Intelligent joint control system and method for livestock breeding environment

Abstract

The invention relates to the technical field of automatic control, in particular to an intelligent joint control system and method for a livestock breeding environment, and the system comprises a load sensing module which collects the current of a motor, extracts the effective load duration, and calculates the total single feeding amount, a heat inversion module which generates the total heat of biological metabolism based on the total amount and a heat increment coefficient, the system comprises a real-time specific enthalpy generation module, a gamma distribution adjustment building heat production rate curve and generating an expected sensible heat power sequence, an enthalpy value compensation module for converting the sequence into an enthalpy value drop amplitude and generating a feedforward compensation target enthalpy value, and a joint control execution module for generating a negative pressure fan control instruction based on a difference value between the real-time specific enthalpy and the target enthalpy value. According to the method, the total feeding amount is calculated, the heat production rate curve is constructed in combination with the heat consumption increasing coefficient, the sensible heat power is rehearsed, the feed-forward compensation target enthalpy value is determined, the draught fan is driven in advance before heat accumulation, prospective intervention based on the metabolic rhythm is achieved, control lag is eliminated, and stable transition of the breeding microenvironment is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to an intelligent joint control system and method for livestock farming environment. Background Technology

[0002] The field of automatic control technology involves constructing closed-loop or open-loop circuits using control devices, sensors, and actuators, and using computers or microprocessors to monitor and adjust the physical parameters of the controlled object in real time so that its operating state conforms to preset logic or standards. Traditional intelligent control systems for livestock farming environments consist of temperature sensors, humidity sensors, and ammonia concentration transmitters installed inside the livestock sheds, connected to a programmable logic controller (PLC) or microcontroller mainboard in the field via an RS485 communication bus or analog interface. The controller reads the real-time data collected by the sensors and compares it with set thresholds in its internal memory. When the monitored value deviates from the set range, the controller outputs a level signal through its I / O port to drive the coils of intermediate relays and AC contactors, thereby connecting the power supply circuits of negative pressure fans, wet curtain pumps, or electric heaters.

[0003] Existing technologies rely solely on sensors deployed in the environment to collect real-time air parameters and compare them with fixed thresholds. This passive feedback regulation mode based on the current environmental state has a significant lag, ignoring the metabolic heat production patterns of organisms as the main heat source. In particular, it does not consider the temporal correlation between the increase in body heat caused by feeding behavior and changes in environmental temperature and humidity. As a result, the control system can only trigger the actuators after the environmental parameters actually deviate, making it difficult to intervene in the heat load in advance before the concentrated release of metabolic heat from organisms. This leads to delayed response in the regulation of the aquaculture environment and drastic fluctuations in temperature and humidity, failing to meet the actual needs of high-density aquaculture for microenvironmental stability and predictive regulation. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent joint control system and method for livestock farming environment.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent joint control system for livestock farming environment, comprising: The load sensing module collects the operating current of the feeding motor, extracts the duration during which the operating current exceeds the no-load threshold as the effective load duration, calculates the product of the effective load duration and the transmission efficiency parameter, generates the total amount of feeding in a single feeding, and transfers the total amount of feeding in a single feeding to the heat inversion module. The heat inversion module generates total biological metabolic heat based on the total amount of a single feeding and the heat consumption coefficient, adjusts the gamma distribution probability density function based on the total biological metabolic heat to construct a metabolic heat production rate curve, samples the metabolic heat production rate curve to generate an expected sensible heat power sequence, and transmits the expected sensible heat power sequence to the enthalpy compensation module. The enthalpy compensation module uses reference ventilation mass flow rate parameters to convert the expected sensible heat power sequence into enthalpy decrease range, calculates the difference between the comfort zone enthalpy benchmark and the enthalpy decrease range, generates a feedforward compensation target enthalpy value, and transmits the feedforward compensation target enthalpy value to the joint control execution module. The joint control execution module calculates the real-time specific enthalpy based on dry-bulb temperature and relative humidity. Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, it generates a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

[0006] As a further aspect of the present invention, the total amount of a single feeding includes the total mass of feed fed and the effective load duration record; the expected sensible heat power sequence includes the time step index within the future prediction period and the sensible heat production power value corresponding to each time step; the feedforward compensation target enthalpy value includes the corrected dynamic air specific enthalpy setpoint and the target enthalpy value reduction range; and the negative pressure fan control command includes the fan unit start / stop control signal and the frequency conversion adjustment duty cycle parameter.

