A mobile henhouse ventilation regulation method and system based on an environment model

Abstract

The application provides a mobile chicken coop ventilation regulation method and system based on an environment model, and relates to the technical field of intelligent breeding, which comprises the following steps: obtaining original temperature data, original humidity data, three-dimensional space wind speed components, an opening geometry distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating an adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; calculating a ventilation compensation demand curve according to the adaptive heat exchange flow field and the opening geometry distribution data set; generating a ventilation intensity regulation instruction set according to the ventilation compensation demand curve; and outputting optimal ventilation control parameters according to the ventilation intensity regulation instruction set. The application realizes adaptive and accurate matching of ventilation response under mobile mutation working conditions, self-inhibition of airflow dead zones and dynamic balance of energy consumption and effects.

Description

Technical Field

[0001] This invention relates to the field of intelligent farming technology, and more specifically, to a method and system for controlling ventilation in mobile chicken houses based on an environmental model. Background Technology

[0002] Mobile chicken houses, as crucial equipment in modern intensive farming, optimize the utilization of pasture resources and promote ecological restoration through periodic spatial displacement. However, the motion-environment interaction during their movement significantly alters the boundary conditions of traditional ventilation control. Existing technologies primarily employ environmental threshold control strategies specific to fixed chicken houses, triggering the stepped start and stop of fans based on preset temperature and humidity parameters. While applicable in static scenarios, these strategies struggle to adapt to mobile conditions. On one hand, the inertial force field disturbances and three-dimensional wind field changes caused by chicken house displacement are not incorporated into the control model, leading to a systematic deviation between wind speed sensor data and the actual environmental airflow field. On the other hand, the spatial heterogeneity of the openings in the enclosure structure exacerbates the internal airflow dead zone effect under the acceleration of movement, and conventional technologies, which only estimate natural infiltration based on a static state, cannot address the dynamic seepage characteristics caused by trajectory changes. Especially when the direction of movement abruptly changes, traditional methods, lacking a synergistic coupling mechanism between motion parameters and environmental infiltration, often result in delayed or excessive ventilation response, causing a sharp increase in energy consumption and drastic fluctuations in the chicken flock's microenvironment.

[0003] The inherent limitations of using static rules to handle dynamic processes severely restrict the environmental control accuracy and energy efficiency of mobile poultry and livestock farming equipment. Based on these shortcomings of existing technologies, there is an urgent need for a mobile chicken house ventilation control method and system based on environmental models. Summary of the Invention

[0004] The purpose of this invention is to provide a ventilation control method for mobile chicken houses based on an environmental model, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for controlling ventilation in a mobile chicken house based on an environmental model, including:

[0006] Acquire raw temperature data, raw humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and raw latitude and longitude coordinate data and time data during the movement of the chicken house, both inside and outside the mobile chicken house.

[0007] A location change rate field is constructed based on the original latitude and longitude coordinate sequence and the time data, and a dynamic environment weight coefficient matrix is ​​calculated by combining the spatial distribution differences of the three-dimensional spatial wind speed components.

[0008] An adaptive heat exchange flow field is generated by fitting the environmental infiltration gradient with the dynamic environmental weight coefficient matrix and the original temperature and humidity data.

[0009] Based on the adaptive heat exchange flow field and the opening geometric distribution dataset, the internal gas stagnation area is inverted using a preset fluid impedance network model, and the ventilation compensation demand curve for maintaining the minimum airflow dead angle is calculated.

[0010] Based on the ventilation compensation demand curve and the location change rate field, the dynamic response of the fan is modeled, and a set of ventilation intensity adjustment instructions is generated.

[0011] Based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, a dynamic game optimization algorithm is used to iteratively correct the fan action sequence and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance.

[0012] Secondly, this application also provides a mobile chicken house ventilation control system based on an environmental model, comprising:

[0013] The acquisition module is used to acquire raw temperature data, raw humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and raw latitude and longitude coordinate data and time data during the movement of the chicken house.

[0014] The construction module is used to construct a location change rate field based on the original latitude and longitude coordinate sequence and the time data, and calculate the dynamic environment weight coefficient matrix by combining the spatial distribution differences of the three-dimensional spatial wind speed components.

[0015] The fitting module is used to fit the environmental infiltration gradient based on the dynamic environmental weight coefficient matrix and the original temperature data and the original humidity data to generate an adaptive heat exchange flow field.

[0016] The inversion module is used to invert the internal gas stagnation area based on the adaptive heat exchange flow field and the opening geometric distribution dataset, and to calculate the ventilation compensation demand curve to maintain the minimum airflow dead angle.

[0017] The modeling module is used to model the dynamic response of the fan based on the ventilation compensation demand curve and the location change rate field, and generate a set of ventilation intensity adjustment instructions.

[0018] The output module is used to iteratively correct the fan action sequence using a dynamic game optimization algorithm based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention constructs a dynamic coupling mechanism between the location change rate field and three-dimensional environmental data, and combines spatial fluid network inversion of the opening distribution of the building envelope with ventilation time delay compensation algorithm to build a dynamic-structure-environment collaborative coupling ventilation control mechanism. This mechanism achieves adaptive and precise matching of ventilation response, self-inhibition of airflow dead zone, and dynamic balance of energy consumption and effect under moving and sudden working conditions. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the ventilation control method for mobile chicken houses based on an environmental model, as described in this embodiment of the invention.

