Antibiotic-free breeding system and method for laying hens

Abstract

The application relates to the technical field of intelligent agriculture, and discloses an egg-laying hen non-antibiotic breeding system and method. The application overcomes the technical defects that a general model cannot adapt to the influence of biological individual heterogeneity and environmental hysteresis by constructing a dynamic metabolic benchmark containing environmental correction logic, realizes dynamic and accurate adaptation of the breeding benchmark, simultaneously, effectively separates and identifies the drug forced intervention signal and the natural physiological recovery signal at the physical level by using parallel time sequence feature extraction and metabolic damping dynamics analysis, solves the problem that hidden illegal drug use is difficult to monitor, in addition, an inviolable physical anti-fake barrier is constructed by combining the antagonistic verification mechanism of the law of conservation of energy, data modification behavior is effectively identified, and the supervision transparency and compliance credibility of the whole process of non-antibiotic breeding are significantly improved.

Description

Technical Field

[0001] This application relates to the field of smart agriculture technology, and in particular to an antibiotic-free egg-laying hen farming system and method. Background Technology

[0002] As consumers continue to demand higher food safety and quality, antibiotic-free farming throughout the entire process has become a core trend and a source of high premiums for the egg-laying hen industry's transformation and upgrading.

[0003] While existing digital poultry farming management systems widely integrate environmental sensing and production data acquisition modules, enabling continuous recording of basic indicators such as feed intake, water consumption, and egg production rate, they suffer from significant technical limitations in the in-depth identification and attribution analysis of abnormal behaviors. Current monitoring technologies typically employ rule-based alarms based on fixed statistical thresholds or general recurrent neural network time-series prediction models to fit production data. These existing technologies primarily focus on identifying numerical abrupt changes in production indicators; that is, the system can only sense whether flock health indicators have declined, but cannot effectively identify the driving factors behind the recovery phase. From a biological metabolic mechanism perspective, antibiotic intervention rapidly blocks the body's inflammatory response and triggers drastic changes in gut microbiota and basal metabolic rate. The resulting physiological function recovery curve exhibits subtle but fundamental temporal morphological differences compared to the natural recovery process relying on the body's own immunity or traditional Chinese medicine treatment, particularly in terms of the recovery slope, fluctuation characteristics, and feed and water coupling relationship. However, existing technologies lack the logical architecture and data analysis model to capture such specific metabolic time fingerprints. They cannot distinguish between the recovery mode under forced chemical intervention and the natural physiological recovery mode at the algorithm level. This causes the system to completely fail when faced with a falsified compliance scenario where illegal drug use occurs but production data shows signs of improvement. As a result, it is difficult to meet the urgent improvement needs of high-standard antibiotic-free farming for transparency, non-invasive supervision and compliance traceability throughout the entire life cycle. Summary of the Invention

[0004] This application proposes an antibiotic-free egg-laying hen farming system and method to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this application adopts the following technical solution: an antibiotic-free egg-laying hen farming system, comprising: a dynamic metabolic baseline construction module, a metabolic residual decoupling module, an intervention attribution determination module, and an energy conservation verification module, wherein;

[0006] The dynamic metabolic baseline construction module is configured to acquire laying hen breed identification and growth week data, execute two-factor embedding logic to generate static and dynamic feature vectors, and generate theoretical dynamic metabolic baseline vectors containing the current environmental stress after feature fusion and environmental stress correction logic.

[0007] The metabolic residual decoupling module is configured to calculate the multidimensional difference between real-time production monitoring data and the theoretical dynamic metabolic benchmark vector to generate the original metabolic residual sequence, which is then input into a dual-channel time-series feature extraction network to extract drug response feature maps and physiological recovery feature maps through the high-frequency sensitive channel and the low-frequency trend channel, respectively.

[0008] The intervention attribution determination module is configured to use a logic gating unit to analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention determination result when the metabolic recovery damping coefficient indicates an overdamped state.

[0009] The energy conservation verification module is configured to execute energy quality conservation verification logic in response to the non-antibiotic intervention judgment result, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

[0010] Furthermore, the specific operation of the dynamic metabolic benchmark construction module in executing the two-factor embedding logic is as follows:

[0011] First, manifold mapping technology is used to map the variety identification data to a high-dimensional vector space to generate a static feature vector that implicitly contains the maximum egg production potential distribution and basal metabolic rate characteristics of the variety.

[0012] Simultaneously, sinusoidal positional encoding technology is used to map the growth age data into a dynamic growth vector that evolves over time;

[0013] Subsequently, a gating mechanism is used to perform element-wise corresponding product operations on the static feature vector and the dynamic growth vector to generate a biological genetic potential vector that reflects a specific variety at a specific growth stage, and this biological genetic potential vector is used as the basic data object for subsequent execution of environmental stress correction logic.

[0014] Furthermore, the specific operations of the dynamic metabolic baseline construction module in executing the environmental stress correction logic are as follows:

[0015] The temperature and humidity index is extracted from historical environmental time series data, and the degree of deviation of the temperature and humidity index from the preset thermal comfort threshold and thermal lethal threshold is calculated, thereby determining the nonlinear stress intensity that exhibits exponential growth characteristics.

[0016] By introducing a biological memory decay term, a convolution integral operation with a forgetting factor is performed on the nonlinear stress intensity within a preset integral time window to obtain a cumulative environmental pressure index that can characterize the environmental lag effect.

[0017] An anisotropic inhibition matrix is ​​constructed, and the cumulative environmental pressure index is transformed into differentiated inhibition weights for different metabolic indicators such as feed intake, water intake and egg production rate using this anisotropic inhibition matrix. The differentiated inhibition weights are then mapped to proportional coefficients under physical constraints using a nonlinear activation function. The feature vector output by the two-factor embedding logic is nonlinearly down-adjusted to generate the theoretical dynamic metabolic baseline vector.

[0018] Furthermore, the specific operation of the metabolic residual decoupling module in generating the original metabolic residual sequence is as follows:

[0019] First, calculate the multidimensional vector difference between the production monitoring data and the theoretical dynamic metabolic baseline vector;

[0020] Obtain the biological metabolic elastic modulus vector characterizing the allowable fluctuation range of a specific variety at the current age, and the benchmark fluctuation potential energy calculated based on historical health data. Use the biological metabolic elastic modulus vector and the benchmark fluctuation potential energy to construct a normalized denominator, and perform a standardization operation on the multidimensional vector difference.

[0021] At the same time, an asymmetric penalty factor is introduced to apply weight amplification to the difference between the value and the theoretical dynamic metabolic benchmark vector, so as to match the asymmetric characteristics of the organism's egg production performance, which is easy to decline but difficult to recover.

[0022] Finally, the hyperbolic tangent function is used to numerically map the processed data, restricting it to a preset normalization interval to generate the original metabolic residual sequence.

[0023] Furthermore, the specific operation of feature extraction performed by the dual-channel temporal feature extraction network in the metabolic residual decoupling module is as follows:

[0024] The original metabolic residual sequence was simultaneously input into both the high-frequency sensitive channel and the low-frequency trend channel;

[0025] In the high-frequency sensitive channel, a second-order difference operation is performed on the original metabolic residual sequence to obtain the acceleration features of the index changes. Then, a non-diffuse convolution kernel of the first preset size is used for convolution processing to extract the drug response feature map reflecting the short-term drastic fluctuations in water intake and food intake.

