A synergistic assessment method for carbon and nitrogen emissions from the entire process of rural human and animal waste utilization

Abstract

The present invention discloses a method for collaboratively evaluating carbon and nitrogen emissions in the whole process of human and animal manure utilization in rural areas, which belongs to the field of carbon and nitrogen emission evaluation. The method comprises the following steps: classifying human and animal manure samples by using a PCA-CPO optimized K-means clustering method to obtain manure classification results; acquiring real-time monitoring data of manure, and constructing a database of dynamic changes in manure properties based on the real-time monitoring data and the manure classification results; optimizing a manure solid-liquid separation process based on the database of dynamic changes in manure properties to obtain optimized manure; optimizing aerobic composting process and anaerobic fermentation treatment based on the optimized manure, and evaluating carbon and nitrogen emissions in the whole process based on a carbon and nitrogen emission accounting method.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon and nitrogen emission assessment, and in particular relates to a method for collaboratively assessing carbon and nitrogen emissions in the entire process of rural human and animal manure utilization. Background Art

[0002] In the whole process of utilizing human and animal manure in rural areas, there are problems such as insufficient treatment of manure and inaccurate assessment of carbon and nitrogen emissions. Manure contains a large amount of organic matter, nitrogen, phosphorus and other nutrients. If not handled properly, it will cause environmental pollution and waste of resources. At the same time, manure will generate carbon and nitrogen emissions in the collection, transportation, storage, treatment and utilization of manure, but there is currently a lack of systematic assessment methods, making it difficult to accurately quantify emissions, resulting in the inability to take targeted emission reduction measures.

[0003] The manure treatment process is complex, involving multiple links such as solid-liquid separation, aerobic composting, and anaerobic fermentation. The carbon and nitrogen emission mechanisms and influencing factors in different links vary greatly. How to establish a carbon and nitrogen emission assessment model for the entire process is a major challenge. During the solid-liquid separation process, it is necessary to consider the impact of separation efficiency on subsequent treatment and carbon and nitrogen emissions; during aerobic composting, it is necessary to consider the impact of factors such as turning frequency, temperature, and moisture content on carbon and nitrogen conversion and emissions; during anaerobic fermentation, it is necessary to consider the impact of raw material composition, temperature, pH value and other factors on methane and ammonia emissions. At the same time, the carbon and nitrogen emissions of different utilization methods (such as returning farmland to the field, biogas utilization, etc.) are also different and need to be evaluated separately.

[0004] In addition, the sources of rural human and livestock manure and sewage are scattered, the composition is complex and varied, and the characteristics of manure and sewage in different regions and farms of different sizes vary greatly, which brings difficulties to the assessment of carbon and nitrogen emissions. The lack of real-time monitoring data of manure and sewage makes it difficult to accurately grasp the dynamic changes of carbon and nitrogen. Therefore, it is urgent to develop a coordinated assessment method for carbon and nitrogen emissions in the entire process of rural human and livestock manure and sewage utilization, overcome the above technical difficulties, and achieve refined management and carbon and nitrogen emission reduction in the manure and sewage treatment process. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a coordinated assessment method for carbon and nitrogen emissions in the whole process of rural human and animal manure utilization to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention provides a method for collaboratively evaluating carbon and nitrogen emissions in the entire process of rural human and animal waste utilization, comprising:

[0007] The PCA-CPO optimized K-means clustering method was used to classify human and animal feces samples to obtain the feces classification results;

[0008] Acquire real-time monitoring data of feces and sewage, and construct a database of dynamic changes in feces and sewage properties based on the real-time monitoring data of feces and sewage and the feces and sewage classification results;

[0009] Optimizing the solid-liquid separation process of feces and sewage based on the database of dynamic changes in feces and sewage properties to obtain optimized feces and sewage;

[0010] Based on the optimized manure, the aerobic composting process and anaerobic fermentation treatment are optimized and based on the carbon and nitrogen emission accounting method, the carbon and nitrogen emissions of the whole process are evaluated.

[0011] Optionally, the process of classifying the feces samples using the PCA-CPO optimized K-means clustering method to obtain feces classification results includes:

[0012] Collecting human and animal feces samples and extracting key characteristic parameters of the human and animal feces samples;

[0013] The key characteristic parameters are reduced in dimension using principal component analysis to obtain pre-processed feces and sewage sample data;

[0014] Initialize CPO parameters and use silhouette coefficient information to construct the optimization objective function;

[0015] Based on the optimization objective function, the CPO algorithm optimizes the K-means clustering by adjusting the number of clusters and the distance metric to obtain an optimized clustering model;

[0016] Based on the optimized clustering model, cluster analysis is performed on the pre-treated feces and sewage sample data to obtain the optimal clustering result;

[0017] A feces and sewage classification result is obtained based on the optimal clustering result.

[0018] Optionally, the process of constructing a database of dynamic changes in manure properties includes:

[0019] Determining the characteristics of each type of manure based on the manure classification results;

[0020] Setting monitoring parameter threshold ranges for different types of feces and sewage based on the feces and sewage classification results;

[0021] Compare the real-time monitoring data of feces and sewage with the threshold range of the monitoring parameters to determine the change in the characteristics of the feces and sewage;

[0022] Based on the changes in the characteristics of the manure, a time series analysis algorithm is used to establish a dynamic change prediction model for the properties of manure for the real-time monitoring data and the laboratory test data corresponding to the animal manure samples;

[0023] Obtaining prediction results based on the dynamic change prediction model of manure properties;

[0024] A database of dynamic changes in fecal sewage properties is constructed based on the prediction results, laboratory test data and real-time monitoring data.

[0025] Optionally, the process of optimizing solid-liquid separation of manure includes:

[0026] Based on the database of dynamic changes in feces and sewage properties, the solid-liquid ratio and viscosity of the feces and sewage are obtained;

[0027] Construct a multivariate linear regression equation between the solid-liquid ratio and the rotation speed and feed rate of the centrifugal separation equipment;

[0028] Performing weight optimization on the multivariate linear regression equation based on a support vector machine to obtain an optimized regression equation;

[0029] Calculating the deviation between the real-time collected data and the predicted value output by the optimized regression equation;

[0030] When the deviation value exceeds the preset threshold, the parameter optimization process is triggered, and the optimized regression equation is optimized twice using a genetic algorithm to obtain the secondary optimized equipment parameters;

[0031] Based on the secondary optimization equipment parameters, the equipment control unit is updated to achieve optimization of solid-liquid separation of manure and sewage.

