Heating control method and system for coop door

Abstract

The invention relates to the technical field of hencoops, in particular to a heating control method and system for a hencoop door, and the method comprises the steps: calculating a target comfort degree deviation value of each hencoop based on the real-time collection of environment parameters of each hencoop; according to the target comfort degree deviation value, generating an execution sequence between the coops; according to the execution sequence, the corresponding coop door and the heating device are controlled to execute the operation, and the control instruction in the execution sequence is adjusted based on the environmental parameter change value after the operation, so that the problem that the existing heating coop door is independently controlled only for a single coop, so that the heating efficiency is improved is solved. Cooperative scheduling and global optimization at a group level cannot be realized, so that the problems of low system energy efficiency and poor thermal environment balance in the hencoop are solved.

Description

Technical Field

[0001] This invention relates to the field of chicken coop technology, specifically to a heating control method and system for chicken coop doors. Background Technology

[0002] Currently, the heating control technology for chicken coop doors on the market is mainly used in large-scale poultry farming or pet breeding facilities. Its function is to regulate the microclimate of the chicken house through automated means, thereby reducing the cost of manual management and providing a suitable growth environment for poultry.

[0003] Common heating control methods often employ PID control strategies based on fixed thresholds. This involves monitoring the real-time temperature inside a single chicken coop using sensors. When the temperature falls below a preset threshold, the auxiliary heating equipment automatically activates and the coop door closes to maintain warmth. Conversely, when the temperature exceeds the preset threshold, heating stops and the coop door opens for ventilation. However, because this control logic only responds independently to the local environmental parameters of a single chicken coop, it cannot coordinate and optimize the heating based on the real-time needs of all coops in the flock. This results in frequent start-stop cycles for heating equipment in multiple coops within similar timeframes, exacerbating energy waste and making it difficult to achieve a balanced and stable thermal environment within the coops under complex and changing external conditions. Summary of the Invention

[0004] To address the technical problems of low system energy efficiency and poor thermal balance within chicken coops caused by the fact that existing chicken coop heating control only controls individual chicken coops independently, thus failing to achieve coordinated scheduling and global optimization at the group level, this application provides a heating control method and system for chicken coop doors.

[0005] The heating control method and system for a chicken coop door provided in this application adopts the following technical solution: A method for controlling the heating of a chicken coop door includes: Based on real-time collection of environmental parameters for each chicken cage, the target comfort deviation value for each chicken cage is calculated. Based on the target comfort deviation value, generate the execution sequence between each chicken cage; According to the execution sequence, the corresponding chicken coop door and heating equipment are controlled to perform operation, and the control instructions in the execution sequence are adjusted based on the changes in environmental parameters after the operation.

[0006] Furthermore, the steps for calculating the target comfort deviation value for each chicken cage based on real-time collection of environmental parameters include: Based on the real-time collection of environmental parameters for each chicken cage, the trend of target temperature parameter changes for each chicken cage in the future period is predicted. Calculate the initial comfort deviation value based on the changing trends of the target temperature parameters and the expected temperature parameters; Based on the activity status data of each chicken cage, the initial comfort deviation value is corrected to obtain the target comfort deviation value.

[0007] Furthermore, based on the real-time collected environmental parameters of each chicken cage, the steps for predicting the trend of target temperature parameter changes in each chicken cage over future periods include: From the internal and external environmental parameters of each chicken cage collected in real time, target internal and external environmental parameters with a correlation greater than a preset correlation with future time periods are obtained, thus obtaining the target environmental parameter sequence for each chicken cage. Based on the target environmental parameter sequence and the physical property parameters of each chicken cage, the trend of the initial temperature parameter change of each chicken cage in the future time period is calculated. Calculate the thermal impact of adjacent chicken cages on each chicken cage based on the positional relationship between them. Based on the heat-affected zone value, the initial temperature parameter change trend of the chicken cage corresponding to the heat-affected zone value is corrected to obtain the target temperature parameter change trend.

[0008] Furthermore, the steps for calculating the initial comfort deviation value based on the changing trends of the target temperature parameter and the expected temperature parameter include: Extract the first, second, and third characteristic differences between the target temperature parameter change trend and the expected temperature parameter change trend in terms of change rate, fluctuation amplitude, and stable time point, respectively. Based on the first characteristic difference, the second characteristic difference, and the third characteristic difference, the rate of change, the fluctuation amplitude, and the first deviation component, the second deviation component, and the third deviation component at the stable time point are calculated. The first deviation component, the second deviation component, and the third deviation component are fused to generate an initial comfort deviation value.

[0009] Furthermore, based on the activity data of each chicken coop, the steps to correct the initial comfort deviation value and obtain the target comfort deviation value include: Behavioral pattern recognition is performed on the activity status data of each chicken cage to obtain the behavioral category pattern of each chicken cage. Query the preset behavior-comfort association rule table to obtain the comfort deviation correction strategy corresponding to each behavior category pattern; Based on the comfort deviation correction strategy, the corresponding initial comfort deviation value is calibrated to obtain the target comfort deviation value for each chicken cage.

