A livestock manure compost odor emission control system and method

Abstract

The present application relates to the technical field of odor emission reduction, in particular to a system and method for odor emission reduction control of livestock and poultry manure compost, in which, through real-time monitoring of gas during the composting process and multi-parameter correlation, the concentration changes of ammonia, hydrogen sulfide and volatile organic compounds and the temperature and humidity data in the composting environment are accurately obtained and digitally processed, providing high-precision basic data for subsequent analysis, through finite element analysis, the diffusion path, rate and coverage area of the gas in the composting body are calculated, the distribution characteristics of the gas concentration in space are quantified, the wind speed, air volume and layout of the ventilation system are optimized, the convolutional neural network is used for pattern recognition of the composting environment gas emission data, the composting frequency and the composting microenvironment are dynamically adjusted according to the gas concentration, the high-value area and the dominant source of gas emission are accurately positioned, and the precise control of the composting fermentation odor emission is realized, thereby improving the systematicness and refinement level of the composting odor treatment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of odor emission reduction, and in particular to an odor emission reduction control system and method for livestock manure composting. BACKGROUND

[0002] The technical field of odor emission reduction aims to effectively control and manage the odor generated in the process of production, life and waste treatment through scientific and systematic methods, reduce the harm of odor to the environment and human health, improve air quality, and protect the ecological environment.

[0003] The odor emission reduction control system for livestock manure composting aims to reduce the emission concentration of harmful gases such as ammonia, hydrogen sulfide and volatile organic compounds released during the composting fermentation process, reduce the pollution to the surrounding environment and the impact on residents' life, and improve the environmental friendliness and resource utilization effect of composting treatment. Through scientific odor management methods, effective control of odor emission is achieved, the ecological environment is protected, and harmless and resourceful treatment of livestock manure is promoted.

[0004] The prior art has problems of single monitoring data, insufficient diffusion path analysis and low positioning accuracy of emission sources in odor emission reduction. Odor monitoring is usually limited to single-point data or data collection at specific time nodes, lacking dynamic tracking of gas concentration in the whole cycle of composting process, and unable to provide complete concentration change trend and its correlation with environmental conditions. For gas diffusion analysis, the existing technology mainly relies on experience judgment or simple mathematical models, and fails to accurately calculate the diffusion path, diffusion rate and coverage range of gas in the composting body, resulting in lack of scientific basis for layout and operation adjustment of ventilation equipment. In the identification of odor sources, the existing technology usually obtains the emission hot spot area through manual detection or limited monitoring means, with low precision, and it is difficult to clearly identify the dominant emission area of high concentration gas and its influence range. These deficiencies make it difficult to quickly eliminate local high gas concentration in the composting fermentation process, and also limit the optimization space of ventilation system and turning operation, making the composting environmental management lack fine control means, increasing the negative impact on the surrounding environment, and reducing the quality and efficiency of composting resource treatment. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and to provide an odor emission reduction control system and method for livestock manure composting.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an odor emission reduction control system for livestock manure composting comprises:

[0007] The gas monitoring module: configure accurate gas sensors, record the concentration of ammonia, hydrogen sulfide and volatile organic compounds in the compost in real time, monitor the temperature and humidity at the same time, and convert the measured data into digital format to obtain primary gas data;

[0008] The gas dynamic analysis module: uses time series analysis to process the primary gas data to determine the periodicity and abnormal points of gas concentration changes, identifies key change nodes through trend analysis, and generates a dynamic gas model;

[0009] The gas diffusion control module: based on the dynamic gas model, uses finite element analysis to analyze the diffusion path and rate of gas in the compost, adjusts the layout and operating parameters of the ventilation system, optimizes the gas distribution through aerodynamic simulation, and generates a gas control strategy;

[0010] The operation parameter adjustment module: based on the gas control strategy, adjusts the composting frequency and ventilation conditions of the composting environment according to real-time monitoring data, and dynamically adjusts the microenvironment of the compost according to the gas concentration, and generates optimized operation parameters;

[0011] The odor source positioning module: based on the optimized operation parameters, uses convolutional neural networks to locate the specific area of the gas emission peak, including the specific area of the fermentation stage, identifies the dominant source of odor emission through pattern recognition, and generates odor source analysis results;

[0012] The odor emission reduction strategy module: according to the odor source analysis results, designs a compost structure adjustment scheme and ventilation improvement measures, reduces the emission of high-concentration gas through air flow optimization, implements specific gas management operations, and generates an odor emission reduction execution plan.

[0013] As a further scheme of the present application, the gas monitoring module includes a sensing data acquisition sub-module, a gas data conversion sub-module, and an environmental data integration sub-module, wherein:

[0014] The sensing data acquisition sub-module: based on the configured accurate gas sensors, real-time concentration monitoring of ammonia, hydrogen sulfide and volatile organic compounds is performed, the gas concentration signal output by the sensor is recorded, and temperature and humidity data are collected synchronously, multiple data acquisition and storage are completed at preset time intervals, and a gas and environmental sensing data set is generated;

[0015] The gas data conversion sub-module: based on the gas and environmental sensing data set, the gas concentration and environmental data are digitized, the collected data are signal decoded and quantized, and the data range is uniformly corrected, the concentration unit conversion and temperature and humidity are calibrated, and standardized gas and environmental data are obtained;

[0016] The environmental data integration submodule integrates time series of ammonia, hydrogen sulfide and volatile organic compound concentration data based on the standardized gas and environmental data, associates and matches temperature and humidity data with the gas concentration data at corresponding time nodes, and generates primary gas data.

[0017] As a further scheme of the present application, the gas dynamic analysis module comprises a data time series processing submodule, a key node identification submodule and a dynamic gas modeling submodule, wherein:

[0018] The data time series processing submodule segments time series of gas concentration and environmental data based on the primary gas data, smoothes and detects abnormal concentration values through data sorting and segmentation identification, extracts periodic changes and locates abnormal points of gas concentration and environmental data, and generates a set of gas concentration periodic and abnormal points.

[0019] The key node identification submodule calculates and analyzes the trend and slope of concentration changes based on the set of gas concentration periodic and abnormal points, locates key nodes by segmenting the nodes in the periodic changes and calculating the change amplitude and direction, filters and classifies periodic feature points, and obtains a key change node feature set.

[0020] The dynamic gas modeling submodule performs mathematical modeling of dynamic changes in gas concentration based on the key change node feature set, integrates key node features and periodic change data, and fits the concentration curve to generate and verify the gas dynamic model, and generates a dynamic gas model.

[0021] As a further scheme of the present application, the gas diffusion control module comprises a diffusion path analysis submodule, a ventilation parameter optimization submodule and a gas distribution simulation submodule, wherein:

[0022] The diffusion path analysis submodule analyzes the gas diffusion path in the compost based on the dynamic gas model, collects data on the gas diffusion area, analyzes the concentration distribution, determines the diffusion rate based on the concentration change data, draws a gas concentration distribution map in the diffusion range, and completes spatial demarcation and path annotation of the gas diffusion range to generate gas diffusion characteristic data.