[0032] Optionally, the process of optimizing the aerobic composting process based on the optimized manure and sewage includes:

[0033] Obtaining manure characteristic parameters and composting requirement parameters based on the optimized manure;

[0034] Constructing a fuzzy control model based on the manure characteristic parameters and composting demand parameters;

[0035] The temperature and moisture content of the compost material are collected in real time, and the temperature and moisture content of the compost material are input into the fuzzy control model to obtain fuzzy control quantities of turning frequency and ventilation volume;

[0036] Defuzzification is performed on the fuzzy control quantity to obtain accurate control values ​​of the flipping frequency and the ventilation volume;

[0037] Based on the precise control amount, the working parameters of the turning equipment and the ventilation equipment are automatically adjusted to realize dynamic regulation of the temperature and moisture content of the compost.

[0038] Optionally, the process of optimizing the anaerobic fermentation process includes:

[0039] Determine the raw material composition data of aerobic composting manure and obtain fermentation condition parameters;

[0040] Constructing a support vector regression model based on the raw material composition data and fermentation condition parameters;

[0041] Obtaining a predicted value of methane yield based on the support vector regression model;

[0042] Determine the optimal raw material composition and optimal fermentation condition parameters based on the predicted methane yield;

[0043] Based on the optimal raw material composition and optimal fermentation condition parameters, an anaerobic fermentation experiment is performed to obtain an anaerobic fermentation residue;

[0044] The physicochemical properties of the anaerobic fermentation residue are analyzed to obtain the carbon and nitrogen contents in the residue.

[0045] Optionally, the process of evaluating the carbon and nitrogen emissions of the whole process includes:

[0046] The emission factor method was used to calculate the carbon and nitrogen emissions from returning farmland to the field based on the optimization process of aerobic composting;

[0047] The emission factor method is used to calculate the carbon and nitrogen emissions of biogas utilization based on the optimized process of anaerobic treatment;

[0048] Based on the carbon and nitrogen emissions from returning the farmland to the field and the carbon and nitrogen emissions from utilizing the biogas, respectively determine the carbon and nitrogen emission reduction potential of returning the farmland to the field and the carbon and nitrogen emission reduction potential of utilizing the biogas;

[0049] Based on the carbon and nitrogen emission reduction potential of returning farmland to farmland and the carbon and nitrogen emission reduction potential of biogas utilization, the carbon and nitrogen emissions of the whole process are evaluated and the optimal resource allocation plan is determined.

[0050] The present invention also provides a computer terminal device, comprising:

[0051] one or more processors;

[0052] A memory, coupled to the processor, for storing one or more programs;

[0053] When the one or more programs are executed by the one or more processors, the one or more processors implement a method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal manure utilization.

[0054] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for collaboratively evaluating carbon and nitrogen emissions from the entire process of utilization of human and animal manure in rural areas is implemented.

[0055] Compared with the prior art, the present invention has the following advantages and technical effects:

[0056] The present invention discloses a method for intelligent treatment and resource utilization based on the characteristics of manure and sewage. The method first uses the K-means clustering algorithm to classify manure and sewage, establishes a dynamic characteristic database, and then optimizes the treatment process for different types of manure and sewage. In the solid-liquid separation stage, the efficiency is improved by adjusting the parameters of the centrifugal equipment; in the aerobic composting process, the fuzzy control algorithm is used to optimize turning and ventilation; during anaerobic fermentation, a methane yield prediction model is constructed to increase biogas production. Finally, a carbon and nitrogen emission accounting method and a full-process collaborative evaluation model are established to quantify and analyze the emission reduction effect. The present invention realizes intelligent control of the entire process of manure and sewage treatment, improves the efficiency of resource utilization, reduces environmental impact, and provides technical support for the scientific treatment and efficient utilization of human and livestock manure and sewage in rural areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0058] Figure 1 It is a flow chart of a method for collaboratively evaluating carbon and nitrogen emissions in the whole process of rural human and animal waste utilization according to an embodiment of the present invention;

[0059] Figure 2 A flowchart for optimizing resource configuration of a monitoring platform is constructed for an embodiment of the present invention;

[0060] Figure 3 A decision flow chart for rural manure treatment based on carbon and nitrogen emissions in each link for the implementation of the present invention;

[0061] Figure 4 This is a structural diagram of the carbon and nitrogen emissions collaborative assessment system for the entire process of rural human and animal manure utilization implemented by the present invention. DETAILED DESCRIPTION

[0062] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0064] Embodiment 1

[0065] like Figure 1As shown, this embodiment provides a method for collaboratively evaluating carbon and nitrogen emissions from the entire process of human and animal manure utilization in rural areas, including the following steps: using a clustering algorithm to classify human and animal manure samples to obtain manure classification results; obtaining real-time monitoring data of manure, and constructing a database of dynamic changes in manure properties based on the real-time monitoring data and the manure classification results; optimizing the manure solid-liquid separation process based on the database of dynamic changes in manure properties to obtain optimized manure; optimizing the aerobic composting process and anaerobic fermentation treatment based on the optimized manure, and evaluating the carbon and nitrogen emissions of the entire process based on the carbon and nitrogen emission accounting method.

[0066] S101. According to the source of manure, breeding scale and regional characteristics, the PCA-CPO optimized K-means clustering method was used to classify manure and obtain the characteristic parameters of different types of manure, including organic matter content, nitrogen and phosphorus content, and moisture content.

[0067] The process of using a clustering algorithm to classify feces and sewage samples and obtain feces and sewage classification results includes: collecting human and animal feces and sewage samples, extracting key characteristic parameters of human and animal feces and sewage samples; using principal component analysis to reduce the dimension of key characteristic parameters to obtain pre-processed feces and sewage sample data; using a K-means clustering algorithm to perform cluster analysis on the pre-processed feces and sewage sample data to obtain the optimal clustering result; and obtaining the feces and sewage classification result based on the optimal clustering result.

