Agricultural rural environment automatic control method based on carbon and nitrogen cooperative emission management and control

Abstract

The invention discloses an agricultural rural environment automatic control method based on carbon and nitrogen collaborative emission control, which comprises the following steps: acquiring carbon and nitrogen collaborative emission data in an agricultural rural production process and a domestic waste resource utilization process, and collecting carbon content information in soil and air; fusing the carbon-nitrogen collaborative emission data and the carbon content information in the soil and the air to generate a unified carbon-nitrogen collaborative emission data set; according to crop types and seasonal changes, establishing a carbon-nitrogen collaborative emission dynamic adjustment model, and judging carbon-nitrogen collaborative emission rules in different crop growth cycles by combining climate conditions, soil properties and farming modes; according to a carbon-nitrogen collaborative emission rule, adopting a machine learning algorithm to train a carbon-nitrogen collaborative emission prediction model, inputting a carbon-nitrogen collaborative emission data set, and outputting a carbon-nitrogen collaborative emission prediction value in a future time period; and according to a prediction result, dynamically adjusting a carbon-nitrogen cooperative emission standard, and generating a management and control scheme adapted to different crop types and seasonal changes.

Description

Technical Field

[0001] The present invention belongs to the field of automation control technology, and specifically relates to an automation control method for agricultural and rural environments based on coordinated carbon and nitrogen emission management. Background Art

[0002] In the agricultural environment automation control method based on carbon-nitrogen co-emission control, a key technical issue is how to formulate and implement dynamic carbon-nitrogen co-emission standards in different links of agricultural production according to the growth characteristics and seasonal changes of different crops. In the agricultural and rural production process and the resource utilization of domestic waste, the sources of carbon-nitrogen co-emissions are diverse, including soil respiration, fertilizer application, agricultural machinery operations, irrigation systems, etc. The carbon-nitrogen co-emissions in these links are not only affected by the crop type, but also by multiple factors such as climatic conditions, soil properties, and farming methods. Therefore, in order to achieve precise carbon-nitrogen co-emission management, it is first necessary to establish a carbon-nitrogen co-emission monitoring system that can comprehensively cover all links of agricultural production.

[0003] However, existing technologies make it difficult to achieve real-time and accurate monitoring of carbon-nitrogen co-emissions in agricultural and rural production processes and the resource utilization of domestic waste. Although agricultural IoT sensors can collect data on carbon content in soil and air, these data often have temporal and spatial heterogeneity and are greatly interfered by environmental factors. For example, the carbon-nitrogen co-emissions produced by soil respiration will vary significantly in different time periods and depths, while the carbon content in the air is easily affected by fluctuations in meteorological conditions such as wind speed and temperature. In addition, different crops have different patterns of carbon absorption and release during their growth cycle, and biological processes such as crop root activity and leaf photosynthesis will also have a dynamic impact on carbon-nitrogen co-emissions. These complex factors make it difficult for a single sensor data to fully reflect the true situation of carbon-nitrogen co-emissions.

[0004] What is even more complicated is that the carbon-nitrogen co-emission standards in agricultural production need to be dynamically adjusted according to different crop types and seasonal changes. For example, rice fields will produce methane emissions under flooded conditions, while dryland crops will release more carbon dioxide. Changes in temperature and humidity in different seasons will also significantly affect soil microbial activity, thereby changing the intensity of carbon-nitrogen co-emissions. Therefore, how to incorporate these complex factors into the formulation of carbon-nitrogen co-emission standards to form a dynamic standard system that can reflect the laws of crop growth and adapt to environmental changes is a technical problem that needs to be solved urgently.

