Pig house environment regulation method and system based on ammonia concentration prediction

Abstract

The application discloses a pig house environment regulation method and system based on ammonia concentration prediction, wherein the method comprises the following steps: acquiring and preprocessing multi-source environment basic data and excrement basic parameters in a pig house; calculating an ammonia volatilization rate contribution factor in a current stage, and combining a historical ammonia concentration sequence to dynamically output an ammonia prediction concentration trend curve; collecting local gas parameters of multiple preset micro-temperature zones in the pig house along a preset direction; dynamically adjusting a regulation strategy based on a coupling degree of the gas parameters and the ammonia prediction concentration trend curve; and matching multiple-dimensional actuators based on meteorological data to comprehensively regulate the internal environment of the pig house. The application overcomes the serious hysteresis of traditional threshold control, eliminates the defects of spatial harmful gas deposition segregation and multiple physical device control contradiction.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology, and more specifically, to a method and system for controlling the environment of pigsties based on ammonia concentration prediction. Background Technology

[0002] In modern large-scale pig farming, the concentration control of harmful gases such as ammonia in pigsties directly affects the healthy development of pigs, feed conversion rate, and the economic benefits of the farm. However, existing pigsty environmental control solutions typically have the following key drawbacks:

[0003] 1. The control logic has a serious lag. Existing control schemes generally adopt simple threshold switching logic, that is, ventilation equipment is only passively turned on or increased when the local ammonia concentration collected by the sensor exceeds the preset safety boundary. This can easily lead to the pig house being in an environment with excessive harmful gases for a long time.

[0004] 2. Harmful gases inside pigsties suffer from severe physical deposition and segregation, as well as local dead zones. In particular, due to its physicochemical properties, ammonia is extremely prone to severe concentration accumulation and stratification in the vertical direction (e.g., near the manure gutter and the pig's breathing zone) or in local micro-temperature zones in pigsties with multiple air ducts and complex internal structures (such as fences and positioning pens), making it difficult to eradicate harmful gases in the core activity areas of pigs. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for controlling the pig house environment based on ammonia concentration prediction, which can solve problems such as control lag and local deposition stratification in the prior art.

[0006] The first aspect of this invention provides a method for controlling the environment of pigsties based on ammonia concentration prediction, comprising the following steps:

[0007] Acquire basic environmental data and excrement parameters from multiple sources within the pigsty and perform preprocessing;

[0008] Based on the basic parameters of the excrement and the basic environmental data from multiple sources, the contribution factor of the ammonia volatilization rate at the current stage is calculated, and the ammonia concentration prediction trend curve is dynamically output in combination with the historical ammonia concentration sequence.

[0009] Local gas parameters of multiple preset micro-temperature zones along a preset direction are collected in the pigsty. A dynamic adjustment and control strategy is based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve. The local gas parameters include at least the local ammonia concentration and the gas flow velocity.

[0010] The system uses meteorological data to match multi-dimensional actuators to implement comprehensive regulation of the internal environment of the pigsty. The multi-dimensional actuators include at least a fresh air fan, a negative pressure fan, a deodorizing spray device, and a manure scraper.

[0011] In this solution, the acquisition and preprocessing of multi-source environmental basic data and excrement basic parameters within the pigsty specifically includes:

[0012] The multi-source environmental basic data includes at least real-time ammonia concentration, ambient temperature, ambient relative humidity, and ventilation rate; the excrement basic parameters include at least excrement surface area and excrement pH value.

[0013] The instantaneous air film transport enhancement factor is calculated by extracting ambient temperature, relative humidity, and ventilation rate. The calculation formula is as follows:

[0014]

[0015] in, The gas transport enhancement factor, The ambient temperature inside the pigsty. The relative humidity inside the pigsty. The local ventilation rate is mentioned above.

[0016] The dynamic gas diffusion control threshold is obtained by multiplying the preset basic ammonia diffusion coefficient by the gas transport enhancement factor, as shown in the following formula:

[0017] ;

[0018] in, The dynamic gas diffusion control threshold is... The basic ammonia diffusion coefficient is given. This refers to the gas transport enhancement factor.

[0019] In this scheme, the calculation of the ammonia volatilization rate contribution factor at the current stage based on the basic parameters of the excrement and the basic environmental data from multiple sources, and the dynamic output of the predicted ammonia concentration trend curve in conjunction with the historical ammonia concentration sequence, specifically includes:

[0020] A source analysis matrix is ​​constructed using the surface area and pH value of the excrement, and the contribution factor of the ammonia volatilization rate at the current stage is calculated by combining it with the dynamic gas diffusion control threshold. The calculation formula is as follows:

[0021] ;

[0022] in, The contributing factor to the ammonia volatilization rate, The pH value of the excrement. The surface area of ​​the excrement. This refers to the total floor area of ​​the pigsty. The dynamic gas diffusion control threshold is defined as follows;

[0023] Extract the historical ammonia concentration sequence within the preset historical sliding window, and capture the inertial evolution trend of harmful gas concentration through first-order difference;

[0024] The ammonia volatilization rate contribution factor and the inertial evolution trend are input into a preset composite prediction model to obtain the ammonia predicted concentration trend curve.