[0068] Furthermore, feature engineering technology is used to extract key characteristic parameters such as organic matter content, nitrogen and phosphorus content, and moisture content from the manure sample data, and feature selection and feature dimensionality reduction are performed to reduce data dimensions and improve clustering efficiency; the K-means clustering algorithm is used to perform clustering analysis on the pretreated manure sample data, and through iterative optimization, the distance between samples in the cluster is minimized to obtain the optimal clustering result; the characteristic parameters of different types of manure obtained by cluster analysis are stored in the database for subsequent manure treatment and resource utilization, and targeted manure treatment plans are formulated according to the characteristics of the manure type.

[0069] As a specific implementation of this embodiment, in the manure sample data, key characteristic parameters such as organic matter content, nitrogen and phosphorus content, and moisture content are extracted by feature engineering technology. For example, the organic matter content of the manure sample is determined by chemical analysis, the total nitrogen content is determined by Kjeldahl nitrogen determination, the total phosphorus content is determined by molybdenum antimony colorimetry, and the moisture content is determined by drying. Then, the principal component analysis (PCA) is used to reduce the dimension of the extracted features, and the first few principal components with a cumulative variance contribution rate of 85% are selected as new features to reduce the data dimension and improve clustering efficiency. Next, the CPO parameters are initialized, and the optimization objective function is constructed using the silhouette coefficient information; based on the optimization objective function, the CPO algorithm optimizes the K-means clustering by adjusting the number of clusters and the distance metric to obtain an optimized clustering model. The specific process includes: in the process of CPO optimizing K-means clustering, the relevant parameters of the CPO algorithm must first be initialized, including the population size, the upper limit of the number of iterations, the boundary of the search space, etc., to ensure the effective operation of the algorithm. Subsequently, an objective function based on the silhouette coefficient is defined, which aims to quantify the quality of the clustering results. By measuring the closeness of the sample point to other points in its cluster and the degree of separation from other clusters, the optimal number of clusters and distance metric are automatically found. Then, the CPO algorithm begins to run iteratively. It simulates the interaction between individuals in the crested porcupine population, continuously adjusts the number and position of cluster centers, and the choice of distance metric to maximize the objective function value, that is, to improve the clustering effect. In each iteration, the algorithm evaluates the pros and cons of the current clustering scheme and updates the position of individuals in the population according to the evaluation results until the preset upper limit of the number of iterations is reached or the objective function value is no longer significantly improved. At this time, the algorithm stops running and outputs the optimal clustering results, including the number of clusters, cluster centers, and the cluster to which each sample point belongs, thereby completing the CPO optimization process for K-means clustering.

[0070] For the pre-treated manure sample data, the optimized clustering model is used for cluster analysis. Through iterative optimization, the cluster center is continuously updated until the sum of the distances between samples in the cluster is minimized, and the optimal clustering result is obtained. For example, the number of clusters K is set to 3, 3 cluster centers are initialized, the Euclidean distance from each sample to the cluster center is calculated, the sample is assigned to the cluster with the closest distance, the cluster center is updated to the mean of the samples in the cluster, and the iteration is repeated until the cluster center no longer changes. Finally, the characteristic parameters of different types of manure obtained by cluster analysis are stored in the database for subsequent manure treatment and resource utilization. According to the clustering results, for manure with high nitrogen and phosphorus and high organic matter content, anaerobic fermentation can be used to produce biogas for resource utilization; for manure with high water content and low nitrogen and phosphorus content, aerobic composting can be used for harmless treatment to generate organic fertilizer. Through feature engineering and K-means clustering algorithm, the classification and feature extraction of manure samples are realized, providing data support and decision-making basis for subsequent manure treatment and resource utilization.

[0071] S102. Using the manure classification results obtained in the first step, the temperature, pH value, and redox potential parameters of the manure are collected in real time through online monitoring equipment. Combined with the organic matter content, nitrogen and phosphorus content, and moisture content test data conducted weekly in the laboratory, a database on the dynamic changes in manure properties is established.

[0072] The process of constructing a database of dynamic changes in manure properties includes: determining the characteristics of each type of manure based on the manure classification results; setting the threshold range of monitoring parameters for different types of manure based on the manure classification results; comparing the real-time monitoring data of manure with the threshold range of monitoring parameters to determine the changes in the characteristics of manure; using a time series analysis algorithm to establish a dynamic change prediction model for manure properties based on the real-time monitoring data and laboratory test data corresponding to livestock manure samples; obtaining prediction results based on the dynamic change prediction model for manure properties; and constructing a database of dynamic changes in manure properties based on the prediction results, laboratory test data and real-time monitoring data.

[0073] Furthermore, according to the feces classification results, the threshold range of monitoring parameters for different types of feces is determined as the preset threshold of the online monitoring equipment. The online monitoring equipment collects parameters such as feces temperature, pH value, and redox potential in real time, and compares the collected data with the preset threshold to determine whether the nature of the feces has changed. If the online monitoring data exceeds the preset threshold range, an early warning is triggered, indicating that the nature of the feces may have changed and further testing is required. The data such as organic matter content, nitrogen and phosphorus content, and moisture content tested weekly in the laboratory are obtained, and they are correlated with the online monitoring data for analysis to determine the specific changes in the nature of feces. Using the time series analysis algorithm, a dynamic change prediction model for feces properties is established based on historical monitoring data and laboratory test data to predict the changing trend of feces properties in the future. According to the prediction results, the preset threshold of the online monitoring equipment is dynamically adjusted to enable it to adapt to changes in the nature of feces and improve the sensitivity and accuracy of monitoring. The online monitoring data, laboratory test data, and prediction results are integrated into the dynamic change database of feces properties to provide data support for subsequent feces treatment and utilization.

