Odor diffusion early warning system for livestock and poultry farm

Abstract

The invention relates to an odor diffusion early warning system for a livestock and poultry farm. The odor diffusion early warning system for the livestock and poultry farm comprises the following steps: acquiring odor emission source data of the livestock and poultry farm; processing information of the deodorization equipment is obtained, wherein the processing information at least comprises the equipment type and the operation state; on the basis of the processing information of the deodorization equipment, deodorization emission components are obtained; obtaining environmental data; the environment data at least comprises topographic data and meteorological data; establishing an early warning model, and obtaining an early warning threshold value and an early warning value based on the farm odor emission source data, the processing information of the deodorization equipment and the environmental data; comparing the early warning threshold with an early warning value to obtain early warning information; the livestock and poultry farm odor diffusion early warning system can efficiently and accurately solve the problems of monitoring and early warning of odor diffusion of a three-dimensional building farm; the real diffusion condition can be accurately reflected, and the response speed and accuracy of the early warning system are improved.

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 breeding industry, large-scale breeding has gradually become the mainstream. Especially in some provinces in the south, there is a large demand for pork consumption, but land resources are limited. The expansion of single-unit breeding often conflicts with the insufficient supply of land resources. With the transformation of the production mode of the breeding industry, how to balance the contradiction between large-scale production and resource protection has become an important issue that needs to be solved urgently. Against this background, three-dimensional building farms have gradually emerged as a new breeding model. This model attempts to solve the problem of excessive land occupation and excessive resource consumption in traditional single-story farms through the design of multi-layer structures. However, with the rapid development of three-dimensional farms, odor pollution problems have gradually emerged, especially the diffusion range and complexity of odor have brought new challenges and become the focus of conflicts between residents and farms.

[0003] In the past few decades, the livestock farming industry has experienced rapid scale and intensive development. Especially in some provinces with large pork consumption and areas with limited land resources, the traditional free-range model has gradually failed to meet the growing demand, which has promoted the emergence of a three-dimensional farming model. The multi-layer structure design of the three-dimensional building farm allows the same area of ​​land to accommodate more breeding units, thereby improving the efficiency of land use. However, although this new farming model solves the problem of space utilization, it also causes a more complex odor diffusion problem.

[0004] In traditional single-layer farms, odor usually diffuses in horizontal space, limited by terrain and meteorological factors with poor air flow. In contrast, the multi-layer structure and high-density breeding environment of three-dimensional farms make the generation, propagation and diffusion of odor more complicated. The multi-layer structure causes odor to diffuse at different heights and is affected by airflow at different levels, making the path of odor diffusion more uncertain. What's more serious is that as the location of farms gradually approaches residential areas, the spread of odor has 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 when weather conditions are bad or wind speeds are low, odor may linger in local areas, causing a serious decline in air quality and threatening the living environment and mental health of residents. As the construction density of vertical farms continues to increase, the odor problem not only affects the daily lives of local residents, but also arouses social concern and doubts about environmental pollution in the aquaculture industry.

[0006] Currently, odor monitoring in most farms relies on manual olfactory recognition and chemical sensors. Manual olfactory recognition methods are highly subjective, and their accuracy and stability are difficult to guarantee due to the influence of multiple factors such as olfactory sensitivity, climatic conditions, and environmental pollution. In addition, manual olfactory recognition requires a lot of manpower and material resources, and cannot achieve real-time monitoring, so it is often difficult to meet the needs of residents and regulatory authorities.

[0007] Chemical sensors are a common odor detection tool that assesses air quality by detecting 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. Secondly, the traditional point monitoring method has limited effect on multi-layered three-dimensional farms, and it is difficult to fully grasp the odor diffusion at different levels. With the expansion of the scale of three-dimensional farms, single-point monitoring methods are difficult to fully capture and accurately predict the diffusion of odor.

[0008] The spread of farm odor is closely related not only to meteorological conditions (such as temperature, humidity, wind speed, etc.), but also to changes in topography (such as mountains, hills, plains, etc.). In complex terrain, the path of airflow is often blocked or guided, causing the odor to gather in certain local areas, thereby aggravating the impact on surrounding residents. Therefore, it is difficult to fully evaluate the spread of odor and its impact on residents by relying solely on traditional monitoring equipment or methods.

[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 breeding 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-mentioned problems. The present invention utilizes the breeding volume, deodorization measures and local meteorological and topographic information of a specific breeding farm to automatically simulate and predict the range of odor diffusion, 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 breeding 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 at least includes equipment type and operation status;

[0015] obtaining deodorized emission components 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, and obtaining 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] The warning threshold is compared with the warning value to obtain warning information.

