A livestock and poultry farm odor diffusion early warning system

Abstract

The present invention relates to an odor diffusion early warning system for livestock and poultry farms. The system includes the following methods: obtaining farm odor emission source data; obtaining processing information for deodorization equipment, the processing information including at least the equipment type and operating status; obtaining deodorization emission components based on the processing information from the deodorization equipment; obtaining environmental data, the environmental data including at least topographic data and meteorological data; establishing an early warning model to obtain an early warning threshold and an early warning value based on the farm odor emission source data, the processing information from the deodorization equipment, and the environmental data; and comparing the early warning threshold and the early warning value to obtain early warning information. The system can efficiently and accurately solve the problem of monitoring and early warning of odor diffusion in three-dimensional building farms; it can accurately reflect the actual diffusion situation, improving the response speed and accuracy of the early warning system.

Description

Technical Field

[0001] The invention belongs to the technical field of odor early warning, and in particular relates to an odor diffusion early warning system for livestock and poultry farms. Background Art

[0002] With the rapid development of the livestock farming industry, large-scale farming has gradually become mainstream. This is particularly true in some southern provinces, where pork consumption is high and land resources are limited. The expansion of individual farms often conflicts with insufficient land supply. With the shift in livestock production methods, balancing large-scale production with resource conservation has become a critical issue that demands urgent resolution. Against this backdrop, three-dimensional farms have emerged as a new farming model. Through their multi-story design, this model attempts to address the excessive land usage and resource consumption of traditional single-story farms. However, with the rapid development of these farms, odor pollution has gradually emerged. The range and complexity of the odor have posed new challenges and become a focal point for conflict between residents and farms.

[0003] Over the past few decades, the livestock farming industry has experienced rapid expansion and intensification. In some provinces with high pork consumption and limited land resources, the traditional free-range farming model has become increasingly inadequate to meet growing demand, leading to the emergence of multi-story farms. The multi-story design of multi-story farms allows for more farms to be accommodated on the same land area, improving land use efficiency. However, while this new farming model addresses space utilization issues, it also creates more complex odor problems.

[0004] In traditional single-story farms, odor typically diffuses horizontally, limited by terrain and meteorological factors that restrict air flow. In contrast, the multi-story structure and high-density farming environment of three-dimensional farms complicate the generation, transmission, and diffusion of odor. The multi-story structure causes odor to diffuse at different heights and be affected by air currents at different levels, making the odor's diffusion path even more uncertain. Furthermore, as farms are increasingly located closer to residential areas, the spread of odor is having an increasingly negative impact on the living environment of surrounding residents.

[0005] Odor emissions have a direct impact on the quality of life and health of surrounding residents. Especially during periods of poor weather or low wind speeds, odor can linger in localized areas, causing a significant decline in air quality and threatening residents' living environment and mental health. As the density of vertical aquaculture farms continues to increase, odor issues not only impact the daily lives of local residents but also spark public concern and questions about environmental pollution from the aquaculture industry.

[0006] Currently, odor monitoring in most farms relies on manual odor detection and chemical sensors. Manual odor detection is subject to significant subjectivity and is subject to multiple factors, including olfactory sensitivity, climatic conditions, and environmental pollution, making its accuracy and stability difficult to guarantee. Furthermore, manual odor detection requires significant manpower and material resources and lacks real-time monitoring, often failing to meet the needs of residents and regulators.

[0007] Chemical sensors are a common odor detection tool, assessing air quality by measuring the concentration of harmful gases such as ammonia and hydrogen sulfide. However, the use of chemical sensors also has some limitations. First, sensors can usually only monitor a single gas or specific gas components and cannot fully reflect the changes in airflow and odor diffusion inside and outside the farm. Second, traditional point monitoring methods are limited in effectiveness for multi-layered farms, making it difficult to fully grasp the odor diffusion situation at different levels. As the scale of farms expands, single-point monitoring methods are difficult to fully capture and accurately predict odor diffusion.

[0008] The spread of farm odor is closely linked not only to meteorological conditions (such as temperature, humidity, and wind speed) but also to topographical variations (such as mountains, hills, and plains). In complex terrain, airflow is often obstructed or diverted, causing odor to accumulate in certain localized areas, exacerbating the impact on surrounding residents. Therefore, relying solely on traditional monitoring equipment or methods makes it difficult to fully assess the spread of odor and its impact on residents.