[0074] As a specific implementation of this embodiment, according to the feces classification results, the threshold range of monitoring parameters for different types of feces is determined, such as organic matter content between 30%-50%, nitrogen and phosphorus content between 1%-3%, and moisture content between 70%-90%, as the preset threshold value of the online monitoring equipment. The online monitoring equipment collects parameters such as feces temperature, pH value, and redox potential in real time, with a sampling frequency of once per hour. The collected data is compared with the preset threshold value to determine whether the nature of the feces has changed. If the online monitoring data exceeds the preset threshold range, such as the organic matter content is lower than 30% or higher than 50%, an early warning is triggered, indicating that the nature of the feces may have changed and further testing is required. The organic matter content, nitrogen and phosphorus content, moisture content and other data tested weekly by the laboratory are obtained, and the Pearson correlation coefficient method is used to correlate them with the online monitoring data. If the correlation coefficient is greater than 8, it is considered that the two sets of data are highly correlated, and the specific changes in the nature of the feces are judged accordingly. Using the ARIMA time series analysis algorithm, a dynamic change prediction model for manure properties is established based on historical monitoring data and laboratory test data. The root mean square error of the model is less than 5%, and the changing trend of manure properties in the next week is predicted. According to the prediction results, the preset threshold of the online monitoring equipment is dynamically adjusted. For example, if the organic matter content is predicted to increase by 5% in the future, the corresponding threshold range is adjusted to 35%-55% to enable it to adapt to the changes in manure properties and improve the sensitivity and accuracy of monitoring. The online monitoring data, laboratory test data, and prediction results are integrated into the dynamic change database of manure properties. The database is managed by MySQL to provide data support for subsequent manure treatment and utilization.

[0075] S103. Based on the manure characteristic data obtained in the first two steps, the rotation speed and feed rate parameters of the centrifugal separation equipment are optimized for the solid-liquid separation process to improve the solid-liquid separation efficiency and reduce the solid content in the liquid manure.

[0076] The process of optimizing the solid-liquid separation of manure and sewage includes: obtaining the solid-liquid ratio and viscosity of manure and sewage based on a database of dynamic changes in manure and sewage properties; constructing a multivariate linear regression equation between the solid-liquid ratio and the rotation speed and feed rate of the centrifugal separation equipment; optimizing the weights of the multivariate linear regression equation based on a support vector machine to obtain an optimized regression equation; calculating the deviation between the real-time collected data and the predicted value output by the optimized regression equation; triggering the parameter optimization process when the deviation exceeds a preset threshold, and using a genetic algorithm to perform secondary optimization on the optimized regression equation to obtain secondary optimized equipment parameters; and updating the equipment control unit based on the secondary optimized equipment parameters to achieve optimization of the solid-liquid separation of manure and sewage.

[0077] Furthermore, according to the characteristic parameters of manure such as the solid-liquid ratio and viscosity, a mathematical model between the manure solid-liquid separation efficiency and the rotational speed and feed rate of the centrifugal separation equipment is established; the support vector machine algorithm is used to train and optimize the key parameters in the above mathematical model based on the existing manure solid-liquid separation experimental data; the optimized rotational speed and feed rate parameters are input into the control unit of the centrifugal separation equipment to automatically adjust the equipment operating parameters; in the process of solid-liquid separation of manure, the rotational speed, feed rate, solid content of liquid manure after separation and other data of the centrifugal separation equipment are collected in real time; the real-time collected data are compared with the predicted value of the mathematical model to calculate the deviation value; if the deviation value exceeds the preset threshold, the parameter optimization process is triggered, and the genetic algorithm is used to perform secondary optimization on the mathematical model parameters; the equipment parameters after secondary optimization are updated to the control unit to realize dynamic optimization of the centrifugal separation process, maximize the solid-liquid separation efficiency, and control the solid content of liquid manure.

[0078] As a specific implementation of this embodiment, according to the characteristic parameters of the solid-liquid ratio and viscosity of feces, a mathematical model between the solid-liquid separation efficiency of feces and the speed and feed rate of the centrifugal separation equipment is established. By collecting the solid-liquid ratio and viscosity data of 100 groups of different feces samples, as well as the corresponding optimal operating parameters of the centrifugal separation equipment, the functional relationship between the solid-liquid separation efficiency and the equipment speed and feed rate is obtained by multivariate linear regression analysis. On this basis, 80 groups of data are selected as training sets and 20 groups of data are selected as test sets. The weight coefficients in the functional relationship are trained and optimized using the support vector machine algorithm. The optimal penalty coefficient C and kernel function parameter g are determined by the grid search method, so that the average relative error of the model on the test set is less than 5%. The speed and feed rate parameters after training and optimization are input into the PLC control unit of the centrifugal separation equipment, and the operating parameters of the equipment are automatically calculated and adjusted according to the real-time collected feces solid-liquid ratio and viscosity data. During the solid-liquid separation process of feces, the speed, feed rate and solid content data of the liquid feces after separation are collected every 10 seconds, and compared with the predicted values ​​of the mathematical model to calculate the deviation value. When the deviation value exceeds 10% for three consecutive times, the parameter optimization process is triggered, and the weight coefficient of the mathematical model is optimized twice using a genetic algorithm. The population size is set to 50, the evolutionary generation is 100, the crossover probability is 8, the mutation probability is 1, and the fitness function is selected as the weighted average of the solid-liquid separation efficiency and the solid content of liquid manure, with weights of 7 and 3 respectively. The equipment parameters after the secondary optimization are updated to the PLC control unit to achieve dynamic optimization of the centrifugal separation process and ensure that the solid content of liquid manure is stable below 3%.

[0079] S104. During aerobic composting, according to the characteristics of manure and composting requirements, a fuzzy control algorithm based on temperature and moisture content is used to optimize the turning frequency and ventilation parameters, so as to achieve regulation of temperature and moisture content and promote the decomposition of organic matter and nitrogen stabilization.

[0080] The process of optimizing the aerobic composting process based on the optimized manure includes: obtaining manure characteristic parameters and composting demand parameters based on the optimized manure; constructing a fuzzy control model based on the manure characteristic parameters and composting demand parameters; collecting the temperature and moisture content of the compost material in real time, and inputting the temperature and moisture content of the compost material into the fuzzy control model to obtain fuzzy control quantities of turning frequency and ventilation volume; defuzzifying the fuzzy control quantities to obtain precise control values ​​of turning frequency and ventilation volume; and automatically adjusting the working parameters of the turning equipment and the ventilation equipment based on the precise control quantities to realize dynamic regulation of the temperature and moisture content of the compost material.