[0023] The ventilation parameter optimization submodule optimizes the layout and parameters of the ventilation system based on the gas diffusion characteristic data, adjusts the ventilation port position by analyzing the influence of the ventilation port position and airflow direction on the diffusion path, sets the ventilation volume based on the gas diffusion rate data and verifies the rationality of the airflow direction, completes the optimization configuration of the ventilation conditions, and establishes a ventilation system optimization scheme.

[0024] The gas distribution simulation submodule: based on the ventilation system optimization scheme, the finite element analysis method is used to simulate the diffusion and distribution of gas in the compost body, and the air flow model is adjusted, the gas concentration distribution map is re-evaluated combined with the simulation results, and the parameter configuration is optimized to generate a gas regulation strategy.

[0025] As a further scheme of the present application, the finite element analysis method is according to the formula:

[0026]

[0027] Wherein: represents the three-dimensional concentration distribution of the compost odor in the compost body at a certain position and time represents the odor release rate, represents the environmental correction coefficient, represents the permeability coefficient of the compost material, represents the diffusion coefficient, represents the three-dimensional spatial coordinates in the compost body, represents the diffusion time, represents the velocity component of the air flow in each direction in the three-dimensional space.

[0028] As a further scheme of the present application, the operating parameter adjustment module includes a real-time monitoring and analysis submodule, a turning frequency adjustment submodule, and a microenvironment adjustment submodule, wherein:

[0029] Real-time monitoring and analysis submodule: based on the gas regulation strategy, real-time monitoring and analysis of gas concentration and environmental data is carried out, monitoring data is collected to analyze the dynamic change trend of temperature, humidity and gas concentration in the pile, and the correlation between gas concentration change and ventilation conditions is processed and corrected combined with sensor data collection data, to obtain real-time monitoring and control data set;

[0030] Turning frequency adjustment submodule: based on the real-time monitoring and control data set, the turning frequency of the compost body is adjusted, the gas concentration change rate and the gas distribution data in the time sequence in the real-time data are analyzed, the turning interval time is adjusted combined with the turning operation record and the new setting is recorded, and the turning frequency adjustment result is generated;

[0031] Microenvironment adjustment submodule: based on the turning frequency adjustment result, the gas concentration is balanced by adjusting the ventilation parameters in the composting area, and the microenvironment conditions in the composting area are optimized combined with the humidity adjustment data to generate optimized operation parameters.

[0032] As a further scheme of the present application, the odor source positioning module includes a regional emission monitoring submodule, a dominant source identification submodule, and an odor characteristic analysis submodule, wherein:

[0033] ​A regional emission monitoring sub-module; based on the optimized operation parameters, the concentration data of ammonia, hydrogen sulfide and volatile organic compounds in the compost fermentation region are collected through a convolutional neural network, the emission concentration changes are recorded combined with time nodes, and the emission peak period is divided, the concentration change data of different regions of the compost pile are analyzed for spatial distribution, and the classification and range of the emission region are calibrated to generate emission peak region data;

[0034] A dominant source identification sub-module; based on the emission peak region data, the dominant odor source in the composting region is analyzed, the concentration of the gas components collected in the emission region is classified to calculate the concentration proportion of each component, the component proportion in different regions is compared to confirm the dominant emission gas type, the high emission point in the region is located through the concentration difference, the dominant odor source region is identified combined with the distribution characteristics of the component concentration and the high emission point in the region, and dominant odor source data is generated;

[0035] An odor characteristic analysis sub-module; based on the dominant odor source data, the odor emission mode and characteristics are extracted, the time sequence distribution characteristics of the emission region are extracted by analyzing the component change law of the dominant odor source region combined with the odor diffusion trajectory in the fermentation stage, the regional emission mode is established combined with the component change characteristics, and the spatial distribution characteristics corresponding to each mode are marked, the mode characteristic labeling of the dominant odor source is completed, and odor source analysis results are generated.

[0036] As a further scheme of the present application, the convolutional neural network is according to the formula:

[0037]

[0038] Wherein: is the node in the composting region at time , is the weight coefficient of the monitoring point , is the actual measured pollutant concentration of the monitoring point at time , is the spatial distance between the node and the monitoring point , is the distance attenuation coefficient and in the original formula, is the environmental correction function, is the fermentation intensity, is the temperature, is the humidity, is the total number of monitoring points.

[0039] As a further scheme of the present application, the odor emission reduction strategy module comprises a structure optimization adjustment submodule, a ventilation condition improvement submodule, and a gas management execution submodule, wherein:

[0040] The structure optimization adjustment submodule optimizes and adjusts the structure of the compost pile according to the odor source analysis result, rearranges the compost pile levels by analyzing the emission path of the high-emission area of the compost pile and combining the area characteristics, optimizes the airflow channel of the high-emission area into a short-distance emission path, and arranges gas diffusion control holes in the emission path to complete the optimization adjustment of the structure of the compost pile, and generates compost pile structure optimization data;

[0041] The ventilation condition improvement submodule adjusts the ventilation condition of the high-emission area based on the compost pile structure optimization data, resets the area and spacing of the ventilation opening of the high-emission area by combining the optimization data, adjusts the running frequency and airflow direction of the ventilation equipment, and completes the optimization condition configuration by increasing and reducing the ventilation channel pressure to match the gas concentration change, and generates a ventilation condition improvement scheme;

[0042] The gas management execution submodule performs dynamic emission management operation of the compost odor based on the ventilation condition improvement scheme, adjusts the gas concentration in the high-emission area in real time by combining the ventilation condition improvement scheme, collects and drains the gas in the emission peak area, adjusts the regional emission dynamics by real-time control of the running parameters of the ventilation equipment, completes the coordination of gas collection and emission, and generates an odor emission reduction execution scheme.

[0043] An odor emission reduction control method for livestock and poultry manure compost, which is executed based on the above-mentioned odor emission reduction control system for livestock and poultry manure compost, comprising the following steps:

[0044] Step one: send the compost material into the fermentation area, insert a gas sensor in the compost pile to monitor the ammonia, hydrogen sulfide and volatile organic compound concentrations, record the temperature and humidity values of the compost area at the same time, and sequentially collect and digitize the concentration values and environmental data of each time node to generate primary gas data;

[0045] Step two: arrange the gas concentration in time sequence based on the primary gas data, calculate the concentration change rate of each group of data, mark the concentration sudden increase point and periodic change interval, integrate the change results according to the time nodes to generate a dynamic gas model;

[0046] Step three: combine the dynamic gas model with the ventilation airflow path of the compost area, calculate the gas diffusion path length and diffusion rate, and generate a compost gas diffusion path distribution characteristic table through correlation analysis of the concentration value and the path position;

[0047] Step four: based on the compost gas diffusion path distribution characteristics table, adjust the ventilation parameters of the compost area, including wind speed value, ventilation pipe layout and air flow distribution state, optimize the operation mode of the existing ventilation system, and generate a gas control strategy;

[0048] Step five: based on the gas control strategy, combined with the composting frequency, temperature and humidity data in the composting area, analyze the dynamic distribution of compost gas concentration, mark the high value point of odor concentration, and locate the emission peak area, and generate a compost odor emission high value point distribution map;

[0049] Step six: based on the compost odor emission high value point distribution map, by adjusting the height of the composting area, the structure of the composting material and the direction of the exhaust air passage, the ventilation conditions in the composting area are optimized, the gas distribution and emission path are dynamically controlled, and the odor emission execution scheme is generated.