[0009] In summary, there is an urgent need for a more efficient and accurate odor diffusion early warning system to meet the special needs of vertical farms and effectively reduce the impact of odor on residential areas. Summary of the Invention

[0010] The purpose of the present invention is to provide a livestock and poultry farm odor diffusion early warning system with a simple structure and reasonable design in order to solve the above problems. The present invention uses the breeding volume, deodorization measures and local meteorological and topographic information of a specific farm to automatically simulate and predict the odor diffusion range, and judge the potential impact on surrounding residents in real time. At the same time, the reverse modeling technology of AERMOD is used to optimize the site selection of the farm to ensure that the impact of odor diffusion is effectively controlled within the residential area. The entire system does not need to rely on manual olfactory identification or chemical sensors, and integrates multiple parameter data to achieve accurate odor management and reasonable site selection in complex environments, thereby improving the efficiency of odor management and the scientific nature of site selection decisions.

[0011] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0012] A livestock and poultry farm odor diffusion early warning system, the system includes the following methods:

[0013] Obtain data on odor emission sources from farms;

[0014] Obtaining processing information of the deodorization equipment, wherein the processing information includes at least equipment type and operating status;

[0015] obtaining a deodorized emission component based on the processing information of the deodorizing device;

[0016] Obtaining environmental data; the environmental data at least includes topographic data and meteorological data;

[0017] Establishing an early warning model to obtain an early warning threshold and an early warning value based on the farm odor emission source data, the processing information of the deodorization equipment, and the environmental data;

[0018] Compare the warning threshold with the warning value to obtain warning information.

[0019] As a further optimization solution of the present invention, the farm odor emission source data includes odor emission amount; the specific method for obtaining the odor emission amount is: obtaining an emission source identification model; obtaining the odor emission amount based on the emission source identification model;

[0020] The formula for odor emission is as follows:

[0021]

[0022] in, is the total ammonia emission, N is the number of livestock, is the maximum daily ammonia emission per livestock;

[0023] Similarly, the maximum daily emissions of hydrogen sulfide, methane and VOCs are obtained.

[0024] As a further optimization solution of the present invention, the device types include:

[0025] Spray / oil spray dust reduction deodorization type, wet scrubbing deodorization type, biofilter type, photocatalytic oxidation deodorization type;

[0026] The operating state includes the on state of the device and the off state of the device; based on the processing information of the deodorization device, the emission concentration change formula is obtained:

[0027] C i,开启 =R i ×T×(1-E i );

[0028] Among them, C i,开启 is the emission concentration of component i when the deodorizing equipment is turned on; R i is the proportion of component i in the total emissions; T is the total odor emissions; E iIt is the removal efficiency of the device.

[0029] As a further optimization scheme of the present invention, obtaining environmental data specifically includes:

[0030] Obtaining data on ground albedo, ground roughness and terrain features;

[0031] Obtaining data on wind speed, wind direction, air temperature, air pressure, humidity and precipitation.

[0032] As a further optimization scheme of the present invention, obtaining the AERMOD model; based on the AERMOD model, obtaining emission source parameters and diffusion patterns, and obtaining the odor impact value.

[0033] As a further optimization scheme of the present invention, obtaining a warning threshold, specifically,

[0034] Obtaining the warning threshold for each component;

[0035] Obtaining the ratio of concentration to threshold;

[0036] Calculating the comprehensive warning index; [[ID=2*]]

[0037] Determining the warning level;

[0038] Calculating the influence range distance D;

[0039] Real-time detecting and generating warning information.

[0040] As a further optimization scheme of the present invention, for the ratio of the concentration of each odor component to the relative threshold, assuming the predicted concentration of chemical component i is Ci and the threshold concentration is Ti, the concentration ratio Ri is: R i =C i / T i ;

[0041] When R i >1, it indicates that the concentration of component i exceeds the standard and a warning signal needs to be issued.

[0042] Furthermore, in order to comprehensively evaluate the impact of odor on the environment, the comprehensive warning index I is calculated by the weighted average method:

[0043]

[0044] Among them, W i is the weight coefficient of component i, which is assigned according to the relative importance of each component to health effects. <www.158>

[0045] As a further optimization scheme of the present invention, according to the comprehensive warning index I, the warning level is divided to evaluate the risk degree of odor to the environment; the warning level is divided as follows:

[0046] 0 < I ≤ 0.5: Low risk, normal range;

[0047] 0.5 < I ≤ 1.0: Medium risk, issue a first-level warning;

[0048] 1.0 < I ≤ 1.5: High risk, issue a second-level warning;

[0049] I > 1.5: Extremely high risk, issue a third-level warning.