[0010] Furthermore, the steps for generating the execution sequence between each chicken coop based on the target comfort deviation value include: Based on the target comfort deviation value of each chicken cage, a target equipment status is generated for the chicken cage door of each chicken cage; Calculate the control commands for each chicken cage door to change from the current device state to the corresponding target device state; Based on the real-time load of the equipment at each chicken coop door and the equipment priority between other chicken coop doors, the timing of each control command is adjusted to generate an execution sequence.

[0011] Furthermore, the steps for adjusting the control instructions in the execution sequence based on changes in environmental parameters after the operation include: The actual control effect of each chicken cage was determined based on the changes in environmental parameters; By comparing the actual control effect of each chicken cage with the corresponding expected control effect, the optimization adjustment strategy for each chicken cage is obtained. Based on the optimization and adjustment strategy, the corresponding control instructions in the execution sequence are adjusted.

[0012] This application also proposes a heating control system for a chicken coop door, including: The data calculation module is used to calculate the target comfort deviation value of each chicken cage based on the environmental parameters collected in real time. The data generation module is used to generate the execution sequence between each chicken cage based on the target comfort deviation value; The data execution module is used to control the corresponding chicken coop door and heating equipment to perform operation according to the execution sequence, and to adjust the control instructions in the execution sequence based on the changes in environmental parameters after the operation.

[0013] Beneficial effects achieved: This application provides a heating control method for chicken coop doors, comprising: calculating a target comfort deviation value for each chicken coop based on real-time acquisition of environmental parameters of each chicken coop; generating an execution sequence between each chicken coop based on the target comfort deviation value; controlling the corresponding chicken coop door and heating equipment to perform operation according to the execution sequence; and adjusting the control instructions in the execution sequence based on the changes in environmental parameters after the operation.

[0014] In this application, the target comfort deviation value of each chicken coop is calculated based on real-time collection of environmental parameters, thereby quantifying the gap between the individual needs of each chicken coop and the ideal state, providing a data foundation for group collaboration. Then, based on these target comfort deviation values, an execution sequence is generated between each chicken coop. This means that the system no longer responds to individual chicken coops in isolation, but integrates the heating control needs of all chicken coops into an orderly scheduling plan. By prioritizing chicken coops with larger deviations or optimizing the start-up and shutdown sequence of equipment, peak energy consumption and conflicting operations are avoided, achieving collaborative scheduling at the group level. Then, the corresponding chicken coop doors and heating equipment are controlled to perform operating operations according to the execution sequence, and the control commands in the execution sequence are adjusted based on the changes in environmental parameters after the operation. This feedback mechanism allows the system to dynamically optimize the control strategy based on actual results, gradually reducing unnecessary energy waste and promoting a balanced distribution of the thermal environment among the chicken coops, thereby improving energy efficiency and stability at the global level. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps of a heating control method for a chicken coop door according to this application; Figure 2 This is a physical schematic diagram of a chicken coop door according to this application; Figure 3 This is a schematic diagram of the power supply equipment for supplying power to the chicken coop door in this application; Figure 4 This is a schematic diagram of a heating control system for a chicken coop door according to this application.

[0016] Explanation of icon numbers: 10. Data calculation module; 20. Data generation module; 30. Data execution module. Detailed Implementation

[0017] The following is in conjunction with the appendix Figures 1-4 This application will be described in further detail.

[0018] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] This application discloses a heating control method for a chicken coop door.

[0021] Please refer to Figure 1 The heating control method for a chicken coop door proposed in this embodiment includes steps S10 to S30: Step S10: Calculate the target comfort deviation value for each chicken cage based on the real-time collection of environmental parameters for each cage.

[0022] Calculating the target comfort deviation value based on real-time collected environmental parameters essentially transforms specific physical monitoring values ​​into an abstract indicator reflecting the urgency of regulation. This calculation process provides the system with a universal scale for comparing the environmental conditions of different chicken coops, allowing the individualized needs of each coop in different locations to be objectively measured and prioritized. By obtaining the target comfort deviation value for each coop, the system can accurately identify which coops require the most intervention and to what extent. This lays a crucial data foundation for subsequent steps to achieve coordinated scheduling and optimal resource allocation at the group level, making global heating control decisions possible.

[0023] Step S20: Generate the execution sequence between each chicken cage based on the target comfort deviation value.

[0024] Generating an execution sequence among chicken coops based on target comfort deviation values ​​essentially integrates dispersed individual control needs into a global scheduling scheme. This generation process establishes a rational allocation and execution order rule—the execution sequence—for limited control resources based on the varying urgency of environmental conditions in each coop, thereby avoiding conflicts in control commands and disorderly competition for system resources. By constructing this execution sequence, an upgrade from responding to individual coop needs to coordinating group behavior can be achieved, enabling control actions to be staggered in time and optimized in spatial distribution, ultimately achieving improved overall system energy efficiency and dynamic equilibrium of the thermal environment within the coops.

[0025] Step S30: According to the execution sequence, control the corresponding chicken coop door and heating equipment to perform operation, and adjust the control instructions in the execution sequence based on the changes in environmental parameters after the operation.

[0026] The process of controlling the equipment to perform operations according to the execution sequence and adjusting based on feedback essentially transforms the static control commands in the execution sequence into dynamic, automatically optimized control commands. The purpose of this process is to verify the effectiveness of the execution sequence by executing control commands, and at the same time, to use the changes in environmental parameters generated by actual operation as feedback signals to calibrate the timing, intensity, or sequence of control commands online. Through this closed-loop heating control method of "operation-evaluation-adjustment", control deviations caused by abnormal conditions such as equipment differences and external interference can be overcome, so that the heating control effect continuously approaches the expected target, and ultimately achieves a steady improvement in control accuracy during continuous operation and an effective guarantee of long-term system stability.