[0050] As a further scheme of the present application, a plurality of gas sensors are added to the composting body in a multi-layer distribution, and the distribution positions are specifically the upper, middle and lower parts of the composting body, so as to ensure that the monitored concentration data fully cover the compost gas diffusion process;

[0051] The optimization of the ventilation parameters specifically includes setting the ventilation time length to fifteen to twenty minutes per hour, and the wind speed range to one to one point five meters per second, so as to ensure uniform air flow distribution in the high value point area and reduce the ventilation operation cost;

[0052] The change rate of the gas concentration sudden increase point in the dynamic gas model is calculated by dividing the concentration change value by the time interval, and the time interval is not more than five minutes;

[0053] The height of the composting body is adjusted to one meter two to one meter eight, so as to control the gas concentration distribution and reduce the odor emission concentration;

[0054] When the monitoring result shows that the diffusion path length is relatively long, the density of the composting body is increased to inhibit the gas diffusion.

[0055] As a further scheme of the present application, the composting area is divided into fermentation zone and maturation zone, ventilation control is strengthened in the fermentation zone, ventilation amount is reduced in the maturation zone, and the ventilation amount ratio is set to three to one;

[0056] A plurality of parallel exhaust air passages are arranged, the distance between each passage is not less than two meters, the diameter of the passage is fifteen to twenty-five centimeters, the composting gas is quickly discharged, and the gas concentration is reduced;

[0057] Odor adsorption materials, including biochar and zeolite, are added in the high value point area, and the specific addition amount is five to eight percent of the total weight of the composting material;

[0058] During the odor concentration peak period, combined with the real-time monitoring data, the ventilation frequency, wind speed and composting body turning interval are automatically adjusted, and a real-time feedback control mechanism is established.

[0059] Compared with the prior art, the application has the advantages and positive effects that:

[0060] 1. In the application, the concentration changes of ammonia, hydrogen sulfide and volatile organic compounds and the temperature and humidity data in the composting environment are accurately obtained through real-time monitoring of gases during composting and multi-parameter correlation, and are digitally processed, thereby providing high-precision basic data for subsequent analysis;

[0061] 2. In the application, the diffusion path, rate and coverage area of the gas in the composting body are calculated through finite element analysis, the spatial distribution characteristics of the gas concentration are quantified, and the wind speed, air volume and layout of the ventilation system are optimized, so that the air flow in the composting area is more uniform, and the local aggregation and diffusion range of high-concentration gas are reduced;

[0062] 3. In the application, the convolutional neural network is used for pattern recognition of the composting environment gas emission data, the turning frequency and composting microenvironment are dynamically adjusted combined with the gas concentration, the high-value area and dominant source of gas emission are accurately positioned, the precise control of the composting fermentation odor emission is realized, and the systematicness and refinement level of the composting odor treatment are improved. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 is a system flowchart of the application;

[0064] Figure 2 is a system framework schematic diagram of the application;

[0065] Figure 3 is a method step schematic diagram of the application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the application more clear, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0067] Please refer to Figure 1 , the application provides a technical scheme: a livestock and poultry manure composting odor emission reduction control system comprises:

[0068] The gas monitoring module is configured with a precise gas sensor to record the concentrations of ammonia, hydrogen sulfide and volatile organic compounds in the compost in real time, monitor the temperature and humidity at the same time, and convert the measurement data into digital format to obtain primary gas data;

[0069] The gas dynamic analysis module processes the primary gas data by time series analysis to determine the periodicity and abnormal points of the gas concentration change, identifies the key change nodes through trend analysis, and generates a dynamic gas model;

[0070] Gas diffusion control module: based on dynamic gas model, using finite element analysis method, analyzing the diffusion path and rate of gas in the compost body, adjusting the layout and operating parameters of the ventilation system, optimizing the gas distribution through aerodynamic simulation, generating gas control strategy;

[0071] Operating parameter adjustment module: based on the gas control strategy, adjusting the composting frequency and ventilation conditions of the composting environment according to real-time monitoring data, and dynamically adjusting the microenvironment of the compost body according to the gas concentration, generating optimized operating parameters;

[0072] Odor source positioning module: based on the optimized operating parameters, using convolutional neural network to locate the specific area of gas emission peak, including the specific area of fermentation stage, identifying the dominant source of odor emission through pattern recognition, generating odor source analysis results;

[0073] Odor emission reduction strategy module: according to the odor source analysis results, designing compost structure adjustment scheme and ventilation improvement measures, reducing high concentration gas emission through air flow optimization, implementing specific gas management operation, generating odor emission reduction execution scheme.

[0074] Please refer to Figure 2 , the gas monitoring module includes sensing data acquisition submodule, gas data conversion submodule and environmental data integration submodule, wherein:

[0075] Sensing data acquisition submodule: based on the configured precise gas sensor, real-time concentration monitoring of ammonia, hydrogen sulfide and volatile organic compounds, through recording the gas concentration signal output by the sensor and synchronously collecting temperature and humidity data, completing the acquisition and storage of multiple data at preset time intervals, generating gas and environmental sensing data set;

[0076] Gas data conversion submodule: based on the gas and environmental sensing data set, digital conversion of gas concentration and environmental data, signal decoding and quantization processing of collected data, and consistency correction of data range, concentration unit conversion and temperature and humidity calibration, obtaining standardized gas and environmental data;

[0077] Environmental data integration submodule: based on the standardized gas and environmental data, time series integration of ammonia, hydrogen sulfide and volatile organic compound concentration data, correlation and matching of temperature and humidity data with gas concentration data at corresponding time nodes, generating primary gas data;

[0078] The sensing data acquisition submodule: based on the configured precise gas sensor, using the gas electrochemical detection method, through the voltage signal of the gas detection unit to determine the real-time concentration of ammonia, hydrogen sulfide and volatile organic compounds, the electrode potential in the electrochemical detection unit is set to 0.7-1.0 volts, the signal response time is set to 100 milliseconds, the current signal collected by the sensor is combined with the temperature compensation coefficient to complete the gas concentration signal output, at the same time, the digital temperature and humidity sensor is used to synchronously acquire the temperature and humidity data in the compost environment, the acquisition frequency is set to 1 time per second, and the acquisition and storage of all sensor data are completed at the preset time interval, and the gas and environmental sensing data set is generated;

[0079] The gas data conversion submodule: based on the gas and environmental sensing data set, using signal decoding and quantization processing method to digitize the gas concentration and environmental data, using linear interpolation method to complete the missing data for ammonia, hydrogen sulfide and volatile organic compounds, using least square method to fit the signal value output by the sensor and the calibration curve, correcting the signal after fitting and unifying the concentration unit to ppm, at the same time, the temperature and humidity data are corrected by dimension consistency correction method, the temperature is unified to Celsius, and the humidity unit is unified to relative humidity percentage, finally the standardized gas and environmental data are generated;

[0080] The environmental data integration submodule: based on the standardized gas and environmental data, using time series data integration method, the gas concentration data is sorted by time node, the concentration data of ammonia, hydrogen sulfide and volatile organic compounds is interpolated by time interval, the concentration change rate is calculated by first-order difference method and inserted into the corresponding time node, the temperature and humidity data are matched and associated with the corresponding gas concentration by dynamic window allocation method, the window time interval is set to 10 minutes, the temperature and humidity data are mapped to the gas concentration time series, and the complete time series data table is formed, and the primary gas data is generated.