[0050] For each chemical component C i (such as ammonia, hydrogen sulfide, methane, and VOCs), the influence range distance D i is calculated as follows:

[0051]

[0052] where T i is the threshold concentration of chemical component i, u is the wind speed, and σ y , σ z are the lateral and vertical diffusion coefficients, and Q i is the emission rate of chemical component i;

[0053] The system predicts the concentration distribution of each component in real time and continuously updates the comprehensive warning index I and the influence range distance D; if the warning index exceeds the set threshold and the influence range distance covers the residential area at the same time, the system will automatically generate a warning message, and the warning message includes the warning level and recommended emergency measures.

[0054] As a further optimization scheme of the present invention, obtain the data of the odor emission sources of the farm; wherein, the data of the odor emission sources of the farm are the estimated data of the odor emission sources of the farm; <00001​​​​​​​​​​​​​​​​​​​​​​​​​​is the height of the upper reflecting surface in the stable layer; F y is a lateral distribution function with a tortuosity;

[0061] Based on the initially set emission rate, the AERMOD model is run to predict the odor concentration at each monitoring point. The predicted value is compared with the actual monitored concentration by calculating the relative error value to determine the accuracy of the model.

[0062] The relative error formula is as follows, which is used to evaluate the degree of difference between the predicted value and the measured value:

[0063]

[0064] where ΔC p is the model-predicted concentration; ΔC m is the actual detected concentration.

[0065] As a further optimization of the present invention, the optimal emission rate is obtained by using inverse modeling in AERMOD to identify the influence range of different candidate locations. For each candidate point, the maximum diffusion distance Di under a specific threshold concentration Ti is calculated by the model to determine the potential impact of each candidate point on the surrounding residential areas. The calculation formula is as follows:

[0066]

[0067] Where T i is the set threshold concentration of odor component i; Q i is the actual emission rate of component i;

[0068] If the diffusion distance D of the candidate point i If it exceeds the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different locations, the candidate point with the smallest impact range will be screened out.

[0069] The beneficial effects of the present invention are: it has higher monitoring efficiency, uses the AERMOD model to simulate odor diffusion, does not rely on manual olfactory identification and traditional chemical sensor deployment, and avoids the tediousness and limitations of manual monitoring; the system's automated processing can greatly improve monitoring efficiency while reducing the demand for manpower and equipment, thereby reducing overall operating costs.

[0070] The present invention has higher prediction accuracy. The AERMOD model can simultaneously consider meteorological conditions (such as wind speed and temperature) and terrain characteristics to provide accurate odor diffusion predictions. Compared with traditional methods that rely on chemical sensors, the model's prediction results are more scientific, objective and reliable. It can accurately predict the spread of odor and help to take effective environmental response measures in advance. Due to the improved prediction accuracy, the system can respond to potential odor hazards more quickly and reduce environmental pollution.

[0071] The present invention has greater adaptability and is particularly suitable for multi-story structures and complex terrain environments such as three-dimensional building farms. It can flexibly respond to different types of odor sources and diffusion scenarios. By optimizing the location of odor sources through reverse modeling technology, it ensures that the emission sources of farms are far away from residential areas. This not only meets actual operational needs, but also effectively reduces the impact of odor on the surrounding environment, enhancing the flexibility and practicality of the system.

[0072] The present invention has a significant environmental protection effect. During the construction phase of a farm, it can predict the spread of odor in advance, help with reasonable site selection, and avoid environmental pollution problems at the source. Through accurate prediction, effective measures can be taken in the early stages of farm construction to avoid complex environmental governance work in the later stages, thereby significantly reducing the high cost of environmental pollution control. The system can monitor changes in odor in real time and issue early warnings to remind farms to strengthen deodorization measures, thereby reducing the impact of odor on surrounding residential areas and improving environmental quality.