[0023] Figure 2 This is a schematic diagram of the ventilation control system for mobile chicken houses based on an environmental model, as described in an embodiment of the present invention.

[0024] The diagram is labeled as follows: 901, Acquisition Module; 902, Construction Module; 903, Fitting Module; 904, Inversion Module; 905, Modeling Module; 906, Output Module. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Example 1:

[0028] This embodiment provides a method for controlling ventilation in mobile chicken houses based on an environmental model.

[0029] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0030] Step S100: Obtain the original temperature data, original humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and original latitude and longitude coordinate data and time data during the movement of the chicken house.

[0031] Based on the dynamic environmental interference characteristics faced by mobile chicken houses in the complex terrain of pastures, the basic data of internal and external physical fields are collected simultaneously, especially the original acquisition of the geometric topological features of the perforated structure and the spatiotemporal imprint of the movement trajectory, so as to provide the underlying input for subsequent dynamic coupling analysis without interference from the preset model.

[0032] Step S200: Construct a location change rate field based on the original latitude and longitude coordinate sequence and time data, and calculate the dynamic environment weight coefficient matrix by combining the spatial distribution differences of the three-dimensional spatial wind speed components.

[0033] To address the core issue of wind speed signal distortion caused by the movement of mobile devices, this step transforms discrete location information into a continuous velocity-direction field. By decoupling the spatial difference between the motion vector and the ambient wind speed, a weighted average of the actual wind field effect is constructed to eliminate mechanical displacement artifacts.

[0034] Step S300: Fit the environmental infiltration gradient based on the dynamic environmental weight coefficient matrix and the original temperature and humidity data to generate an adaptive heat exchange flow field;

[0035] Understandably, this step considers the interaction effect of the spatial non-uniform distribution of temperature and humidity fields in the pasture environment and the inertia of movement. By reconstructing the thermal and humidity parameters differently through a dynamic weight matrix, the traditional independent analysis of temperature and humidity is transformed into a continuous fitting of the thermodynamic comprehensive driving potential field, revealing the spontaneous permeation law of the enclosure structure in the moving state.

[0036] Step S400: Based on the adaptive heat exchange flow field and orifice geometry distribution dataset, the internal gas stagnation area is inverted using the preset fluid impedance network model, and the ventilation compensation demand curve for maintaining the minimum airflow dead angle is calculated.

[0037] To address the issue that non-uniform perforation layout exacerbates airflow stagnation during movement and turbulence, this step utilizes the differential mapping relationship between the structural topology network and the flow field to transform the physical perforation distribution into a dynamic connection diagram of fluid resistance nodes, thereby achieving causal inversion between structural characteristics and the formation mechanism of airflow dead zones.

[0038] Step S500: Model the dynamic response of the fan based on the ventilation compensation demand curve and the location change rate field, and generate a set of ventilation intensity adjustment instructions;

[0039] It should be noted that, in response to the problem of ventilation system response delay caused by sudden changes in movement acceleration, this step establishes a time-varying coupling model of position change characteristics and demand curve. By real-time quantification of movement state change parameters, the delay effect is actively pre-compensated, so that ventilation action and equipment motion state form a dynamic synergy.

[0040] Step S600: Based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, a dynamic game optimization algorithm is used to iteratively correct the fan action sequence and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance.

[0041] Understandably, based on the dual constraints of natural wind variability and energy consumption sensitivity in the pasture scenario, an equivalent game mechanism of command and real-time wind field kinetic energy is proposed. Taking the critical inflection point of the wind turbine characteristic curve as the energy consumption constraint boundary, a decision space that can be autonomously adjusted according to the effect deviation is constructed to achieve control resilience optimization under complex mobile conditions.

[0042] Further, step S200 includes steps S210 to S230.

[0043] Step S210: Perform dynamic modeling of the movement trajectory based on the original latitude and longitude coordinate sequence and time data. Calculate the position change rate field, which includes the movement speed value and the movement direction angle, by using the incremental ratio of the spatial distance between adjacent coordinate points to the time difference.

[0044] Step S220: Perform environmental wind speed compensation processing based on the position change rate field. Obtain the real environmental wind speed vector that eliminates the influence of the chicken house's own movement by performing vector subtraction operation on the projection values ​​of the three-dimensional spatial wind speed components along the movement direction.

[0045] Step S230: Based on the acceleration characteristics of the real environment wind speed vector and the position change rate field, perform environmental effect quantification processing, and generate a dynamic environment weight coefficient matrix by mapping the weight coefficients through the coupling strength function between the sudden change angle of the movement direction and the environmental wind speed vector.