[0081] Please refer to Figure 2 , the gas dynamic analysis module includes data time series processing submodule, key node identification submodule and dynamic gas modeling submodule, wherein:

[0082] The data time series processing submodule: based on the primary gas data, the time series of gas concentration and environmental data is segmented, the data is smoothed and the abnormal concentration value is detected by data sorting and segmentation identification, the periodic change extraction and abnormal point positioning of gas concentration and environmental data are completed, and the gas concentration cycle and abnormal point set are generated;

[0083] Key node identification submodule: based on the gas concentration cycle and the abnormal point set, the trend calculation and slope analysis of the concentration change are carried out, the key node is located by segmenting the nodes in the cycle change and calculating the change amplitude and direction, the cycle feature point screening and classification are completed, and the key change node feature set is obtained;

[0084] Dynamic gas modeling submodule: based on the key change node feature set, the mathematical modeling of the dynamic change of the gas concentration is carried out, the generation and checking of the gas dynamic model are completed by integrating the key node features and the cycle change data and fitting the concentration curve, and the dynamic gas model is generated;

[0085] Data time series processing submodule: based on the primary gas data, the sliding average algorithm is used to segment the gas concentration data and the environmental data in time series, the sliding window size is set to 10 minutes and the sliding window is stepped by every 5 minutes, the segmented data is sorted in time sequence, the sequence data is smoothed by calculating the mean value of each segment of data, and the abnormal concentration value is detected by using the interquartile range method, the difference between the first quartile and the third quartile of the data distribution is calculated, the abnormal detection threshold is set to 1.5 times IQR, the abnormal data is marked and removed by comparing the deviation of each data from the threshold, the cycle change extraction and abnormal point positioning of the gas concentration and environmental data are completed, and the gas concentration cycle and abnormal point set are generated;

[0086] Key node identification submodule: based on the gas concentration cycle and the abnormal point set, the local least square regression method is used to calculate the trend of the time series data of the concentration change, the trend line is fitted for the data points in the time window, and the slope is calculated, the data points with alternating positive and negative slope change directions are selected according to the set threshold, the nodes in the cycle change are segmented by segmentation, the numerical difference of each segment is calculated by using the change amplitude calculation formula, the maximum change amplitude and direction in the segment are marked, all nodes are summarized and selected, and the cycle feature points are classified according to the change amplitude and direction, and the key change node feature set is generated.

[0087] Dynamic gas modeling submodule: based on the key change node feature set, the polynomial fitting method is used to model the dynamic change of the gas concentration, the least square method is used to fit the time series data, the fitting order is set to three, the key node features are used as the control points of fitting, and the cycle change data is interpolated in time sequence, the Lagrange interpolation method is used to calculate the weight of the interpolation points and the control points to generate the concentration curve, the fitting curve is compared with the actual concentration data to calculate the error range, the error standard is set to 2% of the concentration value fluctuation range, and finally the fitting curve is adjusted and checked to generate the dynamic gas model.

[0088] Please refer to Figure 2The gas diffusion control module comprises a diffusion path analysis submodule, a ventilation parameter optimization submodule, and a gas distribution simulation submodule, wherein:

[0089] The diffusion path analysis submodule: based on the dynamic gas model, the gas diffusion path in the compost body is analyzed, the concentration distribution is analyzed by collecting data of the gas diffusion area, the diffusion rate is determined by analyzing the concentration change data, the gas concentration distribution map in the diffusion range is drawn, the spatial demarcation and path marking of the gas diffusion range are completed, and the gas diffusion characteristic data is generated;

[0090] The ventilation parameter optimization submodule: based on the gas diffusion characteristic data, the layout and parameters of the ventilation system are optimized, the position of the ventilation port is adjusted by analyzing the influence of the position of the ventilation port and the airflow direction on the diffusion path, the ventilation volume is set and the rationality of the airflow direction is verified by combining the gas diffusion rate data, the optimization configuration of the ventilation condition is completed, and the ventilation system optimization scheme is established;

[0091] The gas distribution simulation submodule: based on the ventilation system optimization scheme, the finite element analysis method is used to simulate the diffusion and distribution of gas in the compost body, and the airflow model is adjusted, the gas concentration distribution map is re-evaluated and the parameter configuration is optimized combined with the simulation results, and the gas control strategy is generated;

[0092] The diffusion path analysis submodule: based on the dynamic gas model, the diffusion equation analysis method is used to analyze the gas diffusion path in the compost body, the concentration data collected by the sensor in the compost body is grid processed according to the spatial coordinates, the compost body is divided into equal-interval grid units, the gas concentration change rate of each unit is calculated using Fick's diffusion law, the gas concentration gradient in the diffusion area is analyzed and the diffusion rate is calculated combined with the time step, the time step is set to 1 minute to capture the dynamic changes of gas diffusion, the concentration gradient in different regions is calculated by interpolation, the gas concentration distribution map in the diffusion range is drawn, the spatial concentration distribution is threshold divided, the gas concentration diffusion path and boundary range are marked, and the gas diffusion characteristic data is generated;

[0093] The ventilation parameter optimization submodule: based on the gas diffusion characteristic data, the layout and parameters of the ventilation port are optimized using the ventilation system simulation analysis method, the influence of the position of the ventilation port and the airflow direction on the gas diffusion path is analyzed, the spatial coordinates of the existing ventilation port are adjusted, the minimum distance between the ventilation port and the diffusion boundary is set to 0.5 meters, and the deflection angle range of the airflow direction is adjusted to not more than 10 degrees according to the center line direction of the diffusion path, the ventilation volume is calculated combined with the diffusion rate, the ventilation volume parameter is set to the volume of the diffusion area multiplied by the air change frequency, the relationship between the airflow direction and the concentration distribution is simulated by simulation, the rationality of the existing ventilation volume and direction setting is verified, the ventilation condition is optimized, and the ventilation system optimization scheme is established;

[0094] Gas distribution simulation submodule: Based on the ventilation system optimization scheme, the finite element method is used to simulate the gas diffusion and distribution within the compost body. By dividing the compost body space into finite grid cells, the gas concentration is input as a node variable into the simulation model. The continuity equation is used to calculate the gas diffusion rate and diffusion amount within the time step. The time step is set to 1 minute and the spatial step is set to 0.1 meters. By iteratively updating the simulated gas diffusion path and concentration distribution, the simulated airflow model is adjusted. The airflow direction is fitted to the compost gas diffusion boundary. Combined with the simulation results, the gas concentration distribution map is redrawn, the parameter configuration of the ventilation system is further optimized, and a gas control strategy is generated.