[0005] In addition, the decision-making basis of the agricultural environmental automation control system relies on accurate carbon-nitrogen co-emission data, but existing technologies make it difficult to effectively integrate and analyze multi-source data. The data collected by different sensors have different formats, accuracy, and time scales. How to integrate these data into a unified decision-making model while ensuring data quality is also a technical bottleneck. Especially in large-scale agricultural production scenarios, how to achieve efficient and low-cost carbon-nitrogen co-emission monitoring and regulation is still a technical problem that has not been fully solved. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention provides an automated control method for agricultural and rural environments based on carbon-nitrogen coordinated emission management. The automated control system monitors the carbon-nitrogen coordinated emission data in real time, and performs corresponding emission reduction operations according to preset standards. This realizes the intelligent management of carbon-nitrogen coordinated emissions in agricultural and rural production processes and the resource utilization of domestic waste, and effectively improves the accuracy and adaptability of carbon-nitrogen coordinated emission control.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] An agricultural and rural environment automation control method based on carbon-nitrogen coordinated emission control, the method comprising:

[0009] Obtain data on carbon and nitrogen co-emissions in agricultural and rural production processes and in the resource utilization of domestic waste, and collect information on carbon content in soil and air;

[0010] The carbon-nitrogen co-emission data from agricultural and rural production processes and domestic waste resource utilization processes, as well as the carbon content information in soil and air, are integrated to generate a unified carbon-nitrogen co-emission data set;

[0011] According to crop types and seasonal changes, a dynamic adjustment model for carbon-nitrogen co-emission is established, and the laws of carbon-nitrogen co-emission in different crop growth cycles are determined by combining climate conditions, soil properties and farming methods;

[0012] According to the carbon-nitrogen co-emission law in different crop growth cycles, a machine learning algorithm is used to train the carbon-nitrogen co-emission prediction model, input a unified carbon-nitrogen co-emission data set, and output the predicted value of carbon-nitrogen co-emission in the future time period;

[0013] Based on the prediction results, the carbon-nitrogen co-emission standards in the agricultural and rural production processes and the resource utilization of domestic waste are dynamically adjusted to generate management and control plans that adapt to different crop types and seasonal changes.

[0014] Preferably, the method for obtaining carbon-nitrogen co-emission data in agricultural and rural production processes and domestic waste resource utilization processes, and collecting carbon content information in soil and air includes:

[0015] Obtain carbon and nitrogen co-emission data from soil respiration, fertilizer application, agricultural machinery operation, and irrigation systems during agricultural and rural production processes and domestic waste resource utilization;

[0016] Obtain data on carbon content in soil and air based on IoT sensors;

[0017] The sensor data fusion algorithm is used to process the collected data to obtain the values ​​of soil carbon and air carbon;

[0018] Determine whether the values ​​of soil carbon and air carbon exceed the preset thresholds. If so, use machine learning algorithms to analyze the impact of fertilizer, agricultural machinery, and irrigation to determine the main sources of carbon-nitrogen co-emissions.

[0019] A regression analysis model was used to predict the contribution of soil respiration to carbon-nitrogen co-emissions and to generate a distribution map of carbon-nitrogen co-emissions from agricultural production.

[0020] Preferably, the method of fusing the carbon-nitrogen co-emission data in the agricultural and rural production process and the resource utilization process of domestic waste, and the carbon content information in the soil and air to generate a unified carbon-nitrogen co-emission data set includes:

[0021] ;

[0022] Where: is the total carbon and nitrogen co-emissions from farmland ecosystem, kg; for Emission, kg; for Emissions are converted into Emission, kg; for Emissions are converted into Emission, kg; is the carbon and nitrogen co-emission intensity, kg / yuan; is the agricultural output value, yuan.

[0023] Preferably, under flooding conditions, the calculation weight of methane emission is adjusted based on the methane emission characteristics of rice fields through soil microbial activity monitoring data. Emissions are converted into Emissions.

[0024] Preferably, under dryland crop conditions, the dynamic trend of carbon dioxide and nitrogen co-emission is determined through root activity and leaf photosynthesis data.