[0046] Specifically, this process addresses the motion-environment coupling interference problem caused by mobile chicken houses moving through irregular terrain in pastures. It pioneers a method to reconstruct the environmental influence weights from underlying physical quantities: First, based on the original sequence of latitude and longitude coordinates and timestamps, a position change rate field describing the equipment's motion state is directly generated by calculating the real-time ratio of Euclidean distance between adjacent points to the time interval. In this process, the spatial distance increment calculation avoids dependence on GPS speed data, accurately capturing instantaneous speed and direction changes during steep slopes or sharp turns in the pasture. Then, the three-dimensional wind speed components are decomposed into east-west, north-south, and vertical axis data. The motion direction is projected based on the directional angle in the position change rate field. Vector subtraction removes the contamination of wind speed monitoring caused by the equipment's self-movement, solving the data distortion problem of "headwind becoming tailwind" caused by the mechanical displacement of the chicken house in traditional mobile equipment monitoring. Finally, the acceleration characteristics of the position change rate field are combined with the angle between the actual environmental wind speed vector. Φ Construct the coupling strength function Where w represents the coupling strength function, k represents the adjustment coefficient used to adjust the magnitude of the overall weight, and the direction of sudden acceleration. Strengthen the weighting of environmental impacts during rough and bumpy road conditions, cos Φ The dynamic adjustment is achieved based on the relationship between wind direction and equipment movement (e.g., the weight of the feeding area on the leeward side needs to be reduced), forming a dynamic environmental weight coefficient matrix that adapts to the sudden changes in pasture displacement and the heterogeneity of the microenvironment.

[0047] Further, step S300 includes steps S310 to S330.

[0048] Step S310: Based on the dynamic environmental weight coefficient matrix, the environmental impact space is discretized, and the thermal and humidity impact sub-regions are divided according to the differences in the physical characteristics of the temperature field and the humidity field to generate a discrete environmental factor influence spectrum.

[0049] Step S320: Based on the discrete environmental factor influence spectrum and the original temperature and humidity data, perform heat and mass exchange equivalent conversion processing, and obtain the comprehensive heat and humidity exchange potential field through bidirectional correction of the air enthalpy differential operator and the dew point change gradient.

[0050] Step S330: Perform flux mapping processing on the chicken house enclosure structure based on the comprehensive heat and moisture exchange potential field. Generate an adaptive heat exchange flow field by spatial convolution operation between the equivalent pore size distribution function of the porous medium and the potential gradient.

[0051] To address the coupling effect between the spatial heterogeneity of temperature and humidity fields in pasture environments and the microclimate response of mobile devices, this application constructs a hierarchical thermo-humidity driven field reconstruction mechanism. First, based on the spatial distribution characteristics of the dynamic environmental weighting coefficient matrix, the area inside and outside the chicken house is divided into physical sub-regions with different heat and mass exchange sensitivities, such as the strong radiation zone on the sunny side and the shaded zone on the leeward side. Discrete environmental factor influence spectra are generated through quantified partitioning based on the differences in the physical characteristics of the temperature and humidity fields. Then, based on this influence spectrum, the original temperature and humidity data undergo a unified thermodynamic transformation, and heat and mass equivalent quantification is performed using the comprehensive characterization capability of air enthalpy for temperature and humidity. The system processes and introduces a two-way correction for the hysteresis effect of moisture migration by the dew point change gradient (such as the high humidity accumulation phenomenon in the bottom manure belt area), constructing a comprehensive heat and moisture exchange potential field that integrates sensible heat and latent heat. This potential field automatically enhances the weight of the humidity driving force in areas where pasture evaporation is severe. Finally, combining the porous media characteristics of the mobile chicken house's perforated side panels and ventilation windows, the equivalent pore size distribution function is used as a spatial convolution kernel. Through the three-dimensional convolution operation of this kernel with the gradient of the heat and moisture exchange potential field, an adaptive flow mapping is achieved to increase the natural infiltration flux in areas with dense structural openings and suppress eddy current losses in areas with complex feeding opening structures, thus quantifying the heat and mass exchange capacity of the chicken house shell under mobile and bumpy conditions.

[0052] The formula for generating the discrete environmental factor influence spectrum is: ;in W represents the environmental factor influence spectrum at location (x, y); T The temperature sensitivity factor in the dynamic environment weighting coefficient matrix is ​​represented by values ​​of 0.8-0.9 for the sunny side and 0.3-0.4 for the shady side. Indicates the temperature gradient intensity (the maximum temperature difference between the calculated point and its adjacent points; <2℃ in pasture-shaded areas, >5℃ in bare areas); W H Indicates the humidity sensitivity factor (0.7-0.8 for the bottom manure belt area, 0.2-0.3 for the top ventilation area); This represents the humidity diffusion gradient (evaporation effect coefficient in pasture-covered areas, with a value of 1.2-1.5 during the rainy season).

[0053] The formula for constructing the potential field of comprehensive heat and moisture exchange is: Where ψ represents the combined potential field value of heat and moisture exchange; c p dT represents the specific heat capacity of air at constant pressure; h represents the differential temperature increment; fg dH represents the latent heat of water vaporization; λ represents the differential increment of humidity; and λ represents the evaporation inhibition coefficient of pasture (0.6 for grass height > 30cm, and 0.3 for low-grass areas). represents the dew point change gradient operator; e represents the natural base.

[0054] The formula for generating an adaptive heat exchange flow field is expressed as: Where Φ(x,y) represents the heat exchange flow rate at location (x,y); A represents the equivalent aperture distribution function; S represents the structural attenuation factor (0.7 for screen structure, 0.9 for thin steel plate opening); η represents the bump compensation coefficient (1.3 for gravel road surface, 0.8 for flat grassland). Ψ represents the potential field normal gradient (the vertical rate of change of the building envelope), Ψ represents the potential field value under the combined effect of heat and moisture exchange, dx represents the differential length in the x-direction, and dy represents the differential length in the y-direction.