[0007] As a further embodiment of the present invention, the load sensing module includes a current monitoring submodule and a total calculation submodule; The current monitoring submodule collects the operating current of the feeding motor and performs filtering processing on the operating current using a sliding window algorithm to obtain a smooth current value; Determine whether the smoothed current value is greater than the no-load threshold. If the smoothed current value is greater than the no-load threshold, accumulate the duration to generate the effective load duration. The total calculation submodule obtains the effective load duration and the transmission efficiency parameters; The product of the effective load duration and the transmission efficiency parameter is calculated to generate the total amount of a single feeding.

[0008] As a further aspect of the present invention, the heat inversion module includes a total heat calculation submodule, the specific function of which is as follows: Obtain the total amount of feed given in a single feeding and the heat loss coefficient; The total metabolic heat of the organism is generated by multiplying the total amount of food fed in a single feeding by the heat increase coefficient. The total heat of biological metabolism is transferred to the curve construction submodule in the heat inversion module.

[0009] As a further aspect of the present invention, the heat inversion module further includes a curve construction submodule, the specific function of which is as follows: Obtain the total metabolic heat of the organism and the preset peak time parameter; The shape and scale parameters of the gamma distribution probability density function are determined based on the peak time parameter. Using the total metabolic heat, the shape parameter, and the scale parameter, the metabolic heat production rate curve is constructed according to the following formula: ; in, represent The rate of metabolic heat production at any given time. Represents the total metabolic energy of the organism. Represents the shape parameters, This represents the scale parameter. Represents the gamma function. This represents a relative time variable calculated from the start of feeding.

[0010] As a further aspect of the present invention, the heat inversion module further includes a sequence generation submodule, the specific function of which is as follows: Obtain the metabolic heat production rate curve and the time step index within the future prediction period; Based on the time step index, the sampling time point is determined, the function value of the metabolic heat production rate curve at each sampling time point is calculated, and the instantaneous sensible heat power is generated. Based on each instantaneous sensible heat power, the expected sensible heat power sequence is constructed in chronological order.

[0011] As a further aspect of the present invention, the enthalpy compensation module includes an enthalpy conversion submodule, the specific function of which is as follows: Obtain the expected sensible heat power sequence and the reference ventilation mass flow rate parameter; Using the reference ventilation mass flow rate parameter, the sensible heat production power values ​​in the expected sensible heat power sequence are converted into the enthalpy decrease rate item by item according to the following formula: ; in, Representing the The decrease in enthalpy value over each time step The expected sensible heat power sequence represents the first... The sensible heat production power value for each time step. This represents the reference ventilation mass flow rate parameter.

[0012] As a further aspect of the present invention, the enthalpy compensation module further includes a target setting submodule, the specific function of which is as follows: Obtain the comfort zone enthalpy baseline and the enthalpy decrease rate; The difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate is calculated to generate the feedforward compensation target enthalpy.

[0013] As a further aspect of the present invention, the joint control execution module includes an environmental monitoring submodule and a fan control submodule; The environmental monitoring submodule collects the dry bulb temperature and relative humidity of the aquaculture environment; The real-time specific enthalpy was calculated based on a moist air property model. The wind turbine control submodule acquires the real-time specific enthalpy and the feedforward compensation target enthalpy value; Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value is calculated, and the negative pressure fan control command is generated based on the difference.

