Livestock and poultry manure compost nitrogen conservation method and application thereof

Abstract

The invention relates to the technical field of organic waste composting, in particular to a livestock and poultry manure composting nitrogen conservation method and application thereof.The livestock and poultry manure composting nitrogen conservation method comprises the following steps that the temperature, humidity and ventilation environment of composting materials are accurately controlled, nitrogen loss in the composting process is monitored in real time, nitrogen conversion and storage are optimized through an algorithm, and the composting efficiency is improved; calculating variation amplitudes of different stages, adjusting a compost body environment in real time, monitoring and evaluating nitrogen loss through a support vector machine model to ensure that the nitrogen loss does not exceed a set standard, and dynamically adjusting temperature, humidity and a ventilation system of compost. Addition of bacillus and adjustment of oxygen concentration and humidity jointly act on optimization of microbial activity, the nitrogen conversion condition is improved, through combined application of a convolutional neural network and a long-short-term memory network, the influence of the activity of microbial populations on nitrogen conversion is analyzed, a microbial area with high activity is selected, and the nitrogen conversion efficiency is improved. The storage and conversion efficiency of nitrogen is improved, the fertilizer efficiency of compost is ensured, and the pollution to the environment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of organic waste composting, and particularly to a method for nitrogen preservation in livestock and poultry manure composting and its application. Background Art

[0002] The technical field of organic waste composting aims to process and transform organic waste into beneficial organic fertilizers, realizing waste resource utilization, reducing environmental pollution caused by waste, providing nutrients required for plant growth, optimizing the soil microbial environment, and enhancing the water-holding and ventilation capabilities of the soil.

[0003] The method for nitrogen preservation in livestock and poultry manure composting aims to reduce nitrogen loss through a specific composting process, ensure the preservation of available nitrogen in the compost, improve the nitrogen content of the organic fertilizer and its fertilizer efficiency for plants. By regulating conditions such as temperature, humidity, and air circulation during the composting process, optimize microbial activities, reduce nitrogen volatilization and loss, retain more nitrogen sources, enhance the fertilizer efficiency of the compost for the soil, and promote crop growth.

[0004] Existing technologies lack real-time dynamic monitoring and intelligent adjustment mechanisms during the composting process, and cannot precisely control the transformation and preservation of nitrogen during composting. This leads to excessive nitrogen volatilization during the high-temperature period or the heating-up period, and the temperature, humidity, and ventilation conditions of the compost pile cannot be adjusted in a timely manner, making it difficult to effectively control nitrogen loss, resulting in unsatisfactory fertilizer efficiency and a risk of exceeding the nitrogen loss standard. At the same time, the regulation of microbial activity in existing technologies is also relatively rough, and the relationship between the activity of the microbial community and nitrogen transformation cannot be precisely identified, resulting in insufficient optimization and adjustment of microorganisms, affecting the nitrogen transformation efficiency. Therefore, it is difficult for existing technologies to achieve efficient preservation and transformation of nitrogen in actual operations, thereby affecting the fertilizer efficiency of composting, causing resource waste and imposing a burden on the environment. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a method for nitrogen preservation in livestock and poultry manure composting and its application.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A method for nitrogen preservation in livestock and poultry manure composting, comprising the following steps: Step 1: Based on the compost raw materials, control the temperature of the compost materials, simulate the heating-up period, high-temperature period, and cooling-down period, monitor the changes in temperature and humidity in real time through sensors, collect the temperature and humidity data during the composting process, calculate the change amplitude in different stages, and generate the temperature and humidity data of the compost materials. Step 2: Based on the temperature and humidity data of the compost materials, monitor the nitrogen loss during the composting process through sensors, use a support vector machine to evaluate whether it exceeds the standard, and when it exceeds the standard, adjust the temperature, humidity, and ventilation system of the compost pile, conduct real-time monitoring and adjustment, and generate a nitrogen loss control adjustment plan. Step 3: Based on the nitrogen loss control adjustment plan, add Bacillus to the compost, adjust the oxygen concentration and humidity to optimize the microbial activity, monitor the changes in ammonia and carbon dioxide concentrations with sensors, analyze the nitrogen conversion situation, and generate data on microbial activity and nitrogen conversion; Step 4: Based on the data on microbial activity and nitrogen conversion, use convolutional neural networks and long short-term memory networks to analyze the impact of microbial activities on nitrogen conversion, select microbial regions with strong activity, adjust the temperature, humidity, and oxygen concentration of the compost pile, and generate a microbial population optimization and environmental adjustment plan; Step 5: Based on the microbial population optimization and environmental adjustment plan, implement environmental regulation, and monitor the nitrogen conversion rate in real time. If the expected effect is not achieved, further adjust the temperature, humidity, and ventilation inside the compost pile to generate a nitrogen conversion rate improvement plan.

[0007] As a further solution of the present invention, the specific steps for generating the temperature and humidity data of the compost material are as follows: S101: Based on the type of compost raw materials, adjust the heating equipment of the composting materials, set the heating power and duration, heat the composting materials in stages to the set temperature, conduct temperature monitoring, record the temperature changes during the heating-up, high-temperature, and cooling-down periods, and generate a set of composting stage heating parameters; S102: Based on the set of composting stage heating parameters, deploy temperature and humidity sensors at different depths of the compost, collect the temperature and humidity change data inside the compost pile in real time, analyze the data, and calculate the change range of temperature and humidity to generate stratified temperature and humidity change data; S103: Based on the stratified temperature and humidity change data, merge the temperature and humidity changes during the heating-up, high-temperature, and cooling-down periods, establish a temperature and humidity change curve, and generate the temperature and humidity data of the compost material.