[0073] The odor diffusion early warning system, based on the AERMOD model, offers significant advantages in improving monitoring efficiency, enhancing prediction accuracy, adapting to complex environments, and promoting environmental protection. It not only effectively enhances the accuracy of odor monitoring and early warning, but also provides a scientific basis for decision-making in the site selection, operation, and management of farms, while significantly reducing the cost of environmental pollution control, thus possessing high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of the process of an early warning system for odor diffusion in livestock and poultry farms of the present invention;

[0075] Figure 2 It is a schematic flow chart of the method of Example 2 of the present invention. DETAILED DESCRIPTION

[0076] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0077] Example 1

[0078] refer to Figure 1 The flowchart of a livestock and poultry farm odor diffusion early warning system is shown, and the system includes the following methods:

[0079] Obtaining farm odor emission source data; the method for obtaining the data can be any method in the existing technology, such as establishing a model to estimate the farm odor emission source by combining the breeding volume and production activities with literature data;

[0080] Obtaining processing information of the deodorization equipment, wherein the processing information includes at least equipment type and operating status;

[0081] obtaining a deodorized emission component based on the processing information of the deodorizing device;

[0082] Obtaining environmental data; the environmental data at least includes topographic data and meteorological data;

[0083] Establishing an early warning model to obtain an early warning threshold and an early warning value based on the farm odor emission source data, the processing information of the deodorization equipment, and the environmental data;

[0084] Compare the warning threshold with the warning value to obtain warning information.

[0085] Furthermore, the method for obtaining the odor emission source data of the farm can be:

[0086] Investigate the amount of livestock and production activities to determine the overall emission source scale of the farm;

[0087] Refer to the literature data to analyze the typical odor component emissions of each livestock in the farm;

[0088] Establish an emission source identification model, use MATLAB software to simulate typical odor emission sources, and establish a model to maximize the estimation of total odor emissions;

[0089] The process of determining the overall emission source scale of the farm through breeding volume and production activities is as follows:

[0090] Assuming that the farm has no deodorization measures or the deodorization measures are closed, all pigs are in a high-emission stage. Based on the maximum daily emissions of each pig, ammonia emissions are about 17 grams / day, hydrogen sulfide is about 2.5 grams / day, methane emissions are 110 grams / day, and volatile organic compounds (VOCs) emissions are 8 grams / day. Each cow emits between 50-90 grams of ammonia, about 6-10 grams of hydrogen sulfide, about 150-250 grams of methane, and about 12-15 grams of VOCs; each sheep emits about 5-10 grams of ammonia, about 1.5-3 grams of hydrogen sulfide, about 30-50 grams of methane, and about 2-5 grams of VOCs; and each chicken emits about 0.5-2 grams of ammonia, about 0.1-0.5 grams of hydrogen sulfide, about 0.05-0.2 grams of methane, and about 0.5-1 gram of VOCs. Other poultry, such as ducks and geese, have relatively small emissions, primarily ammonia, with lower levels of methane and VOCs. Based on the emission parameters for each livestock type, it is possible to accurately estimate the overall odor emissions from a farm by adding together and calculating the total amount of each type of emission in mixed livestock breeding sites.

[0091] The typical odorous gases include: ammonia (NH3), hydrogen sulfide (H2S), methane (CH4) and volatile organic compounds (VOCs). The farm odor emission source data includes odor emission volume;

[0092] Obtain an emission source identification model; obtain odor emission based on the emission source identification model;

[0093] The formula for odor emission is as follows:

[0094]

[0095] in, is the total ammonia emission, N is the number of livestock, is the maximum daily ammonia emission per livestock;

[0096] Similarly, the maximum daily emissions of hydrogen sulfide, methane and VOCs are obtained.

[0097] Further,

[0098] By analyzing the operating status of the deodorization equipment, the odor emission components under different working conditions are estimated. The process is as follows:

[0099] Analyze the types and operation status of deodorization equipment;

[0100] Analyze the removal efficiency of odor emission components according to each deodorization equipment;

[0101] Obtain the removal efficiency of each deodorization equipment based on the specific farm equipment;

[0102] Calculate the change in emission concentration based on the component proportions and the operating status of the deodorization equipment;

[0103] Specifically, the device types include:

[0104] Spray / oil spray dust reduction deodorization type, wet scrubbing deodorization type, biofilter type, photocatalytic oxidation deodorization type;

[0105] The removal efficiency of the deodorization equipment and methods for odor emission components is as follows:

[0106] Spray / oil spray dust reduction and deodorization: removal efficiency is ammonia (30%-50%), hydrogen sulfide (30%-50%).

[0107] Wet scrubbing deodorization: removal efficiency is ammonia (70%-90%), hydrogen sulfide (70%-90%), VOCs (60%-80%).

[0108] Biofilter: Removal efficiency is ammonia (50%-80%), hydrogen sulfide (50%-80%), VOCs (70%-90%).