[0025] During the prediction process, if the absolute value of the residual between the actual ammonia concentration at three consecutive sampling points and the predicted ammonia concentration trend curve is greater than a preset residual threshold, the residual is reverse-compensated to the ammonia volatilization rate contribution factor for weight feedforward correction.

[0026] In this scheme, the collection of local gas parameters in multiple preset micro-temperature zones along a preset direction within the pigsty, and the dynamic adjustment and control strategy based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve, specifically includes:

[0027] Obtain the initial multi-level hazardous gas risk thresholds set. , and And based on the dynamic prediction of the highest concentration within the preset period. Determine the reference frequency for the fresh air unit control. The initial value, where,

[0028] like ,but ;

[0029] like ,but ;

[0030] like ,but ;

[0031] like ,but ;

[0032] Obtain the local ammonia concentration gradient along the vertical direction of the pigsty, where,

[0033] If the rate of increase in local ammonia concentration exceeds a preset rate of change threshold and the concentration gradient in the vertical direction is lower than a preset lower limit, then the control reference frequency is dynamically increased. The value, the corresponding frequency adjustment amplitude is: ,in, This is the frequency increase range. The first proportionality coefficient, This represents the rate of increase in local concentration. The preset rate of change threshold;

[0034] If the rate of increase in local ammonia concentration is lower than a preset rate of change threshold and the concentration gradient is higher than a preset upper limit, then the fan base frequency value will be dynamically reduced, and the reduction magnitude will be as follows: At the same time, the bottom exhaust branch is activated, among which, This represents the frequency reduction range. This is the second proportionality coefficient. For local ammonia concentration gradient, This is a preset upper limit for the gradient.

[0035] In this scheme, the dynamic adjustment and control strategy based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve further includes:

[0036] The horizontal and vertical spaces of the pigsty are divided into multiple independent multidimensional cross-control grids, and a target concentration balance curve is preset for each grid.

[0037] Local ammonia concentrations within each cross-control grid are collected. A preset control algorithm is used to independently control the fan blade deflection angle and the auxiliary local exhaust fans within each grid, ensuring that the harmful gas concentration field within the pigsty satisfies a target dynamic spatiotemporal equilibrium relationship. The expression for this target dynamic spatiotemporal equilibrium relationship is as follows:

[0038] ;

[0039] in, for Monitor the ammonia concentration at the bottom of the pigsty near the manure ditch. for The ammonia concentration at the exhaust vent area on the top of the pigsty should be monitored regularly. for The difference in the concentration of harmful gases at any given time;

[0040] The target harmful gas concentration difference decreases linearly from its initial value to its equilibrium final value over time, and the rate of decrease is positively correlated with the total exhaust power of the multi-dimensional actuator.

[0041] In this solution, the method further includes extracting the ambient temperature and relative humidity of the external environment for composite scene matching, specifically including:

[0042] If the external ambient temperature is lower than the first external ambient temperature threshold, it is determined to be the first scenario. The frequency of the global negative pressure fan is reduced first, while the air heat recovery fan inside the house is started and the circulation cycle of the manure cleaning scraper is extended to the first cleaning time.

[0043] If the relative humidity of the external environment is greater than the preset high humidity threshold and the external temperature is higher than the second external temperature threshold, it is determined to be the second scenario. The spray deodorization device is controlled to reduce the atomization particle size and spray frequency. At the same time, the negative pressure fan is switched to the target mode and the cleaning scraper cycle is shortened to the second cleaning time.

[0044] Otherwise, the normal environmental automatic control mode will be executed.

[0045] A second aspect of the present invention also provides a pigsty environment control system based on ammonia concentration prediction, comprising a memory and a processor. The memory includes a program for a pigsty environment control method based on ammonia concentration prediction. When the processor executes the program for ammonia concentration prediction-based pigsty environment control, it performs the following steps:

[0046] Acquire basic environmental data and excrement parameters from multiple sources within the pigsty and perform preprocessing;

[0047] Based on the basic parameters of the excrement and the basic environmental data from multiple sources, the contribution factor of the ammonia volatilization rate at the current stage is calculated, and the ammonia concentration prediction trend curve is dynamically output in combination with the historical ammonia concentration sequence.

[0048] Local gas parameters of multiple preset micro-temperature zones along a preset direction are collected in the pigsty. A dynamic adjustment and control strategy is based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve. The local gas parameters include at least the local ammonia concentration and the gas flow velocity.

[0049] The system uses meteorological data to match multi-dimensional actuators to implement comprehensive regulation of the internal environment of the pigsty. The multi-dimensional actuators include at least a fresh air fan, a negative pressure fan, a deodorizing spray device, and a manure scraper.

[0050] A third aspect of the present invention provides a computer-readable storage medium comprising a machine program for a method of controlling the environment of a pigsty based on ammonia concentration prediction. When the program is executed by a processor, it implements the steps of the method of controlling the environment of a pigsty based on ammonia concentration prediction as described in any of the preceding claims.

[0051] The fourth aspect of the present invention provides a computer program product, which includes computer program code. When the computer program code is run on a computer, the computer implements the steps of a pigsty environment control method based on ammonia concentration prediction as described in any of the preceding claims.

[0052] The fifth aspect of the present invention provides an electronic device, the electronic device comprising: a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the steps of a pigsty environment control method based on ammonia concentration prediction as described in any of the preceding claims.