[0019] As a further optimization scheme 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 emission; T is the total odor emission; E iIt is the removal efficiency of the device.

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

[0030] Obtain the albedo of the ground, the roughness of the ground and the terrain feature data;

[0031] Obtain the wind speed, wind direction, air temperature, air pressure, humidity, precipitation data.

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

[0033] As a further optimization scheme of the present invention, obtain the warning threshold. Specifically,

[0034] Obtain the warning threshold of each component;

[0035] Obtain the ratio of concentration to threshold;

[0036] Calculate the comprehensive warning index;

[0037] Determine the warning level;

[0038] Calculate the influence range distance D;

[0039] Detect in real time and generate 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, then the concentration ratio Ri is: R i =C i / T i ;

[0041] When R i >1, it means 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, calculate the comprehensive warning index I 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.

[0045] As a further optimization scheme of the present invention, according to the comprehensive warning index I, divide the warning level 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), its influence range distance D i The calculation formula is as follows:

[0051]

[0052] where T i is the threshold concentration of chemical component i, u is the wind speed, σ y , σ z is the lateral and vertical diffusion coefficient, 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 at the same time the influence range distance covers the residential area, the system will automatically generate a warning message, and the warning message includes the warning level and the 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;

[0055] Obtain environmental data; wherein, the environmental data are the data of the preliminary site selection point;

[0056] Pollutant concentration at a specified distance and height:

[0057]

[0058] The lateral distribution function F y is defined as:

[0059]

[0060] where C(x, y, z) represents the pollutant concentration at the point (x, y, z); Q is the emission rate of the emission source (initial estimated value); u represents the effective wind speed, expressed as the diffusion speed; σ y and σ z are the lateral and vertical diffusion parameters, respectively, related to the atmospheric stability and turbulence intensity; h es is the effective plume height, z ieffis the height of the upper reflecting surface in the stable layer; F y is a lateral distribution function with 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 scheme of the present invention, the optimal emission rate is obtained, and the inverse modeling of AERMOD is used 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 is beyond the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different positions, the candidate point with the smallest impact range will be screened out.

[0069] The beneficial effects of the present invention are as follows: the present invention 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; system automation 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 take into account meteorological conditions (such as wind speed, temperature, etc.) and terrain characteristics at the same time, and provide accurate odor diffusion prediction. Compared with the traditional method that relies on chemical sensors, the prediction results of the model are more scientific, objective and reliable, and can accurately predict the diffusion of odor, which is helpful to make effective environmental response measures in advance. Due to the improvement of prediction accuracy, the system can respond to potential odor hazards more quickly and reduce environmental pollution.

[0071] The present invention has stronger 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. It optimizes the location of odor sources through reverse modeling technology to ensure that the emission sources of farms are far away from residential areas, which not only meets actual operational needs, but also effectively reduces the impact of odor on the surrounding environment, thereby enhancing the flexibility and practicality of the system.

[0072] The environmental protection effect of the present invention is remarkable. During the construction stage of a farm, the diffusion range of odor can be predicted in advance, which helps to reasonably select a site and avoid environmental pollution problems from the source. Through accurate prediction, effective measures can be taken in the early stage of farm construction to avoid complex environmental governance work in the later stage, thereby greatly reducing the high cost of environmental pollution control. The system can monitor the 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 of the present invention has significant advantages in improving monitoring efficiency, enhancing prediction accuracy, adapting to complex environments, and promoting environmental protection. It can not only effectively improve the accuracy of odor monitoring and early warning, but also provide a scientific decision-making basis for the site selection, operation and management of farms, while greatly reducing the cost of environmental pollution control, and has 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 diagram of the method flow of Example 2 of the present invention. DETAILED DESCRIPTION

[0076] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here 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. Technical personnel 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 schematic diagram of a livestock and poultry farm odor diffusion early warning system process is shown, and the system includes the following methods:

[0079] Obtaining farm odor emission source data; the method for obtaining the data may be any method in the prior art, 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 at least includes equipment type and operation status;

[0081] obtaining deodorized emission components 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, and obtaining 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] The warning threshold is compared 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 breeding volume 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. According to the maximum daily emission of each pig, the ammonia emission is about 17 grams / day, the hydrogen sulfide is about 2.5 grams / day, the methane emission is 110 grams / day, and the volatile organic compound (VOCs) emission is 8 grams / day. The ammonia emission of each cow is between 50-90 grams, hydrogen sulfide is about 6-10 grams, methane is about 150-250 grams, and VOCs is about 12-15 grams; the ammonia emission of each sheep is about 5-10 grams, hydrogen sulfide is about 1.5-3 grams, methane is about 30-50 grams, and VOCs is about 2-5 grams; and the ammonia emission of each chicken is about 0.5-2 grams, hydrogen sulfide is about 0.1-0.5 grams, methane is about 0.05-0.2 grams, and VOCs is about 0.5-1 gram. The emissions of other poultry, such as ducks and geese, are relatively small, mainly ammonia, with lower methane and VOCs emissions. Based on the emission parameters of the above livestock, the total amount of various emissions can be calculated by superimposing them in places where multiple livestock are raised together, and the overall odor emissions of the farm can be accurately estimated.