[0109] Photocatalytic oxidation deodorization: removal efficiency of VOCs (more than 70%);

[0110] The operating state includes the on state of the device and the off state of the device; based on the processing information of the deodorization device, an emission concentration change formula is obtained;

[0111] The formula for calculating the change in emission concentration based on the component proportion and the operating status of the deodorization equipment is as follows:

[0112] C i,关闭 =R i ×T

[0113] C i,开启 =C i,关闭 ×(1-E i )

[0114] Or expanded to:

[0115] C i,开启 =R i ×T×(1-E i );

[0116] Among them, C i,开启 is the emission concentration of component i when the deodorizing equipment is turned on; R i is the proportion of component i in the total emissions; T is the total odor emissions; E i is the removal efficiency of the equipment.

[0117] Furthermore, terrain and meteorological data were obtained from public resources and weather stations. The process was as follows:

[0118] Get terrain data;

[0119] Refine terrain data processing;

[0120] Obtain weather data;

[0121] Processing of topographic and meteorological data and application to models;

[0122] The terrain data adopts public terrain information sources, such as SRTM and ASTERGDEM data, to obtain terrain characteristics around the farm, including terrain roughness and terrain map data at different heights.

[0123] Terrain data is available for free download from the Geospatial Data Cloud website. SRTM data has a resolution of 90 meters, while ASTER GDEM data has a resolution of 30 meters, covering the entire global land surface. Based on the scale of the farm and the accuracy requirements of the research, appropriate datasets were selected to ensure high-precision terrain data. This terrain data also includes terrain roughness and contour lines at different heights, which are used as terrain input in the AERMOD model.

[0124] Refining terrain data involves parameterizing surface albedo, surface roughness, and terrain characteristics (e.g., mountainous or plain). These parameters can influence the accurate prediction of odor diffusion paths. The AERMAP module converts raw terrain data into a format readable by AERMOD, ensuring more accurate airflow simulations over complex terrain.

[0125] The meteorological data is sourced from local official weather stations or the National Meteorological Science Data Center, covering wind speed, wind direction, temperature, air pressure, humidity, precipitation, etc. Furthermore, historical meteorological data is collected over a long period of time to account for seasonal variations and daytime and nighttime wind field characteristics.

[0126] During the processing of topographic and meteorological data, Kriging was used to optimize the data to improve the accuracy of matching meteorological conditions with topographic features. The processed meteorological data was input into the AERMOD model through the AERMET module and integrated with the topographic data generated by AERMAP to simulate the dynamic changes in odor diffusion.

[0127] Furthermore, the AERMOD model is obtained; based on the AERMOD model, the emission source parameters and diffusion pattern are obtained, and the odor impact value is obtained.

[0128] Specifically:

[0129] By collecting complete meteorological and topographic data, the data is input into the AERMOD model to simulate the odor diffusion range and generate contour maps. The specific process is as follows:

[0130] Set emission source parameters;

[0131] Select the diffusion mode;

[0132] Run the AERMOD model for calculation;

[0133] Result output and visualization;

[0134] The emission source parameters include emission rate (Q), emission height (H), emission rate and pollutant type (such as ammonia, hydrogen sulfide, methane and VOCs). According to the characteristics of different pollutants, their diffusion coefficients and sedimentation characteristics are set.

[0135] The diffusion mode used by the model is determined based on meteorological data, and a diffusion mode suitable for daytime mixed layer conditions and nighttime stable layer conditions is selected to ensure accurate simulation under different meteorological conditions.

[0136] After completing all parameter settings, the odor diffusion calculation is performed through the AERMOD main program. The process is as follows:

[0137] The model uses the Gaussian diffusion equation for calculation based on the input turbulence parameters, pollutant emission source characteristics, and terrain influences. The calculation formula is as follows:

[0138]

[0139] Where C(x,y,z) is the odor concentration at the location (x,y,z) from the emission source, Q is the emission rate, u is the wind speed, σ y and σ z are the lateral and vertical diffusion coefficients, respectively, and H is the height of the emission source.

[0140] The final results are output as isoconcentration maps, visually demonstrating the odor's diffusion range and the impact on the surrounding environment, providing a basis for subsequent decision-making. The model's calculation results can effectively assess the potential impact of farm odor on residential areas.