[0025] Preferably, the method for determining the carbon-nitrogen synergistic emission law in different crop growth cycles in combination with climate conditions, soil properties and farming methods includes:

[0026] Obtain historical data on crop types and growth cycles, combine climate conditions, soil properties and farming methods, integrate multi-source data and pre-process;

[0027] The time series analysis method is used to segment the carbon and nitrogen co-emission data of crop growth stages and extract the carbon and nitrogen co-emission characteristics of each stage.

[0028] According to the dynamic adjustment model of carbon-nitrogen synergistic emission, the carbon-nitrogen synergistic emission in different crop growth cycles is calculated; the dynamic adjustment model of carbon-nitrogen synergistic emission is: DNDC model;

[0029] If there are significant differences in soil properties and tillage methods, a clustering algorithm is used to group the data to determine the carbon and nitrogen synergistic emission patterns of each group;

[0030] Through the dynamic adjustment model of carbon-nitrogen co-emission, the carbon-nitrogen co-emission data are mapped to a unified time series to generate the carbon-nitrogen co-emission curve during the crop growth stage;

[0031] According to the clustering results and carbon-nitrogen co-emission curves, the trend of carbon-nitrogen co-emission in different crop types and growth stages was analyzed;

[0032] The regression algorithm is used to optimize the dynamic adjustment model of carbon-nitrogen synergistic emission, and the carbon-nitrogen synergistic emission law of the final crop growth cycle is obtained.

[0033] Preferably, the established carbon-nitrogen synergistic emission prediction model is:

[0034] ;

[0035] Where: Raw data on carbon and nitrogen co-emissions according to crop type and seasonality; The generated sequence is obtained by accumulating the original data series of carbon and nitrogen co-emission according to crop type and seasonal changes; is the development coefficient; is the ash action amount;

[0036] After evolution calculation, it becomes the whitening equation:

[0037] ;

[0038] Posterior difference ratio Verify the accuracy of the model. It indicates that the prediction model accuracy is qualified.

[0039] Preferably, according to the prediction results, the method of dynamically adjusting the carbon and nitrogen synergistic emission standards in the agricultural and rural production process and the resource utilization process of domestic waste, and generating a management and control plan adapted to different crop types and seasonal changes includes:

[0040] If the deviation between the predicted value and the actual value exceeds the preset threshold, the support vector machine algorithm is used to group the data for training and adjust the parameters of the carbon-nitrogen co-emission prediction model;

[0041] Re-outputting the carbon-nitrogen co-emission prediction value according to the adjusted carbon-nitrogen co-emission prediction model parameters;

[0042] The gradient descent algorithm is used to optimize the parameters of the carbon-nitrogen co-emission prediction model, determine the deviation between the predicted value and the actual value, and update the parameters of the carbon-nitrogen co-emission prediction model;

[0043] Based on the optimized carbon-nitrogen synergistic emission prediction model, a carbon-nitrogen synergistic emission control plan that adapts to different crop types and seasonal changes is generated.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention discloses an automated control method for agricultural and rural environments based on carbon-nitrogen coordinated emission control. The carbon-nitrogen coordinated emission data in agricultural and rural production processes and the resource utilization of domestic waste are collected through sensors, and a multi-source data fusion algorithm is used to integrate heterogeneous data. A dynamic carbon-nitrogen coordinated emission model is established in combination with crop characteristics and environmental conditions. In view of the different characteristics of rice fields and dryland crops, the coordinated emission characteristics of methane and carbon dioxide nitrogen are considered respectively. The carbon-nitrogen coordinated emission prediction model is trained using a machine learning algorithm, and the carbon-nitrogen coordinated emission standard is dynamically adjusted according to the prediction results to generate a control plan adapted to different crops and seasons. The present invention monitors the carbon-nitrogen coordinated emission data in real time through an automated control system, and performs corresponding emission reduction operations according to preset standards, thereby realizing the intelligent management of carbon-nitrogen coordinated emissions in agricultural and rural production processes and the resource utilization of domestic waste, and effectively improving the accuracy and adaptability of carbon-nitrogen coordinated emission control. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1This is a flow chart of an agricultural and rural environment automation control method based on carbon-nitrogen coordinated emission control according to an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of carbon and nitrogen synergistic emission sources in a farmland ecosystem according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Embodiment 1