[0026] In the low-frequency trend channel, local average pooling is performed on the original metabolic residual sequence to filter out high-frequency noise. Then, convolution processing is performed using an expanded convolution kernel with a hole structure that is larger than the first preset size to expand the receptive field and extract physiological recovery feature maps that reflect the long-term evolution trend of egg production rate.

[0027] Finally, the time dimension alignment operation is performed on the output drug response feature map and physiological recovery feature map.

[0028] Furthermore, the specific operation of the intervention attribution determination module in calculating the metabolic recovery damping coefficient is as follows:

[0029] First, a drug effect delay operator is introduced to perform time axis translation on the physiological recovery feature map to compensate for the biological lag time between drug onset and organ function repair, so that it is strictly aligned with the drug response feature map in time.

[0030] Subsequently, the time derivative of the drug response feature map is calculated to obtain the rate of change of time characterizing the rate of symptom resolution, and the intervention response coupled gradient tensor is constructed by combining it with the aligned physiological recovery feature map.

[0031] Finally, a biological metabolic inertia factor based on the coupling of laying hen breed identification and growth cycle age is introduced. This biological metabolic inertia factor quantitatively characterizes the degree of metabolic stagnation in a specific breed of laying hen when switching from a pathological state back to a healthy state at the current growth stage. The metabolic recovery damping coefficient is solved by nonlinear ratio calculation, where the numerator of the calculation is based on the logarithmic transformation value of the rate of change over time, and the denominator is based on the recovery amplitude of the physiological recovery feature map after being weighted by the biological metabolic inertia factor.

[0032] Furthermore, the specific operation of the intervention attribution determination module in generating the hidden antibiotic intervention determination result using the logic gating unit is as follows:

[0033] A preset biological limit damping threshold is set, which is calculated based on the maximum theoretical rate at which the immune system of the target laying hen breed can clear pathogens without drug intervention.

[0034] The calculated metabolic recovery damping coefficient is compared with the biological limit damping threshold.

[0035] If the metabolic recovery damping coefficient is greater than the biological limit damping threshold, the current metabolic recovery process is determined to be in an overdamped state that violates natural biological inertia, and is identified as a forced intervention caused by external chemical drugs, thereby triggering the generation of a hidden antibiotic intervention judgment result.

[0036] If the metabolic recovery damping coefficient is less than or equal to the biological limit damping threshold, it is determined to be an underdamped state that conforms to the laws of natural immunity.

[0037] Furthermore, the specific operations of the energy conservation verification module in executing the energy-mass conservation verification logic are as follows:

[0038] First, real-time feed intake monitoring data is acquired, and then converted into effective metabolizable energy intake using a preset metabolizable energy conversion coefficient.

[0039] Subsequently, the biological metabolic inertia factor generated by the intervention attribution determination module is used as the biological quality characterization parameter, and the cumulative environmental pressure index generated by the dynamic metabolic benchmark construction module is used as the environmental correction parameter.

[0040] Based on Kleiber's law, a dynamic metabolic expenditure equation is constructed that includes an environmentally coupled basal metabolic energy term and an effective output energy term. The environmentally coupled basal metabolic energy term is calculated by performing a power-law operation on the biological metabolic inertia factor and combining it with an exponential correction term of the cumulative environmental pressure index.

[0041] Finally, the ratio of the difference between the effective metabolic energy intake and the theoretical total energy expenditure obtained through the dynamic metabolic expenditure equation is calculated to generate the thermodynamic consistency residual.

[0042] Furthermore, the specific steps for the energy conservation verification module to generate a forged compliance alert are as follows:

[0043] A preset bioenergy conservation tolerance range is defined, which defines the allowable deviation range of energy input and output within the normal physiological fluctuation range of an organism;

[0044] The calculated thermodynamic consistency residuals are compared with the bioenergy conservation tolerance range to determine the threshold.

[0045] When the thermodynamic consistency residual exceeds the tolerance range of bioenergy conservation, the anomaly attribution logic is executed:

[0046] If the effective metabolizable energy intake is found to be lower than the theoretical total energy consumption, it is determined to be a falsification of egg production data.

[0047] If the effective metabolizable energy intake is detected to be higher than the theoretical total energy consumption and the intervention attribution determination module determines that it is a non-antibiotic intervention, it is determined to be a hidden disease consumption or a falsification of feed data.

[0048] The final output includes a fake compliance alert with a specific attribution type and locks the compliance certification status.

[0049] A method for antibiotic-free egg-laying hen farming includes the following steps:

[0050] Step S1: Obtain the breed identification and growth week data of laying hens, execute the two-factor embedding logic to generate static and dynamic feature vectors, and generate a theoretical dynamic metabolic baseline vector containing the current environmental stress through feature fusion and combined with environmental stress correction logic.

[0051] Step S2: Calculate the multidimensional difference between the real-time production monitoring data and the theoretical dynamic metabolic benchmark vector to generate the original metabolic residual sequence, and input it into the dual-channel time-series feature extraction network to extract the drug response feature map and physiological recovery feature map through the high-frequency sensitive channel and the low-frequency trend channel, respectively.

[0052] Step S3: Analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map using a logic gating unit, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention judgment result when the coefficient indicates an overdamped state.

[0053] Step S4: In response to the non-antibiotic intervention determination result, execute the energy quality conservation verification logic, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

[0054] The beneficial effects of this invention are as follows:

[0055] This invention overcomes the technical shortcomings of general models that cannot adapt to the heterogeneity of biological individuals and the effects of environmental lag by constructing a dynamic metabolic benchmark that includes environmental correction logic. It achieves dynamic and accurate adaptation of the aquaculture benchmark. At the same time, by using parallel temporal feature extraction and metabolic damping kinetic analysis, it effectively separates and identifies drug-induced intervention signals and natural physiological recovery signals at the physical level, solving the problem of difficult monitoring of covert illegal drug use. In addition, by combining the antagonistic verification mechanism of the law of conservation of bioenergy, an inviolable physical anti-counterfeiting barrier is constructed, effectively identifying data manipulation behavior and significantly improving the regulatory transparency and compliance credibility of the entire antibiotic-free aquaculture process. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort:

[0057] Figure 1 This is a system framework diagram of the present invention;

[0058] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1, as Figure 1As shown, the present invention provides an antibiotic-free egg-laying hen farming system, including a dynamic metabolic baseline construction module, a metabolic residual decoupling module, an intervention attribution determination module, and an energy conservation verification module.

[0061] In this embodiment, the dynamic metabolic baseline construction module generates a reference baseline that includes both the genetic characteristics of laying hen breeds and dynamically reflects the impact of current environmental stress. The specific execution logic of the dynamic metabolic baseline construction module is as follows:

[0062] The dynamic metabolic baseline construction module is configured to acquire laying hen breed identification and growth week data, execute two-factor embedding logic to generate static and dynamic feature vectors, and generate a theoretical dynamic metabolic baseline vector containing the current environmental stress through feature fusion and combined with environmental stress correction logic.