[0141] Furthermore, we obtain the warning threshold, specifically,

[0142] Obtain warning thresholds for each component;

[0143] obtain the concentration to threshold ratio;

[0144] Calculate the comprehensive early warning index;

[0145] Determine the warning level;

[0146] Calculation of impact range distance D;

[0147] Real-time detection and generation of early warning information.

[0148] Specifically, the warning thresholds for each component are mainly based on the recommended concentrations of national and international standards, usually based on the lowest level of impact on human health. The specific thresholds are as follows:

[0149] Ammonia (NH3): The warning threshold is set at 1 ppm, based on the minimum irritation concentration of ammonia to the respiratory tract;

[0150] Hydrogen sulfide (H2S): The warning threshold is 0.1ppm, based on the strong odor detection level of hydrogen sulfide;

[0151] Methane (CH4): The warning threshold is set at 50ppm, mainly to prevent the risk of explosion caused by methane accumulation;

[0152] Volatile organic compounds (VOCs): The warning threshold varies depending on the type of VOC (such as benzene, toluene, xylene, etc.). The comprehensive VOC threshold is usually set at 0.5ppm to accommodate the VOC concentration in the odor commonly found in farms.

[0153] Specifically, calculate the proportion of the concentration relative threshold of each odor component. Let the predicted concentration of chemical component i be Ci and the threshold concentration be Ti, then the concentration ratio Ri is: R i = C i / T i ;

[0154] When R i > 1, it indicates that the concentration of component i exceeds the standard, and a warning signal needs to be issued.

[0155] Furthermore, in order to comprehensively evaluate the impact of odors on the environment, calculate the comprehensive warning index I through the weighted average method:

[0156]

[0157] where, W i is the weight coefficient of component i, which is allocated according to the relative importance of each component to health effects.

[0158] The following are the recommended weight coefficients:

[0159] Ammonia (NH3): ≈ 0.25;

[0160] Hydrogen sulfide (): ≈ 0.35;

[0161] Methane (CH4): ≈ 0.10;

[0162] Volatile organic compounds (VOCs): ≈ 0.30. <​​​​​​​​​​​​​​​​​​​​​​​​​​Among them, T i is the threshold concentration of chemical component i, u is the wind speed, σ y , σ z are the lateral and vertical diffusion coefficients, Q i is the emission rate of chemical component i;

[0171] The system predicts the concentration distribution of each component in real time and continuously updates the comprehensive warning index I and the impact range distance D. If the warning index exceeds the set threshold and the impact range distance covers residential areas, the system will automatically generate a warning message, which includes the warning level and recommended emergency measures.

[0172] Based on the completion of odor diffusion prediction and early warning, the inverse modeling technology of the AERMOD model is further used to optimize the site selection of the farm, so as to minimize the impact of the new site on the surrounding residential areas while meeting environmental standards.

[0173] Example 2

[0174] refer to Figure 2 As shown in the flowchart of the method, to achieve the above purpose, this application provides a supplementary method for guiding the site selection of farms using the inverse modeling technology of the AERMOD model. This method is an additional method for the above early warning system. Based on preset data, the impact of odor is evaluated, and the optimal site location is thus inferred. The specific process is as follows:

[0175] Step S1: Obtain environmental data and estimated farm odor emission source data;

[0176] Step S2: Obtaining the AERMOD model;

[0177] Step S3: obtaining a comparison between the predicted and measured concentrations;

[0178] Step S4: iteratively adjusting the emission rate;

[0179] Step S5: Determine the optimal position based on the inverse modeling results.

[0180] Based on the above system method, the difference from Example 1 is that the odor emission source data of the farm is obtained; wherein the odor emission source data of the farm is estimated odor emission source data of the farm;

[0181] Obtain environmental data; wherein the environmental data is preliminary site selection data;

[0182] The preliminary site selection process is as follows: Multiple locations within the candidate area are selected as preliminary sites. Initial emission source parameters are set for each candidate site, including the estimated farm size and emissions. Factors such as proximity to residential areas and surrounding wind direction and speed are considered based on the geographic and environmental characteristics of each location. These data can be obtained from literature references or historical data to ensure that the preliminary site meets reasonable physical requirements.

[0183] The basic concentration formula in AERMOD is as follows, which is used to estimate the pollutant concentration at a specified distance and height:

[0184]

[0185] Transverse distribution function F y Defined as:

[0186]

[0187] where C(x,y,z) represents the pollutant concentration at point (x,y,z); Q is the emission rate of the emission source (initial estimate); u represents the effective wind speed, expressed as the diffusion velocity; σ y and σ z are the lateral and vertical diffusion parameters, respectively, which are related to the stability and turbulence intensity of the atmosphere; h es is the effective plume height, z ieff is the height of the upper reflecting surface in the stable layer; F y is a zigzag lateral distribution function.