[0055] Further, step S400 includes steps S410 to S430.

[0056] Step S410: Perform pore fluid dynamics modeling based on the pore geometric distribution dataset, and generate a dynamic pore connectivity network diagram by determining the topological connectivity of the angle between the spatial distance vector and the normal of adjacent pores.

[0057] Step S420: Based on the dynamic aperture connectivity network diagram and the adaptive heat exchange flow field, the stagnation region is identified. The geometric boundary characteristics of the gas stagnation region are determined by calculating the spatial differential relationship between the mean gradient of the flow difference between the orifices and the local static pressure difference. Step S430: Based on the geometric boundary characteristics of the gas stagnation region, the compensation air volume is iteratively solved. The ventilation compensation demand curve is generated by the flow distribution ratio optimization algorithm under the minimum airflow velocity threshold constraint.

[0058] This step chain uses a pre-defined fluid impedance network model as its physical core. In the orifice fluid dynamics modeling process, based on predefined orifice connectivity judgment rules (i.e., when the included angle between the normals of adjacent orifices is less than a pre-defined threshold of 45 degrees, an impedance path is automatically generated), the geometric distribution of orifices is transformed into a dynamic network diagram with directional impedance attributes. In the stagnation area identification stage, the inherent impedance characteristics of this network directly participate in the differential calculation of the mean gradient of the flow difference between orifices, ensuring that the vortex effect generated by the bumps and vibrations of the gravel road surface is quantified as a static pressure anomaly point under impedance constraints. Finally, in the compensation airflow solution, the minimum airflow velocity threshold is transformed into an equivalent pressure difference boundary through a pre-defined impedance-flow conversion equation, driving the iterative optimization of the flow distribution ratio within the impedance network topology framework. This ensures that high-impedance areas such as the feeding port receive sufficient compensation airflow under the turning and braking conditions of avoiding haystacks. The flow distribution ratio optimization algorithm under the minimum airflow velocity threshold constraint is expressed as follows: ; where Q comp (t) represents the total compensated air volume requirement at time t; A i This represents the equivalent ventilation area of ​​the i-th retention zone; α represents the sum of the areas of all retention zones; i represents the retention zone number; n represents the total number of retention zones; α represents the basic area allocation coefficient; β represents the dynamic compensation factor for steering conditions. Indicates the directional change response intensity of region i; v min Indicates the minimum airflow velocity threshold; This represents the road surface bump attenuation coefficient. For gravel roads, the value is 0.7-0.8 (high frequency suppression), and for flat grass, it is 1.0 (no attenuation).

[0059] Further, step S500 includes steps S510 to S530.

[0060] Step S510: Extract the dynamic features of the movement based on the position change rate field. Obtain the characteristic parameters of the sudden change in movement state by calculating the directional derivative of the velocity vector and the acceleration modulus.

[0061] Step S520: Based on the characteristic parameters of the sudden change in the moving state and the demand curve of the ventilation compensation amount, perform time delay effect quantification processing, and generate the ventilation delay compensation time constant by performing differential correction calculation on the slope of the demand curve through acceleration.

[0062] Step S530: Perform pre-compensation processing of fan operation based on ventilation delay compensation time constant, and reconstruct the ventilation intensity adjustment instruction set by shifting the demand curve time axis and the response attenuation compensation coefficient of acceleration constraint.

[0063] Specifically, addressing the ventilation response lag issue caused by sudden movements such as abrupt stops and turns in unstructured pasture conditions, this application constructs an inertial-time-delay collaborative compensation mechanism: First, based on the spatial distribution characteristics of the velocity vector of the position change rate field, the characteristic parameters representing the sudden changes in equipment motion are calculated by fusing the velocity direction derivative (i.e., the rate of change of the angle of travel direction per unit time) with the synthetic acceleration scalar. This process quantifies the changes in angular velocity during sudden turns when avoiding pasture piles and the acceleration impact during steep slope climbing into identifiable dynamic fingerprints. Then, the characteristic parameters of this sudden change are differentially corrected with the real-time slope of the ventilation compensation demand curve. The calculation (i.e., when a sharp change in direction is detected, the steep drop in the slope of the demand curve is enhanced) generates a ventilation delay compensation time constant that adapts to different turbulence intensities. The technical essence of this is to infer the theoretical hysteresis of the hydraulic response of the fan system by inversely calculating the intensity of the mechanical motion change. Finally, the fan action pre-compensation is implemented in combination with this time constant. On the one hand, the demand curve is moved forward along the time axis to eliminate the inherent time lag. On the other hand, the response attenuation compensation coefficient is dynamically reconstructed based on the acceleration amplitude. A comprehensive set of ventilation intensity adjustment instructions is generated to match the complex road conditions of the pasture. This process has particularly optimized the air volume overshoot suppression capability of the mobile chicken house walking mechanism under the condition of short-term strong vibration when turning between haystacks.