[0063] The specific operation of the dynamic metabolic benchmark construction module in executing the two-factor embedding logic is as follows:

[0064] First, the variety identification data is mapped to a high-dimensional vector space using manifold mapping technology, generating a static feature vector that implicitly contains the maximum egg production potential distribution and basal metabolic rate characteristics of the variety.

[0065] In this embodiment, manifold mapping is not a table lookup operation, but rather a process based on manifold learning that converts discrete variety identifiers into continuous numerical representations.

[0066] The distribution of this static feature vector in the vector space directly corresponds to the biological upper limit of the laying hen breed under ideal conditions. For example, for breeds with high egg production potential, the numerical weight of the egg production dimension in their vector is higher; for breeds with low basal metabolic rate, the numerical distribution of the energy consumption dimension in their vector exhibits a specific low-energy consumption pattern. This mapping method can capture subtle differences in laying hens at the genetic level.

[0067] Simultaneously, the growth period data is mapped to a dynamic growth vector that evolves over time using sinusoidal positional encoding technology. In this step, the growth period is no longer regarded as a single scalar value, but is encoded by sine and cosine functions of different frequencies, and transformed into a multi-dimensional vector.

[0068] Each dimension of this dynamic growth vector represents a different frequency component in the egg-laying hen's growth cycle, thus enabling the mathematical expression of periodic temporal characteristics such as peak egg production and egg production decline.

[0069] Subsequently, the static feature vector and the dynamic growth vector are multiplied element-wise using a gating mechanism to generate a biological genetic potential vector that reflects a specific variety at a specific growth stage. This biological genetic potential vector is then used as the basic data object for subsequent execution of environmental stress correction logic.

[0070] In this step, the element-level corresponding product operation refers to multiplying the values ​​at corresponding positions in the static feature vector and the dynamic growth vector respectively, thereby realizing the activation or suppression of the static genetic characteristics of the variety by utilizing the dynamic changes over time.

[0071] For example, during the peak laying period, the dynamic growth vector enhances the feature values ​​in the static feature vector that are related to egg production; while during the brooding period, it enhances the feature values ​​related to skeletal development.

[0072] The resulting biological genetic potential vector represents the standard metabolic state that this breed of laying hen should have on the current number of days under ideal laboratory conditions without any external environmental interference.

[0073] The specific operation of the dynamic metabolic baseline construction module in executing the environmental stress correction logic is as follows:

[0074] The core of this environmental stress correction logic lies in calculating the cumulative environmental stress index, which employs a time-series convolution integral algorithm with memory functionality.

[0075] Specifically, the temporal convolution integral algorithm first defines an integration time window that traces back historical data. In this embodiment, the window is preferably set to 48 to 72 hours. The reason for this setting is that after a laying hen suffers heat stress, the decline in its egg production performance usually has a lag of two to three days, and this window can fully cover this physiological impact cycle.

[0076] The integrand in the integral operation first includes a term for calculating nonlinear stress intensity.

[0077] The system acquires the temperature and humidity index at historical moments in real time and introduces a data validity weighting coefficient. This coefficient is used to reset the weighting to zero when the sensor data is abnormal, thereby eliminating noise interference.

[0078] Subsequently, the system calculates the extent to which the temperature and humidity index exceeds the preset thermal comfort threshold, and divides it by the difference between the thermal mortality threshold and the thermal comfort threshold to obtain the normalized environmental pressure ratio. In this embodiment, the thermal comfort threshold is set to 72 and the thermal mortality threshold is set to 90. These two values ​​are derived from the breeding manual standards and veterinary pathological limits of the target laying hen breed.

[0079] In order to simulate the biological characteristics of nonlinear and rapidly deteriorating damage caused by extreme environments to organisms, the system performs an exponential operation on the above-mentioned normalization ratio. The exponential parameter is the nonlinear stress factor. In this embodiment, the nonlinear stress factor is preferably set to 2.0, that is, the square relationship is used to fit the nonlinear growth trend between environmental pressure and organism damage.

[0080] In addition, by using maximum value filtering logic, we ensure that the calculated stress intensity is zero when the environment is within the comfort zone, thus preventing the physical paradox of negative values ​​offsetting historical stress.

[0081] Another core part of the integrand is the biological memory decay term, which is an exponential decay function with the natural logarithm as the base. The exponent of this function is composed of the biological memory decay factor multiplied by the time difference.

[0082] The physical function of this term is to give the model a forgetting function, that is, the further back in time the historical heat stress is, the smaller its weight in influencing the current metabolism.

[0083] In this embodiment, the biological memory decay factor is preferably set to 0.05. This value is derived from regression analysis of the metabolic half-life data of stress hormones (such as cortisol) and heat shock proteins in laying hens, and can accurately reflect the rate at which the body repairs itself from a stress state.

[0084] Finally, the system continuously accumulates or discretely sums the product of the nonlinear stress intensity and the biological memory decay term within the integration time window to obtain an accurate cumulative environmental stress index.

[0085] In fact, the process of generating the theoretical dynamic metabolic baseline vector is a vector correction process based on the principle of anisotropic inhibition. The core of this process is to calculate the performance retention coefficient and use this coefficient to perform element-wise corresponding product operation on the biological genetic potential vector generated in the previous step.

[0086] Specifically, the operational logic first involves the application of anisotropic inhibition matrix, which is a learnable or preset multidimensional parameter matrix that quantifies the differential inhibitory effect of a single environmental stress index on metabolic indicators of different dimensions.

[0087] In this embodiment, the weighting coefficient of the anisotropic inhibition matrix for the feed intake dimension is preferably set to a high sensitivity value of 0.8 to 1.2, while the weighting coefficient for the egg production rate dimension is preferably set to a low sensitivity value of 0.2 to 0.4. The values ​​are based on the study of poultry heat stress physiology, that is, the inhibitory effect of high temperature on poultry feeding behavior is direct and intense, while the inhibitory effect on the reproductive system is relatively delayed and slow.

[0088] The system multiplies the cumulative environmental pressure index with the anisotropic suppression matrix mentioned above and adds a bias vector. The purpose of introducing the bias vector is to set an effective threshold to prevent the reference vector from fluctuating unnecessarily due to weak environmental noise.

[0089] Subsequently, the system uses a sigmoid nonlinear activation function to map the results of the above linear combination.

[0090] The activation function outputs a value between zero and one, which physically represents the performance loss rate caused by environmental stress.

[0091] The use of this activation function is based on adherence to biological and physical constraints:

[0092] On the one hand, performance loss cannot be negative;

[0093] On the other hand, even under infinite environmental pressure, the upper limit of performance loss in organisms can only be 100%, and the sigmoid function can perfectly fit this saturation characteristic.

[0094] Finally, the system subtracts the calculated performance loss rate from the unit vector to obtain the performance retention coefficient.

[0095] By multiplying the retention coefficient element-wise with the biological genetic potential vector representing the ideal state, the corrected theoretical dynamic metabolic baseline vector can be obtained.