[0188] According to the above formula, the concentration distribution of odor in the horizontal and vertical directions can be calculated, providing a data basis for subsequent model adjustment and concentration prediction.

[0189] Meteorological and topographic data collection involves collecting long-term meteorological data from authoritative data centers (such as national weather stations or geospatial data websites), including wind speed, direction, temperature, and humidity, to capture seasonal and diurnal variations. Simultaneously, terrain data is acquired using SRTM or ASTERGDEM to generate terrain characteristics (including surface roughness and elevation) around each candidate point. This data is then fed into the AERMOD model for preprocessing.

[0190] The comparison process between the predicted and measured concentrations is as follows: Based on the initially set emission rate, the AERMOD model is run to predict the odor concentration at each monitoring point. The predicted values ​​are then compared with the actual monitored concentrations by calculating the relative error to determine the accuracy of the model.

[0191] The relative error formula is as follows, which is used to evaluate the degree of difference between the predicted value and the measured value:

[0192]

[0193] where ΔC p is the model-predicted concentration; ΔC m is the actual detection concentration;

[0194] If the calculated relative error is large, it indicates that there is a large deviation between the model prediction value and the actual concentration. At this time, it is necessary to adjust the emission rate and other parameters and repeatedly run the model until the predicted value is close to the actual data and the error is within an acceptable range.

[0195] The purpose of the iterative adjustment process of the emission rate is to find an optimal emission rate through repeated model simulations so that the model output can accurately reflect the on-site odor concentration distribution. The specific process is as follows:

[0196] By successively adjusting the emission rate Q of the emission source and re-running the AERMOD model, the predicted value is continuously optimized until the relative error RD between the model prediction result and the measured value is minimized or reaches the preset allowable error range.

[0197] The process of determining the optimal position based on the inverse modeling results is as follows:

[0198] To obtain the optimal emission rate, inverse modeling with AERMOD was used to identify the impact ranges of different candidate locations. For each candidate point, the model calculated the maximum diffusion distance Di under a specific threshold concentration Ti to determine the potential impact of each candidate point on the surrounding residential areas. The calculation formula is as follows:

[0199]

[0200] Where T i is the set threshold concentration of odor component i; Q i is the actual emission rate of component i;

[0201] If the diffusion distance D of the candidate point i If it exceeds the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different locations, the candidate point with the smallest impact range will be screened out.

[0202] Example 1 above proposes an automated odor early warning method, which includes real-time monitoring of odor concentration and setting early warning thresholds for each odor component. When the model-predicted odor concentration reaches or exceeds the threshold, the system automatically triggers an early warning signal and generates corresponding control recommendations. This method includes real-time concentration detection, early warning threshold setting, and an automatic response mechanism.

[0203] In Example 2, the inverse modeling capabilities of the AERMOD model were utilized to simulate the odor diffusion range by setting different site selection scenarios, providing scientific guidance for farm site selection. This method includes optimizing the site selection modeling process and scientifically evaluating the site location based on the diffusion results to ensure that the site selection minimizes the impact of odor on residential areas.