[0064] The formula for the characteristic parameters of the abrupt change in the mobile state is: Where K represents the characteristic parameter of the abrupt change in the mobile state; Indicates the rate of change of direction angle; a effRepresents the equivalent acceleration modulus; ω c This represents the trajectory curvature factor.

[0065] The formula for generating the ventilation delay compensation time constant is: ;in, This represents the ventilation delay compensation time constant; This represents the instantaneous slope of the demand curve; σ represents the curvature of the demand curve; μ represents the hydraulic damping coefficient of the wind turbine; σ represents the attenuation factor of the pasture road surface; and φ represents the overshoot suppression gain.

[0066] The formula for reconstructing the ventilation intensity command is: ;in, Indicates the ventilation intensity command value; This represents the demand curve after time shift; ζ represents the impeller inertia coefficient. The second derivative of the direction angle is represented by ε; ε represents the surge suppression factor. This indicates the effective rotational speed of the impeller.

[0067] Further, step S600 includes steps S610 to S630.

[0068] Step S610: Quantify the ventilation effect deviation based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components. Calculate the ventilation effect deviation characteristic value by converting the kinetic energy equivalent of the commanded air volume and the measured wind speed vector.

[0069] Step S620: Perform energy consumption constraint boundary modeling based on the characteristic value of ventilation effect deviation, and generate dynamic energy consumption constraint boundary function by detecting the inflection point of the second derivative of the fan power-air volume characteristic curve.

[0070] Step S630: Perform multi-objective collaborative optimization based on the characteristic value of ventilation effect deviation and the boundary function of dynamic energy consumption constraint. Output the optimal ventilation control parameter sequence through the variable weight gradient descent iteration of the effect-energy consumption game matrix.

[0071] It should be noted that, based on the dual constraints of ventilation stability and equipment energy consumption sensitivity of mobile chicken coops under sudden changes in natural wind conditions on pastures, this process creates a dynamic equilibrium intelligent decision-making mechanism: First, the theoretical air volume of the ventilation command is converted into equivalent air kinetic energy. Simultaneously, the actual wind field kinetic energy is reconstructed based on measured values ​​of wind speed components in the east-west / north-south / vertical three-dimensional space. Through the kinetic energy equivalent conversion of the deviation values ​​between the two (essentially an energy level benchmark excluding equipment movement interference), a ventilation effect deviation characteristic value is constructed. This process directly quantifies the airflow energy loss rate when grassland gusts penetrate the side mesh. Then, based on this deviation characteristic value, wind... The system's energy consumption constraint elastic adjustment mechanism automatically calibrates the optimal energy consumption boundary function under the current operating conditions by analyzing the second derivative characteristics of the fan power change curve with air volume (i.e., detecting the inflection point of power efficiency change with flow rate). This technology avoids the rigidity defect of fixed energy consumption threshold under the battery life limit of ranch equipment. Finally, a dynamic game matrix is ​​established with ventilation effect deviation as the vertical axis and energy consumption ratio as the horizontal axis. A gradient descent optimization strategy that autonomously adjusts the weight coefficients according to the deviation amplitude is adopted to iteratively generate the optimal control parameter sequence that takes into account both sudden environmental disturbances and equipment lifespan under the constraint of ensuring minimum airflow velocity in the chicken house.

[0072] Example 2:

[0073] like Figure 2 As shown, this embodiment provides a mobile chicken coop ventilation control system based on an environmental model. The system includes:

[0074] The acquisition module 901 is used to acquire the original temperature data, original humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and original latitude and longitude coordinate data and time data during the movement of the chicken house.

[0075] Module 902 is used to construct a location change rate field based on the original latitude and longitude coordinate sequence and time data, and to calculate the dynamic environment weight coefficient matrix by combining the spatial distribution differences of the three-dimensional wind speed components.

[0076] The fitting module 903 is used to fit the environmental infiltration gradient based on the dynamic environmental weight coefficient matrix and the original temperature data and original humidity data to generate an adaptive heat exchange flow field.

[0077] The inversion module 904 is used to invert the internal gas stagnation area based on the adaptive heat exchange flow field and the opening geometry distribution dataset, and to calculate the ventilation compensation demand curve to maintain the minimum airflow dead angle.

[0078] Modeling module 905 is used to model the dynamic response of the fan based on the ventilation compensation demand curve and the location change rate field, and generate a set of ventilation intensity adjustment instructions.

[0079] The output module 906 is used to iteratively correct the fan action sequence based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed component, and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance.

[0080] In one specific embodiment of this application, the construction module 902 includes:

[0081] The first building unit is used to perform dynamic modeling of the movement trajectory based on the original latitude and longitude coordinate sequence and time data. It calculates the position change rate field containing the movement speed value and the movement direction angle by using the incremental ratio of the spatial distance and time difference between adjacent coordinate points.

[0082] The second building unit is used to perform environmental wind speed compensation processing based on the position change rate field. It obtains the real environmental wind speed vector by vector subtraction of the projection values ​​of the three-dimensional spatial wind speed components along the movement direction, thus eliminating the influence of the chicken house's own movement.

[0083] The third building unit is used to quantify the environmental effects based on the acceleration characteristics of the real environmental wind speed vector and the rate of change of position field. It generates a dynamic environmental weight coefficient matrix by mapping the weight coefficients through the coupling strength function between the abrupt change angle of the movement direction and the environmental wind speed vector.