[0096] This dynamic metabolic baseline vector is no longer a theoretical value under ideal laboratory conditions. Instead, it not only inherits the genetic potential of the variety but also eliminates the inevitable losses caused by the current accumulated environmental pressure, thus forming a real, objective, and practical dynamic compliance baseline under the current harsh environment.

[0097] In this embodiment, the metabolic residual decoupling module is a key bridge connecting the theoretical benchmark and the final attribution determination, used to physically separate the mixed short-term drug stimulation signals and long-term physiological recovery signals. The specific execution logic of the metabolic residual decoupling module is as follows:

[0098] The metabolic residual decoupling module is configured to calculate the multidimensional difference between real-time production monitoring data and theoretical dynamic metabolic baseline vector to generate the original metabolic residual sequence. This sequence is then input into a dual-channel time-series feature extraction network, which extracts drug response feature maps and physiological recovery feature maps through the high-frequency sensitive channel and the low-frequency trend channel, respectively.

[0099] The specific operations of the metabolic residual decoupling module in generating the original metabolic residual sequence are as follows:

[0100] First, calculate the multidimensional vector difference between the production monitoring data and the theoretical dynamic metabolic baseline vector;

[0101] In this step, the system receives real-time data on feed intake, water consumption, and egg production rate, and performs a subtraction operation on the corresponding dimension of these data with the theoretical dynamic metabolic baseline vector generated in the previous step.

[0102] The purpose of this operation is to obtain the absolute amount by which actual performance deviates from theoretical expectations.

[0103] Obtain the biological metabolic elastic modulus vector characterizing the allowable fluctuation range of a specific variety at the current age, and the benchmark fluctuation potential energy calculated based on historical health data. Use the biological metabolic elastic modulus vector and the benchmark fluctuation potential energy to construct a normalized denominator, and perform a standardization operation on the multidimensional vector difference.

[0104] In this step, the biometabolic elastic modulus vector is a key regulation parameter, which represents the permissible natural dispersion or robustness among individuals in a particular breed of laying hens at the current growth stage.

[0105] In this embodiment, for flocks during peak egg production, the modulus value is preferably set to 0.05, which means that the system considers fluctuations within 5% of the baseline as normal physiological phenomena.

[0106] For flocks in the later stages of egg production, this value is preferably increased to 0.15. The value is based on statistical data on the evenness of the flock at different ages in the standard breeding manual for this breed of laying hens, ensuring that the system will not generate false alarms during the stage when the individual differences in the flock naturally increase.

[0107] The system multiplies the elastic modulus vector with the square root of the baseline wave potential energy, thereby converting the potential energy in the energy dimension into a wave scalar in the amplitude dimension, thus constructing a normalized denominator that conforms to biological characteristics.

[0108] The baseline variability potential energy represents the inherent energy fluctuation level of various indicators under historical health conditions, and is used to eliminate the dimensional differences between feed intake (grams) and egg production rate (percentage).

[0109] Subsequently, the system divides the multidimensional vector difference obtained from the aforementioned calculation by the normalized denominator to complete the biological resilience standardization of the data.

[0110] At the same time, an asymmetric penalty factor is introduced to apply weight amplification to the difference between the numerical value and the theoretical dynamic metabolic benchmark vector, so as to match the asymmetric characteristic that the rate of decline in the egg production performance of the organism is faster than the rate of recovery.

[0111] In this step, the system introduces a weighted term based on exponential logic, the core parameter of which is an asymmetric penalty factor.

[0112] The technical utility of the asymmetric penalty factor lies in establishing an asymmetric bias sensitivity mechanism, that is, setting the system to have a significantly higher detection gain for negative deviations (numerical decreases) in performance indicators than for positive deviations (numerical increases).

[0113] In this embodiment, the asymmetric penalty factor is preferably set to 1.5 to 2.0.

[0114] Its value is based on the statistical characteristics of poultry pathology, namely, in the early stage of a disease outbreak, the rate of decline in production performance is usually more than 1.5 times the natural fluctuation rate.

[0115] By combining this asymmetric penalty factor with the sign function, when the monitored data is below the baseline, the residual value will be amplified exponentially. This not only highlights potential abnormal signals but also conforms to the asymmetric dynamic characteristics of the organism's rapid decline under pathological stress and slow recovery during the physiological repair period.

[0116] Finally, the hyperbolic tangent function is used to numerically map the processed data, restricting it to a preset normalization interval to generate the original metabolic residual sequence.

[0117] The system inputs the data, after asymmetric weighting, into the hyperbolic tangent activation function.

[0118] The hyperbolic tangent activation function smoothly compresses and maps input values ​​of any range to a closed interval from negative one to positive one. This not only eliminates the damage to subsequent neural network weights caused by extreme outliers (such as instantaneous massive data caused by sensor failure), but also ensures the distribution stability of the input data, thereby generating the original metabolic residual sequence that contains both biological elasticity characteristics and numerical stability.

[0119] The specific operation of the dual-channel temporal feature extraction network in the metabolic residual decoupling module for feature extraction is as follows:

[0120] The original metabolic residual sequence was simultaneously input into both the high-frequency sensitive channel and the low-frequency trend channel;

[0121] This implementation uses a parallel dual-channel structure to replace the general recurrent neural network architecture, aiming to solve the problem of aliasing between drug intervention signals and physiological recovery signals in the frequency domain through a structured frequency domain separation mechanism.

[0122] In the high-frequency sensitive channel, a second-order difference operation is performed on the original metabolic residual sequence to obtain the acceleration features of the index changes. Then, a non-diffuse convolution kernel of the first preset size is used for convolution processing to extract the drug response feature map reflecting the short-term drastic fluctuations in water intake and food intake.

[0123] Specifically, the channel first performs second-order difference processing on the sequence.

[0124] The technical utility of second-order difference operations lies in extracting the acceleration characteristics or dynamic mutation characteristics of data changes, rather than the absolute magnitude of the values ​​themselves. This is based on the fact that, in terms of dynamics, artificial drug administration or acute poisoning events manifest as drastic mutations in drinking water and food intake indicators within a very short time window.

[0125] Subsequently, the system performs convolution processing using a first preset size non-diffuse convolution kernel.

[0126] In this embodiment, the size of the convolution kernel is preferably set to three. The pharmacokinetic basis for this value is that the average onset time of intestinal rejection after oral ingestion of a drug is usually three to twelve hours. Using a small-sized convolution kernel can accurately focus on this short time window.

[0127] Combined with a hole-free dense convolution operation, this channel can selectively extract and amplify short-term, intense signals with a pulse-like oscillation pattern, mapping them into a high-dimensional drug response feature map.

[0128] In the low-frequency trend channel, local average pooling is performed on the original metabolic residual sequence to filter out high-frequency noise. Then, convolution processing is performed using an expanded convolution kernel with a hole structure that is larger than the first preset size to expand the receptive field and extract physiological recovery feature maps that reflect the long-term evolution trend of egg production rate.

[0129] The purpose of this channel is to suppress short-term fluctuations and extract long-term evolution trends.

