; they are combined into a multi-factor comprehensive evaluation index to comprehensively reflect the health status of soil resources and the potential for sustainable use; each data is quantified through weighted calculation to determine its impact on soil health and sustainability, and the determination of weights relies on the analysis of historical data and the knowledge and experience of experts in the field; for example, the balance weight of soil carbon input and output is set according to the dynamic characteristics of the carbon cycle. If historical data show that the balance between the decomposition of plant residues and the release of carbon dioxide by soil respiration has a significant impact on soil carbon storage, then this weight is set high; similarly, soil The weights of soil respiration rate and carbon fixation capacity vary according to their impact on the long-term health of the soil; if expert experience shows that an increase in soil respiration rate will lead to a decrease in carbon storage, the weight of this data will be adjusted accordingly; by multiplying each data by the corresponding weight, and then quantifying each data into a comprehensive sustainability score based on a nonlinear mapping relationship, this score reflects the overall health of soil resources and predicts its potential for sustainable use in the present and future; assuming that the original data collected by the system at a certain moment is, carbon distribution C(z)=0.8, temperature T=20, moisture W=0.5; the data of the weighted data is, weighted carbon distribution C(z) 加权 =0.8×0.4=0.32, weighted temperature T 加权 =20×0.4=8, weighted moisture W 加权 =0.5×0.4=0.2; Calculate the nonlinear respiratory rate, substitute the weighted data into the nonlinear mapping relationship, and calculate the R weighted assuming β = 0.5 and γ = 0.1 , which in turn generates a score for the respiratory rate.

[0029] The calculation of the comprehensive sustainability score takes into account current soil conditions and simulates the long-term impacts of different soil management measures on soil resources. For example, the system simulates changes in tillage practices, such as switching from conventional tillage to conservation tillage, which may reduce soil erosion and increase soil organic matter content, thereby improving the soil's carbon fixation capacity. Adjustments to irrigation strategies, such as switching from flooding to drip irrigation, reduce water waste and improve water use efficiency, thereby affecting soil respiration rate and nutrient loss. Optimization of fertilization methods, such as precision fertilization, improves fertilizer use efficiency, reduces nutrient loss, and increases crop yields. By comparing the comprehensive sustainability scores obtained under different soil management measures, the system identifies which measures can improve the sustainability of soil resources. For example, on one farm, managers compared three soil management measures: conventional tillage, organic tillage, and conservation tillage. Through real-time monitoring and evaluation, conservation tillage received the highest comprehensive sustainability score and was therefore identified as the most effective soil management measure, which helps improve the sustainability of soil resources.

[0030] 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 soil resource sustainable utilization assessment and prediction system, characterized in that: include: The data collection and preprocessing module collects data on soil respiration rate, carbon distribution and other environmental variables, and preprocesses the collected data; A multi-level carbon distribution-respiration rate mapping model constructs a nonlinear mapping relationship between carbon content changes and soil respiration based on preprocessed data, and updates carbon distribution in real time according to changes in respiration rate; Soil resource optimization map, based on the carbon distribution in the multi-level carbon distribution-respiration rate mapping model, generates a soil resource optimization map through a dynamic optimization algorithm, analyzes future soil changes under different soil management measures, and represents soil resource utilization parameters through nodes in the soil resource optimization map; The multi-factor comprehensive evaluation index is used to quantify the sustainability of soil resource utilization. It integrates data on carbon distribution, respiration rate and other environmental variables in the soil, quantifies the multi-factor comprehensive evaluation index into a score based on a multi-level carbon distribution-respiration rate mapping model, and provides a comprehensive sustainability evaluation result.

2. A soil resource sustainable utilization assessment and prediction system according to claim 1, characterized in that: The data acquisition and preprocessing module performs real-time monitoring and collection through multiple data acquisition devices, including soil respiration rate sensors installed in the soil at different depths to capture carbon dioxide in the soil during respiration; carbon content sensors monitor soil carbon storage at different levels, and environmental parameter sensors collect temperature, humidity and soil moisture; the data acquisition and preprocessing module preprocesses the collected data, removes interference signals and reduces noise, and uses interpolation to fill in any missing values ​​to keep the data complete; The standardized preprocessed data will be input into the multi-level carbon distribution-respiration rate mapping model.