[0084] In one specific embodiment of this application, the fitting module 903 includes:

[0085] The first fitting unit is used to discretize the environmental impact space based on the dynamic environmental weight coefficient matrix, divide the thermal and humidity impact sub-regions by the difference in physical characteristics of the temperature field and humidity field, and generate a discrete environmental factor influence spectrum.

[0086] The second fitting unit is used to perform heat and mass exchange equivalent conversion processing based on the discrete environmental factor influence spectrum and the original temperature and humidity data. Through bidirectional correction of the air enthalpy differential operator and the dew point change gradient, the comprehensive action potential field of heat and moisture exchange is obtained.

[0087] The third fitting unit is used to perform flux mapping processing of the chicken house enclosure structure based on the comprehensive heat and moisture exchange potential field. It generates an adaptive heat exchange flow field by performing spatial convolution operation between the equivalent pore size distribution function of the porous medium and the potential gradient.

[0088] In one specific embodiment of this application, the inversion module 904 includes:

[0089] The first inversion unit is used to perform pore fluid dynamics modeling based on the pore geometric distribution dataset. It generates a dynamic pore connectivity network graph by determining the topological connectivity between the spatial distance vector of adjacent pores and the normal.

[0090] The second inversion unit is used to identify the stagnation region based on the dynamic aperture connectivity network diagram and the adaptive heat exchange flow field. It determines the geometric boundary characteristics of the gas stagnation region by calculating the spatial differential relationship between the mean gradient of the flow difference between the apertures and the local static pressure difference.

[0091] The third inversion unit is used to perform iterative solution of compensation air volume based on the geometric boundary characteristics of the gas stagnation zone. It generates the ventilation compensation demand curve through a flow distribution ratio optimization algorithm under the minimum airflow velocity threshold constraint.

[0092] In one specific embodiment of this application, the modeling module 905 includes:

[0093] The first modeling unit is used to extract the dynamic features of movement based on the position change rate field. By calculating the directional derivative of the velocity vector and the acceleration modulus, the characteristic parameters of the sudden change in movement state are obtained.

[0094] The second modeling unit is used to quantify the time delay effect based on the characteristic parameters of the sudden change in the moving state and the demand curve of the ventilation compensation amount. It generates the ventilation delay compensation time constant by performing differential correction calculation on the slope of the demand curve through acceleration.

[0095] The third modeling unit is used to perform pre-compensation processing of fan operation based on the ventilation delay compensation time constant. It reconstructs the ventilation intensity adjustment instruction set by shifting the demand curve time axis and the response attenuation compensation coefficient of acceleration constraint.

[0096] In one specific embodiment of this application, the output module 906 includes:

[0097] The first output unit is used to quantify the ventilation effect deviation based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed component. It calculates the ventilation effect deviation characteristic value by converting the kinetic energy equivalent of the command air volume and the measured wind speed vector.

[0098] The second output unit is used to perform energy consumption constraint boundary modeling based on the characteristic value of ventilation effect deviation. It generates a dynamic energy consumption constraint boundary function by detecting the inflection point of the second derivative of the fan power-air volume characteristic curve.

[0099] The third output unit is used to perform multi-objective collaborative optimization based on the characteristic value of ventilation effect deviation and the dynamic energy consumption constraint boundary function. Through the variable weight gradient descent iteration of the effect-energy consumption game matrix, the optimal ventilation control parameter sequence is output.

[0100] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling ventilation in a mobile chicken coop based on an environmental model, characterized in that, include: Acquire raw temperature data, raw humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and raw latitude and longitude coordinate data and time data during the movement of the chicken house, both inside and outside the mobile chicken house. A location change rate field is constructed based on the original latitude and longitude coordinate sequence and the time data, and a dynamic environment weight coefficient matrix is ​​calculated by combining the spatial distribution differences of the three-dimensional spatial wind speed components. An adaptive heat exchange flow field is generated by fitting the environmental infiltration gradient with the dynamic environmental weight coefficient matrix and the original temperature and humidity data. Based on the adaptive heat exchange flow field and the opening geometric distribution dataset, the internal gas stagnation area is inverted using a preset fluid impedance network model, and the ventilation compensation demand curve for maintaining the minimum airflow dead angle is calculated. Based on the ventilation compensation demand curve and the location change rate field, the dynamic response of the fan is modeled, and a set of ventilation intensity adjustment instructions is generated. Based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, a dynamic game optimization algorithm is used to iteratively correct the fan action sequence and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance. Specifically, based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, a dynamic game optimization algorithm is used to iteratively correct the fan action sequence, outputting optimal ventilation control parameters that satisfy energy consumption-efficiency balance, including: The ventilation effect deviation is quantified based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components. The characteristic value of the ventilation effect deviation is obtained by converting the kinetic energy equivalent of the command air volume and the measured wind speed vector. Based on the characteristic value of the ventilation effect deviation, energy consumption constraint boundary modeling is performed, and dynamic energy consumption constraint boundary function is generated by detecting the inflection point of the second derivative of the fan power-air volume characteristic curve. Based on the characteristic value of the ventilation effect deviation and the boundary function of the dynamic energy consumption constraint, a multi-objective collaborative optimization process is performed. Through the variable weight gradient descent iteration of the effect-energy consumption game matrix, the optimal ventilation control parameter sequence is output.