[0130] First, high-frequency noise components are smoothly filtered out by local average pooling, and then dilated convolution is used for further processing.

[0131] In this embodiment, the porosity of the expanded convolution kernel is preferably set to four or eight, which determines the receptive field size or effective time span of the convolution kernel on the time axis.

[0132] Furthermore, the values ​​are based on the tissue repair cycle of the reproductive system of laying hens. The recovery of egg production rate usually takes three to five days or longer. The large porosity allows the network to extract features across a long time span, thereby identifying the slow, continuous and smooth upward physiological recovery trend in egg production rate and weight data, and generating a physiological recovery feature map.

[0133] Finally, the time dimension alignment operation is performed on the output drug response feature map and physiological recovery feature map.

[0134] Because the two channels use different convolution strategies and network depths, the output features may have phase deviations on the time axis. The system ensures that the drug response features and physiological recovery features at the same time are strictly aligned in the time dimension by pruning or padding, thus providing time-consistent input data for the next-level module to determine whether there are abnormal recovery phenomena caused by drug intervention.

[0135] In this embodiment, the intervention attribution determination module introduces a damped oscillation model from cybernetics to accurately identify whether there is any drug-induced forced intervention that violates natural laws by analyzing the dynamics of the organism's recovery process. The specific execution logic of the intervention attribution determination module is as follows:

[0136] The intervention attribution determination module is configured to use a logic gating unit to analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention determination result when the metabolic recovery damping coefficient indicates an overdamped state.

[0137] The specific steps for calculating the metabolic recovery damping coefficient using the intervention attribution determination module are as follows:

[0138] First, a drug effect delay operator is introduced to perform time axis translation on the physiological recovery feature map to compensate for the biological lag time between drug onset and organ function repair, so that it is strictly aligned with the drug response feature map in time.

[0139] In this step, the system first applies the drug effect delay operator.

[0140] This drug effect delay operator represents the time difference between when a drug acts on the body (such as eliminating intestinal inflammation or improving appetite) and when the body produces output indicators (such as follicle maturation and egg production).

[0141] In biology, even if a drug takes effect immediately, the recovery of egg production will inevitably lag behind the recovery of feed intake.

[0142] In this embodiment, the time window of the drug effect delay operator is preferably set to 24 to 48 hours. The time window setting range is based on the coupling relationship between pharmacokinetics and poultry reproductive physiology, that is, the average physiological cycle from nutrient absorption recovery to eggshell formation.

[0143] By using the time-series shift operation of the drug effect delay operator, the system achieves phase synchronization of the drug response feature map (representing intervention response events) and the physiological recovery feature map (representing functional repair events) in the time dimension, thus laying a time consistency foundation for accurately quantifying and analyzing the dynamic correlation characteristics between the two.

[0144] Subsequently, the time derivative of the drug response feature map is calculated to obtain the rate of change of time characterizing the rate of symptom resolution, and the intervention response coupled gradient tensor is constructed by combining it with the aligned physiological recovery feature map.

[0145] Specifically, the system calculates the slope of the drug response characteristic map over time. The physical meaning of this slope is the symptom resolution rate, which characterizes how quickly abnormal fluctuations in water or food intake return to calm.

[0146] Subsequently, the system performs tensor operations on the rate of change over time and the physiological recovery feature map after translation and alignment to construct the intervention response coupled gradient tensor. This intervention response coupled gradient tensor quantitatively characterizes the dynamic coupling relationship between the symptom resolution rate and the physiological function recovery amplitude from a mathematical perspective.

[0147] Finally, a biological metabolic inertia factor based on the coupling of laying hen breed identification and growth cycle age is introduced. This biological metabolic inertia factor quantitatively characterizes the degree of metabolic stagnation in a specific breed of laying hen when switching from a pathological state back to a healthy state at the current growth stage. The metabolic recovery damping coefficient is solved by nonlinear ratio calculation, where the numerator of the calculation is based on the logarithmic transformation value of the rate of change over time, and the denominator is based on the recovery amplitude of the physiological recovery feature map after being weighted by the biological metabolic inertia factor.

[0148] In this core calculation step, the system introduces a biological metabolic inertia factor.

[0149] This biological metabolic inertia factor characterizes the resistance to state transitions or response lag exhibited by laying hens of different biomass when metabolic state transitions occur.

[0150] According to the allometric growth law of biological metabolic dynamics, the rate of metabolic state switching of an organism is negatively correlated with biomass, that is, the larger the biomass of an individual, the longer the latency period of its state transition.

[0151] In this embodiment, the calculation of the biological metabolic inertia factor adopts a composite calculation logic that combines the base number of basic varieties with the correction of the growth cycle.

[0152] Specifically, the baseline inertia of the basic breed is first determined. For heavy brown-shelled laying hens (such as Lohmann Brown), the baseline is preferably set to 1.0, and for light white-shelled laying hens (such as Hy-Line White), the baseline is preferably set to 0.8, in order to quantify the basal metabolic inhibition effect caused by the difference in biomass.

[0153] Subsequently, a growth cycle correction term is introduced to dynamically compensate for the above base. Based on the physiological characteristic that the body's repair rate decreases non-linearly with age, a time-varying correction model containing a logarithmic function is constructed. That is, the final biological metabolic inertia factor is equal to the base inertia of the basic variety multiplied by the decay correction coefficient, where the decay correction coefficient is positively correlated with the natural logarithm of (current growth cycle age / sexual maturity cycle age).

[0154] The technical advantage of this logic lies in ensuring that the system adapts to the relatively slow natural recovery characteristics of individuals in the later stages of egg production, eliminating the risk of misidentifying the sluggish recovery caused by the natural decline of physiological functions as an underdamped state caused by discontinuation of medication, thereby achieving adaptive attribution determination throughout the entire growth cycle.

[0155] The introduction of this biological metabolic inertia factor enables the normalization of variety-specific physiological response characteristics, effectively preventing the system from misjudging small varieties with high metabolic rates as being catalyzed by external drugs.

[0156] When constructing the nonlinear ratio calculation formula, the numerator uses the natural logarithm of the symptom resolution rate plus one to smooth the data and suppress the influence of extreme outliers; the denominator is the result of exponentially multiplying the recovery amplitude of the physiological recovery feature map by a recovery nonlinear exponent and then multiplying it by the biological metabolic inertia factor.

[0157] In this embodiment, the recovery nonlinearity index is preferably set to 0.5 to 0.8, based on the fact that as the recovery amplitude increases, its marginal contribution to the overall damping characteristics of the system exhibits a nonlinear decreasing characteristic.

[0158] The final calculated metabolic recovery damping coefficient is a dimensionless physical characteristic quantity that precisely quantifies the dynamic response characteristics of the current rehabilitation process.

[0159] The specific operation of the intervention attribution determination module in generating the hidden antibiotic intervention determination result using the logic gating unit is as follows:

[0160] A preset biological limit damping threshold is set, which is calculated based on the maximum theoretical rate at which the immune system of the target laying hen breed can clear pathogens without drug intervention.