[0008] As a further solution of the present invention, the specific steps for generating the nitrogen loss control adjustment plan are as follows: Based on the temperature and humidity data of the compost material, install gas sensors during the composting process to monitor the concentration changes of ammonia and other gases, collect the change data of nitrogen emissions in real time, calculate the gas emission rate and total amount, and generate nitrogen emission fluctuation data; Based on the nitrogen emission fluctuation data, compare the nitrogen emission changes under different temperature and humidity conditions, use support vector machines for multivariate regression analysis, evaluate the impact of temperature, humidity, and oxygen concentration on nitrogen emissions, determine whether the nitrogen emissions exceed the set threshold, and if they exceed the standard, adjust the temperature, humidity, and ventilation system of the compost pile to generate a nitrogen emission adjustment operation record; Based on the nitrogen emission adjustment operation record, combine the temperature and humidity changes and nitrogen emission data, select appropriate environmental regulation parameters, record the operation instructions and their execution effects, and generate a nitrogen loss control adjustment plan.

[0009] As a further solution of the present invention, the support vector machine is calculated according to the formula: Where: is the nitrogen emission prediction value, is the Lagrange multiplier of the support vector, is the kernel function, is the bias term, is the correlation weight coefficient between temperature and humidity and nitrogen emission, is the relative humidity, is the correlation weight coefficient between oxygen concentration and nitrogen emission, is the influence parameter of the compost raw material type.

[0010] As a further solution of the present invention, the specific steps for generating the microbial activity and nitrogen conversion data are as follows: Based on the nitrogen loss control adjustment plan, Bacillus is evenly spread into the compost material in proportion, the oxygen concentration in the compost pile body is controlled within a set range, the humidity is adjusted in real time through the spray system, the growth environment of microorganisms is optimized, and the microbial addition and environment adjustment records are generated; Based on the microbial addition and environment adjustment records, gas sensors are set at different positions of the compost pile body to monitor the changes in ammonia and carbon dioxide concentrations in real time, record the sensor feedback data, and regularly analyze the correlation between the gas concentration changes and the compost temperature and humidity to generate the nitrogen conversion situation monitoring data; Based on the nitrogen conversion situation monitoring data, analyze the influence of different microbial activities on nitrogen conversion during the composting process, combine with the temperature and humidity change data, adjust the environmental conditions in the microbial activity area, and generate the microbial activity and nitrogen conversion data.

[0011] As a further solution of the present invention, the specific steps for generating the microbial population optimization and environment adjustment plan are as follows: Based on the microbial activity and nitrogen conversion data, monitor the microbial activities in different areas of the compost pile body, use a convolutional neural network to extract the spatial characteristics of the microbial communities in different areas, capture the spatial distribution of the microbial communities, and then analyze the time series data through a long short-term memory network to construct the dynamic change trend of microbial activity and nitrogen conversion, and judge the area where the microbial population with strong activity is located to generate the microbial activity area distribution data; Based on the microbial activity area distribution data, select the areas with suitable temperature, humidity and oxygen concentration for key regulation, adjust the ventilation volume, adjust the humidity in the area, and optimize the compost environment through the control system to generate the regional environment optimization adjustment records; Based on the optimized adjustment records of the regional environment, integrate the environmental regulation effects of each region in the compost pile body, and generate a microbial population optimization and environmental adjustment plan according to the optimization and adjustment conditions of different regions.

[0012] As a further solution of the present invention, the convolutional neural network is calculated according to the formula: Where: is the feature map output by the convolutional layer, is the convolutional kernel, is the input data, is the bias term, is the relative humidity, is the influence coefficient of relative humidity on the microbial community distribution, is the influence parameter of the compost raw material type, is the weight coefficient of the influence of the compost raw material type on the microbial activity.

[0013] As a further solution of the present invention, the long short-term memory network is calculated according to the formula: Where: is the cell state at the current moment, is the output of the forget gate, is the cell state at the previous moment, is the output of the input gate, is the candidate memory state at the current moment, is the current temperature and humidity data, is the current oxygen concentration data, is the microbial activity data of the current region, is the weight coefficient, is the weight coefficient, is the weight coefficient.

[0014] As a further solution of the present invention, the specific steps for generating the nitrogen conversion rate improvement plan are as follows: Based on the microbial population optimization and environmental adjustment plan, adjust the temperature, humidity and ventilation system in the compost pile body, monitor the change of nitrogen conversion rate in real time during the composting process, use gas sensors to monitor the ammonia and carbon dioxide concentrations, and generate nitrogen conversion rate monitoring data in combination with the temperature and humidity data in the pile body; Based on the nitrogen conversion rate monitoring data, compare the nitrogen conversion under different environmental conditions during the composting process, judge whether the nitrogen conversion rate reaches the expected target, and if not, adjust the temperature, humidity and ventilation volume in the compost pile body to generate an optimized record of environmental adjustment; Based on the optimized records adjusted according to the environment, monitor the changes in temperature, humidity and gas concentration inside the compost heap, analyze the nitrogen conversion rate after adjustment. If the adjustment does not meet the target, optimize the compost environment and record all adjustment data to generate a plan for improving the nitrogen conversion rate.