2. The ventilation control method for mobile chicken houses based on an environmental model according to claim 1, characterized in that, Based on the original latitude and longitude coordinate sequence and the time data, a location change rate field is constructed, and a dynamic environment weight coefficient matrix is ​​calculated by combining the spatial distribution differences of the three-dimensional spatial wind speed components, including: Based on the original latitude and longitude coordinate sequence and the time data, dynamic modeling of the movement trajectory is performed. By calculating the incremental ratio of the spatial distance between adjacent coordinate points to the time difference, a position change rate field containing the movement speed value and the movement direction angle is obtained. Environmental wind speed compensation processing is performed based on the position change rate field. By vector subtraction of the projection values ​​of the three-dimensional spatial wind speed components along the movement direction, the true environmental wind speed vector that eliminates the influence of the chicken house's own movement is obtained. The environmental effects are quantified based on the acceleration characteristics of the real environment wind speed vector and the position change rate field. A dynamic environment weight coefficient matrix is ​​generated by mapping the coupling strength function between the acceleration characteristics of the position change rate field and the real environment wind speed vector and the acceleration of abrupt changes in direction.

3. The ventilation control method for mobile chicken houses based on an environmental model according to claim 1, characterized in that, An adaptive heat exchange flow field is generated by fitting the environmental infiltration gradient based on the dynamic environmental weighting coefficient matrix, the original temperature data, and the original humidity data, including: Based on the dynamic environmental weight coefficient matrix, the environmental impact space is discretized, and the thermal and humidity impact sub-regions are divided by the difference in physical characteristics of the temperature field and humidity field to generate a discrete environmental factor influence spectrum. Based on the discrete environmental factor influence spectrum, the original temperature data, and the original humidity data, heat and mass exchange equivalent conversion processing is performed. Through bidirectional correction of the air enthalpy differential operator and the dew point change gradient, the comprehensive heat and humidity exchange potential field is obtained. Based on the combined heat and moisture exchange potential field, the flux mapping of the chicken house enclosure structure is performed. An adaptive heat exchange flow field is generated by spatial convolution operation between the equivalent pore size distribution function of the porous medium and the potential gradient.

4. The ventilation control method for mobile chicken houses based on an environmental model according to claim 1, characterized in that, Based on the adaptive heat exchange flow field and the aperture geometry distribution dataset, a preset fluid impedance network model is applied to invert the internal gas retention area, and the ventilation compensation demand curve for maintaining the minimum airflow dead angle is calculated, including: Based on the aperture geometric distribution dataset, the aperture fluid dynamics modeling process is performed. Using a preset fluid impedance network model as the physical kernel, and according to the predefined inter-aperture connectivity judgment rules, the aperture geometric distribution is transformed into a dynamic network diagram with directional impedance attributes, generating a dynamic aperture connectivity network diagram. Based on the dynamic aperture connectivity network diagram and the adaptive heat exchange flow field, the stagnation region is identified, and the geometric boundary characteristics of the gas stagnation region are determined by calculating the spatial differential relationship between the mean gradient of the flow difference between the orifices and the local static pressure difference. The compensation air volume is iteratively solved based on the geometric boundary characteristics of the gas retention zone. The ventilation compensation demand curve is generated by an optimization algorithm for the flow distribution ratio under the constraint of minimum airflow velocity threshold.

5. The ventilation control method for mobile chicken houses based on an environmental model according to claim 1, characterized in that, Based on the ventilation compensation demand curve and the location change rate field, the dynamic response of the fan is modeled, and a ventilation intensity adjustment command set is generated, including: Based on the position change rate field, the movement dynamics feature extraction process is performed, and the movement state change feature parameters are obtained by calculating the directional derivative of the velocity vector and the acceleration modulus. The time delay effect is quantified based on the mobile state change characteristic parameters and the ventilation compensation demand curve. The real-time slope of the ventilation compensation demand curve is differentially corrected by the mobile state change characteristic parameters to generate the ventilation delay compensation time constant. Based on the ventilation delay compensation time constant, the fan action pre-compensation process is performed. By shifting the demand curve time axis and dynamically reconstructing the response attenuation compensation coefficient based on the acceleration amplitude, a ventilation intensity adjustment instruction set is generated.