[0081] Furthermore, the characteristic parameters of manure and the required parameters of the composting process are obtained, including the moisture content, organic matter content, etc. of manure, as well as the target temperature range and moisture content range of the composting process. According to the obtained characteristic parameters of manure and the required parameters of composting, a fuzzy control model based on temperature and moisture content is established, and the fuzzy membership function and fuzzy rule base of temperature and moisture content are set. During the composting process, the temperature and moisture content data of the compost are collected in real time, and the collected data are input into the fuzzy control model for fuzzy reasoning calculation to obtain the fuzzy control quantity of turning frequency and ventilation volume. The fuzzy control quantity is defuzzified to obtain the precise control value of turning frequency and ventilation volume, and the working parameters of the turning equipment and ventilation equipment are adjusted according to the control value to realize the dynamic regulation of the temperature and moisture content of the compost. During the regulation process, the particle swarm optimization algorithm is used to optimize the parameters of the fuzzy control model, and the accuracy and stability of temperature and moisture content control are improved by optimizing the fuzzy membership function and fuzzy rule base. According to the temperature and moisture content of the compost, the frequency of turning and ventilation are adjusted dynamically. When the temperature or moisture content deviates from the target range, the control intensity is increased. When the temperature and moisture content are stable within the target range, the control intensity is reduced to achieve adaptive control of the composting process. After the composting process is completed, the compost is sampled and analyzed to determine the decomposition rate of organic matter and nitrogen content. The analysis results are compared with the preset target values ​​to evaluate the effect of the composting process. Based on the evaluation results, the fuzzy control model and optimization algorithm are improved to provide a reference for the next batch of composting.

[0082] As a specific implementation of this embodiment, when obtaining the characteristic parameters of manure, the moisture content of manure is 75% and the organic matter content is 60% by infrared spectroscopy. At the same time, according to the composting process requirements, the target temperature range of the composting process is set to 55-65°C, and the moisture content range is 55%-60%. Based on the acquired parameters, a fuzzy control model of temperature and moisture content is established, the fuzzy membership function of the temperature is set to a triangular function, the fuzzy membership function of the moisture content is set to a Gaussian function, and a rule base containing 25 fuzzy rules is constructed. During the composting process, the temperature and moisture content data of the compost material are collected in real time using a wireless sensor network, and the data are collected every 10 minutes. The collected data is input into the fuzzy control model, and the fuzzy reasoning calculation is performed by the Mamdani reasoning method to obtain the fuzzy control quantity of the turning frequency and the ventilation volume. The fuzzy control quantity is defuzzified by the center of gravity method, and the turning frequency is 5 times / day and the ventilation volume is 1000 m³ / h. According to the control value, the PLC control system is used to adjust the turning times of the turning equipment and the fan frequency of the ventilation equipment to realize the automatic control of the temperature and moisture content of the compost. In the control process, the particle swarm optimization algorithm is used to optimize the parameters of the fuzzy control model, setting the number of particles to 50, the inertia weight to 7, the acceleration constants c1 and c2 to 5 and 7 respectively, and the maximum number of iterations to 100. By optimizing the parameters of the fuzzy membership function and the rule weights of the fuzzy rule base, the temperature control error is reduced by 2℃ and the moisture content control error is reduced by 3%. According to the changing trends of the temperature and moisture content of the compost, the turning frequency and ventilation volume are dynamically adjusted using the results of fuzzy reasoning. When the temperature is lower than 55℃ or the moisture content is higher than 60%, the turning frequency is increased to 8 times / day and the ventilation volume is increased to 1500 m³ / h; when the temperature and moisture content are stable within the target range for 3 consecutive days, the turning frequency is reduced to 3 times / day and the ventilation volume is reduced to 800 m³ / h. After the composting process was completed, the compost was sampled and analyzed, and the organic matter decomposition rate was 65% and the nitrogen content was 5%, which was basically consistent with the preset target values, indicating that the composting process control effect was good. At the same time, according to the evaluation results, the membership function type of the fuzzy control model and the number of rules in the fuzzy rule base were optimized, providing a reference for the next batch of composting.

[0083] S105. For manure with high moisture content, anaerobic fermentation treatment is used to build a support vector regression methane yield prediction model based on raw material composition, temperature, and pH value factors to optimize fermentation process parameters, increase biogas production, and reduce the carbon and nitrogen content in the residue.

[0084] The process of optimizing the anaerobic fermentation treatment process includes: determining the raw material composition data of manure treated by aerobic composting and obtaining fermentation condition parameters; constructing a support vector regression model based on the raw material composition data and the fermentation condition parameters; obtaining a methane yield prediction value based on the support vector regression model; determining the optimal raw material composition and the optimal fermentation condition parameters based on the methane yield prediction value; performing an anaerobic fermentation experiment based on the optimal raw material composition and the optimal fermentation condition parameters to obtain anaerobic fermentation residues; and performing physical and chemical property analysis on the anaerobic fermentation residues to obtain the carbon and nitrogen content in the residues.

[0085] Further, the raw material composition data of high-moisture content manure is obtained, and the raw material composition data includes organic matter content and carbon-nitrogen ratio;

[0086] Obtaining fermentation condition parameters of high-water content feces, including pH value and temperature;

[0087] According to the raw material composition data and fermentation condition parameters, a support vector regression model is constructed, and the support vector regression model is used to predict the methane yield;

[0088] An optimization algorithm is used to optimize the hyperparameters of the support vector regression model to obtain an optimized support vector regression model;

[0089] According to the optimized support vector regression model, the methane yield under different raw material compositions and fermentation conditions is predicted to obtain the predicted methane yield;

[0090] Determine the optimal raw material composition and optimal fermentation condition parameters based on the predicted methane yield;

[0091] An anaerobic fermentation experiment is carried out under the optimal raw material composition and optimal fermentation condition parameters to obtain anaerobic fermentation residues;

[0092] The physical and chemical properties of the anaerobic fermentation residues were analyzed to obtain the carbon and nitrogen contents in the residues;

[0093] The degradation effect of anaerobic fermentation on manure with high water content was evaluated based on biogas production, methane content and carbon and nitrogen content in the residue;

[0094] Based on the evaluation results, the optimal anaerobic fermentation process parameters are determined to achieve efficient resource utilization of high-moisture content manure.