3. A soil resource sustainable utilization assessment and prediction system according to claim 2, characterized in that: The multi-level carbon distribution-respiration rate mapping model constructs a nonlinear mapping relationship through preprocessed data to establish a nonlinear mapping relationship , where R(t) represents soil respiration rate, which is affected by soil carbon distribution C(z) at different depths, β is the weight parameter of the model, T and W are environmental variables, namely soil temperature and moisture, respectively. is the noise term; , where sin(C(z)) indicates that carbon fluctuates at different depths of soil. It indicates that the metabolic rate of microorganisms responds exponentially to temperature. The parameter γ adjusts the sensitivity of temperature to respiration rate and is set at 0.01-0.

1. ln(W+1) indicates the decreasing effect of water on soil respiration. This model captures the nonlinear mapping relationship between soil respiration rate and carbon distribution and environmental variables. After each respiration rate measurement, the model uses C(z) new =C(z) old +ΔC(z,R(t)) updates the carbon distribution, where ΔC(z,R(t)) is calculated by Calculation, R avg is the historical average respiration rate, Δt is the time interval, and α is the response coefficient; the update of the weight parameter β combines the multilayer perceptron MLP and LSTM to dynamically learn the nonlinear mapping relationship between soil respiration and carbon distribution, through Update the weights, where η is the learning rate, L LSTM is the loss function, which represents the difference between the model prediction value and the actual value.

4. A soil resource sustainable utilization assessment and prediction system according to claim 3, characterized in that: The update of the weight parameter β combines MLP and LSTM, starting from the input layer, where MLP receives static feature data including soil carbon distribution, temperature, and moisture; After the static feature data is transformed nonlinearly through multiple hidden layers, a preliminary respiratory rate prediction value is generated. The LSTM receives the output respiratory rate prediction value from the MLP and the historical respiratory rate data to form an input sequence. Because LSTM can capture the time dependency of the input sequence and process dynamic features, at each time step, LSTM manages the data through a gating mechanism to generate a predicted value R of the respiratory rate at the current moment. LSTM After each model run, LSTM calculates the predicted value R LSTM Compared with the actual measured value R observed The loss function between .

5. The soil resource sustainable utilization assessment and prediction system according to claim 1, characterized in that: The soil resource optimization map extracts real-time carbon distribution data and respiratory rate prediction values ​​through a multi-level carbon distribution-respiration rate mapping model. The soil resource sustainable utilization assessment and prediction system combines a variety of soil management measures, including tillage depth, irrigation method and fertilization strategy, and defines soil management measures as nodes. In the process of map construction, the carbon distribution under different soil management measures is obtained by inputting data of different soil management measures into the multi-level carbon distribution-respiration rate mapping model, thereby simulating the effects of different soil management measures under a soil condition. Evaluation indicators are defined, and the evaluation indicators quantify the impact of each management measure on soil resource utilization efficiency based on the simulation results. By evaluating the resource utilization efficiency of each node and comparing the evaluation indicators under different management measures, the impact of different measures on carbon storage and respiratory rate can be identified. By combining previous soil data and real-time monitoring data, the system generates a dynamic soil resource optimization map to show the relationship and effect between various soil management measures. Through a multi-objective optimization algorithm, the potential effect of each management measure is quantitatively analyzed, and the analysis results are visualized through the path in the soil resource optimization map, so that users can intuitively understand the long-term and short-term benefits of various management measures.

6. A soil resource sustainable utilization assessment and prediction system according to claim 1, characterized in that: The multi-factor comprehensive evaluation index obtains the latest soil status data through real-time monitoring sensors, constructs a multi-factor comprehensive evaluation index, and integrates multiple soil data, including soil carbon distribution, soil respiration rate and environmental variables; each data is calculated in a weighted manner, and the weight is determined by historical data analysis and expert knowledge. The weighted data is quantified into a comprehensive sustainability score based on a nonlinear mapping relationship, which reflects the overall health status and sustainable utilization potential of soil resources; by comparing the comprehensive sustainability scores under different soil management measures, the system identifies which measures can improve the sustainability of soil resources; through the visualization of the evaluation results of the soil resource optimization map, the user can intuitively understand the impact of different soil management measures on the sustainability of soil resources.

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