[0161] The biological limiting damping threshold represents the maximum theoretical recovery rate boundary that this breed of laying hen can achieve under natural physiological conditions by relying on endogenous immune mechanisms or compliant plant-derived preparations.

[0162] In this embodiment, the biological limit damping threshold is preferably set to 0.95. The value of 0.95 is based on the statistical analysis of a large-scale antibiotic-free aquaculture control group. That is, in the sample without external chemical intervention, the damping coefficient distribution of 99% of the natural recovery process is below this value.

[0163] Therefore, this biological limit damping threshold constitutes the critical decision boundary for compliance determination, and any recovery rate exceeding this boundary is considered to deviate from the established biological laws.

[0164] The calculated metabolic recovery damping coefficient is compared with the biological limit damping threshold.

[0165] The system executes numerical comparison logic to monitor the damping coefficient value in real time for each monitoring cycle.

[0166] If the metabolic recovery damping coefficient is greater than the biological limit damping threshold, the current metabolic recovery process is determined to be in an overdamped state that violates natural biological inertia, and is identified as a forced intervention caused by external chemical drugs, thereby triggering the generation of a hidden antibiotic intervention judgment result.

[0167] When the calculated coefficient exceeds the threshold (e.g., greater than 0.95), it means that the system is in an overdamped state.

[0168] At this time, the data characteristics show that the symptom indicators exhibit a step-like decline within a very short time window, while the egg production rate shows a monotonous linear increase.

[0169] This transient and potent suppression of pathological responses does not conform to the laws of natural biology and is systematically identified as a forced metabolic change caused by the intervention of potent antibiotics.

[0170] Therefore, the system identified it as a covert antibiotic intervention and issued a high-risk alert.

[0171] If the metabolic recovery damping coefficient is less than or equal to the biological limit damping threshold, it is determined to be an underdamped state that conforms to the laws of natural immunity.

[0172] When the coefficient is below the threshold, it means that the system is in an underdamped state.

[0173] The data at this point exhibits a gradual recovery trajectory accompanied by damped oscillations, reflecting the natural physiological process of dynamic balance between the body's immune system and pathogenic factors.

[0174] Based on this, the system determined that the recovery process complied with antibiotic-free aquaculture standards and was either a result of natural self-healing or physiological conditioning after the use of compliant traditional Chinese medicine preparations.

[0175] In this embodiment, the energy conservation verification module uses the first law of thermodynamics to perform a physical-level consistency audit on the judgment results of the preceding module. Its logic lies in utilizing the inviolable physical fact that bioenergy input and output must follow the law of conservation of matter to identify data manipulation or logical paradoxes. The specific execution logic of the energy conservation verification module is as follows:

[0176] The energy conservation verification module is configured to execute energy quality conservation verification logic in response to the non-antibiotic intervention judgment result, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

[0177] The specific operations of the energy conservation verification module in executing the energy mass conservation verification logic are as follows:

[0178] First, real-time feed intake monitoring data is acquired, and then converted into effective metabolizable energy intake using a preset metabolizable energy conversion coefficient.

[0179] In this step, the system first reads the real-time physical weight data of feed intake transmitted by the feed tower sensor.

[0180] Since physical weight cannot directly represent energy, the system needs to convert it into energy units. Specifically, the system multiplies the real-time feed intake with the feed metabolizable energy conversion coefficient and multiplies it by a feed utilization coefficient to deduct the waste.

[0181] In this embodiment, the feed metabolizable energy conversion coefficient is preferably set to 11,700 kJ per kilogram.

[0182] The setting of 11,700 kilojoules per kilogram is based on the weighted calculation of the standard metabolizable energy values ​​of raw materials such as corn and soybean meal in the feed formula.

[0183] The feed utilization coefficient is preferably set to 0.95 to 0.98 to eliminate the loss of feed spillage and uneaten feed, which is based on the vibration waveform analysis of the feed line sensor.

[0184] The final calculated effective metabolic energy intake is the total energy supply input into the biological system.

[0185] Subsequently, the biological metabolic inertia factor generated by the intervention attribution determination module is used as the biological quality characterization parameter, and the cumulative environmental pressure index generated by the dynamic metabolic benchmark construction module is used as the environmental correction parameter.

[0186] This step demonstrates the synergistic effect of system-wide parameter reuse.

[0187] The system directly retrieves the biological metabolic inertia factor (characterizing the mass of an individual organism) and the cumulative environmental pressure index (characterizing the environmental heat load) from the preceding modules.

[0188] Based on Kleiber's law, a dynamic metabolic expenditure equation is constructed that includes an environmentally coupled basal metabolic energy term and an effective output energy term. The environmentally coupled basal metabolic energy term is calculated by performing a power-law operation on the biological metabolic inertia factor and combining it with an exponential correction term of the cumulative environmental pressure index.

[0189] In this step, the system constructs a dynamic theoretical energy consumption model, which consists of two parts: basal metabolic energy for sustaining life and effective output energy for production.

[0190] For the environmentally coupled basal metabolic energy term, the system performs a power-law operation on the biological metabolic inertia factor with an exponent of 0.75 based on Kleiber's law in biology to calculate the basal metabolic rate under standard conditions.

[0191] Subsequently, a multidimensional environmental composite correction logic is introduced, which uses the composite environmental stress index as the exponent to perform power operation on the natural constant, thereby generating a comprehensive environmental metabolic compensation term.

[0192] The composite environmental stress index is derived by weighted coupling of the thermal environmental pressure component and the gas toxicity pressure component.

[0193] Among them, the thermal environment pressure component is calculated based on the deviation of the temperature and humidity index from the thermal comfort zone;

[0194] The gas toxicity pressure component is calculated based on the real-time monitoring of ammonia and carbon dioxide concentrations by sensors.

[0195] The biological basis for this logic is that for every 10 ppm increase in ammonia concentration, the maintenance energy required by the body to repair the respiratory mucosa and perform liver detoxification functions will increase non-linearly.

[0196] Therefore, the adjustment coefficient in the environmental stress correction term is set as a composite gradient vector, which corresponds to the increase in heat dissipation energy consumption caused by each unit increase in temperature and humidity index, and the increase in tissue repair energy consumption caused by each threshold unit increase in ammonia concentration, thereby accurately eliminating the hidden non-productive energy loss caused by harsh microenvironment.

[0197] This logic ensures that the system can quantitatively characterize the energy coupling relationship between environmental heat load and production performance decline, verify the thermodynamic consistency of production capacity decline under heat stress conditions, and thus avoid misclassifying expected performance decline induced by environmental factors as data anomalies. For the effective output energy item, the system multiplies the real-time monitored egg production weight and body weight increment by the corresponding energy conversion constant and sums them up.

[0198] The preferred energy conversion constant for egg production is set at 6 kilojoules per gram, and the preferred energy conversion constant for weight gain is set at 20 kilojoules per gram. Both settings are based on standard parameters of poultry bioenergetics.

[0199] Finally, the ratio of the difference between the effective metabolic energy intake and the theoretical total energy expenditure obtained through the dynamic metabolic expenditure equation is calculated to generate the thermodynamic consistency residual.