[0027] In one feasible implementation, step S10 may specifically include steps S11 to S13: Step S11: Based on the environmental parameters of each chicken cage collected in real time, predict the trend of the target temperature parameter change of each chicken cage in the future period.

[0028] Predicting the future trend of target temperature parameters for each chicken cage based on real-time collected environmental parameters essentially extends the system's perception dimension from the current instantaneous state to the impending process of change. This predictive process enables the system to perceive the dynamics of environmental evolution, providing early signals about future needs for control commands. This shifts the control logic from passively responding to adverse situations to proactively guiding the environment towards an ideal state. By predicting the trend of target temperature parameters, the system can identify potential problems that have not yet reached critical thresholds but are developing unfavorably, thus triggering appropriate control actions in advance. This effectively avoids drastic environmental fluctuations, significantly improves the timeliness and smoothness of control, and ultimately achieves advanced early-stage stability of the thermal environment within the chicken cages and optimizes system operating efficiency.

[0029] Step S12: Calculate the initial comfort deviation value based on the changing trends of the target temperature parameters and the expected changing trends of the temperature parameters.

[0030] A comfort assessment mechanism based on dynamic trends is established. By comparing the predicted trend of target temperature parameter changes with the expected trend of temperature parameter changes, the initial comfort deviation value is calculated. Its function is to elevate the standard of comfort measurement from a static temperature point to a dynamic process of change. This shifts the focus of the system from whether the environmental parameter value at a certain moment meets the standard to whether the evolution direction and path of the environmental parameter in the future meets the ideal expectation. This enables the system to identify potential risks that are developing in an unfavorable direction even if the current environmental parameters are still within the acceptable range. This allows for early identification and warning of comfort deviations, providing a basis for decision-making to take forward-looking control measures, and ultimately making control commands more precise and smooth.

[0031] Step S13: Based on the activity status data of each chicken cage, the initial comfort deviation value is corrected to obtain the target comfort deviation value.

[0032] The initial comfort deviation value is corrected based on the activity status data of each chicken cage. The purpose of this correction process is to construct a closed-loop judgment logic with a biological verification link. By introducing the activity status data of the chicken flock (such as aggregation status, call frequency, etc.), which is the most direct indicator reflecting their cold and heat perception, the initial comfort deviation calculated solely from environmental parameters is tested and calibrated. This avoids misjudgments caused by sensor errors, inaccurate models, or the existence of local microenvironments. The final target comfort deviation value not only depends on the monitoring and prediction of the physical world, but also obtains physiological and behavioral confirmation from the subjects being cared for—the chicken flock itself, thereby greatly improving the accuracy and reliability of control commands.

[0033] Specifically, step S11 may include steps S111 to S114: Step S111: From the internal and external environmental parameters of each chicken cage collected in real time, obtain the target internal environmental parameters and target external environmental parameters that have a correlation degree greater than the preset correlation degree with the future time period, and obtain the target environmental parameter sequence for each chicken cage.

[0034] First, a correlation degree is calculated for both internal and external environmental parameters. This correlation degree measures the strength of the correlation between changes in the corresponding environmental state and changes in the environmental state in future periods. When the calculated correlation degree exceeds a preset threshold, the environmental parameter corresponding to that correlation degree is marked as a high-correlation target parameter. Then, these selected high-correlation target internal environmental parameters and target external environmental parameters are arranged and combined according to their collection time order to form a target environmental parameter sequence for each chicken coop. In this way, the most indicative key parameters for predicting future environmental changes are identified from multiple environmental parameters, which are the data sequences used to predict future temperature changes. This effectively eliminates irrelevant variables and noise interference, allowing subsequent prediction calculations to focus on the truly important data dimensions, thereby significantly improving the accuracy of trend prediction while ensuring the real-time nature of the prediction.

[0035] It should be noted that the correlation degree is calculated by analyzing the matching degree between the real-time change characteristics of each environmental parameter and the system's preset future environmental change pattern. Specifically, the system maintains a preset template sequence for each environmental parameter, representing its ideal change pattern. This preset template sequence defines a typical change pattern in which the environmental parameter is highly correlated with future environmental state changes over future time periods. During real-time calculation, the system extracts the data sequence of the current environmental parameter within the most recent sliding time window and calculates the matching degree between this data sequence and the preset template sequence using a temporal similarity algorithm. This matching degree is quantified as the correlation degree of the environmental parameter.

[0036] Step S112: Based on the target environmental parameter sequence and the physical property parameters of each chicken cage, calculate the trend of the initial temperature parameter change of each chicken cage in the future time period.

[0037] First, a mathematical model of chicken cage environmental change based on thermodynamic principles is established. This model characterizes the temperature change of the chicken cage as a dynamic process driven by the sequence of target environmental parameters and constrained by the physical property parameters of the chicken cage (such as thermal resistance of the enclosure structure, internal space volume, ventilation volume, etc.).