[0053] The present invention discloses a method and system for controlling the environment of pigsties based on ammonia concentration prediction, which has the following beneficial effects:

[0054] 1. By integrating the physicochemical parameters of excrement (pH, area) with the instantaneous gas film diffusion law, the lag limitation of relying solely on historical data for prediction is overcome, enabling the prediction and evolution of ammonia peaks several hours in advance, thus achieving prevention before disease occurs.

[0055] 2. By introducing a vertical local concentration gradient and cross-controlled grid field, the ammonia concentration in the animal's breathing zone is forcibly compressed within a safe range through the fine frequency conversion of the actuator and the local branch ventilation.

[0056] 3. Multi-actuator coordinated control, energy saving and consumption reduction. Among them, through the multi-dimensional cross-control grid and the dynamic spatiotemporal balance relationship of the target, the fine coordinated operation of actuators such as fresh air fans, negative pressure fans and local exhaust fans is realized. Attached Figure Description

[0057] Figure 1 A schematic diagram illustrating the steps of a pigsty environmental control method based on ammonia concentration prediction according to the present invention is shown.

[0058] Figure 2 A flowchart illustrating a scenario application of the present invention's method for controlling pigsty environment based on ammonia concentration prediction is shown.

[0059] Figure 3 A schematic diagram of the composite scenario matching process in a pigsty environment control method based on ammonia concentration prediction according to the present invention is shown.

[0060] Figure 4 A block diagram of a pigsty environmental control system based on ammonia concentration prediction according to the present invention is shown. Detailed Implementation

[0061] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0062] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0063] Specifically, Figure 1 The diagram illustrates the steps of a method for controlling the environment of a pigsty based on ammonia concentration prediction according to the present invention.

[0064] like Figure 1 As shown, this invention discloses a method for controlling the environment of pigsties based on ammonia concentration prediction, comprising the following steps:

[0065] S102, acquire basic environmental data and excrement parameters from multiple sources in the pigsty and perform preprocessing;

[0066] S104, Calculate the contribution factor of ammonia volatilization rate at the current stage based on the basic parameters of the excrement and the basic data of the multi-source environment, and dynamically output the ammonia concentration prediction trend curve in combination with the historical ammonia concentration sequence.

[0067] S106, Collect local gas parameters of multiple preset micro-temperature zones along a preset direction in the pigsty, and dynamically adjust the control strategy based on the coupling measurement of the gas parameters and the ammonia gas predicted concentration trend curve.

[0068] S108 uses meteorological data to match multi-dimensional actuators to comprehensively regulate the internal environment of pigsties.

[0069] It should be noted that, in this embodiment, the technical point of the present invention lies in the multi-dimensional collaborative predictive regulation based on source characteristics and spatial field distribution. Specifically, it is applied to the field of intelligent control of pig house environment in large-scale pig farming. In these scenarios, the present invention can dynamically calculate the ammonia volatilization potential by collecting data such as excrement area, pH value, and indoor temperature and humidity, and predict the concentration change in advance for the next 15-30 minutes by combining historical concentration trends, and pre-adjust the reference frequency of the fresh air fan accordingly. At the same time, by deploying a sensor grid in the vertical and horizontal directions of the pig house, the concentration gradient and rise rate are monitored in real time, and the fan frequency is dynamically fine-tuned and the bottom exhaust is selectively turned on or the wing plate angle is adjusted to eliminate ammonia stratification and deposition. Finally, it can also intelligently match comprehensive regulation strategies for special scenarios such as winter and high temperature and humidity based on external meteorological data to achieve efficient, precise, and energy-saving control of the breeding environment.

[0070] Specifically, in this embodiment, the local gas parameters include at least the local ammonia concentration and gas flow velocity. The multi-dimensional actuator includes at least a fresh air unit, a negative pressure fan, a deodorizing spray device, and a manure scraper. First, multi-source environmental data and basic excrement parameters are acquired through temperature, humidity, ammonia, and wind speed sensors installed in the pigsty, as well as excrement image recognition or manual input. Then, the controller calculates a prediction curve periodically (e.g., every minute), predicting that ammonia levels will rise from 8 ppm to 12 ppm in the next 15 minutes. Simultaneously, ammonia sensors are installed at the bottom (near the manure ditch), middle (pig breathing zone), and top (exhaust vent) of the pigsty to read the data in real time. Based on the predicted maximum concentration, the controller first sets an initial speed for the fresh air fan, such as medium. Then, if it is found that the concentration at the bottom is rising rapidly, but the concentration difference between the breathing zone and the bottom is small (indicating that ammonia is rapidly diffusing upwards), the controller immediately increases the fan speed. Conversely, if the concentration at the bottom is very high but the breathing zone is very low (indicating that ammonia is settling at the bottom), the controller will reduce the main fan speed and simultaneously turn on the branch fan specifically for drawing air from the bottom. Finally, the controller judges based on the outdoor temperature: if it is very cold outside, it reduces the speed of the main fan, turns on the heat recovery device, and lengthens the manure cleaning interval; if it is hot and humid outside, it adjusts the spray to a fine mist, sprays less, and scrapes manure more frequently.

[0071] According to an embodiment of the present invention, the acquisition and preprocessing of multi-source environmental basic data and excrement basic parameters in the pigsty specifically includes:

[0072] The multi-source environmental basic data includes at least real-time ammonia concentration, ambient temperature, ambient relative humidity, and ventilation rate; the excrement basic parameters include at least excrement surface area and excrement pH value.