[0200] The system calculates the absolute value of the difference between effective metabolic energy intake and theoretical total energy consumption, and divides this difference by effective metabolic energy intake to obtain a dimensionless percentage value, namely the thermodynamic consistency residual. This residual quantifies the degree to which the current monitoring data violates the law of conservation of energy.

[0201] The specific steps for the energy conservation verification module to generate a fake compliance alert are as follows:

[0202] A preset bioenergy conservation tolerance range is defined, which defines the allowable deviation range of energy input and output within the normal physiological fluctuation range of an organism.

[0203] The bioenergy conservation tolerance range represents the permissible error range caused by uncontrollable factors such as individual differences in digestibility and absorption rates and small fluctuations in feed energy value.

[0204] In this embodiment, the tolerance range for bioenergy conservation is preferably set to 10% to 15%. This range is derived from the statistical variance of large-scale population energy metabolism balance experiments and covers natural fluctuations within a 95% confidence interval. Setting the tolerance range for bioenergy conservation enhances the sensitivity and specificity of the balance system and prevents false alarms triggered by normal biological noise.

[0205] The calculated thermodynamic consistency residuals are compared with the bioenergy conservation tolerance range to determine the threshold.

[0206] The system compares the calculated residual value with the preset tolerance threshold in real time.

[0207] When the thermodynamic consistency residual exceeds the tolerance range of bioenergy conservation, the anomaly attribution logic is executed:

[0208] Once the residual exceeds the limit, it indicates a serious physical-logical paradox in the current data, and the system enters the attribution branch:

[0209] If the effective metabolizable energy intake is detected to be lower than the theoretical total energy consumption, it is determined to be a falsification of egg production data;

[0210] When the effective metabolic energy intake is significantly lower than the theoretical total energy consumption, this energy state violates the physical constraint of the law of conservation of biological energy.

[0211] This data anomaly pattern logically maps to a false increase in output data, corresponding to a counter-reaction of artificially tampering with egg production data to cover up the actual decline in production performance. Based on this, the system judges it as a falsification of egg production data.

[0212] If the effective metabolizable energy intake is detected to be higher than the theoretical total energy consumption and the intervention attribution determination module determines that it is a non-antibiotic intervention, it is determined to be a hidden disease consumption or a falsification of feed data.

[0213] When the effective metabolic energy intake is significantly higher than the theoretical total energy consumption, and the preceding modules have ruled out changes in metabolic kinetics caused by drug intervention, it indicates that there is unquantified hidden energy dissipation or abnormal input data in the system.

[0214] This data characteristic is technically attributed to non-productive consumption caused by pathological factors (such as nutrient allocation shifts due to parasitic infections) or false exaggeration of feed data. Based on this, the system determines it to be either hidden disease consumption or falsified feed data.

[0215] The final output includes a fake compliance alert with a specific attribution type and locks the compliance certification status.

[0216] Based on the above judgment results, the system generates an alarm log containing the specific fraud type and immediately freezes the anti-counterfeiting certification status of the current batch of products until manual verification is intervened, thereby realizing a data anti-counterfeiting closed loop based on physical laws.

[0217] Example 2, as Figure 2 As shown, the present invention also provides a method for antibiotic-free egg-laying hen farming, comprising the following steps;

[0218] Step S1: Obtain the breed identification and growth week data of laying hens, execute the two-factor embedding logic to generate static and dynamic feature vectors, and generate a theoretical dynamic metabolic baseline vector containing the current environmental stress through feature fusion and combined with environmental stress correction logic.

[0219] Step S2: Calculate the multidimensional difference between the real-time production monitoring data and the theoretical dynamic metabolic benchmark vector to generate the original metabolic residual sequence, and input it into the dual-channel time-series feature extraction network to extract the drug response feature map and physiological recovery feature map through the high-frequency sensitive channel and the low-frequency trend channel, respectively.

[0220] Step S3: Analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map using a logic gating unit, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention judgment result when the coefficient indicates an overdamped state.

[0221] Step S4: In response to the non-antibiotic intervention determination result, execute the energy quality conservation verification logic, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

[0222] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An antibiotic-free egg-laying hen farming system, characterized in that, include: The system includes a dynamic metabolic baseline construction module, a metabolic residual decoupling module, an intervention attribution determination module, and an energy conservation verification module. The dynamic metabolic baseline construction module is configured to acquire laying hen breed identification and growth week data, execute two-factor embedding logic to generate static and dynamic feature vectors, and generate theoretical dynamic metabolic baseline vectors containing the current environmental stress after feature fusion and environmental stress correction logic. The metabolic residual decoupling module is configured to calculate the multidimensional difference between real-time production monitoring data and the theoretical dynamic metabolic benchmark vector to generate the original metabolic residual sequence, which is then input into a dual-channel time-series feature extraction network to extract drug response feature maps and physiological recovery feature maps through the high-frequency sensitive channel and the low-frequency trend channel, respectively. The intervention attribution determination module is configured to use a logic gating unit to analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention determination result when the metabolic recovery damping coefficient indicates an overdamped state. The energy conservation verification module is configured to execute energy quality conservation verification logic in response to the non-antibiotic intervention judgment result, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

2. The antibiotic-free egg-laying hen farming system according to claim 1, characterized in that, The specific operations of the dynamic metabolic benchmark construction module in executing the two-factor embedding logic are as follows: First, manifold mapping technology is used to map the variety identification data to a high-dimensional vector space to generate a static feature vector that implicitly contains the maximum egg production potential distribution and basal metabolic rate characteristics of the variety. Simultaneously, sinusoidal positional encoding technology is used to map the growth age data into a dynamic growth vector that evolves over time; Subsequently, a gating mechanism is used to perform element-wise corresponding product operations on the static feature vector and the dynamic growth vector to generate a biological genetic potential vector that reflects a specific variety at a specific growth stage, and this biological genetic potential vector is used as the basic data object for subsequent execution of environmental stress correction logic.

3. The antibiotic-free egg-laying hen farming system according to claim 2, characterized in that, The specific operations of the dynamic metabolic baseline construction module in executing the environmental stress correction logic are as follows: The temperature and humidity index is extracted from historical environmental time series data, and the degree of deviation of the temperature and humidity index from the preset thermal comfort threshold and thermal lethal threshold is calculated, thereby determining the nonlinear stress intensity that exhibits exponential growth characteristics. By introducing a biological memory decay term, a convolution integral operation with a forgetting factor is performed on the nonlinear stress intensity within a preset integral time window to obtain a cumulative environmental pressure index that can characterize the environmental lag effect. An anisotropic inhibition matrix is ​​constructed, and the cumulative environmental stress index is transformed into differential inhibition weights for different metabolic indicators such as feed intake, water intake and egg production rate using this anisotropic inhibition matrix. The differential inhibition weights are then mapped to proportional coefficients under physical constraints using a nonlinear activation function. The feature vector output by the two-factor embedding logic is nonlinearly down-adjusted to finally generate the theoretical dynamic metabolic baseline vector.