[0095] Please see Figure 2 Finite element analysis method, according to the formula:

[0096]

[0097] in: This indicates the location of the compost odor within the compost mass. and time The three-dimensional concentration distribution Indicates the rate of odor release. Represents the environmental correction factor. This indicates the permeability coefficient of the composting material. Indicates the diffusion coefficient. Represents the three-dimensional spatial coordinates within the compost body. Indicates diffusion time. Represents the velocity components of airflow in each direction in three-dimensional space;

[0098] Execution process: First, based on the odor generation rate during composting... The diffusion coefficient was determined by considering the internal environmental conditions of the compost body. and environmental correction factor The velocity components of airflow in the three-dimensional directions of the compost body were calculated through the optimization scheme of the ventilation system. and the permeability coefficient of composting materials Next, the compost body is modeled as a three-dimensional space, and its coordinates are discretized. And based on diffusion time The process of odor diffusion within the compost body is simulated. Finally, the formula is discretized and iteratively solved to generate an odor concentration distribution map within the compost body. Based on the simulation results, the ventilation system configuration and compost parameters are optimized to generate an odor reduction and control strategy, thereby achieving precise control and emission reduction of compost odor.

[0099] Please see Figure 2The operation parameter adjustment module comprises a real-time monitoring and analysis submodule, a turning frequency adjustment submodule, and a microenvironment adjustment submodule, wherein:

[0100] The real-time monitoring and analysis submodule: based on the gas regulation strategy, real-time monitoring and analysis of gas concentration and environmental data are performed, dynamic change trends of temperature and humidity and gas concentration in the pile body are collected and analyzed, correlation between gas concentration change and ventilation conditions is processed and corrected based on sensor collected data, and real-time monitoring and control data sets are obtained;

[0101] The turning frequency adjustment submodule: based on the real-time monitoring and control data sets, the turning frequency of the compost pile is adjusted, the gas concentration change rate and the gas distribution data in the time series in the real-time data are analyzed, the turning interval time is adjusted in combination with the turning operation record and the new setting is recorded, and the turning frequency adjustment result is generated;

[0102] The microenvironment adjustment submodule: based on the turning frequency adjustment result, the gas concentration is balanced by adjusting the ventilation parameters in the composting area, and the microenvironment conditions in the composting area are optimized in combination with the humidity adjustment data, and the optimized operation parameters are generated;

[0103] The real-time monitoring and analysis submodule: based on the gas regulation strategy, dynamic data collection method is used to monitor the gas concentration and environmental data in the composting area in real time, the concentration data of ammonia, hydrogen sulfide and volatile organic compounds are collected by the sensor array, the sampling frequency is set to 1 time per minute, the temperature and humidity data are collected synchronously, the collected multiple data are preprocessed by dynamic weighted average algorithm, the weighted coefficient is set to 0.7 for concentration data and 0.3 for temperature and humidity data, the smoothing processing of multiple parameter data in time series is completed, the correlation between gas concentration change and ventilation conditions is processed by using Pearson correlation analysis method, the correlation threshold is set to 0.5, the deviation value in the correlation data is corrected, and the real-time monitoring and control data sets are generated;

[0104] The turning frequency adjustment submodule: based on the real-time monitoring and control data sets, the trend decomposition algorithm is used to calculate the gas concentration change rate, the time series window size is set to 1 hour, the concentration change value in the sequence is decomposed into long-term trend, periodic component and random fluctuation, the amplitude of the periodic component change is fitted in combination with the turning interval time, the turning interval time is recalculated based on the historical turning operation record, the turning time is controlled within the range of 30 to 60 minutes, the adjustment result is recorded, the turning time point is associated with the concentration change trend, and the turning frequency adjustment result is generated;

[0105] Microenvironment adjustment submodule: based on the turning frequency adjustment result, a multi-objective optimization algorithm is used to dynamically adjust the ventilation parameters in the composting area, taking the wind speed, air volume and ventilation time of the ventilation system as optimization variables, setting the ventilation speed to 0.5-2.0 m / s, the ventilation volume to 5%-10% of the area volume, and the ventilation time to 10 minutes, searching for the optimal combination of ventilation parameters through a particle swarm optimization algorithm, interpolating the humidity data according to the regional distribution, adjusting the humidity to the range of 40%-60%, and dynamically updating the microenvironment conditions according to the humidity and gas concentration to generate optimized operation parameters.

[0106] Please refer to Figure 2 The odor source positioning module includes a regional emission monitoring submodule, a dominant source identification submodule, and an odor characteristic analysis submodule, wherein:

[0107] The regional emission monitoring submodule; based on the optimized operation parameters, the concentration data of ammonia, hydrogen sulfide and volatile organic compounds in the composting fermentation area are collected through a convolutional neural network, the emission concentration changes are recorded in combination with the time nodes and the emission peak period is divided, the concentration change data of different regions of the pile body are analyzed in spatial distribution, and the classification and range of the emission region are calibrated to generate emission peak region data;

[0108] The dominant source identification submodule; based on the emission peak region data, the dominant odor source in the composting area is analyzed, the concentration proportion of each component is calculated by classifying the gas component concentration collected in the emission region, the dominant emission gas type is confirmed by comparing the component proportion in different regions, the high emission point in the region is located by the concentration difference, the dominant odor source region is identified by combining the component concentration and the distribution characteristics of the high emission point in the region, and the dominant odor source data is generated;

[0109] The odor characteristic analysis submodule; based on the dominant odor source data, the odor emission mode and characteristics are extracted, the time sequence distribution characteristics of the emission region are extracted by analyzing the component change law of the dominant odor source region combined with the odor diffusion trajectory in the fermentation stage, the regional emission mode is established combined with the component change characteristics, and the spatial distribution characteristics corresponding to each mode are marked, the mode characteristic labeling of the dominant odor source is completed, and the odor source analysis result is generated;

[0110] Regional emission monitoring sub-module: based on the optimized operation parameters, the gas concentration of the compost fermentation area is collected and analyzed by using convolutional neural network, a three-layer convolutional neural network model is built, the real-time concentration data of ammonia, hydrogen sulfide and volatile organic compounds are taken as input, the number of convolution kernels in the first layer is 32, the convolution kernel size is set to 3*3, the activation function uses ReLU, the step is 1, the pooling layer uses maximum pooling, the pooling window is 2*2, the collected gas concentration data is feature extracted, the number of convolution kernels in the second layer is 64 and the same setting is used, the time series variation characteristics of the data are deeply extracted, the last layer is a fully connected layer, the output distribution concentration value and the classification result of the time node are obtained, the emission peak period is divided by comparing the change trend of the gas concentration in different time periods, the concentration change data of different regions is analyzed, the classification and range of the emission area are completed by marking the spatial position, and the emission peak area data is generated;

[0111] Dominant source identification sub-module: based on the emission peak area data, the dominant odor source in the composting area is analyzed by using the component concentration classification algorithm, the ammonia, hydrogen sulfide and volatile organic compound concentration data collected in the emission area are classified and processed, the concentration proportion of each component is calculated, the threshold range is set by using the component concentration classification method, ammonia is set to 30% to 60%, hydrogen sulfide is set to 10% to 30%, and volatile organic compounds are set to 20% to 50%, the dominant emission gas type is confirmed by comparing the component proportion data in different regions, the specific position of the high emission point is determined by comparing the gas concentration difference by using the concentration difference calculation method, the distribution data of the high emission point is combined with the regional component concentration to analyze, the identification of the dominant odor source area is completed, and the dominant odor source data is generated;

[0112] Odor characteristic analysis sub-module: based on the dominant odor source data, the odor emission mode and characteristics are extracted by using the time series distribution characteristic extraction algorithm, the gas components in the dominant odor source area are analyzed by time series, the odor concentration data in the fermentation stage is divided into multiple time windows, the window size is set to 1 hour, the component change rule is extracted by calculating the variance value of the component change in the window, the change direction of the odor diffusion trajectory is marked combined with the diffusion path data in the fermentation stage, the emission concentration in the region is clustered according to time period, the feature points of the clustering result are fitted to generate the time series distribution characteristics of the emission area, the region mode in different emission stages is classified and labeled combined with the component change trend, the mode characteristics of the dominant odor source are labeled, and the odor source analysis result is generated.