[0095] As a specific implementation of this embodiment, in order to obtain the raw material composition data of high-water content feces, an elemental analyzer can be used to determine the organic content, the Kjeldahl method can be used to determine the total nitrogen content, and the carbon-nitrogen ratio can be calculated. For example, a batch of feces samples was measured to have an organic content of 68% and a total nitrogen content of 1%, then the carbon-nitrogen ratio is 33. When obtaining the fermentation condition parameters of feces, a pH meter can be used to determine the pH value, and a temperature sensor can be used to monitor the fermentation temperature in real time. According to the collected raw material composition data and fermentation condition parameters, a support vector regression model is constructed using MATLAB software, and the grid search method is used to optimize the model's hyperparameters, such as the penalty factor C and the kernel function parameter g, to obtain an optimized support vector regression model. The model is used to predict the methane yield under different raw material compositions and fermentation conditions. By comparing the prediction results, the optimal raw material composition is determined to be 70% organic matter content, 30 carbon-nitrogen ratio, and the optimal fermentation conditions are pH 5 and temperature 35°C. Under these conditions, an anaerobic fermentation experiment was carried out to determine the biogas production and methane content, and the physical and chemical properties of the fermentation residue were analyzed. The results showed that under these conditions, the daily biogas production was 5 m³, the methane content reached 65%, and the total nitrogen content in the residue was reduced to 2%, achieving efficient resource utilization of manure. Based on the evaluation results, an optimal process parameter database for anaerobic fermentation of manure with high moisture content was established to provide a reference for subsequent engineering applications.

[0096] S106. In combination with the resource utilization of manure by returning farmland to fields and using biogas, a carbon and nitrogen emission accounting method based on the combination of life cycle assessment (LCA) and emission factor method is established. Through full life cycle assessment, the carbon and nitrogen emissions and emission reduction potential of different treatment and utilization links are quantified.

[0097] The process of evaluating carbon and nitrogen emissions throughout the entire process includes: using the emission factor method to calculate the carbon and nitrogen emissions from returning farmland to the field in the optimization process based on aerobic composting; using the emission factor method to calculate the carbon and nitrogen emissions from biogas utilization in the optimization process based on anaerobic treatment; based on the carbon and nitrogen emissions from returning farmland to the field and the carbon and nitrogen emissions from biogas utilization, respectively determine the carbon and nitrogen emission reduction potential of returning farmland to the field and the carbon and nitrogen emission reduction potential of biogas utilization; based on the carbon and nitrogen emission reduction potential of returning farmland to the field and the carbon and nitrogen emission reduction potential of biogas utilization, evaluate the carbon and nitrogen emissions of the entire process and determine the optimal resource allocation plan.

[0098] Furthermore, if Figure 2As shown in the figure, life cycle assessment software is used to construct life cycle inventories for farmland return and biogas utilization. The emission factor method is used to combine the amount of manure treatment, activity level data and emission factors to calculate the carbon and nitrogen emissions of each link. The full life cycle assessment method is used to quantitatively evaluate the carbon and nitrogen emissions of farmland return and biogas utilization, and the carbon and nitrogen emissions of each link are obtained. By comparing the carbon and nitrogen emissions of each link in different manure resource utilization methods, the carbon and nitrogen emission reduction potential of each method is determined. According to the carbon and nitrogen emission reduction potential, the multi-objective optimization algorithm is used to obtain the optimal configuration scheme for manure resource utilization to maximize the carbon and nitrogen emission reduction potential. For the optimal configuration scheme, the scenario analysis method is used to set different scenario parameters to simulate and evaluate its carbon and nitrogen emission reduction effect. Through sensitivity analysis, the key parameters affecting the carbon and nitrogen emission reduction effect are determined to provide a decision-making basis for optimizing the resource utilization of manure. A carbon and nitrogen emission monitoring platform for manure resource utilization is constructed to obtain and analyze carbon and nitrogen emission data of each link in real time, dynamically optimize resource allocation, and continuously improve the carbon and nitrogen emission reduction effect.

[0099] As a specific implementation method of this embodiment, the emission factor method is used to calculate the carbon and nitrogen emissions in the process of returning farmland to the field and biogas utilization. By collecting activity level data such as the amount of manure returned to the field and biogas production, combined with methane and nitrous oxide emission factors, the carbon and nitrogen emissions of each link are calculated. Using the life cycle assessment software SimaPro, a life cycle inventory of two modes of returning farmland to the field and biogas utilization is constructed, and the carbon and nitrogen emission data are input. The CML2001 method is used for quantitative evaluation. The carbon emissions of returning farmland to the field and biogas utilization are 15kg CO2-eq / kg and 08kg CO2-eq / kg, respectively, and the nitrogen emissions are 0.12kgN2O-N / kg and 0.05kg N2O-N / kg, respectively. A multi-objective optimization model is constructed based on TOPSIS and entropy weight method, with the maximum carbon and nitrogen emission reduction as the objective function, considering the constraints such as the amount of manure produced, transportation radius, and the proportion of returning to the field, and the genetic algorithm is used to solve it, and the optimal ratio of returning farmland to the field and biogas utilization is 6:4. Under the optimal configuration scheme, different scenario parameters such as the amount of manure generated and the transportation distance were set, and the scenario analysis method was used to simulate and predict the carbon and nitrogen emission reduction effect. Through sensitivity analysis, it was identified that the amount of manure generated and the proportion of manure returned to the field are the key factors affecting carbon and nitrogen emission reduction. A carbon and nitrogen emission monitoring platform for manure resource utilization based on the Internet of Things was constructed, and RFID, sensors and other technologies were used to collect data such as the amount of manure returned to the field and biogas production in real time. Big data analysis algorithms were used to mine and analyze the data, optimize resource allocation plans, continuously improve carbon and nitrogen emission reduction effects, and realize intelligent management of manure resource utilization.

[0100] S107. Comprehensively consider the carbon and nitrogen emission characteristics of each link in manure treatment, build a coordinated assessment model of carbon and nitrogen emissions in the whole process of rural human and livestock manure utilization, adopt a method combining material flow and emission factors to quantitatively analyze the carbon and nitrogen emission reduction effects, and guide manure treatment and resource utilization.

[0101] like Figure 3 As shown in the figure, the material flow data of each link of rural human and livestock manure and sewage treatment are obtained, including the amount of manure and sewage generated, collected, treated and resourced; the carbon and nitrogen emissions of each link are calculated according to the carbon and nitrogen emission factors of different treatment processes, and the carbon and nitrogen emission inventory of the whole process of manure and sewage treatment is obtained; a carbon and nitrogen emission assessment model for the whole process of rural human and livestock manure and sewage treatment is constructed, and the carbon and nitrogen emission characteristics and material flow data of each link of manure and sewage treatment are comprehensively considered; machine learning algorithms such as support vector machines or random forests are used to train the carbon and nitrogen emission assessment model to improve the prediction accuracy and generalization ability of the model; the constructed carbon and nitrogen emission assessment model is used to quantitatively analyze the carbon and nitrogen emission reduction effects of different manure and sewage treatment and resource utilization schemes, and identify key emission reduction links; targeted manure and sewage treatment optimization measures are formulated for key emission reduction links, such as optimizing treatment process parameters, improving resource utilization rate, etc., to achieve the purpose of carbon and nitrogen synergistic emission reduction; through scenario analysis and sensitivity analysis, the carbon and nitrogen emission reduction potential under different policy and technical scenarios is evaluated, providing a quantitative basis for scientific decision-making in rural manure and sewage treatment.