4. The antibiotic-free egg-laying hen farming system according to claim 3, characterized in that, The specific operations of the metabolic residual decoupling module in generating the original metabolic residual sequence are as follows: First, calculate the multidimensional vector difference between the production monitoring data and the theoretical dynamic metabolic baseline vector; Obtain the biological metabolic elastic modulus vector characterizing the allowable fluctuation range of a specific variety at the current age, and the benchmark fluctuation potential energy calculated based on historical health data. Use the biological metabolic elastic modulus vector and the benchmark fluctuation potential energy to construct a normalized denominator, and perform a standardization operation on the multidimensional vector difference. At the same time, an asymmetric penalty factor is introduced to apply weight amplification to the difference between the value and the theoretical dynamic metabolic benchmark vector, so as to match the asymmetric characteristics of the organism's egg production performance, which is easy to decline but difficult to recover. Finally, the hyperbolic tangent function is used to numerically map the processed data, restricting it to a preset normalization interval to generate the original metabolic residual sequence.

5. The antibiotic-free egg-laying hen farming system according to claim 4, characterized in that, The specific operation of the dual-channel temporal feature extraction network in the metabolic residual decoupling module for feature extraction is as follows: The original metabolic residual sequence was simultaneously input into both the high-frequency sensitive channel and the low-frequency trend channel; In the high-frequency sensitive channel, a second-order difference operation is performed on the original metabolic residual sequence to obtain the acceleration features of the index changes. Then, a non-drilling convolution kernel of the first preset size is used for convolution processing to extract the drug response feature map reflecting the short-term drastic fluctuations in water intake and food intake. In the low-frequency trend channel, local average pooling is performed on the original metabolic residual sequence to filter out high-frequency noise. Then, convolution processing is performed using an expanded convolution kernel with a hole structure that is larger than the first preset size to expand the receptive field and extract physiological recovery feature maps that reflect the long-term evolution trend of egg production rate. Finally, the time dimension alignment operation is performed on the output drug response feature map and physiological recovery feature map.

6. The antibiotic-free egg-laying hen farming system according to claim 5, characterized in that, The specific steps for calculating the metabolic recovery damping coefficient using the intervention attribution determination module are as follows: First, a drug effect delay operator is introduced to perform time axis translation on the physiological recovery feature map to compensate for the biological lag time between drug onset and organ function repair, so that it is strictly aligned with the drug response feature map in time. Subsequently, the time derivative of the drug response feature map is calculated to obtain the rate of change of time characterizing the rate of symptom resolution, and the intervention response coupled gradient tensor is constructed by combining it with the aligned physiological recovery feature map. Finally, a biological metabolic inertia factor based on the coupling of laying hen breed identification and growth cycle age is introduced. This biological metabolic inertia factor quantitatively characterizes the degree of metabolic stagnation in a specific breed of laying hen when switching from a pathological state back to a healthy state at the current growth stage. The metabolic recovery damping coefficient is solved by nonlinear ratio calculation, where the numerator is based on the logarithmic transformation value of the rate of change over time, and the denominator is based on the recovery amplitude of the physiological recovery feature map after being weighted by the biological metabolic inertia factor.

7. The antibiotic-free egg-laying hen farming system according to claim 6, characterized in that, The specific operation of the intervention attribution determination module in generating the hidden antibiotic intervention determination result using the logic gating unit is as follows: A preset biological limit damping threshold is set, which is calculated based on the maximum theoretical rate at which the immune system of the target laying hen breed can clear pathogens without drug intervention. The calculated metabolic recovery damping coefficient is compared with the biological limit damping threshold. If the metabolic recovery damping coefficient is greater than the biological limit damping threshold, the current metabolic recovery process is determined to be in an overdamped state that violates natural biological inertia, and is identified as a forced intervention caused by external chemical drugs, thereby triggering the generation of a hidden antibiotic intervention judgment result. If the metabolic recovery damping coefficient is less than or equal to the biological limit damping threshold, it is determined to be an underdamped state that conforms to the laws of natural immunity.

8. The antibiotic-free egg-laying hen farming system according to claim 7, characterized in that, The specific operations of the energy conservation verification module in executing the energy mass conservation verification logic are as follows: First, real-time feed intake monitoring data is acquired, and then converted into effective metabolizable energy intake using a preset metabolizable energy conversion coefficient. Subsequently, the biological metabolic inertia factor generated by the intervention attribution determination module is used as the biological quality characterization parameter, and the cumulative environmental pressure index generated by the dynamic metabolic benchmark construction module is used as the environmental correction parameter. Based on Kleiber's law, a dynamic metabolic expenditure equation is constructed that includes an environmentally coupled basal metabolic energy term and an effective output energy term. The environmentally coupled basal metabolic energy term is calculated by performing a power-law operation on the biological metabolic inertia factor and combining it with an exponential correction term of the cumulative environmental pressure index. Finally, the ratio of the difference between the effective metabolic energy intake and the theoretical total energy expenditure obtained through the dynamic metabolic expenditure equation is calculated to generate the thermodynamic consistency residual.

9. The antibiotic-free egg-laying hen farming system according to claim 8, characterized in that, The specific steps for the energy conservation verification module to generate a fake compliance alert are as follows: A preset bioenergy conservation tolerance range is defined, which defines the allowable deviation range of energy input and output within the normal physiological fluctuation range of an organism; The calculated thermodynamic consistency residuals are compared with the bioenergy conservation tolerance range to determine the threshold. When the thermodynamic consistency residual exceeds the tolerance range of bioenergy conservation, the anomaly attribution logic is executed: If the effective metabolizable energy intake is found to be lower than the theoretical total energy consumption, it is determined to be a falsification of egg production data. If the effective metabolizable energy intake is detected to be higher than the theoretical total energy consumption and the intervention attribution determination module determines that it is a non-antibiotic intervention, it is determined to be a hidden disease consumption or a falsification of feed data. The final output includes a fake compliance alert with a specific attribution type and locks the compliance certification status.

10. A method for antibiotic-free egg-laying hen farming, based on the antibiotic-free egg-laying hen farming system according to any one of claims 1-9, characterized in that, Includes the following steps: Step S1: Obtain the breed identification and growth week data of laying hens, execute the two-factor embedding logic to generate static and dynamic feature vectors, and generate a theoretical dynamic metabolic baseline vector containing the current environmental stress through feature fusion and combined with environmental stress correction logic. Step S2: Calculate the multidimensional difference between the real-time production monitoring data and the theoretical dynamic metabolic benchmark vector to generate the original metabolic residual sequence, and input it into the dual-channel time-series feature extraction network to extract the drug response feature map and physiological recovery feature map through the high-frequency sensitive channel and the low-frequency trend channel, respectively. Step S3: Analyze the ratio of the time change rate of the drug response feature map to the recovery amplitude of the physiological recovery feature map using a logic gating unit, calculate the metabolic recovery damping coefficient, and generate a hidden antibiotic intervention judgment result when the metabolic recovery damping coefficient indicates an overdamped state. Step S4: In response to the non-antibiotic intervention determination result, execute the energy quality conservation verification logic, construct a balance equation including energy intake, basal metabolic energy and energy output, and generate a forgery compliance alarm when energy input and output data are detected to violate the bioenergy conservation constraint.

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