[0052] like Figure 1 As shown, an embodiment of the present invention discloses an agricultural and rural environment automation control method based on carbon-nitrogen coordinated emission control, the method comprising:

[0053] Obtain data on carbon and nitrogen co-emissions in agricultural and rural production processes and in the resource utilization of domestic waste, and collect information on carbon content in soil and air;

[0054] The carbon-nitrogen co-emission data from agricultural and rural production processes and domestic waste resource utilization processes, as well as the carbon content information in soil and air, are integrated to generate a unified carbon-nitrogen co-emission data set;

[0055] According to crop types and seasonal changes, a dynamic adjustment model for carbon-nitrogen co-emission is established, and the laws of carbon-nitrogen co-emission in different crop growth cycles are determined by combining climate conditions, soil properties and farming methods;

[0056] According to the carbon-nitrogen co-emission law in different crop growth cycles, a machine learning algorithm is used to train the carbon-nitrogen co-emission prediction model, input a unified carbon-nitrogen co-emission data set, and output the predicted value of carbon-nitrogen co-emission in the future time period;

[0057] Based on the prediction results, the carbon-nitrogen co-emission standards in the agricultural and rural production processes and the resource utilization of domestic waste are dynamically adjusted to generate management and control plans that adapt to different crop types and seasonal changes.

[0058] In this embodiment, the method for obtaining carbon-nitrogen co-emission data in the agricultural and rural production process and the resource utilization process of domestic waste, and collecting carbon content information in the soil and air includes:

[0059] Obtain carbon and nitrogen co-emission data from soil respiration, fertilizer application, agricultural machinery operation, and irrigation systems during agricultural and rural production processes and domestic waste resource utilization;

[0060] Obtain data on carbon content in soil and air based on IoT sensors;

[0061] The sensor data fusion algorithm is used to process the collected data to obtain the values ​​of soil carbon and air carbon;

[0062] Determine whether the values ​​of soil carbon and air carbon exceed the preset thresholds. If so, use machine learning algorithms to analyze the impact of fertilizer, agricultural machinery, and irrigation to determine the main sources of carbon-nitrogen co-emissions.

[0063] A regression analysis model was used to predict the contribution of soil respiration to carbon-nitrogen co-emissions and to generate a distribution map of carbon-nitrogen co-emissions from agricultural production.

[0064] In this embodiment, in view of the temporal and spatial heterogeneity of sensor data, a multi-source data fusion algorithm is used to integrate data of different time scales, spatial ranges and precisions to generate a unified carbon and nitrogen co-emission data set, including:

[0065] Acquire sensor data of different time scales and spatial ranges, and handle temporal and spatial heterogeneity based on multi-source data fusion algorithms;

[0066] Use data preprocessing technology to eliminate noise and outliers in sensor data and improve data quality;

[0067] Mapping data of different precisions into a unified time series and spatial grid through spatiotemporal alignment methods;

[0068] If there are missing values ​​during the data fusion process, an interpolation algorithm is used to complete the data set;

[0069] Clustering algorithms were used to analyze the spatial distribution characteristics of carbon-nitrogen co-emission data and identify high-emission and low-emission areas.

[0070] According to the time series analysis model, the periodic change law and trend characteristics of carbon and nitrogen co-emission data are determined;

[0071] Generate a unified carbon-nitrogen co-emission data set to provide data support for subsequent carbon-nitrogen co-emission management and optimization.

[0072] Specifically, Figure 2As shown in the figure, the method of integrating the carbon-nitrogen co-emission data in the agricultural and rural production process and the resource utilization of domestic waste, and the carbon content information in the soil and air to generate a unified carbon-nitrogen co-emission data set includes:

[0073] ;

[0074] Where: is the total carbon and nitrogen co-emissions from farmland ecosystem, kg; for Emission, kg; for Emissions are converted into Emission, kg; for Emissions are converted into Emission, kg; is the carbon and nitrogen co-emission intensity, kg / yuan; is the agricultural output value, yuan.