[0038] During the calculation, the target environmental parameter sequence is used as the input to the mathematical model of chicken cage environmental change. At the same time, the physical property parameters of the chicken cage corresponding to the target environmental parameter sequence are used as the key coefficients of the mathematical model of chicken cage environmental change. Through numerical simulation, the evolution trajectory of the chicken cage environmental change parameters in the future period is deduced, that is, the trend of the initial temperature parameter change. This trend of the initial temperature parameter change not only reflects the influence of the external environmental driving force, but also profoundly reflects the unique thermal characteristics of different chicken cages due to differences in structure, size, and equipment. Thus, personalized and accurate trend prediction is achieved for each chicken cage, providing a highly customized decision basis for subsequent control.

[0039] The specific implementation process for predicting the changing trend of the initial temperature parameters is as follows: the future period is divided into multiple small time steps. Within each time step, based on the target environmental parameter sequence (such as external temperature and solar radiation intensity) and the physical property parameters of the chicken coop at the current moment, the environmental parameter values ​​such as the internal temperature of the chicken coop are updated through the heat balance equation. This calculation process is iterated at each time step, using the output result of the previous step as the input condition for the next step. The simulation is gradually advanced in this way, and finally the calculation results of all time steps are connected to form a complete trajectory that shows how the environmental parameters of the chicken coop gradually evolve in the future period, that is, the changing trend of the initial temperature parameters.

[0040] The heat balance equation is expressed as: In the above expression, C represents the total equivalent heat capacity of the air and internal structure inside the chicken cage; This indicates the rate of temperature change inside the chicken coop over time. This indicates the heat gain or loss inside the chicken coop; Indicates the thermal power of the heating equipment; This refers to objects that allow light to pass through, such as solar radiation entering a chicken coop through a window. This refers to the heat generated by the chickens' own metabolism inside the cage. This refers to the heat lost by the walls, roof, and other enclosure structures of the chicken coop through heat conduction with the external environment; This indicates heat loss due to ventilation (opening and closing of the chicken coop door, natural ventilation, or mechanical ventilation).

[0041] For example, after calculating the heat gain or loss inside the chicken cage at the current moment based on the target environmental parameter sequence and the physical property parameters of the chicken cage, the heat gain or loss is divided by the total equivalent heat capacity inside the chicken cage to obtain the temperature change rate at the current moment. The temperature change rate is multiplied by the time step to obtain the temperature difference. The current temperature is added to the temperature difference to obtain the predicted temperature for the first minute in the future.

[0042] By combining the predicted temperature of the first minute in the future with the environmental parameters of the second minute in the target environmental parameter sequence, the predicted temperature of the first minute in the future is obtained. For example, if the external temperature of the first minute in the future is 14.9°C, and the current external temperature is 15°C, the heat gain / loss can be calculated as -52W. Then, the temperature change rate of the first minute in the future is obtained. Multiplying the temperature change rate of the first minute in the future by the time step, the temperature difference is obtained. Adding the temperature difference to the predicted temperature of the first minute in the future, the predicted temperature of the second minute in the future is obtained. And so on, combining the temperature change rates at each moment, the trend of environmental parameter changes is obtained.

[0043] Step S113: Calculate the thermal impact value of adjacent chicken cages on each chicken cage based on the positional relationship between each chicken cage.

[0044] First, based on the spatial layout of the chicken coops, the set of adjacent chicken coops for each target chicken coop, along with their spacing and orientation, are determined. Then, based on the principles of radiative and convective heat transfer in thermodynamics, each adjacent chicken coop is considered a heat source. Using a simplified model based on the Stefan-Boltzmann law and Newton's law of cooling, the heat flux intensity generated by the heat source on the target chicken coop through radiation and air convection is calculated. The radiative heat flux is directly proportional to the fourth power difference between the absolute temperatures of the adjacent and target chicken coops and inversely proportional to the square of the distance. The convective heat transfer is directly proportional to the temperature difference and the contact area. Finally, the heat flux intensity vectors generated by all adjacent chicken coops are superimposed and converted into the equivalent influence degree on the internal temperature of the target chicken coop, which is the thermal influence value of the adjacent chicken coop on the corresponding chicken coop. This quantitatively assesses the complex thermal interactions existing in the chicken coop group, upgrading the originally isolated temperature prediction model into a collaborative prediction system that can reflect the real heat transfer network relationship. This significantly improves the accuracy of predicting the temperature change trend of each chicken coop, especially for chicken coop groups that are adjacent and have significant temperature differences, effectively predicting the spatial diffusion process of heat.

[0045] Step S114: Based on the heat impact value, correct the initial temperature parameter change trend of the chicken cage corresponding to the heat impact value to obtain the target temperature parameter change trend.

[0046] First, the thermal impact value is divided by the total equivalent heat capacity of the air and internal structure inside the corresponding chicken cage, converting the thermal impact value into the rate of temperature change affecting the temperature inside the corresponding chicken cage. Then, this rate of temperature change is used as a dynamic correction term and is added to the initial temperature parameter change trend of the chicken cage in real time. That is, when predicting each future time step, after calculating the rate of temperature change caused by the thermal balance of the corresponding chicken cage itself, the rate of temperature change caused by the thermal impact of adjacent chicken cages is added simultaneously. Through this dynamic superposition correction over time step, a comprehensive temperature change curve that reflects both the thermal dynamics of each chicken cage and the thermal synergy effect of the surrounding chicken cages is finally obtained, which is the target temperature parameter change trend. This breaks the traditional prediction mode of treating each chicken cage as an independent system. By quantifying and incorporating the spatial thermal interaction effect, it can accurately capture the diffusion and equilibrium process of heat in the chicken cage group, thereby significantly improving the overall accuracy of predicting the temperature distribution and change trend within the chicken cage group.