[0015] The application method of the nitrogen preservation method for livestock and poultry manure composting includes the following steps: First, during the composting process, use temperature and humidity sensors to monitor the changes in temperature and humidity of the compost materials in real time, collect the compost environment data during the heating period, high-temperature period and cooling period, calculate the amplitude of temperature and humidity changes, and generate the temperature and humidity data of the compost materials. Next, use the temperature and humidity data and gas sensors to monitor the nitrogen loss situation during the composting process in real time, evaluate the nitrogen loss amount, and judge whether it exceeds the set safety threshold. If the nitrogen loss exceeds the standard, automatically adjust the temperature, humidity and ventilation system of the compost heap. Finally, while adjusting the compost environment, add Bacillus to the compost to promote microbial activities, monitor the changes in ammonia and carbon dioxide concentrations through sensors, analyze the nitrogen conversion situation, and based on the microbial activity and nitrogen conversion data, select the microbial area with strong activity for optimization and adjustment to generate qualified organic fertilizers.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. In the present invention, by precisely controlling the temperature, humidity and ventilation environment of the compost materials, monitoring the nitrogen loss during the composting process in real time, optimizing the nitrogen conversion and preservation through algorithms, calculating the change amplitude in different stages, and adjusting the environment of the compost heap in real time. 2. In the present invention, monitor and evaluate the nitrogen loss through the support vector machine model to ensure that the nitrogen loss does not exceed the set standard, dynamically adjust the temperature, humidity and ventilation system of the compost, and the addition of Bacillus and the adjustment of oxygen concentration and humidity act together on the optimization of microbial activity, improving the nitrogen conversion situation. 3. In the present invention, through the combined application of convolutional neural network and long short-term memory network, analyze the influence of the activity of microbial populations on nitrogen conversion, select the microbial area with stronger activity, improve the nitrogen preservation and conversion efficiency, ensure the fertilizer efficiency of the compost and reduce environmental pollution. Brief Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the main steps of the present invention; Figure 2 It is a detailed schematic diagram of S1 of the present invention; Figure 3 It is a detailed schematic diagram of S2 of the present invention; Figure 4 It is a detailed schematic diagram of S3 of the present invention; Figure 5Schematic diagram for the refinement of S4 of the present invention; Figure 6 Schematic diagram for the refinement of S5 of the present invention. Detailed implementation manners

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Please refer to Figure 1 , the present invention provides a technical solution: a method for nitrogen conservation in livestock and poultry manure composting, comprising the following steps: S1: Based on the compost raw materials, control the temperature of the composting materials, simulate the heating-up period, high-temperature period and cooling-down period, monitor the temperature and humidity changes in real time through sensors, collect the temperature and humidity data during the composting process, calculate the change amplitude in different stages, and generate the temperature and humidity data of the composting materials; S2: Based on the temperature and humidity data of the composting materials, monitor the nitrogen loss during the composting process through sensors, use a support vector machine to evaluate whether it exceeds the standard, and adjust the temperature, humidity and ventilation system of the composting heap in real time when it exceeds the standard, and generate a nitrogen loss control and adjustment plan; S3: Based on the nitrogen loss control and adjustment plan, add Bacillus to the compost, adjust the oxygen concentration and humidity, optimize the microbial activity, monitor the changes in ammonia and carbon dioxide concentrations with sensors, analyze the nitrogen conversion situation, and generate the microbial activity and nitrogen conversion data; S4: Based on the microbial activity and nitrogen conversion data, use a convolutional neural network and a long short-term memory network to analyze the impact of microbial activities on nitrogen conversion, select the microbial regions with strong activity, adjust the temperature, humidity and oxygen concentration of the composting heap, and generate a microbial population optimization and environmental adjustment plan; S5: Based on the microbial population optimization and environmental adjustment plan, implement environmental regulation, monitor the nitrogen conversion rate in real time, and if the expected effect is not achieved, further adjust the temperature, humidity and ventilation inside the composting heap to generate a nitrogen conversion rate improvement plan.