[0075] Among them, under flooding conditions, according to the characteristics of methane emission in rice fields, the calculation weight of methane emission is adjusted through soil microbial activity monitoring data. Specifically:

[0076] Obtain soil microbial activity monitoring data under flooded rice fields, and extract methane emission characteristics based on soil properties and climate conditions;

[0077] The monitoring data were processed in sections using the time series analysis method to obtain the characteristic values ​​of methane emissions in different growth periods;

[0078] According to soil properties and farming methods, a methane emission weight adjustment model is established to correct the calculation weights;

[0079] If there are significant differences in flooding degree and soil properties, clustering algorithms are used to group the data and determine the methane emission patterns of each group;

[0080] Through the weight adjustment model, the methane emission data is mapped to a unified time series to generate the methane emission curve under flooded rice field conditions;

[0081] The regression algorithm is used to optimize the weight adjustment model and determine the final methane emission calculation weight;

[0082] Based on the optimization results and emission curves, the changing trend of methane emissions under flooded rice field environment was analyzed.

[0083] Based on the weight of methane emissions, Emissions are converted into Emissions. Specific:

[0084] ;

[0085] Where: For rice fields Emission factor, kg / hm², i represents the type of paddy field, is the rice planting area corresponding to the emission factor, hm²; 25 is Conversion Equivalence coefficient.

[0086] Among them, under dryland crop conditions, the methods for determining the dynamic trend of carbon dioxide and nitrogen co-emission through root activity and leaf photosynthesis data include:

[0087] Obtain root activity and leaf photosynthesis data of dryland crops, combine soil moisture and soil temperature conditions, and extract the dynamic characteristics of carbon dioxide and nitrogen co-emission;

[0088] The sequence analysis method was used to segment the root activity and leaf photosynthesis data to obtain the characteristic values ​​of carbon dioxide and nitrogen co-emission in the growth stage.

[0089] According to soil type and crop species, a weight adjustment model for the coordinated emission of carbon dioxide and nitrogen is established to correct the calculation weights;

[0090] If there are significant differences in soil moisture and soil temperature, clustering is used to group the data to determine the co-emission patterns of carbon dioxide and nitrogen in each group;

[0091] Through the weight adjustment model, the CO2-nitrogen co-emission data are mapped to a unified time series to generate the CO2-nitrogen co-emission curve under dryland crop conditions;

[0092] The weight adjustment model is optimized by regression calculation to determine the final calculation weights of carbon dioxide and nitrogen co-emissions;

[0093] Based on the optimization results and emission curves, the changing trend of carbon dioxide and nitrogen co-emissions under dryland crop conditions was analyzed.

[0094] Specific: Calculation The emissions method is as follows:

[0095] ;

[0096] Where: is the amount of agricultural input of the jth type, kg; is the carbon and nitrogen synergistic emission coefficient of the jth agricultural input; 44 / 12 is The molecular weight ratio of C.

[0097] Among them, the calculation of farmland ecosystem Emissions are converted into The emissions method is as follows:

[0098] ;

[0099] Where: is the direct nitrogen emission, kg; is the indirect nitrogen emission, kg; 44 / 28 is Ratio of molecular weight to N; 298 Conversion Equivalence coefficient.

[0100] In this embodiment, the method for determining the carbon-nitrogen synergistic emission rules in different crop growth cycles in combination with climate conditions, soil properties and farming methods includes:

[0101] Obtain historical data on crop types and growth cycles, combine climate conditions, soil properties and farming methods, integrate multi-source data and pre-process;

[0102] The time series analysis method is used to segment the carbon and nitrogen co-emission data of crop growth stages and extract the carbon and nitrogen co-emission characteristics of each stage.