[0047] Specifically, step S12 may include steps S121 to S123: Step S121: Extract the first, second, and third characteristic differences between the target temperature parameter change trend and the expected temperature parameter change trend in terms of change rate, fluctuation amplitude, and stabilization time point, respectively.

[0048] ① For the rate of change, the first characteristic difference is obtained by calculating the instantaneous rate of change of the target temperature parameter and the expected temperature parameter at each time point, and by statistically analyzing the average absolute deviation of these instantaneous rates of change over the entire time series.

[0049] ② For the fluctuation range, the difference between the maximum and minimum values ​​of the target temperature parameter change trend and the expected temperature parameter change trend is calculated by identifying the local extreme points, and the difference is compared to obtain the second characteristic difference.

[0050] ③ For stable time points, by defining stable state conditions, such as the rate of change of multiple consecutive time points being lower than a threshold, the time when the change trend of the target temperature parameter and the expected change trend of the temperature parameter reach this state are detected respectively, and the time difference between them is calculated to obtain the third feature difference.

[0051] By deepening the comparison of simple temperature values ​​into a refined comparison of the dynamic form of trends, the quality of the predicted trend can be comprehensively evaluated from three dimensions: agility, stability, and timeliness of the change process. This provides a more comprehensive and essential characteristic basis for subsequent calculation of comfort deviation, rather than relying solely on the endpoint temperature error.

[0052] Step S122: Based on the first characteristic difference, the second characteristic difference, and the third characteristic difference, calculate the rate of change, the fluctuation amplitude, and the first deviation component, the second deviation component, and the third deviation component at the stable time point.

[0053] A baseline weight is set for each of the three feature dimensions: rate of change, fluctuation amplitude, and stable time point. Then, the actual weight coefficients of each feature dimension are dynamically adjusted according to the age and health status of the chickens in the corresponding cages. The obtained first feature difference, second feature difference, and third feature difference are multiplied by the corresponding dynamic weight coefficients and normalized. Finally, the first deviation component of the rate of change, the second deviation component of the fluctuation amplitude, and the third deviation component of the stable time point are output. This realizes the multi-dimensional quantitative assessment and standardized integration of trend differences, and converts feature differences with different physical meanings into comparable standardized deviation components, laying the foundation for the subsequent generation of comprehensive initial comfort deviation values.

[0054] It should be noted that the dynamic weighting coefficient is a value that is dynamically adjusted based on the real-time status of the chicken coop, such as the age and health status of the chickens. It is used to quantify the relative importance of characteristic dimensions such as rate of change, fluctuation amplitude, and stable time point in comfort assessment.

[0055] Step S123: The first deviation component, the second deviation component, and the third deviation component are fused to generate an initial comfort deviation value.

[0056] The first, second, and third deviation components are used as three independent input variables. Then, based on the real-time monitored state of the chicken coop (such as flock behavior and external environmental stress), a set of corresponding weight coefficients are dynamically obtained from a preset rule base. Next, each deviation component is amplified through nonlinear mapping such as squaring and exponential mapping to amplify its negative effects beyond the reasonable range. Finally, the mapped component values ​​are multiplied by their corresponding dynamic weights and summed to obtain the initial comfort deviation value. This approach considers both the absolute magnitude of the deviation in each dimension and reflects the changes in the relative importance of each dimension under the current environment through dynamic weights. This allows the generated initial comfort deviation value to quantify the overall deviation between the trend of the target temperature parameter and the trend of the expected temperature parameter.

[0057] Specifically, step S13 may include steps S131 to S133: Step S131: Perform behavioral pattern recognition on the activity status data of each chicken cage to obtain the behavioral category pattern of each chicken cage.

[0058] Real-time data on the activity status of chickens inside the cages is collected, including sound signals of call frequency and pitch changes obtained by a microphone array, and chicken movement vectors and spatial distribution heatmaps obtained by a visual sensor through a background subtraction algorithm. These activity status data are then standardized and preprocessed to form feature vectors with uniform timestamps. These feature vectors are then matched and calculated in real time with a pre-set feature library of typical behavior patterns. This feature library contains feature templates for standard behavior patterns such as "huddling together", "open-mouth breathing", "restless running", and "uniform distribution". When the similarity between a feature vector and a standard template exceeds a set threshold, the current chicken cage is determined to be in that behavior category.

[0059] Step S132: Query the preset behavior-comfort association rule table to obtain the comfort deviation correction strategy corresponding to each behavior category mode.