[0073] The instantaneous air film transport enhancement factor is calculated by extracting ambient temperature, relative humidity, and ventilation rate. The calculation formula is as follows:

[0074]

[0075] in, The gas transport enhancement factor, The ambient temperature inside the pigsty. The relative humidity inside the pigsty. The local ventilation rate is mentioned above.

[0076] The dynamic gas diffusion control threshold is obtained by multiplying the preset basic ammonia diffusion coefficient by the gas transport enhancement factor, as shown in the following formula:

[0077] ;

[0078] in, The dynamic gas diffusion control threshold is... The basic ammonia diffusion coefficient is given. This refers to the gas transport enhancement factor.

[0079] It should be noted that, in this embodiment, for ammonia to volatilize, it must first pass through a thin gas film on the surface of the excrement. The thickness and permeability of the gas film will change with the temperature, humidity and wind speed in the pigsty. For example, when the temperature is high, the molecules move faster and the gas film resistance is low; when the humidity is high, there is more water vapor in the gas film, which may also affect the diffusion of ammonia. Therefore, this embodiment designs a gas transport enhancement factor formula, converting temperature, humidity and wind speed into a coefficient greater than 1. Multiplying this coefficient by a standard ammonia diffusion coefficient yields a dynamic diffusion control threshold. The dynamic value is much more accurate than using a fixed coefficient directly, and can truly reflect the ease with which ammonia escapes from the source under the current environment.

[0080] Specifically, in this embodiment, data from the temperature and humidity sensor and the anemometer are read. Assuming the current temperature inside the pigsty is 28°C, the relative humidity is 70%, and the local ventilation rate is 1.5 m / s, the data is substituted into the formula: first calculate (28-25)=3, then calculate (70-65)=5, and then... If the basic ammonia diffusion coefficient If the value is 0.000015 square meters per second, then the dynamic diffusion control threshold is 1.24 × 0.000015 = 0.0000186 square meters per second. This value is 24% larger than the static value, indicating that ammonia is more likely to volatilize from excrement under the current environment. This dynamic value is stored for the next step of calculating the volatilization rate. This dynamic value will be recalculated every time there is a change in temperature, humidity or wind speed, so as to follow the environmental changes in real time.

[0081] According to an embodiment of the present invention, the step of calculating the contribution factor of ammonia volatilization rate at the current stage based on the basic parameters of the excrement and the basic environmental data from multiple sources, and dynamically outputting the predicted ammonia concentration trend curve by combining the historical ammonia concentration sequence, specifically includes:

[0082] A source analysis matrix is ​​constructed using the surface area and pH value of the excrement, and the contribution factor of the ammonia volatilization rate at the current stage is calculated by combining it with the dynamic gas diffusion control threshold. The calculation formula is as follows:

[0083] ;

[0084] in, The contributing factor to the ammonia volatilization rate, The pH value of the excrement. The surface area of ​​the excrement. This refers to the total floor area of ​​the pigsty. The dynamic gas diffusion control threshold is defined as follows;

[0085] Extract the historical ammonia concentration sequence within the preset historical sliding window, and capture the inertial evolution trend of harmful gas concentration through first-order difference;

[0086] The ammonia volatilization rate contribution factor and the inertial evolution trend are input into a preset composite prediction model to obtain the ammonia predicted concentration trend curve.

[0087] During the prediction process, if the absolute value of the residual between the actual ammonia concentration at three consecutive sampling points and the predicted ammonia concentration trend curve is greater than a preset residual threshold, the residual is reverse-compensated to the ammonia volatilization rate contribution factor for weight feedforward correction.

[0088] It should be noted that, in this embodiment, the factors affecting the future ammonia concentration are divided into two parts: one is the fresh ammonia generated at the source, and the other is the ammonia already present in the air that will continue for a period of time. For the source-generated part, the pH value (natural logarithm) of the excrement and the proportion of the excrement's surface area are multiplied by a previously calculated dynamic diffusion coefficient to derive an ammonia evaporation rate contribution factor. The higher the pH value, the larger the excrement's surface area, and the larger the diffusion coefficient, the larger this factor, indicating that the source is vigorously releasing ammonia. For the historical inertia part, the concentration data from a past period (e.g., the past 10 minutes) can be used for first-order difference analysis to determine whether the concentration is rising or falling, and at what rate. These two pieces of information are then fed into a composite prediction model (e.g., weighted summation) to obtain a prediction curve for the future concentration. Furthermore, the model itself can self-correct; for example, if the predicted value differs significantly from the actual value several times in a row, this error is compensated back into the contribution factor, resulting in a more accurate prediction the next time.