[0014] A method for intelligent joint control of livestock farming environment, the method being executed based on the aforementioned intelligent joint control system for livestock farming environment, includes the following steps: S1. Collect the operating current of the feeding motor, extract the duration during which the operating current exceeds the no-load threshold as the effective load duration, calculate the product of the effective load duration and the transmission efficiency parameter, and generate the total amount of feeding in a single feeding. S2. Generate total biological metabolic heat based on the total amount of single feeding and the heat consumption coefficient, adjust the gamma distribution probability density function based on the total biological metabolic heat to construct a metabolic heat production rate curve, and sample the metabolic heat production rate curve to generate the expected sensible heat power sequence. S3. Using the reference ventilation mass flow rate parameter, the expected sensible heat power sequence is converted into the enthalpy decrease rate. The difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate is calculated to generate the feedforward compensation target enthalpy. S4. Calculate the real-time specific enthalpy based on dry-bulb temperature and relative humidity. Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, generate a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the total amount of feed per feeding is accurately calculated by collecting the operating current of the feeding motor and extracting the effective load duration. The metabolic heat production rate curve is constructed by combining the heat loss coefficient and the gamma distribution probability density function, thereby predicting the sensible heat power sequence of the organism in the future period after feeding. The expected heat load is converted into the enthalpy decrease rate by using the specific heat capacity parameter, and the feedforward compensation target enthalpy value is established accordingly. Before the actual occurrence of environmental heat accumulation, the negative pressure fan is driven in advance based on the difference between the real-time specific enthalpy and the target value, realizing a forward-looking thermal environment intervention based on the biological metabolic rhythm. This effectively eliminates the drawbacks of traditional feedback control lag and ensures a smooth transition and precise constant thermodynamic state of the microenvironment in the breeding space. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall architecture of the intelligent joint control system for livestock farming environment of the present invention. Figure 2 This is a flowchart of the operation of the load sensing module of the present invention; Figure 3 This is a flowchart of the operation of the heat inversion module of the present invention; Figure 4 This is a flowchart of the enthalpy compensation module operation of the present invention; Figure 5 This is a flowchart of the operation of the joint control execution module of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0018] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0019] Please see Figure 1 and Figure 2 This invention provides a technical solution: an intelligent joint control system for livestock farming environment, comprising: The load sensing module collects the operating current of the feeding motor, extracts the duration of the operating current exceeding the no-load threshold as the effective load duration, calculates the product of the effective load duration and the transmission efficiency parameter, generates the total amount of feeding in a single feeding, and transfers the total amount of feeding in a single feeding to the heat inversion module. The total amount of feed fed at one time includes the total mass of feed fed and the duration of effective load. The load sensing module includes a current monitoring submodule and a total load calculation submodule; The current monitoring submodule collects the operating current of the feeding motor and uses a sliding window algorithm to filter the operating current to obtain a smooth current value. Determine if the smoothed current value is greater than the no-load threshold. If the smoothed current value is greater than the no-load threshold, accumulate the duration to generate the effective load duration. The total quantity calculation submodule obtains the effective load duration and transmission efficiency parameters; Calculate the product of the effective payload duration and the transmission efficiency parameter to generate the total amount of feed per cycle.

[0020] The load sensing module first performs data acquisition using Hall current sensors installed at the three-phase input terminals of the feed motor. The Hall current sensor is selected from ACS712 or equivalent industrial-grade sensors. The sensor captures the single-phase or three-phase current on the stator side of the feed motor in real time at a sampling frequency of 50Hz and converts the analog signal into a digital signal, which is then input to the STM32F4 series microcontroller. The STM32F4 series microcontroller establishes a length of... A time-series sliding window is used to store the most recent 10 sampled instantaneous current values. Whenever a new current sample value is obtained... The earliest sampled value enters the queue. Once removed, the system performs an arithmetic mean operation on the data within the window to obtain a smoothed current value. This process aims to eliminate high-frequency noise interference caused by motor startup, mechanical vibration, or power grid fluctuations.

[0021] The aforementioned sliding window algorithm is a signal processing method that moves a fixed-length window interval over time series data and calculates the statistical characteristics (such as the average value) of the data within that window. It can effectively filter out high-frequency random noise and retain the low-frequency trend characteristics of the signal.

[0022] Regarding the no-load threshold ( The system is configured to enter "benchmark calibration mode" after initial installation or equipment maintenance. In this mode, the feeding drive chain (such as an auger or chain) is idle, and the motor runs for 30 minutes. The system continuously collects smoothed current values ​​during this period and calculates their statistical distribution. The no-load threshold is set to the average current value during the calibration period plus 5 times the standard deviation, i.e. For example, in a certain measured calibration, the no-load average current... It is 2.10A, with a standard deviation of 2.10. If the value is 0.04A, then the no-load threshold is calculated. A. This setting ensures that current fluctuations are only considered as effective load when the motor actually overcomes the resistance of the feed, avoiding misjudgments caused by idling fluctuations.

[0023] In actual operation, the STM32F4 series microcontroller will smooth the current value in real time. Compare the values ​​with the no-load threshold of 2.30A. If A. The system determines that the motor is currently in a material conveying state and adds the current time step (e.g., 20ms) to the effective load duration counter. Assuming that within a complete morning feeding cycle, the system detects a cumulative period of 450 seconds where the smoothing current value exceeds 2.30A, which is the effective load duration... s.