[0113] Please refer to Figure 2 , the convolutional neural network is according to the formula:

[0114]

[0115] wherein: is the node in the composting area at time is the pollutant concentration, is the weight coefficient of the monitoring point , is the actual measured pollutant concentration of the monitoring point at time , is the spatial distance between the node and the monitoring point , is the distance attenuation coefficient, similar to in the original formula, is the environmental correction function, is the fermentation intensity, is the temperature, is the humidity, is the total number of monitoring points;

[0116] Execution process: First, collect the pollutant concentration data of the monitoring points in the composting fermentation area , including the actual concentration values of ammonia, hydrogen sulfide and volatile organic compounds, calculate the spatial distance between each monitoring point and the node to be analyzed , decompose the three-dimensional spatial structure of the composting area through geometric modeling to determine the specific value, then perform error analysis on the monitoring equipment, fit the weight coefficient based on the historical data distribution of the equipment, reflecting the influence of the measurement accuracy of different equipment on the overall concentration distribution, then measure the environmental parameters of the composting area, including the fermentation intensity , temperature and humidity of the area where the monitoring point is located, adjust the influence of environmental factors on the diffusion process of pollutants by fitting the correction function , the correction function combines experimental data to fit parameters to ensure accurate reflection of the comprehensive effect of humidity, temperature and fermentation intensity on diffusion, finally, substitute all parameters into the formula to calculate the pollutant concentration of each node in the composting area , generate the pollutant concentration distribution map of the composting area, mark the emission peak area and period, provide scientific basis for optimizing the ventilation configuration of the composting and developing odor emission reduction strategies.

[0117] Please refer to Figure 2 , the odor emission reduction strategy module includes a structure optimization adjustment submodule, a ventilation condition improvement submodule and a gas management execution submodule, wherein:

[0118] The structure optimization adjustment submodule adjusts the structure of the compost pile based on the odor source analysis results. The high-emission area of the compost pile is analyzed using a short-path analysis algorithm. The gas diffusion path in the high-emission area is segmented, and the length of each segment is calculated and sorted. The long paths with a length exceeding 10% of the average value are marked as optimization objects. The compost pile is divided into three layers based on the regional characteristics analysis. The airflow channel in the middle layer is adjusted to shorten the original airflow path to 70% of the average distance. Diffusion control holes are arranged to optimize the local diffusion range of the gas. The diameter of the diffusion control holes is set to 10-15 cm, and the hole spacing is set to 50 cm. The compost pile structure optimization data is generated.

[0119] The ventilation condition improvement submodule adjusts the ventilation conditions of the high-emission area based on the compost pile structure optimization data. The area and spacing of the ventilation openings in the high-emission area are reconfigured based on the optimization data. The operating frequency and airflow direction of the ventilation equipment are adjusted. The ventilation channel pressure is increased or decreased to match the gas concentration changes. The optimized condition configuration is completed, and the ventilation condition improvement scheme is generated.

[0120] The gas management execution submodule performs dynamic emission management operations for the compost odor based on the ventilation condition improvement scheme. The gas concentration in the high-emission area is real-time regulated based on the ventilation condition improvement scheme. The gas in the emission peak area is collected and drained. The regional emission dynamics are adjusted by real-time control of the ventilation equipment operating parameters. The gas collection and emission coordination is completed, and the odor emission reduction execution scheme is generated.

[0121] The structure optimization adjustment submodule: based on the odor source analysis results, a short-path analysis algorithm is used to analyze the emission path of the high-emission area of the compost pile. The gas diffusion path in the high-emission area is segmented, and the length of each segment is calculated and sorted. The long paths with a length exceeding 10% of the average value are marked as optimization objects. The compost pile is divided into three layers based on the regional characteristics analysis. The airflow channel in the middle layer is adjusted to shorten the original airflow path to 70% of the average distance. Diffusion control holes are arranged to optimize the local diffusion range of the gas. The diameter of the diffusion control holes is set to 10-15 cm, and the hole spacing is set to 50 cm. The compost pile structure optimization data is generated.

[0122] The ventilation condition improvement submodule: based on the compost pile structure optimization data, a pressure distribution analysis method is used to adjust the ventilation conditions of the high-emission area. The area of the ventilation opening is set to 20-30 cm. The spacing of the ventilation opening is dynamically adjusted based on the diffusion concentration gradient in the high-emission area. The ventilation opening spacing is set to 1-2 m. The frequency range of the ventilation equipment operating frequency is set to 5-10 times / hour. The deflection angle of the airflow direction is corrected based on the regional pressure variation analysis. The deflection angle is set to 5-15 degrees. The operating parameters of the ventilation channel are dynamically adjusted based on the matching calculation of the gas concentration and pressure distribution data in the high-emission area. The optimized configuration of the ventilation conditions is completed, and the ventilation condition improvement scheme is generated.

[0123] The gas management execution submodule: based on the ventilation condition improvement scheme, the dynamic emission of the compost odor is managed by using a real-time control algorithm. The gas concentration data in the emission peak area are collected in real time, and the operation mode of the ventilation equipment is adjusted by combining the parameter configuration of the ventilation condition improvement. The wind speed of the ventilation equipment is set to 0.5-2.5 m / s, and the opening and closing time of the equipment operation is adjusted in real time according to the gas concentration. The opening and closing time range is set to 10-20 minutes each time. The gas concentration distribution is dynamically monitored through the diffusion path drainage hole set in the high emission area. The data with emission concentration exceeding the set threshold are marked as control points, and the parameter adjustment range of the ventilation equipment is contracted. At the same time, the high-concentration gas is discharged through the drainage path, the balance adjustment of the gas concentration in the area is completed, and the odor emission reduction execution scheme is generated.

[0124] Please refer to Figure 3 A method for controlling odor emission reduction of livestock and poultry manure compost, comprising the following steps:

[0125] Step one: send the compost material into the fermentation area, insert the gas sensor in the compost body, monitor the ammonia, hydrogen sulfide and volatile organic compound concentration, record the temperature and humidity value of the compost area, collect and digitize the concentration value and environmental data of each time node in turn, and generate primary gas data;

[0126] Step two: based on the primary gas data, arrange the gas concentration in time sequence, calculate the concentration change rate of each group of data, mark the concentration sudden increase point and periodic change interval, integrate the change results according to the time node, and generate a dynamic gas model;

[0127] Step three: combine the dynamic gas model with the ventilation airflow path of the compost area, calculate the gas diffusion path length and diffusion rate, generate a compost gas diffusion path distribution characteristic table through the correlation analysis of concentration value and path position;

[0128] Step four: based on the compost gas diffusion path distribution characteristic table, adjust the ventilation parameters of the compost area, including wind speed value, ventilation pipeline layout and airflow distribution state, optimize the operation mode of the existing ventilation system, and generate a gas control strategy;

[0129] Step five: based on the gas control strategy, combine the composting area turning frequency, temperature and humidity data, analyze the dynamic distribution of compost gas concentration, mark the high value point of odor concentration, and locate the emission peak area, and generate a compost odor emission high value point distribution map;

[0130] Step six: based on the compost odor emission high value point distribution map, adjust the compost height, compost structure and exhaust passage airflow direction in the compost area, optimize the ventilation condition in the compost area, dynamically control the gas distribution and emission path, and generate an odor emission reduction execution scheme.