[0089] Specifically, in this embodiment, it is assumed that the current pH value of the excrement is 8.0, the excrement area accounts for 30% of the total area of ​​the pigsty, and the dynamic diffusion coefficient is 0.0000186, based on the formula... The calculated result is 0.0000116. Then, the historical ammonia concentration sequence of the past 10 minutes is read, for example, [5.2, 5.5, 5.9, 6.4, 7.0] ppm. After first-order differencing, the average rate of increase is approximately 0.45 ppm / minute. A prediction model is then set up, for example, for the next... Concentration per minute = Current concentration + ( The calculated value is (volatility increment + inertial velocity) multiplied by time. This allows for the prediction of the concentration at each time point over the next 15 minutes, resulting in an upward curve. If the residual threshold is set to 0.5 ppm, then when the actual concentrations at three consecutive sampling points (e.g., 7.2, 7.6, 8.1) differ from the predicted values ​​(7.0, 7.3, 7.5) by 0.2, 0.3, and 0.6 respectively, and the third difference of 0.6 exceeds the threshold, this residual of 0.6 will be added to the threshold. Then, it moves up and re-predicts the subsequent points, thereby achieving adaptive correction.

[0090] According to an embodiment of the present invention, the method of collecting local gas parameters in multiple preset micro-temperature zones along a preset direction within the pigsty, and dynamically adjusting and controlling the system based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve, specifically includes:

[0091] Obtain the initial multi-level hazardous gas risk thresholds set. , and And based on the dynamic prediction of the highest concentration within the preset period. Determine the reference frequency for the fresh air unit control. The initial value, where,

[0092] like ,but ;

[0093] like ,but ;

[0094] like ,but ;

[0095] like ,but ;

[0096] Obtain the local ammonia concentration gradient along the vertical direction of the pigsty, where,

[0097] If the rate of increase in local ammonia concentration exceeds a preset rate of change threshold and the concentration gradient in the vertical direction is lower than a preset lower limit, then the control reference frequency is dynamically increased. The value, the corresponding frequency adjustment amplitude is: ,in, This is the frequency increase range. The first proportionality coefficient, This represents the rate of increase in local concentration. The preset rate of change threshold;

[0098] If the rate of increase in local ammonia concentration is lower than a preset rate of change threshold and the concentration gradient is higher than a preset upper limit, then the fan base frequency value will be dynamically reduced, and the reduction magnitude will be as follows: At the same time, the bottom exhaust branch is activated, among which, This represents the frequency reduction range. This is the second proportionality coefficient. For local ammonia concentration gradient, This is a preset upper limit for the gradient.

[0099] It should be noted that this embodiment specifically describes how fast the fan should rotate and when to change the rotation speed, such as... Figure 2 The diagram shows a schematic of adjusting the baseline frequency. First, based on the predicted highest concentration falling within which risk range (low, medium, or high), a baseline frequency is set for the fresh air system. For example, in high risk, the frequency is set at high speed. However, since the distribution of ammonia in space is dynamic, two indicators need special attention: one is the rate of concentration increase (…). One is the vertical concentration gradient (the concentration difference between the bottom and the top). If the concentration rises rapidly but the vertical gradient is small, it means that ammonia is being generated quickly and spreading rapidly throughout the space. In this case, the exhaust fan must be increased to expel the ammonia before it spreads into the pig's breathing zone. Conversely, if the concentration rises slowly but the vertical gradient is large, it means that the ammonia has settled at the bottom. In this case, if the main fan is increased further, it will stir up the high concentration of ammonia at the bottom and bring it to the breathing zone. The correct approach is to reduce the speed of the main fan and start the branch line that is specifically designed to ventilate the bottom to prevent stirring.

[0100] Specifically, in this embodiment, the predicted highest concentration is first determined... To determine the reference frequency, for example, to set , , The first, second, and third frequency parameters correspond to 50Hz, 35Hz, and 20Hz, respectively. If prediction... Let's set it first. Then, the rate of increase in concentration is calculated in real time using ammonia sensors installed at the bottom (0.2 meters high) and the breathing zone (0.7 meters high). and gradient Assuming a preset rate of change threshold The lower limit of the gradient is 2 ppm / m, and the upper limit of the gradient is 5 ppm / m. (According to actual measurements...) (Clearly exceeded), while the gradient is only 1.5 ppm / m (below the lower limit of 2 ppm / m), indicating that the concentrations above and below are similar, and ammonia is rapidly diffusing. At this point, calculations... Assuming This corresponds to an increase of 3Hz, making the fan frequency 53Hz. Another scenario: If... (Below the threshold), while the gradient reaches 6 ppm / m (above the upper limit of 5 ppm / m), indicating that a very high concentration has accumulated at the bottom. At this point, calculations... Assuming If the frequency is reduced by 8Hz, the fan frequency will become 42Hz, and the auxiliary exhaust branch at the bottom (such as the small exhaust fans on both sides of the manure ditch) will be turned on at the same time.

[0101] According to an embodiment of the present invention, the dynamic adjustment and control strategy based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve further includes:

[0102] The horizontal and vertical spaces of the pigsty are divided into multiple independent multidimensional cross-control grids, and a target concentration balance curve is preset for each grid.

[0103] Local ammonia concentrations within each cross-control grid are collected. A preset control algorithm is used to independently control the fan blade deflection angle and the auxiliary local exhaust fans within each grid, ensuring that the harmful gas concentration field within the pigsty satisfies a target dynamic spatiotemporal equilibrium relationship. The expression for this target dynamic spatiotemporal equilibrium relationship is as follows:

[0104] ;

[0105] in, for Monitor the ammonia concentration at the bottom of the pigsty near the manure ditch. for The ammonia concentration at the exhaust vent area on the top of the pigsty should be monitored regularly. for The difference in the concentration of harmful gases at any given time;

[0106] The target harmful gas concentration difference decreases linearly from its initial value to its equilibrium final value over time, and the rate of decrease is positively correlated with the total exhaust power of the multi-dimensional actuator.