[0103] According to the dynamic adjustment model of carbon-nitrogen synergistic emission, the carbon-nitrogen synergistic emission in different crop growth cycles is calculated; the dynamic adjustment model of carbon-nitrogen synergistic emission is: DNDC model;

[0104] If there are significant differences in soil properties and tillage methods, a clustering algorithm is used to group the data to determine the carbon and nitrogen synergistic emission patterns of each group;

[0105] Through the dynamic adjustment model of carbon-nitrogen co-emission, the carbon-nitrogen co-emission data are mapped to a unified time series to generate the carbon-nitrogen co-emission curve during the crop growth stage;

[0106] According to the clustering results and carbon-nitrogen co-emission curves, the trend of carbon-nitrogen co-emission in different crop types and growth stages was analyzed;

[0107] The regression algorithm is used to optimize the dynamic adjustment model of carbon-nitrogen synergistic emission, and the carbon-nitrogen synergistic emission law of the final crop growth cycle is obtained.

[0108] In this embodiment, the established carbon-nitrogen synergistic emission prediction model is:

[0109] ;

[0110] Where: Raw data on carbon and nitrogen co-emissions according to crop type and seasonality; The generated sequence is obtained by accumulating the original data series of carbon and nitrogen co-emission according to crop type and seasonal changes; is the development coefficient; is the ash action amount;

[0111] After evolution calculation, it becomes the whitening equation:

[0112] ;

[0113] Posterior difference ratio Verify the accuracy of the model. It indicates that the prediction model accuracy is qualified.

[0114] Among them, the posterior difference ratio ( ) is a concept in Bayesian statistics that measures the relative probability of two hypotheses given data. It is often used in Bayesian hypothesis testing and can be expressed as:

[0115] ;

[0116] in, In the given data In the case of The posterior probability of In the given data In the case of The posterior probability of .

[0117] The posterior probability can be calculated by Bayes' theorem, which is expressed as:

[0118] ;

[0119] in, It is the likelihood probability of data D under the assumption that H is true; is the prior probability of hypothesis H; is the marginal probability of data D, which can be obtained by summing over all possible hypotheses:

[0120] ;

[0121] Therefore, the posterior difference ratio can be rewritten as:

[0122] ;

[0123] here, They appear in both the numerator and the denominator, so they cancel each other out.

[0124] Calculation steps: 1. Determine the assumptions: define two assumptions and 2. Collect data: Collect relevant data 3. Calculate the likelihood probability: Calculate the likelihood probability of the data under each hypothesis and 4. Determine the prior probability: Determine the prior probability of the hypothesis and 5. Calculate the posterior difference ratio: Use the above formula to calculate the posterior difference ratio .

[0125] In this embodiment, a machine learning algorithm is used to train a carbon-nitrogen co-emission prediction model, and crop growth characteristics, environmental conditions, and sensor data are input to output a predicted value of carbon-nitrogen co-emission in a future time period, including:

[0126] Obtain the growth characteristic data of the crop growth stage, and extract the crop growth dynamic information by combining the ambient temperature, ambient humidity, soil temperature and soil moisture;

[0127] Fuse sensor data, build a dataset of crop growth and environmental conditions, and generate initial training samples;

[0128] The random forest algorithm is used to input the initial training samples, train the carbon-nitrogen co-emission prediction model, and obtain the initial values ​​of the model parameters;

[0129] If the difference between the ambient temperature and the soil temperature exceeds the preset threshold, the support vector machine algorithm is used to group the data for training and adjust the model parameters;

[0130] Based on the time series data, crop growth characteristics and environmental conditions are input, and the trained model is used to output the predicted carbon emissions for the future time period;

[0131] The gradient descent algorithm is used to optimize the model parameters, determine the deviation between the predicted value and the actual value, and update the model parameters;

[0132] Generate an optimized carbon-nitrogen coordinated emission prediction model, input real-time crop growth characteristics, environmental conditions and sensor values, and output carbon emission prediction values ​​for future time periods.