[0131] The gas sensor is added to the compost body in a multi-layer distribution, and the distribution positions are specifically the upper part, the middle part and the lower part of the compost body, so as to ensure that the concentration data monitored fully cover the gas diffusion process of the compost;

[0132] The optimization of the ventilation parameter specifically includes setting the ventilation time length to fifteen minutes to twenty minutes per hour, and the wind speed range to one meter to one point five meters per second, so as to ensure that the airflow distribution is uniform in the high value point area, and to reduce the ventilation operation cost;

[0133] The change rate of the gas concentration sudden increase point in the dynamic gas model is calculated by dividing the concentration change value by the time interval, and the time interval is not more than five minutes;

[0134] The compost body height is adjusted to one meter two to one meter eight, so as to control the gas concentration distribution and reduce the odor emission concentration;

[0135] When the monitoring result shows that the diffusion path length is long, the compost body density is increased to inhibit the gas diffusion.

[0136] The compost area is divided into a fermentation zone and a maturation zone, ventilation regulation is strengthened in the fermentation zone, and the ventilation amount is reduced in the maturation zone, and the ventilation amount ratio is set to three to one;

[0137] A plurality of parallel exhaust channels are arranged, the distance between each channel is not less than two meters, and the channel diameter is fifteen centimeters to twenty five centimeters, so as to quickly exhaust the compost gas and reduce the gas concentration;

[0138] Odor adsorption materials including biochar and zeolite are added to the high value point area, and the specific addition amount is five percent to eight percent of the total weight of the compost material;

[0139] During the peak period of the odor concentration, the ventilation frequency, the wind speed and the compost body turning interval are automatically adjusted in combination with the real-time monitoring data, and a real-time feedback control mechanism is established.

[0140] The above is only a preferred embodiment of the present application, and does not limit the form of the present application, and any person skilled in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments still belongs to the protection scope of the technical solution of the present application.

Claims

1. An odor reduction and control system for livestock and poultry manure composting, characterized in that, The system includes: Gas monitoring module: Equipped with a precise gas sensor, it records the concentrations of ammonia, hydrogen sulfide, and volatile organic compounds in the compost in real time, while also monitoring temperature and humidity, and converting the measurement data into digital format to obtain primary gas data; Gas dynamic analysis module: The primary gas data is processed using time series analysis to determine the periodicity and outliers of gas concentration changes, key change nodes are identified through trend analysis, and a dynamic gas model is generated. Gas diffusion control module: Based on the dynamic gas model, the finite element analysis method is used to analyze the diffusion path and rate of gas in the compost body, adjust the layout and operating parameters of the ventilation system, optimize the gas distribution through aerodynamic simulation, and generate a gas control strategy. Operating parameter adjustment module: Based on the gas control strategy, the turning frequency and ventilation conditions of the composting environment are adjusted according to real-time monitoring data, and the microenvironment of the compost pile is dynamically adjusted according to the gas concentration to generate optimized operating parameters; Odor Source Location Module: Based on the optimized operating parameters, a convolutional neural network is used to locate the specific area of ​​gas emission peak, including specific areas during the fermentation stage. The dominant source of odor emission is identified through pattern recognition, and odor source analysis results are generated. Odor reduction strategy module: Based on the odor source analysis results, design a stack structure adjustment plan and ventilation improvement measures, reduce the emission of high-concentration gases through airflow optimization, implement specific gas management operations, and generate an odor reduction implementation plan; The gas diffusion control module includes a diffusion path analysis submodule, a ventilation parameter optimization submodule, and a gas distribution simulation submodule, wherein: Diffusion path analysis submodule: Based on the dynamic gas model, the gas diffusion path in the compost body is analyzed. By collecting data on the gas diffusion area and analyzing the concentration distribution, the diffusion rate is determined by combining the concentration change data, a gas concentration distribution map within the diffusion range is drawn, the spatial delineation and path labeling of the gas diffusion range are completed, and gas diffusion characteristic data are generated. Ventilation parameter optimization submodule: Based on the gas diffusion characteristic data, optimize the layout and parameters of the ventilation system. By analyzing the influence of the vent location and airflow direction on the diffusion path, readjust the vent location. Combine the gas diffusion rate data to set the ventilation volume and verify the rationality of the airflow direction, complete the optimized configuration of ventilation conditions, and establish an optimized ventilation system scheme. Gas distribution simulation submodule: Based on the ventilation system optimization scheme, the finite element analysis method is used to simulate the diffusion and distribution of gas in the compost body, and the airflow model is adjusted. The gas concentration distribution map is re-evaluated and the parameter configuration is optimized based on the simulation results to generate a gas control strategy. The operating parameter adjustment module includes a real-time monitoring and analysis submodule, a turning frequency adjustment submodule, and a microenvironment regulation submodule, wherein: Real-time monitoring and analysis submodule: Based on the gas control strategy, it monitors and analyzes real-time gas concentration and environmental data, collects monitoring data and analyzes the dynamic change trends of temperature, humidity and gas concentration in the stack, and processes and corrects the correlation between gas concentration changes and ventilation conditions by combining sensor data to obtain real-time monitoring and control dataset. The compost turning frequency adjustment submodule: Based on the real-time monitoring and control dataset, the compost turning frequency is adjusted, the gas concentration change rate and gas distribution data in the time series are analyzed in the real-time data, the turning interval time is adjusted in combination with the turning operation record and the new settings are recorded, and the turning frequency adjustment result is generated. Microenvironment regulation submodule: Based on the results of the turning frequency adjustment, the gas concentration is balanced by adjusting the ventilation parameters in the composting area, and the microenvironmental conditions in the composting area are optimized by combining humidity adjustment data to generate optimized operating parameters.

2. The odor reduction and control system for livestock and poultry manure composting according to claim 1, characterized in that, The gas monitoring module includes a sensor data acquisition submodule, a gas data conversion submodule, and an environmental data integration submodule, wherein: Sensor data acquisition submodule: Based on the configured precision gas sensor, it performs real-time concentration monitoring of ammonia, hydrogen sulfide and volatile organic compounds. By recording the gas concentration signal output by the sensor and simultaneously collecting temperature and humidity data, it completes the acquisition and storage of multiple data at preset time intervals to generate a gas and environmental sensor data set. Gas data conversion submodule: Based on the gas and environmental sensor data set, it performs digital conversion of gas concentration and environmental data, performs signal decoding and quantization processing on the collected data and performs consistency correction on the data range, calibrates the concentration unit conversion and temperature and humidity, and obtains standardized gas and environmental data. Environmental data integration submodule: Based on the standardized gas and environmental data, it performs time series integration of ammonia, hydrogen sulfide and volatile organic compound concentration data, associates temperature and humidity data with gas concentration data and matches corresponding time nodes to generate primary gas data.