[0107] It should be noted that, in this embodiment, in specific implementation, assuming the pigsty is 30 meters long and 15 meters wide, it is divided into 3 rows and 3 columns of 9 grids in the horizontal direction, each grid being approximately 10 meters × 5 meters; in the vertical direction, three heights are set: 0.3 meters (near the manure ditch), 0.7 meters (breathing zone), and 1.5 meters (exhaust vent). An ammonia sensor is installed at each intersection point (e.g., the 0.7-meter height in the 2nd row and 3rd column). A small, independently adjustable auxiliary exhaust fan is installed near the exhaust vent of each grid, and the wing plates of the main fan are also independently controllable in different areas.

[0108] Specifically, in this embodiment, the initial detected average concentration at the bottom was 15 ppm, and the average concentration at the top was 5 ppm, with a difference of 10 ppm. The total exhaust power is currently 70% of the rated power, therefore it is set... The concentration decreased linearly at a rate of 0.33 ppm / min, reaching zero after 30 minutes. Then, the controllers for each grid cell operated independently: for example, the lower left front grid cell had a higher concentration, so its auxiliary exhaust fan increased its speed, and the fan blade angle in that area was increased; while the right rear grid cell had a more uniform concentration, so its fan operated at a lower speed. Data from all grid cells was collected every few seconds, and adjustments were made continuously to ensure the measured concentration was within acceptable limits. It consistently approaches the target curve. If the total exhaust power is increased midway, the rate at which the target difference decreases will increase accordingly, resulting in a more agile overall response.

[0109] According to embodiments of the present invention, such as Figure 3 As shown, the method further includes extracting the ambient temperature and relative humidity of the external environment for composite scene matching, specifically including:

[0110] If the external ambient temperature is lower than the first external ambient temperature threshold, it is determined to be the first scenario. The frequency of the global negative pressure fan is reduced first, while the air heat recovery fan inside the house is started and the circulation cycle of the manure cleaning scraper is extended to the first cleaning time.

[0111] If the relative humidity of the external environment is greater than the preset high humidity threshold and the external temperature is higher than the second external temperature threshold, it is determined to be the second scenario. The spray deodorization device is controlled to reduce the atomization particle size and spray frequency. At the same time, the negative pressure fan is switched to the target mode and the cleaning scraper cycle is shortened to the second cleaning time.

[0112] Otherwise, the normal environmental automatic control mode will be executed.

[0113] It should be noted that this embodiment specifically explains how to intelligently adjust the internal control strategy according to the weather conditions of the pigsty area. In winter, because it is very cold outside, if the fans are turned on as vigorously as in summer, the pigs will suffer from cold stress, and a large amount of heat will be lost, resulting in high energy consumption. Therefore, in winter, the priority should be to keep warm, reduce ventilation appropriately, utilize heat recovery fans, and lengthen the manure cleaning cycle—because excrement ferments slowly in cold weather, there is no need to scrape it frequently. In summer, when it is hot and humid, spraying water for deodorization will increase humidity, making the pigs more uncomfortable. Moreover, in a high-humidity environment, ammonia is more easily adsorbed on the pig's mucous membrane. Therefore, in summer, when it is hot and humid, the spray should be adjusted to a finer mist, the spraying frequency should be reduced, ventilation and dehumidification should be strengthened, and the manure cleaning cycle should be shortened, because manure ferments particularly quickly in a hot and humid environment.

[0114] Specifically, in this embodiment, data such as temperature, humidity, and wind speed are collected. A first external ambient temperature threshold is preset to 5°C, a second external ambient temperature threshold to 25°C, and a high humidity threshold to 80%. When the external ambient temperature is 2°C (below 5°C), it is determined to be the first scenario (corresponding to a winter scenario). At this time, the frequency limit of the global negative pressure fan is limited to 40% of its rated value; the air heat recovery fan is automatically turned on to preheat the incoming cold air using the exhaust warm air; the operating cycle of the manure scraper is extended from the default 2 hours to the first cleaning duration (e.g., 4 hours); when the external ambient temperature is 35°C and the relative humidity is 85% (above 25°C), the system is activated. If the humidity is above 80%, it is determined to be the second scenario (corresponding to the summer scenario). At this time, the controller will reduce the atomized particles of the deodorizing spray device from 100 microns to 50 microns (fineer) and reduce the spraying frequency from once every 5 minutes to once every 15 minutes. The negative pressure fan will switch from the normal mode to the "high air volume, low static pressure" mode to maximize the air exchange rate. The cleaning scraper cycle will be shortened from 2 hours to the second cleaning time (e.g., 1 hour). Correspondingly, if the weather conditions do not meet the above two special conditions, the system will automatically adjust according to the normal prediction + feedback mode. All scenario switching is seamless and automatic to achieve intelligent automated control.

[0115] Figure 4 A block diagram of a pigsty environmental control system based on ammonia concentration prediction according to the present invention is shown.