[0024] Subsequently, the total calculation submodule calls the preset transmission efficiency parameters ( The transmission efficiency parameter was obtained through a physical weighing calibration experiment: During the calibration phase, the total mass of feed (e.g., 35 kg) transported by the motor under effective load for a specific time (e.g., 100 seconds) was recorded, and the efficiency was calculated. kg / s. This parameter is stored in non-volatile memory. The system performs a multiplication operation to calculate the product of the effective payload duration and the transmission efficiency parameter: kg. This value represents the total amount of feed fed in this single feeding, including the total feed mass of 157.5 kg and the effective load duration of 450 s. The data is packaged and sent to the next-level heat inversion module via the bus.

[0025] Table 1. Feeding motor load monitoring and total calculation data. ; As shown in Table 1, by comparing the smoothing current with the threshold, the system accurately quantifies the actual feeding process, eliminates the idling period of the motor, and ensures the accuracy of the basic data for subsequent heat calculation.

[0026] Please see Figure 1 and Figure 3 The heat inversion module generates total biological metabolic heat based on the total amount of a single feeding and the heat consumption coefficient. It then adjusts the gamma distribution probability density function based on the total biological metabolic heat to construct a metabolic heat production rate curve. The module samples the metabolic heat production rate curve to generate a expected sensible heat power sequence and then transmits the expected sensible heat power sequence to the enthalpy compensation module. The expected sensible heat power sequence includes the time step index for the future prediction period and the sensible heat power value corresponding to each time step. The heat inversion module includes a total heat calculation submodule, the specific functions of which are as follows: Obtain the total amount of feed per feeding and the heat increase coefficient; Calculate the product of the total amount of food fed in a single feeding and the heat increase coefficient to generate total metabolic heat. The total heat of biological metabolism is transferred to the curve construction submodule in the heat inversion module; The heat inversion module also includes a curve construction submodule, the specific functions of which are as follows: Obtain total metabolic heat and preset peak time parameters; Determine the shape and scale parameters of the gamma distribution probability density function based on the peak time parameter; Using total metabolic heat, shape parameters, and scale parameters, a metabolic heat production rate curve is constructed according to the following formula: ; in, represent The rate of metabolic heat production at any given time. Represents total metabolic energy. Represents shape parameters, Represents the scale parameter. Represents the gamma function. Represents a relative time variable calculated from the start of feeding; The heat inversion module also includes a sequence generation submodule, the specific functions of which are as follows: Obtain the metabolic heat production rate curve and the time step index for the future prediction period; The sampling time point is determined based on the time step index, the function value of the metabolic heat production rate curve at each sampling time point is calculated, and the instantaneous sensible heat power is generated. Based on each instantaneous sensible heat power, a sequence of expected sensible heat power is constructed in chronological order.

[0027] The total heat calculation submodule first receives the total amount of feed for a single feeding from the load sensing module. kg. The system retrieves the heat gain coefficient stored in the SQL database ( The heat gain coefficient reflects the additional heat effect (Specific Dynamic Action, SDA) generated during the digestion and metabolism of a specific type of feed in an animal. This coefficient is based on calorimetric laboratory data: for a compound feed specifically designed for growing-finishing pigs, the average total heat gain generated in the following 4 hours after ingestion of 1 kg of feed was measured to be 950 kJ / kg. Therefore, the heat gain coefficient was set... kJ / kg. The system performs a multiplication operation: kJ. This value represents the total metabolic energy generated during this feeding activity.

[0028] Subsequently, the curve construction submodule constructs a metabolic heat production rate curve based on the gamma distribution probability density function. First, the shape parameters are determined. With scale parameters The system obtains the preset peak time parameter. This parameter is set to the time when the peak heat production is reached after feeding, which is set to 5400 seconds (i.e., 1.5 hours) after the start of feeding, based on the physiological model of pigs. Based on the gamma distribution characteristics, the relationship between the peak occurrence time and the parameter is as follows: To determine a unique solution, the system introduces empirical shape parameters. Based on the digestibility characteristics of the feed, set (Dimensionless, representing the concentration of energy release). Based on this, the system calculates scale parameters. .