[0020] Please refer to Figure 2 , the specific steps for generating the temperature and humidity data of the composting materials are as follows: S101: Based on the type of compost raw materials, adjust the heating equipment of the composting materials, set the heating power and duration, heat the composting materials in stages to the set temperature, monitor the temperature, and record the temperature changes during the heating-up, high-temperature and cooling-down periods to generate a set of composting stage heating parameters; S102: Based on the set of composting stage heating parameters, deploy temperature and humidity sensors at different depths of the composting heap, collect the temperature and humidity change data inside the composting heap in real time, analyze the data and calculate the change amplitude of the temperature and humidity to generate the stratified temperature and humidity change data; S103: Based on the hierarchical temperature and humidity change data, merge the temperature and humidity changes in the heating-up period, high-temperature period, and cooling-down period, establish a temperature and humidity change curve, and generate the temperature and humidity data of the compost materials. S101: Based on the type of compost raw materials, use the PID control algorithm to adjust the heating equipment of the composting materials, set the heating power and duration. Specifically, by setting the proportional coefficient Kp of the PID controller to 1.5, the integral coefficient Ki to 0.5, and the differential coefficient Kd to 0.2, and adjust in combination with the power range of the heating equipment. Adjust the heating power through the heating equipment controller, and heat the compost materials in stages to the set temperature according to the initial temperature and target temperature of the compost materials. Conduct temperature monitoring, record the temperature changes in the heating-up, high-temperature, and cooling-down periods, and generate a set of composting stage heating parameters. S102: Based on the set of composting stage heating parameters, deploy temperature and humidity sensors at different depths of the compost, use the Kalman filter algorithm to smooth the temperature and humidity data collected by the sensors. Set the initial state covariance to 1.0, the process noise covariance to 0.5, and the measurement noise covariance to 0.1. Real-time collect the temperature and humidity change data inside the compost pile, analyze the data and calculate the change range of temperature and humidity, and use the least squares method to fit the temperature and humidity data to generate hierarchical temperature and humidity change data. S103: Based on the hierarchical temperature and humidity change data, merge the temperature and humidity changes in the heating-up period, high-temperature period, and cooling-down period. Use the interpolation method, specifically use cubic spline interpolation to smoothly transition the temperature and humidity data of each stage, set the interpolation step size to 0.1, ensure the smooth connection of temperature and humidity changes in different stages, and generate the temperature and humidity data of the compost materials.

[0021] Please refer to Figure 3 , and the specific steps to generate the nitrogen loss control adjustment plan are as follows: S201: Based on the temperature and humidity data of the compost materials, install gas sensors during the composting process, monitor the concentration changes of ammonia and other gases, real-time collect the change data of nitrogen emissions, calculate the gas emission rate and total amount, and generate nitrogen emission fluctuation data. S202: Based on the nitrogen emission fluctuation data, compare the nitrogen emission changes under different temperature and humidity conditions, use the support vector machine for multivariate regression analysis, evaluate the impact of temperature, humidity, and oxygen concentration on nitrogen emissions, judge whether the nitrogen emissions exceed the set threshold. If it exceeds the standard, adjust the temperature, humidity, and ventilation system of the compost pile, and generate a nitrogen emission adjustment operation record. S203: Based on the nitrogen emission adjustment operation record, combine the temperature and humidity changes and nitrogen emission data, select appropriate environmental adjustment parameters, record the operation instructions and their execution effects, and generate a nitrogen loss control adjustment plan. S201: Based on the temperature and humidity data of the composting materials, install gas sensors during the composting process, use the PWM signal modulation algorithm to adjust the sampling frequency and accuracy of the gas sensors, set the sampling interval to 1 second, and estimate the changes in ammonia and other gas concentrations within each sampling period. By collecting the changing data of nitrogen emissions in real time, calculate the gas emission rate and total amount, and use the Euler Method to integrate the time series data of gas emissions to generate nitrogen emission fluctuation data; S202: Based on the nitrogen emission fluctuation data, compare the changes in nitrogen emissions under different temperature and humidity conditions, perform multivariate regression analysis using a support vector machine, set the kernel function as the radial basis function, set the parameter C to 1.0, and gamma to 0.5. Use this algorithm to analyze the effects of temperature, humidity, and oxygen concentration on nitrogen emissions, and determine whether the nitrogen emissions exceed the set threshold. Set the threshold to 5 ppm. If it exceeds the standard, adjust the temperature, humidity, and ventilation system of the composting pile body through the PID control algorithm, and generate a nitrogen emission adjustment operation record; S203: Based on the nitrogen emission adjustment operation record, combined with the temperature and humidity changes and nitrogen emission data, use the gradient descent algorithm to optimize and select suitable environmental adjustment parameters, set the learning rate to 0.01, and the number of iterations to 1000 times. Record the operation instructions and their execution effects, and generate a nitrogen loss control adjustment plan.

[0022] Support vector machine, according to the formula: Where: is the nitrogen emission prediction value, is the Lagrange multiplier of the support vector, is the kernel function, is the bias term, is the correlation weight coefficient between temperature, humidity and nitrogen emissions, is the relative humidity, is the correlation weight coefficient between oxygen concentration and nitrogen emissions, is the influence parameter of the compost raw material type; Execution process: First, by real-time monitoring the temperature, humidity, oxygen concentration and compost raw material type data, obtain the support vector and Lagrange multiplier through the training process of the support vector machine, and calculate the similarity of each input feature through the kernel function . Then, based on the multi-dimensional data input, the model calculates the predicted value of nitrogen emissions during the composting process, and considers the effects of temperature, humidity and oxygen concentration on nitrogen emissions. Respectively, through the weight coefficients and Adjust to optimize the accuracy of model prediction, generate an optimized record of nitrogen emission adjustment operations, provide data support for the dynamic adjustment of temperature, humidity, and ventilation volume during the composting process, and maximize the retention effect of nitrogen resources.