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

[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] Furthermore,

[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 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 proportion 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 deodorizing 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, the emission concentration change formula is obtained;

[0111] The formula for calculating the change in emission concentration according to 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 emission; T is the total odor emission; E i is the removal efficiency of the equipment.

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

[0118] Get terrain data;

[0119] Refine terrain data processing;

[0120] Get 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 the terrain characteristics around the farm, including terrain roughness and terrain map data at different heights.

[0123] The terrain data is available for free download from the Spatial Geographic Data Cloud website. The SRTM data has a resolution of 90 meters and the ASTER GDEM data has a resolution of 30 meters, covering the global land surface. According to the scale of the farm and the accuracy requirements of the research, the appropriate dataset is selected to ensure the high accuracy of the terrain data. These terrain data also include terrain roughness and contour data of different heights, which are used for terrain input processing in the AERMOD model.

[0124] The processing of the refined terrain data includes parameterizing the ground albedo, ground roughness and terrain features (such as mountains or plains), which can affect the accurate prediction of the odor diffusion path. Through the AERMAP module, the original terrain data is converted into a format that can be read by AERMOD to ensure more accurate airflow simulation under complex terrain.

[0125] The meteorological data is derived from local official meteorological stations or the National Meteorological Science Data Center, covering wind speed, wind direction, temperature, air pressure, humidity, precipitation, etc. In addition, historical meteorological data over a long period of time is collected to take into account seasonal changes and wind field characteristics during the day and night.

[0126] During the processing of the terrain and meteorological data, Kriging interpolation method (Kriging) is used for data optimization to improve the accuracy of matching meteorological conditions with terrain features. The processed meteorological data is input into the AERMOD model through the AERMETS module and integrated with the terrain data generated by AERMAP to simulate the dynamic changes of 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] Specific:

[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 are set, including emission rate (Q), emission height (H), emission rate and pollutant types (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 according to the meteorological data, and the diffusion mode suitable for the daytime mixed layer conditions and the 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 influence. The calculation formula is as follows:

[0138]

[0139] Where C(x,y,z) is the odor concentration at the distance from the emission source (x,y,z), 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 result is output in the form of an isoconcentration line map, which intuitively shows the diffusion range of the odor and the impact area on the surrounding environment, providing a basis for subsequent decision-making. The calculation results of the model can effectively evaluate the potential impact of farm odor on residential areas.

[0141] Further, the warning threshold is obtained, specifically,

[0142] Obtain warning thresholds for each component;

[0143] Obtaining a concentration to threshold ratio;

[0144] Calculate the comprehensive early warning index;

[0145] Determine the warning level;

[0146] Calculation of the 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 1ppm, 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 according to the type of VOCs (such as benzene, toluene, xylene, etc.). The comprehensive VOC threshold is usually set at 0.5ppm to adapt to the VOCs concentration in the odor commonly found in farms.

[0153] Specifically, calculate the ratio 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 by the weighted average method:

[0156]

[0157] where W i is the weight coefficient of component i, which is assigned 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.

[0163] Specifically, according to the comprehensive warning index I, divide the warning levels to evaluate the risk degree of odors to the environment; the warning level division is as follows:

[0164] 0 < I ≤ 0.5: Low risk, normal range;

[0165] 0.5 < I ≤ 1.0: Medium risk, issue a first-level warning;

[0166] 1.0 < I ≤ 1.5: High risk, issue a second-level warning;

[0167] I > 1.5: Extremely high risk, issue a third-level warning.

[0168] The system combines the impact range distance predicted by the AERMOD model to evaluate the maximum diffusion distance of each chemical component in the odor at a specific concentration. For each chemical component C i (such as ammonia, hydrogen sulfide, methane, and VOCs), the calculation formula for its impact range distance D i is as follows:

[0169]

[0170] 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 the residential area, the system will automatically generate warning information, which includes the warning level and recommended emergency measures.

[0172] On the basis of completing the 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 the environmental standards.

[0173] Example 2

[0174] refer to Figure 2 The method flow chart shown in the figure, in order to achieve the above purpose, this application provides a method for guiding the site selection of farms by using the reverse modeling technology of the AERMOD model. This method is an additional method of the above early warning system. Based on the preset data, the odor impact is evaluated, so as to infer the best site selection point. 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 result.