[0133] In this embodiment, according to the prediction results, the method of dynamically adjusting the carbon and nitrogen synergistic emission standards in the agricultural and rural production process and the resource utilization process of domestic waste, and generating a management and control plan adapted to different crop types and seasonal changes includes:

[0134] If the deviation between the predicted value and the actual value exceeds the preset threshold, the support vector machine algorithm is used to group the data for training and adjust the parameters of the carbon-nitrogen co-emission prediction model;

[0135] Re-outputting the carbon-nitrogen co-emission prediction value according to the adjusted carbon-nitrogen co-emission prediction model parameters;

[0136] The gradient descent algorithm is used to optimize the parameters of the carbon-nitrogen co-emission prediction model, determine the deviation between the predicted value and the actual value, and update the parameters of the carbon-nitrogen co-emission prediction model;

[0137] Based on the optimized carbon-nitrogen synergistic emission prediction model, a carbon-nitrogen synergistic emission control plan that adapts to different crop types and seasonal changes is generated.

[0138] In this embodiment, the carbon-nitrogen co-emission data is monitored in real time through an automated control system, and combined with preset dynamic standards, it is determined whether to trigger regulatory measures and perform corresponding emission reduction operations.

[0139] Obtain the carbon-nitrogen co-emission data stream collected by the monitor, and combine it with the dynamic standard to determine whether the carbon-nitrogen co-emission exceeds the preset threshold. If the carbon-nitrogen co-emission data stream exceeds the preset threshold, trigger the control measures and call the actuator to start the emission reduction operation. Update the carbon-nitrogen co-emission data stream in real time through the automated control system to determine the execution effect of the control measures. Use the random forest algorithm to analyze the relationship between the data stream and the dynamic standard and optimize the judgment conditions. According to the optimized judgment conditions, adjust the trigger mechanism to generate more accurate control measures. Use the support vector machine algorithm to classify the data stream and determine the priority of the emission reduction operation. Monitor the execution effect of the emission reduction operation in real time through the control system, update the data stream and feed it back to the monitor.

[0140] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An agricultural and rural environment automation control method based on carbon and nitrogen coordinated emission control, characterized in that: The method comprises: Obtain data on carbon and nitrogen co-emissions in agricultural and rural production processes and in the resource utilization of domestic waste, and collect information on carbon content in soil and air; The carbon-nitrogen co-emission data from agricultural and rural production processes and domestic waste resource utilization processes, as well as the carbon content information in soil and air, are integrated to generate a unified carbon-nitrogen co-emission data set; According to crop types and seasonal changes, a dynamic adjustment model for carbon-nitrogen co-emission is established, and the laws of carbon-nitrogen co-emission in different crop growth cycles are determined by combining climate conditions, soil properties and farming methods; According to the carbon-nitrogen co-emission law in different crop growth cycles, a machine learning algorithm is used to train the carbon-nitrogen co-emission prediction model, input a unified carbon-nitrogen co-emission data set, and output the predicted value of carbon-nitrogen co-emission in the future time period; Based on the prediction results, the carbon-nitrogen co-emission standards in the agricultural and rural production processes and the resource utilization of domestic waste are dynamically adjusted to generate management and control plans that adapt to different crop types and seasonal changes.

2. The method according to claim 1, characterized in that Methods for obtaining carbon and nitrogen co-emission data in agricultural and rural production processes and domestic waste resource utilization processes, and collecting carbon content information in soil and air include: Obtain carbon and nitrogen co-emission data from soil respiration, fertilizer application, agricultural machinery operation, and irrigation systems during agricultural and rural production processes and domestic waste resource utilization; Obtain data on carbon content in soil and air based on IoT sensors; The sensor data fusion algorithm is used to process the collected data to obtain the values ​​of soil carbon and air carbon; Determine whether the values ​​of soil carbon and air carbon exceed the preset thresholds. If so, use machine learning algorithms to analyze the impact of fertilizer, agricultural machinery, and irrigation to determine the main sources of carbon-nitrogen co-emissions. A regression analysis model was used to predict the contribution of soil respiration to carbon-nitrogen co-emissions and to generate a distribution map of carbon-nitrogen co-emissions from agricultural production.