[0029] The aforementioned gamma distribution probability density function refers to a continuous probability distribution model, which is often used to describe waiting time or energy release processes. It has a right-skewed shape and can accurately simulate the rapid rise and slow decay characteristics in the biological metabolic heat production process.

[0030] Using the parameters determined above, the system calls the following formula to construct the metabolic heat production rate curve: ; in, Indicates the number of days since feeding began. The instantaneous metabolic heat production rate at a given time, expressed in kilowatts (kW), reflects the real-time heat dissipation power of the animal population. The total metabolic heat obtained from the previous steps is 149,625 kJ, which is used as the integral total area constraint curve; the shape parameter is 2.5, which determines the skewed distribution characteristics of the heat production curve. The scaling parameter, with a value of 3600, determines the extent of the curve's extension on the time axis. For the gamma function, , , as part of the normalization factor; This is a relative time variable calculated from the start of feeding; is the base of the natural logarithm.

[0031] The sequence generation submodule generates a time-step index sequence based on the predicted future time period (e.g., the next hour, with a step size of 5 minutes). (Unit: seconds, relative to the start of feeding). In Taking seconds (1 hour) as an example, let's perform a practical calculation: First, calculate the normalization constant term: Calculate the time variable: Calculate the exponential decay term: Substitute into the formula: ; kW.

[0032] The system performs the above calculations at each time step to generate the instantaneous sensible heat power at each sampling time point, and arranges them in chronological order to construct the expected sensible heat power sequence. This sequence not only contains numerical values ​​but also corresponding relative time indices, accurately quantifying the heat load increment at different time points after feeding, providing a dynamic feedforward input for subsequent enthalpy compensation. The innovative application of this formula lies in utilizing the asymmetric nature of the gamma distribution to accurately fit the "rapid rise, slow decay" heat production pattern of organisms after feeding. Compared to the traditional constant heat source assumption, this significantly improves the real-time performance and accuracy of heat load prediction.

[0033] Please see Figure 1 and Figure 4 The enthalpy compensation module uses reference ventilation mass flow rate parameters to convert the expected sensible heat power sequence into enthalpy decrease rate, calculates the difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate, generates a feedforward compensation target enthalpy value, and transmits the feedforward compensation target enthalpy value to the joint control execution module. The feedforward compensation target enthalpy value includes the corrected dynamic air specific enthalpy setpoint and the target enthalpy value reduction range; The enthalpy compensation module includes an enthalpy conversion submodule, the specific functions of which are as follows: Obtain the expected sensible heat power sequence and reference ventilation mass flow rate parameters; Using the reference ventilation mass flow rate parameter, the sensible heat production power values ​​in the expected sensible heat power sequence are converted into enthalpy decrease rates one by one according to the following formula: ; in, Representing the The rate of decrease in enthalpy over a given time step. The expected sensible heat power sequence is in the first... The sensible heat production power value for each time step. This represents the reference ventilation mass flow rate parameter; The enthalpy compensation module also includes a target setting submodule, the specific functions of which are as follows: Obtain the baseline enthalpy value and the rate of enthalpy decrease in the comfort zone; The difference between the comfort zone enthalpy baseline and the enthalpy decrease rate is calculated to generate the feedforward compensation target enthalpy.

[0034] The enthalpy conversion submodule first receives the expected sensible heat power sequence from the heat inversion module. Taking the [number]th [unit] in the sequence as an example... Taking the data at a time step as an example, the corresponding sensible heat production power value at this time... kW (i.e., 11.51 kJ / s). Simultaneously, the system acquires reference ventilation mass flow rate parameters. This parameter is set based on the fan operating conditions required to maintain the minimum air exchange rate in the breeding shed. Assuming the minimum ventilation mode set for the current season corresponds to operating two variable frequency fans, whose rated total mass flow rate is calibrated as follows: kg / s. This value represents the total mass of air flowing through the aquaculture environment per unit time, carrying away heat.

[0035] The system uses reference ventilation mass flow rate parameters to convert sensible heat production power into enthalpy decrease rate item by item according to the following formula: ; in, Representing the The decrease in air enthalpy required to eliminate bioheating at each time step is expressed in kilojoules per kilogram (kJ / kg). Representing the The expected sensible heat production power for each time step is 11.51kW, or 11.51kJ / s. The representative reference ventilation mass flow rate is taken as 25.0 kg / s. The formula, based on the principle of energy conservation, uses division to calculate the power required to offset the energy loss. The heat source, with a flow rate of The energy difference per unit mass required to be generated in the airflow.