[0023] Please refer to Figure 4 , and the specific steps to generate microbial activity and nitrogen transformation data are as follows: S301: Based on the nitrogen loss control adjustment plan, evenly spread Bacillus subtilis into the composting materials according to a certain proportion, control the oxygen concentration in the composting pile body within the set range, adjust the humidity in real time through the spray system, optimize the growth environment of microorganisms, and generate records of microbial addition and environmental adjustment; S302: Based on the records of microbial addition and environmental adjustment, set gas sensors at different positions in the composting pile body, monitor the changes in ammonia and carbon dioxide concentrations in real time, record the sensor feedback data, and regularly analyze the correlation between gas concentration changes and composting temperature and humidity to generate monitoring data on nitrogen transformation; S303: Based on the monitoring data of nitrogen transformation, analyze the impact of different microbial activities on nitrogen transformation during the composting process, combine with the temperature and humidity change data, adjust the environmental conditions in the microbial activity area, and generate microbial activity and nitrogen transformation data; S301: Based on the nitrogen loss control adjustment plan, evenly spread Bacillus subtilis into the composting materials according to a certain proportion, simulate the distribution of the strain using the diffusion equation, set the strain diffusion rate to 0.02 cm / s, and dynamically adjust the strain spreading amount according to the changes in temperature, humidity, and oxygen concentration in the composting pile body. Monitor the oxygen concentration in the composting pile body in real time through a gas sensor, set the target oxygen concentration to 18%-21%, control the oxygen concentration within the set range, adjust the humidity in real time through the spray system, use the PID control algorithm to adjust the humidity, set the humidity set value to 60%-65%, optimize the growth environment of microorganisms, and generate records of microbial addition and environmental adjustment; S302: Based on the records of microbial addition and environmental adjustment, set gas sensors at different positions in the composting pile body, monitor the changes in ammonia and carbon dioxide concentrations in real time, smooth the sensor feedback data using the Kalman filter algorithm, set the initial state covariance to 1.0, the process noise covariance to 0.2, and the measurement noise covariance to 0.1, record the sensor feedback data, and analyze the correlation between gas concentration changes and composting temperature and humidity using the Pearson correlation coefficient to generate monitoring data on nitrogen transformation; S303: Based on the monitoring data of nitrogen transformation, analyze the influence of different microbial activities on nitrogen transformation during the composting process. Use the support vector regression algorithm to model the relationship between microbial activity and nitrogen transformation. Set the kernel function as RBF, the C value as 1.0, and the gamma value as 0.5. Combine the temperature and humidity change data, adjust the environmental conditions in the microbial activity area, optimize and adjust the parameters through the gradient descent method, set the learning rate as 0.01, and the maximum number of iterations as 500, to generate the data of microbial activity and nitrogen transformation.

[0024] Please refer to Figure 5 , and the specific steps to generate the microbial population optimization and environmental adjustment plan are as follows: S401: Based on the data of microbial activity and nitrogen transformation, monitor the microbial activities in different areas of the compost pile. Use the convolutional neural network to extract the spatial features of the microbial communities in different areas, capture the spatial distribution of the microbial communities, and then analyze the time series data through the long short-term memory network, construct the dynamic change trend of microbial activity and nitrogen transformation, determine the area where the microbial population with strong activity is located, and generate the data of the distribution of microbial activity areas; S402: Based on the data of the distribution of microbial activity areas, select the areas with appropriate temperature, humidity and oxygen concentration for key regulation, adjust the ventilation volume, adjust the humidity in the area, optimize the composting environment through the control system, and generate the record of the optimized adjustment of the regional environment; S403: Based on the record of the optimized adjustment of the regional environment, integrate the environmental adjustment effects of each area in the compost pile, and generate the microbial population optimization and environmental adjustment plan according to the optimized adjustment conditions of different areas; S401: Based on the data of microbial activity and nitrogen transformation, monitor the microbial activities in different areas of the compost pile. Use the convolutional neural network to extract the spatial features of the microbial communities in different areas. Set the number of convolutional layers as 3 layers, use a 5x5 convolutional kernel for each layer, the stride as 1, the padding method as same, and the activation function as ReLU, to capture the spatial distribution of the microbial communities. Then analyze the time series data through the long short-term memory network, set the number of units as 100, the learning rate as 0.01, and the batch size as 64, construct the dynamic change trend of microbial activity and nitrogen transformation, determine the area where the microbial population with strong activity is located, and generate the data of the distribution of microbial activity areas; S402: Based on the data of the distribution of microbial activity areas, select the areas with appropriate temperature, humidity and oxygen concentration for key regulation. Use the PID control algorithm to adjust the ventilation volume, set the proportional coefficient Kp as 1.2, the integral coefficient Ki as 0.8, and the differential coefficient Kd as 0.1. Regulate the humidity in the area through the control system, set the humidity target value as 65%, and the control error threshold for adjusting the humidity as 5%, to optimize the composting environment and generate the record of the optimized adjustment of the regional environment; S403: Based on the records of optimizing and adjusting the regional environment, integrate the environmental regulation effects of each region in the compost heap body. Adopt the clustering analysis algorithm, set the number of clusters to 3, and randomly initialize the initial center points. By calculating the environmental regulation effects of each region, generate an optimization plan for the microbial population and environmental adjustment according to the optimization and adjustment conditions of different regions.