[0116] like Figure 4 As shown, this invention discloses a pigsty environment control system based on ammonia concentration prediction, including a memory and a processor. The memory includes a program for a pigsty environment control method based on ammonia concentration prediction. When the processor executes the program for ammonia concentration prediction-based pigsty environment control, it performs the following steps:

[0117] Acquire basic environmental data and excrement parameters from multiple sources within the pigsty and perform preprocessing;

[0118] Based on the basic parameters of the excrement and the basic environmental data from multiple sources, the contribution factor of the ammonia volatilization rate at the current stage is calculated, and the ammonia concentration prediction trend curve is dynamically output in combination with the historical ammonia concentration sequence.

[0119] Local gas parameters of multiple preset micro-temperature zones along a preset direction are collected in the pigsty. A dynamic adjustment and control strategy is based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve. The local gas parameters include at least the local ammonia concentration and the gas flow velocity.

[0120] The system uses meteorological data to match multi-dimensional actuators to implement comprehensive regulation of the internal environment of the pigsty. The multi-dimensional actuators include at least a fresh air fan, a negative pressure fan, a deodorizing spray device, and a manure scraper.

[0121] It should be noted that when the pig house environment control system based on ammonia concentration prediction disclosed in this invention is applied, the specific process corresponds to the pig house environment control method based on ammonia concentration prediction described in the above embodiments. Since the specific implementation details of the system application are consistent with the content of the above-mentioned pig house environment control method based on ammonia concentration prediction, no further details will be provided in this embodiment.

[0122] A third aspect of the present invention provides a computer-readable storage medium comprising a program for a method of controlling the environment of a pigsty based on ammonia concentration prediction. When the program for controlling the environment of a pigsty based on ammonia concentration prediction is executed by a processor, it implements the steps of the method for controlling the environment of a pigsty based on ammonia concentration prediction as described in any of the preceding claims.

[0123] The fourth aspect of the present invention provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to execute any of the methods described in the embodiments of the pigsty environment control method based on ammonia concentration prediction.

[0124] The fifth aspect of the present invention provides an electronic device, the electronic device comprising: a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the steps of a pigsty environment control method based on ammonia concentration prediction as described in any of the preceding claims.

[0125] The terms “component,” “module,” “system,” etc., used in this specification are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0126] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0127] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0131] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0132] This invention discloses a method and system for controlling the pig house environment based on ammonia concentration prediction. It solves the lag of traditional threshold control through predictive control, and eliminates the conflict between the deposition and separation of harmful gases and equipment control through spatial fine-grained control and multi-dimensional synergistic strategies. It significantly improves the environmental quality and breeding efficiency of pig houses, while achieving the goal of energy conservation and consumption reduction.

[0133] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for controlling the environment of pigsties based on ammonia concentration prediction, characterized in that, Includes the following steps: Acquire basic environmental data and excrement parameters from multiple sources within the pigsty and perform preprocessing; Based on the basic parameters of the excrement and the basic environmental data from multiple sources, the contribution factor of the ammonia volatilization rate at the current stage is calculated, and the ammonia concentration prediction trend curve is dynamically output in combination with the historical ammonia concentration sequence. Local gas parameters of multiple preset micro-temperature zones along a preset direction are collected in the pigsty. A dynamic adjustment and control strategy is based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve. The local gas parameters include at least the local ammonia concentration and the gas flow velocity. The system uses meteorological data to match multi-dimensional actuators to implement comprehensive regulation of the internal environment of the pigsty. The multi-dimensional actuators include at least a fresh air fan, a negative pressure fan, a deodorizing spray device, and a manure scraper.

2. The method for controlling the pigsty environment based on ammonia concentration prediction according to claim 1, characterized in that, The acquisition and preprocessing of multi-source environmental basic data and excrement basic parameters within the pigsty specifically includes: The multi-source environmental basic data includes at least real-time ammonia concentration, ambient temperature, ambient relative humidity, and ventilation rate; the excrement basic parameters include at least excrement surface area and excrement pH value. The instantaneous air film transport enhancement factor is calculated by extracting ambient temperature, relative humidity, and ventilation rate. The calculation formula is as follows: in, The gas transport enhancement factor, The ambient temperature inside the pigsty. The relative humidity inside the pigsty. The local ventilation rate is mentioned above. The dynamic gas diffusion control threshold is obtained by multiplying the preset basic ammonia diffusion coefficient by the gas transport enhancement factor, as shown in the following formula: ； in, The dynamic gas diffusion control threshold is... The basic ammonia diffusion coefficient is given. This refers to the gas transport enhancement factor.

3. The method for controlling the pigsty environment based on ammonia concentration prediction according to claim 2, characterized in that, The calculation of the ammonia volatilization rate contribution factor at the current stage based on the excrement baseline parameters and the multi-source environmental baseline data, and the dynamic output of the ammonia concentration prediction trend curve in conjunction with the historical ammonia concentration sequence, specifically includes: A source analysis matrix is ​​constructed using the surface area and pH value of the excrement, and the contribution factor of the ammonia volatilization rate at the current stage is calculated by combining it with the dynamic gas diffusion control threshold. The calculation formula is as follows: ； in, The contributing factor to the ammonia volatilization rate, The pH value of the excrement. The surface area of ​​the excrement. This refers to the total floor area of ​​the pigsty. The dynamic gas diffusion control threshold is defined as follows; Extract the historical ammonia concentration sequence within the preset historical sliding window, and capture the inertial evolution trend of harmful gas concentration through first-order difference; The ammonia volatilization rate contribution factor and the inertial evolution trend are input into a preset composite prediction model to obtain the ammonia predicted concentration trend curve. During the prediction process, if the absolute value of the residual between the actual ammonia concentration at three consecutive sampling points and the predicted ammonia concentration trend curve is greater than a preset residual threshold, the residual is reverse-compensated to the ammonia volatilization rate contribution factor for weight feedforward correction.