[0102] As a specific implementation method of this embodiment, through field research and questionnaire surveys, it was obtained that the amount of human and animal manure produced in a village in one year was 1,200 tons, and the collection amount was 1,000 tons, of which 800 tons were used for biogas fermentation and 200 tons were used for composting. According to existing studies, the carbon emission factors of biogas fermentation and composting processes are 15 and 25, respectively, and the nitrogen emission factors are 0.2 and 0.4, respectively. Using material flow data and emission factors, it is calculated that the carbon emissions of the biogas fermentation link are 120 tons and the nitrogen emissions are 16 tons; the carbon emissions of the composting link are 50 tons and the nitrogen emissions are 8 tons. Based on the above data, a carbon and nitrogen emission assessment model for the whole process of rural human and animal manure treatment is constructed, and the support vector machine algorithm is used for training. Through 5-fold cross validation, the average prediction accuracy of the model reaches more than 90%. Using the trained model, the carbon and nitrogen emission reduction effects of different manure treatment schemes are evaluated, and it is found that increasing the proportion of biogas fermentation can effectively reduce carbon and nitrogen emissions. Therefore, optimization measures were formulated to increase the biogas fermentation ratio from 80% to 90% and reduce the composting ratio to 10%. The simulation analysis showed that the optimized carbon emissions were reduced to 105 tons and nitrogen emissions were reduced to 14 tons, which were 15% and 15% less than the original plan, respectively. Further scenario analysis was carried out to evaluate the carbon and nitrogen emission reduction potential by considering different scenarios such as a 20% increase in manure production and a 10% increase in biogas utilization. The results showed that an increase in manure production would lead to an increase in carbon and nitrogen emissions, but by increasing biogas utilization, some of the increase could be offset, achieving a reduction in carbon and nitrogen emission intensity of more than 5%. The comprehensive evaluation results provide a scientific basis for local governments to formulate optimization strategies for manure treatment, which will help achieve the coordinated reduction of carbon and nitrogen emissions in rural human and livestock manure treatment.

[0103] According to the carbon and nitrogen emission factors of different treatment processes, the carbon and nitrogen emissions of each link are calculated to obtain the carbon and nitrogen emission list of the whole process of manure treatment.

[0104] Obtain carbon and nitrogen emission factor data for different manure and sewage treatment processes and establish an emission factor database. For each manure and sewage treatment process, divide the various treatment links, and calculate the carbon and nitrogen emissions of each link according to the corresponding emission factors in the emission factor database. Summarize the carbon and nitrogen emissions of each link to obtain the total carbon and nitrogen emissions of the whole process of this manure and sewage treatment process. Compare and analyze the total carbon and nitrogen emissions of the whole process of different manure and sewage treatment processes to determine which process has lower carbon and nitrogen emissions. According to the division of treatment links and carbon and nitrogen emission calculation results of various manure and sewage treatment processes, generate a carbon and nitrogen emission inventory for the whole process of manure and sewage treatment. Using data visualization technology, the carbon and nitrogen emission inventory of the whole process of manure and sewage treatment is presented in the form of charts for easy analysis and comparison. Through machine learning algorithms such as regression analysis and decision trees, a prediction model between manure and sewage treatment process parameters and carbon and nitrogen emissions is established to provide a basis for optimizing manure and sewage treatment processes and reducing carbon and nitrogen emissions.

[0105] As a specific implementation method of this embodiment, in order to obtain carbon and nitrogen emission factor data of different manure treatment processes, the carbon and nitrogen emission factors of common manure treatment processes such as anaerobic fermentation, aerobic composting, and biological filter can be collected by consulting relevant literature, and an emission factor database containing key emissions such as methane, nitrous oxide, and ammonia can be established. Taking the anaerobic fermentation process as an example, it can be divided into manure collection, pretreatment, anaerobic fermentation, and biogas utilization. According to the emission factors of each link of the anaerobic fermentation process in the emission factor database, such as the methane emission factor of the collection link is 0.2kg / t manure, and the nitrous oxide emission factor of the anaerobic fermentation link is 0.035kg / t manure, etc., the carbon and nitrogen emissions of each link are calculated separately, and then the total carbon and nitrogen emissions of the whole process of the anaerobic fermentation process are summarized. Similarly, the total carbon and nitrogen emissions of other processes such as aerobic composting and biological filter can be calculated. By comparing the carbon and nitrogen emission levels of different processes, it can be judged that the carbon and nitrogen emissions of the anaerobic fermentation process are relatively low. Based on the calculation of carbon and nitrogen emissions from each process, a carbon and nitrogen emission inventory for the entire process of manure and sewage treatment is generated, and bar charts, pie charts and other data visualization methods are used to intuitively present the characteristics and differences of carbon and nitrogen emissions in each link of different manure and sewage treatment processes. Using the collected process parameters (such as temperature, residence time, etc.) and carbon and nitrogen emission data, through multivariate linear regression, decision tree and other machine learning algorithms, a prediction model for manure and sewage treatment process parameters and carbon and nitrogen emissions is established, the influence weight of each parameter on carbon and nitrogen emissions is analyzed, and the control parameters are optimized to minimize carbon and nitrogen emissions while ensuring the effect of manure and sewage treatment.

[0106] Embodiment 2

[0107] like Figure 4 As shown, the present invention provides a coordinated assessment system for carbon and nitrogen emissions in the whole process of human and animal manure utilization in rural areas, which mainly includes: a manure classification module, which is used for cluster analysis to obtain characteristic parameters; a dynamic monitoring module, which is used to establish a database of manure property changes; a solid-liquid separation module, which is used to optimize centrifugal equipment parameters to improve efficiency; an aerobic composting module, which is used to optimize turning and ventilation to promote the decomposition of organic matter; an anaerobic fermentation module, which is used to predict methane yield and optimize fermentation technology; a carbon and nitrogen assessment module, which is used to quantify emission reduction and guide resource utilization.