[0060] A preset behavior-comfort association rule table is established, indexed by behavior category patterns and containing corresponding comfort deviation correction strategies. These strategies explicitly define the direction and magnitude of the correction. When a recognized behavior category pattern is input, it is precisely matched against the index key in the preset behavior-comfort association rule table. If a match is successful, the corresponding comfort deviation correction strategy is returned. This strategy typically includes a correction coefficient calculation formula or a specific correction value. For example, when the behavior category pattern is "gathering for warmth," the returned comfort deviation correction strategy might be "increase the initial comfort deviation value by 20% of its current value." This establishes a rapid conversion path from biological behavioral semantics to control parameters. By transforming abstract animal behavioral knowledge into quantifiable operational instructions that can be directly executed by the controller, precise closed-loop control based on biofeedback is achieved, significantly improving the accuracy and reliability in responding to complex changes in the biological environment.

[0061] Step S133: According to the comfort deviation correction strategy, the corresponding initial comfort deviation value is calibrated to obtain the target comfort deviation value for each chicken cage.

[0062] The comfort deviation correction strategy obtained from the query is parsed. This comfort deviation correction strategy clearly specifies the type of correction operation, the amount of correction, and possible constraints. For example, the comfort deviation correction strategy may be "additive calibration: +0.5" or "multiplicative calibration: multiply by coefficient 1.2", or more complex conditional operations such as "if the initial value is greater than the threshold X, then perform additive calibration, otherwise perform multiplicative calibration", etc.

[0063] The initial comfort deviation value is then used as an operand, and the corresponding arithmetic or logical operations are performed strictly according to the algorithm described in the comfort deviation correction strategy to generate a new value that has been corrected by biological behavior feedback. This value is the target comfort deviation value.

[0064] The obtained target comfort deviation value integrates monitoring data from the physical world with real feedback from the biological world, enabling it to not only reflect the quality of the physical environment but also accurately characterize the actual comfort of the flock, thereby greatly improving the accuracy of control decisions and the level of animal welfare.

[0065] In one feasible implementation, step S20 may specifically include steps S21 to S23: Step S21: Based on the target comfort deviation value of each chicken cage, generate the target equipment status for the chicken cage door of each chicken cage.

[0066] The target comfort deviation value is divided into different control levels according to the numerical range. Each control level corresponds to a preset equipment state combination scheme. The equipment state combination scheme defines in detail the target values ​​of parameters such as the opening degree of the chicken coop door and the power level of the heating equipment.

[0067] When a specific target comfort deviation value is input, the control level to which it belongs is determined by querying the mapping rules. Then, the equipment state combination scheme corresponding to the control level is invoked to generate the specific target state of the chicken coop door and its associated heating equipment. For example, when a high target comfort deviation value is received (indicating an urgent need for heating), the target equipment state of "the chicken coop door is completely closed and the heating equipment is running at maximum power" will be generated. In this way, the abstract value representing the degree of demand is transformed into an executable control target at the equipment level, providing a clear setpoint basis for the subsequent generation of specific control commands, thereby ensuring that the control strategy can accurately respond to the actual thermal comfort needs of the chicken coop.

[0068] Step S22: Calculate the control command for each chicken cage door to change from the current device state to the corresponding target device state.

[0069] By comparing the current and target states of the chicken coop door, the algorithm identifies the control variables that need to be changed (such as door opening and heater power) and their target values. Then, based on the preset constraints in the equipment physical characteristic library (such as maximum motor speed and heater power change rate), the algorithm uses a reverse recursive path planning method to calculate a series of operation instructions and their time parameters required for a smooth transition from the current state to the target state. For example, when the chicken coop door needs to be changed from a fully open state to a half-open state, the algorithm generates a precise control instruction set containing "starting the motor at rated speed and braking to stop after running for a specific time" and ensures that the acceleration and deceleration processes conform to the mechanical load characteristics.

[0070] Among them, the equipment physical characteristics library is a database that stores the inherent physical parameters and operating constraints of chicken coop doors and related heating equipment. By presetting key parameters such as the maximum speed of the motor and the gradual change rate of the heater power, it provides the necessary physical constraint basis for the path planning of control commands.

[0071] Step S23: Adjust the timing of each control command according to the real-time load of each chicken cage door and the priority of the equipment between other chicken cage doors to generate an execution sequence.

[0072] The system uses a load monitor to read the current and temperature values ​​of the drive motors of each chicken coop door in real time as the real-time load of the equipment. Simultaneously, each chicken coop door is assigned a numerical priority tag based on its target comfort deviation value. A priority queue management algorithm is then used to sort all generated control commands according to the priority number of the chicken coop door to which it belongs, with higher-priority commands placed at the front of the queue. A time-slice allocation mechanism is then introduced, prioritizing the retrieval of commands from the front of the queue within each scheduling cycle. The system checks whether the estimated load of the relevant equipment exceeds a safety threshold when executing the command; if so, the subsequent command is delayed until the next cycle with a lower load. Finally, a mutex lock mechanism is used to control access to the shared power line, ensuring that only a limited number of high-power devices can be started simultaneously. Through strict load control and priority scheduling, the system effectively avoids current surges caused by the simultaneous start-up of multiple high-power heating devices, ensuring timely response to urgent needs while maintaining the stable operation of the entire power supply system.

[0073] In one feasible implementation, step S30 may specifically include steps S31 to S33: Step S31: Determine the actual control effect of each chicken cage based on the changes in environmental parameters.