[0036] Substitute the values ​​into the calculation: kJ / kg. The calculation results indicate that in order to neutralize the additional heat load of 11.51kW from feeding, the air or environmental control target entering the breeding area needs to have an additional cooling (or enthalpy reduction) capacity of 0.4604kJ / kg above the baseline, or in other words, the target set point needs to be reduced by this amount to reserve thermal buffer space.

[0037] The target setting submodule then obtains the comfort zone enthalpy benchmark. The comfort zone enthalpy benchmark is set based on the enthalpy-humidity chart of moist air and the physiological comfort zone of the cultured organisms. The aforementioned enthalpy-humidity chart is a graph describing the relationship between various thermodynamic state parameters of moist air (such as dry-bulb temperature, wet-bulb temperature, relative humidity, moisture content, enthalpy, etc.), and is a fundamental tool for calculating air conditioning processes. For example, setting a target dry-bulb temperature... relative humidity The baseline specific enthalpy obtained by looking up a table or by calculation kJ / kg.

[0038] The system calculates the difference between the comfort zone enthalpy baseline and the enthalpy decrease rate, and generates a feedforward compensation target enthalpy. : Substitute the values: kJ / kg.

[0039] The system uses the calculated value of 52.0396 kJ / kg as the corrected dynamic air specific enthalpy setpoint, along with the target enthalpy reduction of 0.4604 kJ / kg. This process is repeated at each prediction time step, generating a series of dynamically adjusted target values. In this way, the system does not wait for the temperature to rise before acting, but rather reduces the target enthalpy value in advance based on the predicted heat production trend, achieving "feedforward" compensation.

[0040] Please see Figure 1 and Figure 5 The joint control execution module calculates the real-time specific enthalpy based on dry-bulb temperature and relative humidity. For comparison results where the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, it generates a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

[0041] The control commands for the negative pressure fan include the fan unit start / stop control signals and the frequency converter duty cycle parameters; The joint control execution module includes an environmental monitoring submodule and a fan control submodule; The environmental monitoring submodule collects dry-bulb temperature and relative humidity data for the aquaculture environment. Real-time specific enthalpy calculated based on a moist air properties model; The fan control submodule acquires the real-time specific enthalpy and the target enthalpy value for feedforward compensation; For the comparison results where the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value is calculated, and a negative pressure fan control command is generated based on the difference.

[0042] The environmental monitoring submodule collects environmental data using multiple PT100 platinum resistance temperature sensors and capacitive polymer humidity sensors distributed throughout the breeding shed. The system performs a weighted average of the data from multiple points every 10 seconds to obtain the current average dry-bulb temperature of the breeding environment. and relative humidity .

[0043] Based on the moist air property model, the system calculates the real-time specific enthalpy. Although the specific model formula is the general thermodynamic equation, its calculation logic is described here: the system calls a built-in function, and the input... and Calculate the saturated water vapor pressure. (Approximating using the Magnus formula), combined with Calculate the actual water vapor partial pressure Thus, the moisture content is obtained. (kg / kg dry air). Final calculated specific enthalpy: The real-time specific enthalpy under the current environmental conditions is calculated by the STM32F4 series microcontroller. kJ / kg.

[0044] The fan control submodule obtains real-time specific enthalpy. kJ / kg and the feedforward compensation target enthalpy from the enthalpy compensation module kJ / kg. The system performs a comparison and judgment: The result is true. This indicates that the current environmental heat content is higher than the ideal target after considering the feeding heat effect, and ventilation must be enhanced to remove excess heat.

[0045] The system calculates the difference between the two: kJ / kg.

[0046] Based on this difference, the system generates control commands for the negative pressure fan. The control logic employs a hierarchical PID algorithm. The aforementioned hierarchical PID algorithm refers to a feedback control strategy that divides the control process into multiple levels and uses proportional, integral, and derivative components within each level to process the error, thereby generating precise control quantities. First, based on the magnitude of the difference... The control range is determined by kJ / kg. The activation threshold for the "powered ventilation range" is set at 3.0 kJ / kg. Because... The system determines that additional fan units need to be started. The specific instructions are generated as follows: 1. Fan unit start / stop control signal: Issue a command to close the contactors of constant-speed fans 2 and 3, putting them into operation. 2. Variable frequency drive duty cycle parameter: For variable frequency fan 1, calculate the output using the PID formula. Proportional coefficient. Integral coefficient Assuming the integral term was 0 at the previous time step, the current proportional output... Including the basic ventilation volume (e.g., 30%), the total duty cycle command is calculated as follows: Since the duty cycle limit is 100%, the system sets the instruction output to... .