[0108] Embodiment 3

[0109] This embodiment also provides a computer terminal device, including:

[0110] one or more processors;

[0111] A memory, coupled to the processor, for storing one or more programs;

[0112] When one or more programs are executed by one or more processors, the one or more processors implement a coordinated assessment method for carbon and nitrogen emissions from the entire process of rural human and animal manure utilization.

[0113] Embodiment 4

[0114] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal manure utilization is implemented.

[0115] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization, characterized in that: The following steps are involved: The PCA-CPO optimized K-means clustering method was used to classify human and animal feces samples to obtain the feces classification results; The process of classifying feces and sewage samples by using the PCA-CPO optimized K-means clustering method to obtain feces and sewage classification results includes: collecting human and animal feces and sewage samples, extracting key characteristic parameters of the human and animal feces and sewage samples; using principal component analysis to reduce the dimension of the key characteristic parameters to obtain pre-processed feces and sewage sample data; initializing CPO parameters, and using silhouette coefficient information to construct an optimization objective function; based on the optimization objective function, the CPO algorithm optimizes K-means clustering by adjusting the number of clusters and the distance metric to obtain an optimized clustering model; based on the optimized clustering model, clustering analysis is performed on the pre-processed feces and sewage sample data to obtain an optimal clustering result; based on the optimal clustering result, a feces and sewage classification result is obtained; Acquire real-time monitoring data of feces and sewage, and construct a database of dynamic changes in feces and sewage properties based on the real-time monitoring data of feces and sewage and the feces and sewage classification results; Optimizing the solid-liquid separation process of feces and sewage based on the database of dynamic changes in feces and sewage properties to obtain optimized feces and sewage; Based on the optimized manure, the aerobic composting process and anaerobic fermentation treatment are optimized and based on the carbon and nitrogen emission accounting method, the carbon and nitrogen emissions of the whole process are evaluated.

2. The method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization according to claim 1 is characterized in that: The process of building a database of dynamic changes in manure properties includes: Determining the characteristics of each type of manure based on the manure classification results; Setting monitoring parameter threshold ranges for different types of feces and sewage based on the feces and sewage classification results; Compare the real-time monitoring data of feces and sewage with the threshold range of the monitoring parameters to determine the change in the characteristics of the feces and sewage; Based on the changes in the characteristics of the manure, a time series analysis algorithm is used to establish a dynamic change prediction model for the properties of manure for the real-time monitoring data and the laboratory test data corresponding to the animal manure samples; Obtaining prediction results based on the dynamic change prediction model of manure properties; A database of dynamic changes in fecal sewage properties is constructed based on the prediction results, laboratory test data and real-time monitoring data.

3. The method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization according to claim 1 is characterized in that: The process of optimizing solid-liquid separation of manure includes: Based on the database of dynamic changes in feces and sewage properties, the solid-liquid ratio and viscosity of the feces and sewage are obtained; Construct a multivariate linear regression equation between the solid-liquid ratio and the rotation speed and feed rate of the centrifugal separation equipment; Performing weight optimization on the multivariate linear regression equation based on a support vector machine to obtain an optimized regression equation; Calculating the deviation between the real-time collected data and the predicted value output by the optimized regression equation; When the deviation value exceeds the preset threshold, the parameter optimization process is triggered, and the optimized regression equation is optimized twice using a genetic algorithm to obtain the secondary optimized equipment parameters; Based on the secondary optimization equipment parameters, the equipment control unit is updated to achieve optimization of solid-liquid separation of manure and sewage.

4. The method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization according to claim 3 is characterized in that: The process of optimizing the aerobic composting process based on the optimized manure and sewage includes: Obtaining manure characteristic parameters and composting requirement parameters based on the optimized manure; Constructing a fuzzy control model based on the manure characteristic parameters and composting demand parameters; The temperature and moisture content of the compost material are collected in real time, and the temperature and moisture content of the compost material are input into the fuzzy control model to obtain fuzzy control quantities of turning frequency and ventilation volume; Defuzzification is performed on the fuzzy control quantity to obtain accurate control values ​​of the flipping frequency and the ventilation volume; Based on the precise control amount, the working parameters of the turning equipment and the ventilation equipment are automatically adjusted to realize dynamic regulation of the temperature and moisture content of the compost.

5. The method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization according to claim 4 is characterized in that: The process of optimizing the anaerobic digestion process includes: Determine the raw material composition data of aerobic composting manure and obtain fermentation condition parameters; Constructing a support vector regression model based on the raw material composition data and fermentation condition parameters; Obtaining a predicted value of methane yield based on the support vector regression model; Determine the optimal raw material composition and optimal fermentation condition parameters based on the predicted methane yield; Based on the optimal raw material composition and optimal fermentation condition parameters, an anaerobic fermentation experiment is performed to obtain an anaerobic fermentation residue; The physicochemical properties of the anaerobic fermentation residue are analyzed to obtain the carbon and nitrogen contents in the residue.

6. The method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization according to claim 5 is characterized in that: The process of assessing carbon and nitrogen emissions from the entire process includes: The emission factor method was used to calculate the carbon and nitrogen emissions from returning farmland to the field based on the optimization process of aerobic composting; The emission factor method is used to calculate the carbon and nitrogen emissions of biogas utilization based on the optimized process of anaerobic treatment; Based on the carbon and nitrogen emissions from returning the farmland to the field and the carbon and nitrogen emissions from utilizing the biogas, respectively determine the carbon and nitrogen emission reduction potential of returning the farmland to the field and the carbon and nitrogen emission reduction potential of utilizing the biogas; Based on the carbon and nitrogen emission reduction potential of returning farmland to farmland and the carbon and nitrogen emission reduction potential of biogas utilization, the carbon and nitrogen emissions of the whole process are evaluated and the optimal resource allocation plan is determined.

7. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the coordinated assessment method of carbon and nitrogen emissions from the entire process of rural human and animal manure utilization as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for collaboratively evaluating carbon and nitrogen emissions from the entire process of rural human and animal waste utilization as described in any one of claims 1-6 is implemented.

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