[0074] After the control command is executed, the numerical sequence of environmental parameters (such as temperature and humidity) inside the chicken coop is collected in real time, and the amount or rate of change of these parameters within a specific time period before and after the operation is calculated. For example, by comparing the actual temperature rise inside the chicken coop after the heating equipment is turned on with the initial value recorded when the operation is executed, the specific value of the temperature change is obtained. Then, these measured change data are directly compared with the expected target value corresponding to this control operation. For example, the actual temperature change rate is subtracted from the expected temperature change rate to obtain the deviation value. This deviation value is a quantitative representation of the actual control effect, realizing an objective measurement of the real impact of the control command. This allows the system to evaluate the effectiveness of the control strategy based on the actual physical response rather than a purely theoretical model, providing an accurate data basis for subsequent optimization and adjustment.

[0075] Step S32: Compare the actual control effect of each chicken cage with the corresponding expected control effect to obtain the optimization adjustment strategy for each chicken cage.

[0076] The deviation value is obtained by subtracting the actual control effect from the target value of the expected control effect. Then, a matching query is performed in a preset adjustment rule lookup table based on the sign and magnitude of the deviation value. This preset adjustment rule lookup table is indexed by the deviation value and stores the corresponding specific adjustment instructions. For example, when the actual heating rate is lower than the expected value and the deviation exceeds a certain threshold, the query result is "increase the power instruction of the corresponding heating device in the execution sequence by 5%". This achieves deterministic self-correction of the control strategy and can quantify the gap between the actual control effect and the expected target of each control operation into specific parameter adjustment amounts, thereby ensuring that the expected target can be achieved more accurately when the same or similar control tasks are executed next time.

[0077] Step S33: Adjust the corresponding control instructions in the execution sequence according to the optimization and adjustment strategy.

[0078] The system analyzes and optimizes the specific operation commands and parameter modification values ​​contained in the optimization strategy. Then, it locates the control command that needs to be adjusted in the execution sequence and directly replaces the corresponding parameter fields in the original command with the new parameter values. For example, when the optimization strategy requires "increasing the power command by 5%", the system will find the corresponding control command of the heating device in the execution sequence, multiply its parameter value by 1.05, and update and replace it, while keeping other parts of the control command unchanged. This achieves real-time online optimization of the control command. The system can immediately correct the specific parameters of subsequent control commands based on the latest operating data, so that the control behavior can quickly adapt to changes in equipment characteristics and fluctuations in environmental conditions, thereby continuously maintaining control accuracy and system performance.

[0079] Furthermore, Figure 2 This application describes a chicken coop door, wherein 10 is an aluminum crossbeam, 20 is a guide rail, 30 is a roller shutter door, and 40 is an equipment panel, which is equipped with an ultrasonic speaker, a display panel, and a switch indicator light, etc. Figure 3 The power supply equipment for the chicken coop door consists of 50 solar panels and 60 grounding spikes.

[0080] This application also provides a heating control system for a chicken coop door, referring to... Figure 4 As shown, the heating control system for the chicken coop door includes: Data calculation module 10 is used to calculate the target comfort deviation value of each chicken cage based on the environmental parameters collected in real time. The data generation module 20 is used to generate the execution sequence between each chicken cage based on the target comfort deviation value; The data execution module 30 is used to control the corresponding chicken coop door and heating equipment to perform operation according to the execution sequence, and to adjust the control instructions in the execution sequence based on the changes in environmental parameters after the operation.

[0081] It is feasible; the data calculation module 10 is also used for: Based on the real-time collection of environmental parameters for each chicken cage, the trend of target temperature parameter changes for each chicken cage in the future period is predicted. Calculate the initial comfort deviation value based on the changing trends of the target temperature parameters and the expected temperature parameters; Based on the activity status data of each chicken cage, the initial comfort deviation value is corrected to obtain the target comfort deviation value.

[0082] It is feasible; the data calculation module 10 is also used for: From the internal and external environmental parameters of each chicken cage collected in real time, target internal and external environmental parameters with a correlation greater than a preset correlation with future time periods are obtained, thus obtaining the target environmental parameter sequence for each chicken cage. Based on the target environmental parameter sequence and the physical property parameters of each chicken cage, the trend of the initial temperature parameter change of each chicken cage in the future time period is calculated. Calculate the thermal impact of adjacent chicken cages on each chicken cage based on the positional relationship between them. Based on the heat-affected zone value, the initial temperature parameter change trend of the chicken cage corresponding to the heat-affected zone value is corrected to obtain the target temperature parameter change trend.

[0083] It is feasible; the data calculation module 10 is also used for: Extract the first, second, and third characteristic differences between the target temperature parameter change trend and the expected temperature parameter change trend in terms of change rate, fluctuation amplitude, and stable time point, respectively. Based on the first characteristic difference, the second characteristic difference, and the third characteristic difference, the rate of change, the fluctuation amplitude, and the first deviation component, the second deviation component, and the third deviation component at the stable time point are calculated. The first deviation component, the second deviation component, and the third deviation component are fused to generate an initial comfort deviation value.

[0084] It is feasible; the data calculation module 10 is also used for: Behavioral pattern recognition is performed on the activity status data of each chicken cage to obtain the behavioral category pattern of each chicken cage. Query the preset behavior-comfort association rule table to obtain the comfort deviation correction strategy corresponding to each behavior category pattern; Based on the comfort deviation correction strategy, the corresponding initial comfort deviation value is calibrated to obtain the target comfort deviation value for each chicken cage.