[0047] Finally, the joint control execution module outputs negative pressure fan control commands to the motor driver, including "start fan groups 2 and 3" and "variable frequency fan 1 duty cycle 100%". The increase in fan speed leads to an increase in indoor negative pressure, and fresh air from outside rushes in. By replacing the high-enthalpy indoor air, the environmental state gradually approaches the feedforward compensation target enthalpy value of 52.04 kJ / kg, thereby removing heat in advance before the feeding heat effect reaches its peak and smoothing out temperature fluctuations.

[0048] Table 2. Examples of Key Parameters for the Joint Control Execution Module ; Referring to Table 2, the system uses real-time specific enthalpy and dynamically adjusted target enthalpy for closed-loop control, ensuring that the control commands respond to the current environment and predict future heat loads.

[0049] A method for intelligent joint control of livestock farming environment, wherein the method is based on the aforementioned intelligent joint control system for livestock farming environment, includes the following steps: S1. Collect the operating current of the feeding motor, extract the duration of the operating current exceeding the no-load threshold as the effective load duration, calculate the product of the effective load duration and the transmission efficiency parameter, and generate the total amount of feeding in a single feeding. S2. Generate total biomass heat based on total feeding amount and heat consumption coefficient, construct metabolic heat production rate curve by adjusting gamma distribution probability density function based on total biomass heat, and generate expected sensible heat power sequence by sampling metabolic heat production rate curve. S3. Using the reference ventilation mass flow rate parameter, the expected sensible heat power sequence is converted into the enthalpy decrease rate. The difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate is calculated to generate the feedforward compensation target enthalpy. S4. Calculate the real-time specific enthalpy based on dry-bulb temperature and relative humidity. For comparison results where the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, generate a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

[0050] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the scope of protection defined by the claims of the present invention.

Claims

1. An intelligent joint control system for livestock farming environment, characterized in that, The system includes: The load sensing module collects the operating current of the feeding motor, extracts the duration during which the operating current exceeds the no-load threshold as the effective load duration, calculates the product of the effective load duration and the transmission efficiency parameter, generates the total amount of feeding in a single feeding, and transfers the total amount of feeding in a single feeding to the heat inversion module. The heat inversion module generates total biological metabolic heat based on the total amount of a single feeding and the heat consumption coefficient, adjusts the gamma distribution probability density function based on the total biological metabolic heat to construct a metabolic heat production rate curve, samples the metabolic heat production rate curve to generate a expected sensible heat power sequence, and transmits the expected sensible heat power sequence to the enthalpy compensation module. The enthalpy compensation module uses reference ventilation mass flow rate parameters to convert the expected sensible heat power sequence into enthalpy decrease range, calculates the difference between the comfort zone enthalpy benchmark and the enthalpy decrease range, generates a feedforward compensation target enthalpy value, and transmits the feedforward compensation target enthalpy value to the joint control execution module. The joint control execution module calculates the real-time specific enthalpy based on dry-bulb temperature and relative humidity. Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, it generates a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

2. The intelligent joint control system for livestock breeding environment according to claim 1, characterized in that, The total amount of a single feeding includes the total mass of feed fed and the duration of the effective load. The expected sensible heat power sequence includes the time step index within the future prediction period and the sensible heat power value corresponding to each time step. The feedforward compensation target enthalpy value includes the corrected dynamic air specific enthalpy setpoint and the target enthalpy value reduction range. The negative pressure fan control command includes the fan unit start / stop control signal and the frequency conversion adjustment duty cycle parameter.

3. The intelligent joint control system for livestock breeding environment according to claim 2, characterized in that, The load sensing module includes a current monitoring submodule and a total calculation submodule; The current monitoring submodule collects the operating current of the feeding motor and performs filtering processing on the operating current using a sliding window algorithm to obtain a smooth current value; Determine whether the smoothed current value is greater than the no-load threshold. If the smoothed current value is greater than the no-load threshold, accumulate the duration to generate the effective load duration. The total calculation submodule obtains the effective load duration and the transmission efficiency parameters; The product of the effective load duration and the transmission efficiency parameter is calculated to generate the total amount of a single feeding.