6. A mobile chicken coop ventilation control system based on an environmental model, characterized in that, include: The acquisition module is used to acquire raw temperature data, raw humidity data, three-dimensional spatial wind speed components, geometric distribution dataset of openings on the surface of the chicken house, and raw latitude and longitude coordinate data and time data during the movement of the chicken house. The construction module is used to construct a location change rate field based on the original latitude and longitude coordinate sequence and the time data, and calculate the dynamic environment weight coefficient matrix by combining the spatial distribution differences of the three-dimensional spatial wind speed components. The fitting module is used to fit the environmental infiltration gradient based on the dynamic environmental weight coefficient matrix and the original temperature data and the original humidity data to generate an adaptive heat exchange flow field. The inversion module is used to invert the internal gas stagnation area based on the adaptive heat exchange flow field and the opening geometric distribution dataset, and to calculate the ventilation compensation demand curve to maintain the minimum airflow dead angle. The modeling module is used to model the dynamic response of the fan based on the ventilation compensation demand curve and the location change rate field, and generate a set of ventilation intensity adjustment instructions. The output module is used to iteratively correct the fan action sequence using a dynamic game optimization algorithm based on the ventilation intensity adjustment instruction set and the three-dimensional spatial wind speed components, and output the optimal ventilation control parameters that satisfy the energy consumption-effect balance. Specifically, based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components, a dynamic game optimization algorithm is used to iteratively correct the fan action sequence, outputting optimal ventilation control parameters that satisfy energy consumption-efficiency balance, including: The ventilation effect deviation is quantified based on the ventilation intensity adjustment command set and the three-dimensional spatial wind speed components. The characteristic value of the ventilation effect deviation is obtained by converting the kinetic energy equivalent of the command air volume and the measured wind speed vector. Based on the characteristic value of the ventilation effect deviation, energy consumption constraint boundary modeling is performed, and dynamic energy consumption constraint boundary function is generated by detecting the inflection point of the second derivative of the fan power-air volume characteristic curve. Based on the characteristic value of the ventilation effect deviation and the boundary function of the dynamic energy consumption constraint, a multi-objective collaborative optimization process is performed. Through the variable weight gradient descent iteration of the effect-energy consumption game matrix, the optimal ventilation control parameter sequence is output.

7. The mobile chicken coop ventilation control system based on an environmental model according to claim 6, characterized in that, The building module includes: The first construction unit is used to perform dynamic modeling of the movement trajectory based on the original latitude and longitude coordinate sequence and the time data, and to obtain a position change rate field containing the movement speed value and the movement direction angle by calculating the incremental ratio of the spatial distance between adjacent coordinate points to the time difference. The second construction unit is used to perform environmental wind speed compensation processing based on the position change rate field. By performing vector subtraction of the projection values ​​of the three-dimensional spatial wind speed components along the movement direction, the true environmental wind speed vector that eliminates the influence of the chicken house's own movement is obtained. The third construction unit is used to perform environmental effect quantification processing based on the acceleration characteristics of the real environment wind speed vector and the position change rate field, and to generate a dynamic environment weight coefficient matrix by mapping weight coefficients through the coupling strength function of the angle between the acceleration characteristics of the position change rate field and the real environment wind speed vector and the acceleration of abrupt changes in direction.

8. The mobile chicken coop ventilation control system based on an environmental model according to claim 6, characterized in that, The fitting module includes: The first fitting unit is used to perform spatial discretization of environmental effects based on the dynamic environmental weight coefficient matrix, divide the thermal and humidity effects into sub-regions based on the differences in physical characteristics of the temperature field and humidity field, and generate a discrete environmental factor influence spectrum. The second fitting unit is used to perform heat and mass exchange equivalent conversion processing based on the discrete environmental factor influence spectrum, the original temperature data, and the original humidity data, and obtain the comprehensive heat and humidity exchange potential field through bidirectional correction of the air enthalpy differential operator and the dew point change gradient. The third fitting unit is used to perform flux mapping processing of the chicken house enclosure structure based on the comprehensive heat and moisture exchange potential field. It generates an adaptive heat exchange flow field by performing spatial convolution operation between the equivalent pore size distribution function of the porous medium and the potential gradient.

9. The mobile chicken coop ventilation control system based on an environmental model according to claim 6, characterized in that, The inversion module includes: The first inversion unit is used to perform pore fluid dynamics modeling based on the pore geometric distribution dataset. Using a preset fluid impedance network model as the physical kernel, and based on the predefined inter-pore connectivity judgment rules, the pore geometric distribution is transformed into a dynamic network diagram with directional impedance attributes, generating a dynamic aperture connectivity network diagram. The second inversion unit is used to identify the stagnation region based on the dynamic aperture connectivity network diagram and the adaptive heat exchange flow field, and to determine the geometric boundary characteristics of the gas stagnation region by calculating the spatial differential relationship between the mean gradient of the inter-aperture flow difference and the local static pressure difference. The third inversion unit is used to perform iterative solution processing of the compensation air volume based on the geometric boundary characteristics of the gas stagnation zone, and generate the ventilation compensation demand curve through the flow distribution ratio optimization algorithm under the minimum airflow velocity threshold constraint.

10. The mobile chicken coop ventilation control system based on an environmental model according to claim 6, characterized in that, The modeling module includes: The first modeling unit is used to perform motion dynamics feature extraction processing based on the position change rate field, and obtain the motion state change feature parameters by calculating the directional derivative of the velocity vector and the acceleration modulus. The second modeling unit is used to perform time delay effect quantification processing based on the mobile state change characteristic parameters and the ventilation compensation demand curve, and to perform differential correction calculation on the real-time slope of the ventilation compensation demand curve through the mobile state change characteristic parameters to generate a ventilation delay compensation time constant. The third modeling unit is used to perform pre-compensation processing of fan operation based on the ventilation delay compensation time constant, and to generate a set of ventilation intensity adjustment instructions by shifting the demand curve time axis and dynamically reconstructing the response attenuation compensation coefficient based on the acceleration amplitude.

Images