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

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

[0182] The process of determining the preliminary site selection is as follows: multiple locations are selected as preliminary site selection points in the candidate area. Initial parameters of the emission source are set for each candidate point, including the expected breeding scale and emission volume. According to the geographical and environmental characteristics of each location, factors such as the distance from the residential area and the surrounding wind direction and speed are considered. These data can be obtained from literature reference values ​​or historical data to ensure that the preliminary site selection meets reasonable physical condition 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 the 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 speed; 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 It 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] The meteorological and topographic data collection collects long-term meteorological data, including wind speed, wind direction, temperature, humidity, etc., through authoritative data centers (such as national weather stations or geospatial data websites) to capture seasonal and daily changes. At the same time, terrain data is obtained through SRTM or ASTERGDEM to generate terrain features around each candidate point (including surface roughness, altitude, etc.), and these data are input 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 value is compared with the actual monitored concentration by calculating the relative error value 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 detected 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. In this case, it is necessary to adjust the emission rate and other parameters and repeat the model until the prediction 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 in repeated model simulations so that the output of the model can accurately reflect the odor concentration distribution on site. 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 reverse modeling results is as follows:

[0198] To obtain the optimal emission rate, the inverse modeling of AERMOD is used 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:

[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 is beyond the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different positions, the candidate point with the smallest impact range will be screened out.

[0202] The above embodiment 1 proposes an automated odor warning method, including real-time monitoring of odor concentration and setting warning thresholds for each odor component. When the odor concentration predicted by the model reaches or exceeds the threshold, the system will automatically trigger a warning signal and generate corresponding control suggestions. The protection of this method point includes real-time concentration detection, warning threshold setting and automatic response mechanism.

[0203] In the above-mentioned Example 2, the inverse modeling function of the AERMOD model is used to simulate the odor diffusion range by setting different site selection schemes to provide scientific guidance for the site selection of the farm. The method includes optimizing the modeling process of site selection and scientifically evaluating the site selection location based on the diffusion results to ensure that the site selection can minimize the impact of odor on residential areas.

[0204] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representation 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 may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

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

[0206] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope 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 the data of 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 the environmental data; the environmental data at least includes the terrain data and the 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.

2. The livestock and poultry farm odor diffusion early warning system according to claim 1 is characterized by: 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 the 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.

3. The livestock and poultry farm odor diffusion early warning system according to claim 2 is characterized by: The equipment type includes: Spray / oil injection dust and odor reduction type, wet scrubbing odor reduction type, biological filter 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 emission concentration change formula: 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 emission; T is the total odor emission; E i is the removal efficiency of the equipment.

4. The livestock and poultry farm odor diffusion early warning system according to claim 3 is characterized by: The specific method for obtaining the environmental data is: Obtain the data of the ground albedo, ground roughness, and terrain characteristics; Obtain the data of wind speed, wind direction, air temperature, air pressure, humidity, and precipitation.

5. The livestock and poultry farm odor diffusion early warning system according to claim 4, 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.

6. The livestock and poultry farm odor diffusion early warning system according to claim 5, characterized in that: Obtain the early warning threshold. Specifically, Obtain the early warning threshold for each component; Obtain the ratio of the concentration to the threshold; Calculate the comprehensive early warning index; Determine the early warning level; Calculate the influence range distance D; Real-time detect and generate early warning information.

7. The livestock and poultry farm odor diffusion early warning system according to claim 6, characterized in that: The ratio of the concentration of each odor component to the threshold value. 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 ; When R i When >1, it indicates that the concentration of component i exceeds the standard and a warning signal needs to be issued.

8. The livestock and poultry farm odor diffusion early warning system according to claim 6, characterized in that: In order to comprehensively evaluate the impact of the odor 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.

9. The livestock and poultry farm odor diffusion early warning system according to claim 8, characterized in that: According to the comprehensive early warning index I, divide the early warning levels to evaluate the risk degree of the odor to the environment; The early warning levels are divided 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 real-time predicts the concentration distribution of each component 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.

10. The livestock and poultry farm odor diffusion early warning system according to claim 9, characterized in that: Obtain the data of 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 the environmental data; among them, the environmental data is the data of the preliminary site selection point; The pollutant concentration at the specified distance and height: Transverse distribution function F y Defined as: Where C(x,y,z) represents the pollutant concentration at the 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 speed; 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 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 detected concentration; To obtain the optimal emission rate, the inverse modeling of AERMOD is used 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: 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 is beyond the safe range of the residential area, the candidate point will be reselected or the position will be adjusted; by comparing different positions, the candidate point with the smallest impact range will be screened out.

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