[0025] The convolutional neural network, according to the formula: Where: is the feature map output by the convolutional layer, is the convolutional kernel, is the input data, is the bias term, is the relative humidity, is the influence coefficient of relative humidity on the microbial community distribution, is the influence parameter of the compost raw material type, is the weight coefficient of the influence of the compost raw material type on the microbial activity; Execution process: Among them, As the input data, representing the microbial community data of the compost area, through the convolutional kernel Perform convolutional operations to extract the spatial features within each compost area, including the density and activity intensity of the microbial distribution. In order to enhance the adaptability of the model to environmental factors, the relative humidity and the compost raw material type Two important parameters, relative humidity Is adjusted through the weight coefficient To represent the impact of humidity changes on the microbial community. The compost raw material type Is then adjusted through the weight coefficient To reflect the contribution of different raw material types to the microbial activity. The activation function Will output a feature map , The figure represents the spatial distribution of the microbial community in the compost heap body. Next, the feature map As the input, perform time series analysis through the long short-term memory network to capture the dynamic trend of the change in the microbial community activity. Finally, predict the microbial activity and its impact on nitrogen conversion through precise spatial and temporal features, providing a scientific basis for nitrogen retention in the composting process and ensuring the effective optimization of the composting environment to maximize the nitrogen retention effect.

[0026] The long short-term memory network, according to the formula: Where: Is the cell state at the current moment, Is the output of the forget gate, is the cell state at the previous moment, is the output of the input gate, is the candidate memory state at the current moment, is the current temperature and humidity data, is the current oxygen concentration data, is the microbial activity data of the current area, is the weight coefficient, is the weight coefficient, is the weight coefficient; Execution process: First, represents the cell state at the current moment, which integrates the memory state of the previous moment and the current input data, reflecting the dynamic changes of microbial activity and nitrogen transformation. The output of the forgetting gate determines the retention ratio of the cell state at the previous moment at the current moment. The output of the input gate then determines the storage degree of the candidate memory state , indicating the potential changes of microbial activity and nitrogen transformation during the composting process at the current moment. In order to more precisely control the retention of ammonia resources, external factors such as temperature and humidity , oxygen concentration and microbial activity are introduced into the formula, and the influence of these factors on the update of the cell state during the composting process is adjusted through the weight coefficients , and respectively. Temperature and humidity and oxygen concentration have a direct impact on microbial activity, thus determining the rate of nitrogen transformation. Microbial activity further optimizes the nitrogen retention efficiency during the composting process, realizes more efficient nitrogen resource retention, and ensures the minimization of nitrogen loss during the livestock manure composting process.

[0027] Please refer to Figure 6 , and the specific steps to generate a nitrogen conversion rate improvement plan are as follows: S501: Based on the microbial population optimization and environmental adjustment plan, adjust the temperature, humidity and ventilation system in the composting pile body, monitor the change of nitrogen conversion rate during the composting process in real time, use gas sensors to monitor the ammonia and carbon dioxide concentrations, and generate nitrogen conversion rate monitoring data in combination with the temperature and humidity data in the pile body; S502: Based on the nitrogen conversion rate monitoring data, compare the nitrogen conversion under different environmental conditions during the composting process, judge whether the nitrogen conversion rate reaches the expected target. If not, adjust the temperature, humidity and ventilation volume in the composting pile body, and generate an environmental adjustment optimization record; S503: Optimize the record based on environmental adjustment, monitor the changes in temperature, humidity and gas concentration inside the compost heap, analyze the nitrogen conversion rate after adjustment. If the adjustment does not reach the target, optimize the compost environment and record all adjustment data to generate a nitrogen conversion rate improvement plan; S501: Based on the microbial population optimization and environmental adjustment plan, adjust the temperature, humidity and ventilation system inside the compost heap, and monitor the change of nitrogen conversion rate during the composting process in real time. Use gas sensors to monitor the ammonia and carbon dioxide concentrations, set the ammonia concentration threshold at 50 ppm and the carbon dioxide concentration threshold at 1000 ppm. Combine the temperature and humidity data inside the heap, and use the Kalman filter algorithm to filter the ammonia and carbon dioxide concentration data collected by the sensors. Set the initial state covariance at 1.0, the process noise covariance at 0.2, and the measurement noise covariance at 0.1 to generate nitrogen conversion rate monitoring data; S502: Based on the nitrogen conversion rate monitoring data, compare the nitrogen conversion under different environmental conditions during the composting process, and perform regression analysis using the support vector regression algorithm. Set the kernel function as RBF, the C value as 1.0, and the gamma value as 0.5. Use this algorithm to analyze the change of nitrogen conversion rate under different environmental conditions, and judge whether the nitrogen conversion rate reaches the expected target. Set the target value at 0.8. If it does not reach the target, adjust the temperature, humidity and ventilation volume inside the compost heap through the PID control algorithm. Set the proportionality coefficient Kp at 1.5, the integral coefficient Ki at 0.5, and the differential coefficient Kd at 0.2 to generate an environmental adjustment optimization record; S503: Based on the environmental adjustment optimization record, monitor the changes in temperature, humidity and gas concentration inside the compost heap, optimize the environmental adjustment parameters using the gradient descent algorithm, set the learning rate at 0.01 and the maximum number of iterations at 1000. Analyze the nitrogen conversion rate after adjustment. If it does not reach the target, optimize the compost environment again through the PID control algorithm, record all adjustment data, and generate a nitrogen conversion rate improvement plan.