4. The method for controlling the pigsty environment based on ammonia concentration prediction according to claim 1, characterized in that, The method of collecting local gas parameters in multiple preset micro-temperature zones along a preset direction within the pigsty, and dynamically adjusting and controlling the system based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve, specifically includes: Obtain the initial multi-level hazardous gas risk thresholds set. , and And based on the dynamic prediction of the highest concentration within the preset period. Determine the reference frequency for the fresh air unit control. The initial value, where, like ,but ; like ,but ; like ,but ; like ,but ; Obtain the local ammonia concentration gradient along the vertical direction of the pigsty, where, If the rate of increase in local ammonia concentration exceeds a preset rate of change threshold and the concentration gradient in the vertical direction is lower than a preset lower limit, then the control reference frequency is dynamically increased. The value, the corresponding frequency adjustment amplitude is: ,in, This is the frequency increase range. The first proportionality coefficient, This represents the rate of increase in local concentration. The preset rate of change threshold; If the rate of increase in local ammonia concentration is lower than a preset rate of change threshold and the concentration gradient is higher than a preset upper limit, then the fan base frequency value will be dynamically reduced, and the reduction magnitude will be as follows: At the same time, the bottom exhaust branch is activated, among which, This represents the frequency reduction range. This is the second proportionality coefficient. For local ammonia concentration gradient, This is a preset upper limit for the gradient.

5. The method for controlling the pigsty environment based on ammonia concentration prediction according to claim 4, characterized in that, The dynamic adjustment and control strategy based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve further includes: The horizontal and vertical spaces of the pigsty are divided into multiple independent multidimensional cross-control grids, and a target concentration balance curve is preset for each grid. Local ammonia concentrations within each cross-control grid are collected. A preset control algorithm is used to independently control the fan blade deflection angle and the auxiliary local exhaust fans within each grid, ensuring that the harmful gas concentration field within the pigsty satisfies a target dynamic spatiotemporal equilibrium relationship. The expression for this target dynamic spatiotemporal equilibrium relationship is as follows: ； in, for Monitor the ammonia concentration at the bottom of the pigsty near the manure ditch. for The ammonia concentration at the exhaust vent area on the top of the pigsty should be monitored regularly. for The difference in the concentration of harmful gases at any given time; The target harmful gas concentration difference decreases linearly from its initial value to its equilibrium final value over time, and the rate of decrease is positively correlated with the total exhaust power of the multi-dimensional actuator.

6. The method for controlling the pigsty environment based on ammonia concentration prediction according to claim 1, characterized in that, The method also includes extracting the ambient temperature and relative humidity of the external environment for composite scene matching, specifically including: If the external ambient temperature is lower than the first external ambient temperature threshold, it is determined to be the first scenario. The frequency of the global negative pressure fan is reduced first, while the air heat recovery fan inside the house is started and the circulation cycle of the manure cleaning scraper is extended to the first cleaning time. If the relative humidity of the external environment is greater than the preset high humidity threshold and the external temperature is higher than the second external temperature threshold, it is determined to be the second scenario. The spray deodorization device is controlled to reduce the atomization particle size and spray frequency. At the same time, the negative pressure fan is switched to the target mode and the cleaning scraper cycle is shortened to the second cleaning time. Otherwise, the normal environmental automatic control mode will be executed.

7. A pigsty environmental control system based on ammonia concentration prediction, characterized in that, The system includes a memory and a processor. The memory contains a program for controlling the pigsty environment based on ammonia concentration prediction. When the processor executes the program for controlling the pigsty environment based on ammonia concentration prediction, it performs the following steps: Acquire basic environmental data and excrement parameters from multiple sources within the pigsty and perform preprocessing; Based on the basic parameters of the excrement and the basic environmental data from multiple sources, the contribution factor of the ammonia volatilization rate at the current stage is calculated, and the ammonia concentration prediction trend curve is dynamically output in combination with the historical ammonia concentration sequence. Local gas parameters of multiple preset micro-temperature zones along a preset direction are collected in the pigsty. A dynamic adjustment and control strategy is based on the coupling measurement of the gas parameters and the predicted ammonia concentration trend curve. The local gas parameters include at least the local ammonia concentration and the gas flow velocity. The system uses meteorological data to match multi-dimensional actuators to implement comprehensive regulation of the internal environment of the pigsty. The multi-dimensional actuators include at least a fresh air fan, a negative pressure fan, a deodorizing spray device, and a manure scraper.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for a method of controlling the environment of a pigsty based on ammonia concentration prediction. When the program is executed by a processor, it implements the steps of the method for controlling the environment of a pigsty based on ammonia concentration prediction as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, causes the computer to implement the steps of a pigsty environment control method based on ammonia concentration prediction as described in any one of claims 1 to 6.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the steps of the pigsty environment control method based on ammonia concentration prediction as described in any one of claims 1 to 6.

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