4. The intelligent joint control system for livestock breeding environment according to claim 3, characterized in that, The heat inversion module includes a total heat calculation submodule, the specific functions of which are as follows: Obtain the total amount of feed given in a single feeding and the heat loss coefficient; The total metabolic heat of the organism is generated by multiplying the total amount of food fed in a single feeding by the heat increase coefficient. The total heat of biological metabolism is transferred to the curve construction submodule in the heat inversion module.

5. The intelligent joint control system for livestock breeding environment according to claim 4, characterized in that, The heat inversion module also includes a curve construction submodule, the specific function of which is as follows: Obtain the total metabolic heat of the organism and the preset peak time parameter; The shape and scale parameters of the gamma distribution probability density function are determined based on the peak time parameter. Using the total metabolic heat, the shape parameter, and the scale parameter, the metabolic heat production rate curve is constructed according to the following formula: ； in, represent The rate of metabolic heat production at any given time. Represents the total metabolic energy of the organism. Represents the shape parameters, This represents the scale parameter. Represents the gamma function. This represents a relative time variable calculated from the start of feeding.

6. The intelligent joint control system for livestock breeding environment according to claim 5, characterized in that, The heat inversion module further includes a sequence generation submodule, the specific functions of which are as follows: Obtain the metabolic heat production rate curve and the time step index within the future prediction period; Based on the time step index, the sampling time point is determined, the function value of the metabolic heat production rate curve at each sampling time point is calculated, and the instantaneous sensible heat power is generated. Based on each instantaneous sensible heat power, the expected sensible heat power sequence is constructed in chronological order.

7. The intelligent joint control system for livestock breeding environment according to claim 2, characterized in that, The enthalpy compensation module includes an enthalpy conversion submodule, the specific function of which is as follows: Obtain the expected sensible heat power sequence and the reference ventilation mass flow rate parameter; Using the reference ventilation mass flow rate parameter, the sensible heat production power values ​​in the expected sensible heat power sequence are converted into the enthalpy decrease rate item by item according to the following formula: ； in, Representing the The decrease in enthalpy value over each time step The expected sensible heat power sequence represents the first... The sensible heat production power value for each time step. This represents the reference ventilation mass flow rate parameter.

8. The intelligent joint control system for livestock breeding environment according to claim 7, characterized in that, The enthalpy compensation module further includes a target setting submodule, the specific function of which is as follows: Obtain the comfort zone enthalpy baseline and the enthalpy decrease rate; The difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate is calculated to generate the feedforward compensation target enthalpy.

9. The intelligent joint control system for livestock breeding environment according to claim 8, characterized in that, The joint control execution module includes an environmental monitoring submodule and a fan control submodule; The environmental monitoring submodule collects the dry bulb temperature and relative humidity of the aquaculture environment; The real-time specific enthalpy was calculated based on a moist air property model. The wind turbine control submodule acquires the real-time specific enthalpy and the feedforward compensation target enthalpy value; Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value is calculated, and the negative pressure fan control command is generated based on the difference.

10. A method for intelligent joint control of livestock farming environment, characterized in that, The method is used to implement the intelligent joint control system for livestock breeding environment according to any one of claims 1-9, and includes the following steps: S1. Collect the operating current of the feeding motor, extract the duration during which the operating current exceeds the no-load threshold as the effective load duration, calculate the product of the effective load duration and the transmission efficiency parameter, and generate the total amount of feeding in a single feeding. S2. Generate total biological metabolic heat based on the total amount of single feeding and the heat consumption coefficient, adjust the gamma distribution probability density function based on the total biological metabolic heat to construct a metabolic heat production rate curve, and sample the metabolic heat production rate curve to generate the expected sensible heat power sequence. S3. Using the reference ventilation mass flow rate parameter, the expected sensible heat power sequence is converted into the enthalpy decrease rate, the difference between the comfort zone enthalpy benchmark and the enthalpy decrease rate is calculated, and a feedforward compensation target enthalpy is generated. S4. Calculate the real-time specific enthalpy based on dry-bulb temperature and relative humidity. Based on the comparison result that the real-time specific enthalpy is greater than the feedforward compensation target enthalpy value, generate a negative pressure fan control command based on the difference between the real-time specific enthalpy and the feedforward compensation target enthalpy value.

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