3. The odor reduction and control system for livestock and poultry manure composting according to claim 1, characterized in that, The gas dynamic analysis module includes a data time series processing submodule, a key node identification submodule, and a dynamic gas modeling submodule, wherein: Data time series processing submodule: Based on the primary gas data, it performs time series segmentation of gas concentration and environmental data, smooths the data and detects abnormal concentration values ​​through data sorting and segmentation identification, completes the extraction of periodic changes in gas concentration and environmental data and the location of anomalies, and generates a set of gas concentration periods and anomalies. Key node identification submodule: Based on the gas concentration cycle and anomaly point set, perform trend calculation and slope analysis of concentration change, complete the location of key nodes by segmenting nodes in the cycle change and calculating the change amplitude and direction, complete the screening and classification of cycle feature points, and obtain the key change node feature set; Dynamic gas modeling submodule: Based on the key change node feature set, it performs mathematical modeling of the dynamic changes in gas concentration. By integrating key node features and periodic change data and fitting concentration curves, it completes the generation and verification of the gas dynamic model, thus generating a dynamic gas model.

4. The odor reduction and control system for livestock and poultry manure composting according to claim 1, characterized in that, The odor source location module includes a regional emission monitoring submodule, a dominant source identification submodule, and an odor characteristic analysis submodule, wherein: Regional emission monitoring submodule: Based on the optimized operating parameters, it collects concentration data of ammonia, hydrogen sulfide and volatile organic compounds in the composting fermentation area through a convolutional neural network, records emission concentration changes in conjunction with time nodes and divides emission peak periods, performs spatial distribution analysis on the concentration change data of different areas of the compost, and marks the classification and range of emission areas to generate emission peak area data. The dominant odor source identification submodule analyzes the dominant odor source in the composting area based on the emission peak area data. It classifies and calculates the concentration ratio of each component by collecting gas component concentrations in the emission area, compares the component ratios in different areas to identify the dominant emission gas type, locates high emission points in the area by concentration differences, and identifies the dominant odor source area by combining the component concentration and high emission point distribution characteristics in the area, and generates dominant odor source data. The odor feature analysis submodule extracts odor emission patterns and features based on the dominant odor source data. By analyzing the compositional variation patterns of the dominant odor source region and combining them with the odor diffusion trajectory during the fermentation stage, it extracts the temporal distribution characteristics of the emission region. It then establishes regional emission patterns based on the compositional variation characteristics and marks the spatial distribution characteristics corresponding to each pattern. This completes the pattern feature labeling of the dominant odor source and generates odor source analysis results.

5. The odor reduction and control system for livestock and poultry manure composting according to claim 1, characterized in that, The odor reduction strategy module includes a structural optimization and adjustment submodule, a ventilation condition improvement submodule, and a gas management execution submodule, wherein: The structure optimization and adjustment submodule optimizes and adjusts the compost pile structure based on the odor source analysis results. By analyzing the emission paths of high emission areas of the compost pile and combining them with regional characteristics, the pile pile levels are rearranged. The airflow channels of high emission areas are optimized into short-distance emission paths, and gas diffusion control holes are arranged in the emission paths to complete the optimization and adjustment of the pile structure and generate pile structure optimization data. Ventilation improvement submodule: Based on the stack structure optimization data, adjust the ventilation conditions in high-emission areas. By combining the optimization data, reset the area and spacing of the ventilation openings in high-emission areas, adjust the operating frequency and airflow direction of ventilation equipment, and complete the optimization condition configuration by increasing and decreasing the pressure of ventilation channels to match changes in gas concentration, thereby generating a ventilation improvement scheme. The gas management execution submodule performs dynamic emission management of compost odor based on the ventilation improvement scheme. By combining the ventilation improvement scheme with real-time control of gas concentration in high emission areas, the gas in peak emission areas is collected and diverted. By adjusting the regional emission dynamics through real-time control of ventilation equipment operating parameters, the gas collection and emission are coordinated, and an odor reduction execution scheme is generated.

6. A method for odor reduction and control in livestock and poultry manure composting, characterized in that, The odor reduction control system for livestock and poultry manure composting according to any one of claims 1-5 includes the following steps: Step 1: Send the compost material into the fermentation area, insert gas sensors into the compost body to monitor the concentrations of ammonia, hydrogen sulfide and volatile organic compounds, and record the temperature and humidity values ​​of the compost area. Collect and digitize the concentration values ​​and environmental data at each time point to generate primary gas data. Step 2: Based on the primary gas data, arrange the gas concentrations in chronological order, calculate the concentration change rate of each data set, mark the concentration spikes and periodic change intervals, integrate the change results by time nodes, and generate a dynamic gas model. Step 3: Combine the dynamic gas model with the ventilation airflow path of the composting area to calculate the gas diffusion path length and diffusion rate. Through correlation analysis between concentration values ​​and path locations, generate a compost gas diffusion path distribution characteristic table. Step 4: Based on the compost gas diffusion path distribution characteristic table, adjust the ventilation parameters of the composting area, including wind speed, ventilation duct layout and airflow distribution, optimize the operation mode of the existing ventilation system, and generate a gas control strategy. Step 5: Based on the gas control strategy, combined with the data on turning frequency, temperature and humidity in the composting area, analyze the dynamic distribution of compost gas concentration, mark high odor concentration points, locate emission peak areas, and generate a distribution map of high odor emission points in compost. Step Six: Based on the distribution map of high-value points of compost odor emissions, optimize the ventilation conditions in the composting area by adjusting the height of the compost pile, the structure of the compost pile, and the airflow direction of the exhaust channel, dynamically control the gas distribution and emission path, and generate an odor reduction implementation plan.

7. The odor reduction and control method for livestock and poultry manure composting according to claim 6, characterized in that, Multiple layers of gas sensors are added to the compost body, specifically distributed in the upper, middle and lower parts of the compost body, to ensure that the monitored concentration data fully covers the gas diffusion process in the compost. The optimization of ventilation parameters specifically includes setting the ventilation duration to 15 to 20 minutes per hour and the wind speed range to 1 to 1.5 meters per second, to ensure uniform airflow distribution in high-value areas, while reducing ventilation operating costs. In dynamic gas models, the rate of change of gas concentration at the point of sudden increase is calculated by dividing the concentration change by the time interval, which shall not exceed five minutes. The height of the stack was adjusted to 1.2 to 1.8 meters to control the gas concentration distribution and reduce the odor emission concentration.

8. The odor reduction and control method for livestock and poultry manure composting according to claim 6, characterized in that, The composting area is divided into a fermentation zone and a maturation zone. Ventilation control is strengthened in the fermentation zone and ventilation is reduced in the maturation zone. The ventilation ratio is set at three to one. Multiple parallel exhaust channels are set up, with a distance of no less than two meters between each channel and a diameter of 15 to 25 centimeters, to quickly exhaust compost gases and reduce gas concentration; Odor-absorbing materials, including biochar and zeolite, are added to high-value areas, with the specific addition amount being five to eight percent of the total weight of the compost material. During periods of peak odor concentration, the ventilation frequency, wind speed, and pile turning interval are automatically adjusted based on real-time monitoring data, and a real-time feedback control mechanism is established.

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