[0204] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0205] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0206] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A livestock and poultry farm odor diffusion early warning system, characterized in that: The system includes the following methods: Obtain data on the odor emission sources in the farm; Obtain the treatment information of the deodorization equipment, where the treatment information at least includes the equipment type and the operating status; Based on the treatment information of the deodorization equipment, obtain the deodorization emission components; Obtain environmental data; the environmental data at least includes terrain data and meteorological data; Establish an early warning model, and based on the data of the odor emission sources in the farm, the treatment information of the deodorization equipment, and the environmental data, obtain the early warning threshold and the early warning value; Compare the early warning threshold with the early warning value to obtain the early warning information; Among them, the data of the odor emission sources in the farm includes the odor emission volume; the specific method for obtaining the odor emission volume is: obtain an emission source identification model; based on the emission source identification model, obtain the odor emission volume; The formula for the odor emission volume is as follows: in, is the total ammonia emission, N is the number of livestock, is the maximum daily ammonia emission per livestock; Similarly, obtain the maximum daily emission total of hydrogen sulfide, methane, and VOCs; Among them, the equipment types include: Spray / oil injection dust and odor reduction type, wet scrubbing odor reduction type, biofilter type, photocatalytic oxidation odor reduction type; The operating status includes the on state and the off state of the equipment; based on the treatment information of the deodorization equipment, obtain the formula for the change in emission concentration: C i,开启 =R i ×T×(1-E i ); Among them, C i,开启 is the emission concentration of component i when the deodorizing equipment is turned on; R i is the proportion of component i in the total emissions; T is the total odor emissions; E i is the removal efficiency of the equipment; The ratio of the concentration of each odor component to the threshold value is: R i =C i / T i ; When R i When it is >1, it means that the concentration of component i exceeds the standard and an early warning signal needs to be issued; Among them, in order to comprehensively evaluate the impact of odors on the environment, calculate the comprehensive early warning index I by the weighted average method: Among them, W i is the weight coefficient of component i, which is allocated according to the relative importance of each component on health effects; Among them, according to the comprehensive early warning index I, divide the early warning levels to evaluate the risk degree of odors to the environment; the early warning level division is as follows: 0 < I ≤ 0.5: Low risk, normal range; 0.5 < I ≤ 1.0: Medium risk, issue a level 1 early warning; 1.0 < I ≤ 1.5: High risk, issue a level 2 early warning; I > 1.5: Extremely high risk, issue a level 3 early warning; For each chemical component C i (such as ammonia, hydrogen sulfide, methane and VOCs), the impact range is D i The calculation formula is as follows: Among them, T i is the threshold concentration of chemical component i, u is the wind speed, σ y , σ z are the lateral and vertical diffusion coefficients, Q i is the emission rate of chemical component i; The system predicts the concentration distribution of each component in real time and continuously updates the comprehensive early warning index I and the influence range distance D; if the early warning index exceeds the set threshold and at the same time the influence range distance covers the residential area, the system will automatically generate early warning information, and the early warning information includes the early warning level and the recommended emergency measures.

2. The livestock and poultry farm odor diffusion early warning system according to claim 1, characterized in that: The specific method for obtaining environmental data is: Obtain data on the ground albedo, ground roughness, and terrain characteristics; Obtain data on wind speed, wind direction, air temperature, air pressure, humidity, and precipitation; 3. The livestock and poultry farm odor diffusion early warning system according to claim 2, characterized in that: Obtain the AERMOD model; based on the AERMOD model, obtain the emission source parameters and the diffusion model, and obtain the odor impact value; 4. The livestock and poultry farm odor diffusion early warning system according to claim 3, characterized in that: Obtain the early warning threshold. Specifically, Obtain the early warning threshold for each component; Obtain the ratio of concentration to threshold; Calculate the comprehensive early warning index; Determine the early warning level; Calculate the influence range distance D; Detect in real time and generate early warning information.

5. The livestock farm odor diffusion early warning system according to claim 4, characterized in that: Obtain data on the odor emission sources in the farm; among them, the data of the odor emission sources in the farm is the estimated data of the odor emission sources in the farm; Obtain environmental data; among them, the environmental data is the preliminary site selection point data; The pollutant concentration at a specified distance and height: Transverse distribution function F y Defined as: where C(x,y,z) represents the pollutant concentration at point (x,y,z); Q is the emission rate of the emission source (initial estimate); u represents the effective wind speed, expressed as the diffusion velocity; σ y and σ z are the lateral and vertical diffusion parameters, respectively, which are related to the stability and turbulence intensity of the atmosphere; h es is the effective plume height, z ieff is the height of the upper reflecting surface in the stable layer; F y is a lateral distribution function with a tortuosity; Based on the initially set emission rate, run the AERMOD model to predict the odor concentration at each monitoring point, and compare the predicted value with the actually monitored concentration by calculating the relative error value to judge the accuracy of the model; The relative error formula is as follows, which is used to evaluate the difference degree between the predicted value and the measured value: where ΔC p is the model-predicted concentration; ΔC m is the actual detection concentration; To obtain the optimal emission rate, inverse modeling with AERMOD was used to identify the impact ranges of different candidate locations. For each candidate point, the model calculated the maximum diffusion distance Di under a specific threshold concentration Ti to determine the potential impact of each candidate point on the surrounding residential areas. The calculation formula is as follows: Where T i is the set threshold concentration of odor component i; Q i is the actual emission rate of component i; If the diffusion distance D of the candidate point i If it exceeds the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different locations, the candidate point with the smallest impact range will be screened out.

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