3. The method according to claim 1, characterized in that: The method of integrating carbon-nitrogen co-emission data in agricultural and rural production processes and domestic waste resource utilization processes, and carbon content information in soil and air to generate a unified carbon-nitrogen co-emission data set includes: ； Where: is the total carbon and nitrogen co-emissions from farmland ecosystem, kg; for Emission, kg; for Emissions are converted into Emission, kg; for Emissions are converted into Emission, kg; is the carbon and nitrogen co-emission intensity, kg / yuan; is the agricultural output value, yuan.

4. The method according to claim 1, characterized in that: Under flooding conditions, according to the characteristics of methane emission in rice fields, the calculation weight of methane emission was adjusted through soil microbial activity monitoring data. Emissions are converted into Emissions.

5. The method according to claim 1, characterized in that Under dryland crop conditions, the dynamic trends of carbon dioxide and nitrogen co-emissions were determined through root activity and leaf photosynthesis data.

6. The method according to claim 1, characterized in that Combining climate conditions, soil properties and farming methods, methods for determining the carbon and nitrogen synergistic emission patterns during different crop growth cycles include: Obtain historical data on crop types and growth cycles, combine climate conditions, soil properties and farming methods, integrate multi-source data and pre-process; The time series analysis method is used to segment the carbon and nitrogen co-emission data of crop growth stages and extract the carbon and nitrogen co-emission characteristics of each stage. According to the dynamic adjustment model of carbon-nitrogen synergistic emission, the carbon-nitrogen synergistic emission in different crop growth cycles is calculated; the dynamic adjustment model of carbon-nitrogen synergistic emission is: DNDC model; If there are significant differences in soil properties and tillage methods, a clustering algorithm is used to group the data to determine the carbon and nitrogen synergistic emission patterns of each group; Through the dynamic adjustment model of carbon-nitrogen co-emission, the carbon-nitrogen co-emission data are mapped to a unified time series to generate the carbon-nitrogen co-emission curve during the crop growth stage; According to the clustering results and carbon-nitrogen co-emission curves, the trend of carbon-nitrogen co-emission in different crop types and growth stages was analyzed; The regression algorithm is used to optimize the dynamic adjustment model of carbon-nitrogen synergistic emission, and the carbon-nitrogen synergistic emission law of the final crop growth cycle is obtained.

7. The method according to claim 1, characterized in that The established carbon and nitrogen synergistic emission prediction model is: ； Where: Raw data on carbon and nitrogen co-emissions according to crop type and seasonality; The generated sequence is obtained by accumulating the original data series of carbon and nitrogen co-emission according to crop type and seasonal changes; is the development coefficient; is the ash action amount; After evolution calculation, it becomes the whitening equation: ； Posterior difference ratio Verify the accuracy of the model. It indicates that the prediction model accuracy is qualified.

8. The method according to claim 1, characterized in that According to the prediction results, the methods for dynamically adjusting the carbon and nitrogen synergistic emission standards in the agricultural and rural production process and the resource utilization of domestic waste, and generating management and control plans that adapt to different crop types and seasonal changes include: If the deviation between the predicted value and the actual value exceeds the preset threshold, the support vector machine algorithm is used to group the data for training and adjust the parameters of the carbon-nitrogen co-emission prediction model; Re-outputting the carbon-nitrogen co-emission prediction value according to the adjusted carbon-nitrogen co-emission prediction model parameters; The gradient descent algorithm is used to optimize the parameters of the carbon-nitrogen co-emission prediction model, determine the deviation between the predicted value and the actual value, and update the parameters of the carbon-nitrogen co-emission prediction model; Based on the optimized carbon-nitrogen synergistic emission prediction model, a carbon-nitrogen synergistic emission control plan that adapts to different crop types and seasonal changes is generated.

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