[0028] The application method of the nitrogen preservation method for livestock and poultry manure composting includes the following steps: First, during the composting process, use temperature and humidity sensors to monitor the changes in temperature and humidity of the compost materials in real time, collect the compost environment data during the heating period, high-temperature period and cooling period, calculate the temperature and humidity change range, and generate the temperature and humidity data of the compost materials; Next, use the temperature and humidity data and gas sensors to monitor the nitrogen loss situation during the composting process in real time, evaluate the nitrogen loss amount, and judge whether it exceeds the set safety threshold. If the nitrogen loss exceeds the standard, automatically adjust the temperature, humidity and ventilation system of the compost heap; Finally, while adjusting the composting environment, Bacillus is added to the compost to promote microbial activity. The concentration changes of ammonia and carbon dioxide are monitored by sensors to analyze the nitrogen conversion situation. Based on the microbial activity and nitrogen conversion data, the areas with strong microbial activity are selected for optimization and adjustment to produce qualified organic fertilizers.

[0029] The above are only the preferred embodiments of the present invention, and the present invention is not limited to other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical solution content of the present invention is not departed from, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for nitrogen conservation in livestock and poultry manure composting, characterized in that, It includes the following steps: Step 1: Based on the compost raw materials, control the temperature of the compost materials, simulate the heating-up period, high-temperature period, and cooling-down period, monitor the temperature and humidity changes in real time through sensors, collect the temperature and humidity data during the composting process, calculate the change range in different stages, and generate the temperature and humidity data of the compost materials; Step 2: Based on the temperature and humidity data of the compost materials, monitor the nitrogen loss during the composting process through sensors, use a support vector machine to evaluate whether it exceeds the standard. When it exceeds the standard, adjust the temperature, humidity, and ventilation system of the compost heap, conduct real-time monitoring and adjustment, and generate a nitrogen loss control adjustment plan; Step 3: Based on the nitrogen loss control adjustment plan, add Bacillus to the compost, adjust the oxygen concentration and humidity, optimize the microbial activity, monitor the changes in ammonia and carbon dioxide concentrations with sensors, analyze the nitrogen conversion situation, and generate the microbial activity and nitrogen conversion data; Step 4: Based on the microbial activity and nitrogen conversion data, use a convolutional neural network and a long short-term memory network to analyze the impact of microbial activities on nitrogen conversion, select the microbial regions with strong activity, adjust the temperature, humidity, and oxygen concentration of the compost heap, and generate a microbial population optimization and environmental adjustment plan; Step 5: Based on the microbial population optimization and environmental adjustment plan, implement environmental regulation, monitor the nitrogen conversion rate in real time. If the expected effect is not achieved, further adjust the temperature, humidity, and ventilation inside the compost heap to generate a nitrogen conversion rate improvement plan.

2. The method for nitrogen conservation in livestock and poultry manure composting according to claim 1, characterized in that, The specific steps for generating the temperature and humidity data of the compost materials are as follows: Based on the type of compost raw materials, adjust the heating equipment of the compost materials, set the heating power and duration, heat the compost materials in stages to the set temperature, conduct temperature monitoring, record the temperature changes during the heating-up, high-temperature, and cooling-down periods, and generate a set of compost stage heating parameters; Based on the set of compost stage heating parameters, deploy temperature and humidity sensors at different depths of the compost, collect the temperature and humidity change data inside the compost heap in real time, analyze the data and calculate the change range of temperature and humidity, and generate the stratified temperature and humidity change data; Based on the stratified temperature and humidity change data, merge the temperature and humidity changes during the heating-up period, high-temperature period, and cooling-down period, establish a temperature and humidity change curve, and generate the temperature and humidity data of the compost materials.

3. The method for nitrogen conservation in livestock and poultry manure composting according to claim 1, characterized in that, The specific steps for generating the nitrogen loss control adjustment plan are as follows: Based on the temperature and humidity data of the compost materials, install gas sensors during the composting process, monitor the concentration changes of ammonia and other gases, collect the change data of nitrogen emissions in real time, calculate the gas emission rate and total amount, and generate nitrogen emission fluctuation data; Based on the nitrogen emission fluctuation data, compare the changes in nitrogen emissions under different temperature and humidity conditions, use a support vector machine for multivariate regression analysis, evaluate the impact of temperature, humidity, and oxygen concentration on nitrogen emissions, judge whether the nitrogen emissions exceed the set threshold. If it exceeds the standard, adjust the temperature, humidity, and ventilation system of the compost heap to generate a nitrogen emission adjustment operation record; Based on the nitrogen emission adjustment operation record, combine the temperature and humidity changes and nitrogen emission data, select the appropriate environmental adjustment parameters, record the operation instructions and their execution effects, and generate a nitrogen loss control adjustment plan.

4. The method for nitrogen conservation in livestock and poultry manure composting according to claim 3, characterized in that, The support vector machine, according to the formula: Wherein: is the predicted value of nitrogen emissions, is the Lagrange multiplier of the support vector, is the kernel function, is the bias term, is the correlation weight coefficient between temperature and humidity and nitrogen emissions, is the relative humidity, is the correlation weight coefficient between oxygen concentration and nitrogen emissions, is the influence parameter of the compost raw material type.