[0085] It is feasible; the data generation module 20 is also used for: Based on the target comfort deviation value of each chicken cage, a target equipment status is generated for the chicken cage door of each chicken cage; Calculate the control commands for each chicken cage door to change from the current device state to the corresponding target device state; Based on the real-time load of the equipment at each chicken coop door and the equipment priority between other chicken coop doors, the timing of each control command is adjusted to generate an execution sequence.

[0086] It is feasible; the data execution module 30 is also used for: The actual control effect of each chicken cage was determined based on the changes in environmental parameters; By comparing the actual control effect of each chicken cage with the corresponding expected control effect, the optimization adjustment strategy for each chicken cage is obtained. Based on the optimization and adjustment strategy, the corresponding control instructions in the execution sequence are adjusted.

[0087] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for controlling the heating of a chicken coop door, characterized in that, include: Based on real-time collection of environmental parameters for each chicken cage, the target comfort deviation value for each chicken cage is calculated. Based on the target comfort deviation value, an execution sequence is generated between each of the chicken cages; According to the execution sequence, the corresponding chicken coop door and heating equipment are controlled to perform operation, and the control instructions in the execution sequence are adjusted based on the changes in environmental parameters after the operation.

2. The heating control method for a chicken coop door according to claim 1, characterized in that, The step of calculating the target comfort deviation value for each chicken cage based on real-time collection of environmental parameters for each chicken cage includes: Based on the environmental parameters of each chicken cage collected in real time, the trend of the target temperature parameter change of each chicken cage in the future time period is predicted. Calculate the initial comfort deviation value based on the changing trends of the target temperature parameters and the expected temperature parameters; Based on the activity status data of each chicken cage, the initial comfort deviation value is corrected to obtain the target comfort deviation value.

3. The heating control method for a chicken coop door according to claim 2, characterized in that, The step of predicting the trend of target temperature parameters for each chicken cage in a future time period based on real-time collected environmental parameters of each chicken cage includes: From the internal and external environmental parameters of each chicken cage collected in real time, target internal and external environmental parameters with a correlation greater than a preset correlation degree with the future time period are obtained, and a target environmental parameter sequence for each chicken cage is obtained. Based on the target environmental parameter sequence and the physical property parameters of each chicken cage, the trend of the initial temperature parameter change of each chicken cage in the future time period is calculated. Calculate the thermal impact value of adjacent chicken cages on each chicken cage based on the positional relationship between them. Based on the heat effect value, the initial temperature parameter change trend of the chicken cage corresponding to the heat effect value is corrected to obtain the target temperature parameter change trend.

4. The heating control method for a chicken coop door according to claim 2, characterized in that, The step of calculating the initial comfort deviation value based on the changing trend of the target temperature parameter and the changing trend of the expected temperature parameter includes: Extract the first, second, and third characteristic differences between the target temperature parameter change trend and the expected temperature parameter change trend in terms of change rate, fluctuation amplitude, and stable time point, respectively; Based on the first feature difference, the second feature difference, and the third feature difference, the rate of change, the fluctuation amplitude, and the first deviation component, the second deviation component, and the third deviation component at the stable time point are calculated. The first deviation component, the second deviation component, and the third deviation component are fused to generate the initial comfort deviation value.

5. The heating control method for a chicken coop door according to claim 2, characterized in that, The step of correcting the initial comfort deviation value based on the activity status data of each chicken cage to obtain the target comfort deviation value includes: Behavioral pattern recognition is performed on the activity status data of each chicken cage to obtain the behavioral category pattern of each chicken cage. Query the preset behavior-comfort association rule table to obtain the comfort deviation correction strategy corresponding to each behavior category pattern; According to the comfort deviation correction strategy, the corresponding initial comfort deviation value is calibrated to obtain the target comfort deviation value for each chicken cage.

6. The heating control method for a chicken coop door according to claim 1, characterized in that, The step of generating the execution sequence between each chicken cage based on the target comfort deviation value includes: Based on the target comfort deviation value of each chicken cage, a target equipment state is generated for the chicken cage door of each chicken cage; Calculate the control command for each of the chicken coop doors to change from the current device state to the corresponding target device state; Based on the real-time load of each chicken coop door and the device priority between other chicken coop doors, the timing of each control command is adjusted to generate the execution sequence.

7. The heating control method for a chicken coop door according to claim 1, characterized in that, The step of adjusting the control instructions in the execution sequence based on the changes in environmental parameters after the operation includes: Based on the changes in the environmental parameters, the actual control effect of each chicken cage is determined; By comparing the actual control effect of each chicken cage with the corresponding expected control effect, the optimization adjustment strategy for each chicken cage is obtained. According to the optimization and adjustment strategy, the corresponding control instructions in the execution sequence are adjusted.

8. A heating control system for a chicken coop door, characterized in that, include: The data calculation module is used to calculate the target comfort deviation value of each chicken cage based on the environmental parameters collected in real time. The data generation module is used to generate an execution sequence between each of the chicken cages based on the target comfort deviation value; The data execution module is used to control the corresponding chicken coop door and heating equipment to perform operation according to the execution sequence, and to adjust the control instructions in the execution sequence based on the changes in environmental parameters after the operation.

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