5. The method for nitrogen conservation in livestock and poultry manure composting according to claim 1, characterized in that, The specific steps for generating the microbial activity and nitrogen conversion data are as follows: Based on the nitrogen loss control adjustment plan, spread Bacillus in proportion and evenly onto the compost materials, control the oxygen concentration in the compost heap within a set range, adjust the humidity in real time through the spraying system, optimize the growth environment of microorganisms, and generate records of microorganism addition and environment adjustment; Based on the records of microorganism addition and environment adjustment, set gas sensors at different positions in the compost heap, monitor the changes in ammonia and carbon dioxide concentrations in real time, record the data fed back by the sensors, and regularly analyze the correlation between the gas concentration changes and the temperature and humidity of the compost, and generate monitoring data on nitrogen conversion; Based on the monitoring data on nitrogen conversion, analyze the influence of different microbial activities on nitrogen conversion during the composting process, combine the temperature and humidity change data, and adjust the environmental conditions in the microbial activity areas to generate data on microbial activity and nitrogen conversion.

6. The method for nitrogen conservation in livestock and poultry manure composting according to claim 1, characterized in that, The specific steps for generating the microorganism population optimization and environment adjustment plan are as follows: Based on the data on microbial activity and nitrogen conversion, monitor the microbial activities in different areas of the compost heap, use a convolutional neural network to extract the spatial characteristics of the microbial communities in different areas, capture the spatial distribution of the microbial communities, and then analyze the time series data through a long short-term memory network, construct the dynamic change trend of microbial activity and nitrogen conversion, judge the areas where the microbial populations with strong activity are located, and generate data on the distribution of microbial activity areas; Based on the data on the distribution of microbial activity areas, select the areas with suitable temperature, humidity, and oxygen concentration for key regulation, adjust the ventilation volume, adjust the humidity within the areas, and optimize the composting environment through the control system to generate records of regional environment optimization and adjustment; Based on the records of regional environment optimization and adjustment, integrate the environmental regulation effects of each area in the compost heap, and generate a microorganism population optimization and environment adjustment plan for the optimization and adjustment situations of different areas.

7. The method for nitrogen conservation in livestock and poultry manure composting according to claim 6, characterized in that The convolutional neural network, according to the formula: Wherein: is the feature map output by the convolutional layer, is the convolutional kernel, is the input data, is the bias term, is the relative humidity, is the influence coefficient of relative humidity on the microbial community distribution, is the influence parameter of the compost raw material type, is the weight coefficient of the influence of the compost raw material type on the microbial activity.

8. The method for nitrogen conservation in livestock and poultry manure composting according to claim 6, characterized in that, The long short-term memory network, according to the formula: Wherein: is the cell state at the current moment, is the output of the forget gate, is the cell state at the previous moment, is the output of the input gate, is the candidate memory state at the current moment, is the current temperature and humidity data, is the current oxygen concentration data, is the microbial activity data of the current area, is the weight coefficient, is the weight coefficient, is the weight coefficient.

9. The method for nitrogen preservation in livestock and poultry manure composting according to claim 1, characterized in that, The specific steps for generating the nitrogen conversion rate improvement plan are as follows: Based on the microorganism population optimization and environment adjustment plan, adjust the temperature, humidity, and ventilation system in the compost heap, monitor the change in the nitrogen conversion rate during the composting process in real time, use gas sensors to monitor the ammonia and carbon dioxide concentrations, and combine the temperature and humidity data in the heap to generate monitoring data on the nitrogen conversion rate; Based on the monitoring data on the nitrogen conversion rate, compare the nitrogen conversion situations under different environmental conditions during the composting process, judge whether the nitrogen conversion rate reaches the expected target, and if not, adjust the temperature, humidity, and ventilation volume in the compost heap to generate records of environmental adjustment and optimization; Based on the records of environmental adjustment and optimization, monitor the changes in the temperature, humidity, and gas concentration in the compost heap, analyze the adjusted nitrogen conversion rate, and if the adjustment fails to reach the target, optimize the composting environment and record all adjustment data to generate a nitrogen conversion rate improvement plan.

10. Application method of nitrogen conservation method for livestock and poultry manure composting, characterized in that, The method for nitrogen conservation in livestock and poultry manure composting according to any one of claims 1-9 includes the following steps: First, during the composting process, monitor the changes in the temperature and humidity of the compost materials in real time through temperature and humidity sensors, collect the composting environment data during the heating period, high-temperature period, and cooling period, calculate the change range of the temperature and humidity, and generate the temperature and humidity data of the compost materials; Next, the nitrogen loss during the composting process is monitored in real time using temperature and humidity data and gas sensors, and the amount of nitrogen loss is evaluated to determine whether it exceeds the set safety threshold. If the nitrogen loss exceeds the standard, the temperature, humidity, and ventilation system of the compost pile are automatically adjusted. Finally, while adjusting the composting environment, Bacillus is added to the compost to promote microbial activity. The changes in ammonia and carbon dioxide concentrations are monitored through sensors, the nitrogen transformation is analyzed, and based on the microbial activity and nitrogen transformation data, the areas with strong microbial activity are selected for optimization and adjustment to produce qualified organic fertilizers.

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