Livestock and poultry healthy breeding nutrition management system adaptive to growth stage

Abstract

The invention is suitable for the field of livestock and poultry breeding, and provides a livestock and poultry healthy breeding nutrition management system adaptive to a growth stage, and the system comprises a multi-dimensional biological characteristic perspective collection module; the digestion and absorption efficiency evaluation module is connected with the multi-dimensional biological characteristic perspective acquisition module; a social level and metabolism coupling modeling module; the predictive nutrition formula generation module is used for predicting a growth track of a time step length in the future by utilizing a model predictive control algorithm based on the dynamic metabolism model and generating a dynamic formula containing macronutrients and microecologics; a trace precise regulation and control execution module; the method has the beneficial effects that a digestion feedback and social grade compensation mechanism is constructed, and a dynamic formula adaptive to an individual is generated in combination with predictive control. According to the invention, nutrition mismatching is effectively solved, the feed conversion rate and the group growth uniformity are improved, the drug dependence is reduced, and the cooperation of healthy breeding, cost reduction and benefit improvement is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of livestock breeding, and particularly relates to a livestock health breeding nutrition management system adaptive to growth stages. BACKGROUND

[0002] Current livestock breeding nutrition management generally adopts a stage-by-stage feeding method, that is, according to the average age or average weight of a livestock group, a plurality of fixed time windows such as rearing, growing and fattening are mechanically divided, and a single fixed feed formula is provided in each window period. Although the existing automatic feeding equipment realizes timed and quantitative feeding, its core logic is mostly open-loop control, that is, it is only responsible for feeding, without paying attention to the actual conversion efficiency of livestock to feed.

[0003] In a group breeding environment, livestock has a significant social hierarchy. Individuals in a subordinate position often have limited feeding time and high psychological pressure. If a standard energy density feed is provided according to the body weight, the weak individuals cannot intake sufficient nutrition due to the short feeding time, and the strong individuals are prone to intake excessive feed, resulting in excessive fat deposition, which leads to poor uniformity of the group, and the traditional system cannot dynamically compensate for the nutrition density in view of the "food competition". SUMMARY

[0004] The present application aims to provide a livestock health breeding nutrition management system adaptive to growth stages, which aims to solve the problems raised in the background art.

[0005] The present application is implemented as follows: a livestock health breeding nutrition management system adaptive to growth stages, which is applied to a group breeding environment equipped with independent intelligent feeding stations and electronic access control, and is directed to medium and large livestock individuals wearing electronic ear tags, the system comprises: A multi-dimensional biological feature perspective acquisition module is used to construct a digitalized physical profile of livestock individuals based on RFID radio frequency identification technology, to acquire external morphological data of livestock individuals by using sensors arranged in the channel of the intelligent feeding station, and to monitor excrement character data of livestock individuals and feeding competition behavior data outside the feeding station. A digestive absorption efficiency evaluation module is connected with the multi-dimensional biological feature perspective acquisition module, and is used to back-calculate the current intestinal health degree and nutrition absorption rate of livestock based on excrement character, and to generate a digestive correction factor. A social hierarchy and metabolism coupling modeling module is used to analyze the social status index of livestock in a group breeding environment, and to establish a dynamic metabolism model containing social competition pressure compensation in combination with the growth stage. A predictive nutritional formula generation module, based on the dynamic metabolic model, utilizes a model predictive control algorithm to predict the growth trajectory of a future time step, and generates a dynamic formula containing constant nutrients and microecological preparations; A micro-precision regulation execution module is arranged in the intelligent feeding station, and is used for executing instant mixing and differentiated feeding of base feed and functional additives when the electronic ear tag of a specific individual is identified and the electronic access control is locked.

[0006] As a further scheme of the present application, the multi-dimensional biological feature perspective acquisition module comprises: A hyperspectral excrement analysis unit is arranged below a manure leakage floor or a cleaning area, and is used for acquiring a hyperspectral image of livestock and poultry manure, and analyzing residual characteristics of undigested protein, water and specific digestive enzymes; A body size dynamic reconstruction unit utilizes a multi-view depth camera arranged in a restricted channel at an entrance of the intelligent feeding station to reconstruct a three-dimensional point cloud model of livestock and poultry in real time when the individual is stationary or slowly moving, and calculates a body surface area and volume ratio to evaluate a current metabolic heat production type; A competitive behavior quantification unit is used for tracking a queuing waiting time length, a physical collision frequency and an active retreat frequency of an individual in front of a feeding station door by using an RFID card reader and visual assistance.

[0007] As a further scheme of the present application, the digestive absorption efficiency evaluation module comprises: A manure trait classification unit is used for dividing the collected manure image into four grades of normal, watery diarrhea, feed feces and dry knot by using a convolutional neural network; A nutritional loss inversion unit is used for calculating a specific nutritional loss rate in a feed conversion process according to spectral characteristic peak values of nitrogen and phosphorus in the hyperspectral data; An enzymolysis demand calculation unit is used for calculating an additive amount of a specific exogenous enzyme required to improve a current feed conversion rate according to a deviation value obtained by the nutritional loss inversion unit when a feed feces characteristic or undigested protein is high, and converting the dose into a part of a digestion correction factor.

[0008] As a further scheme of the present application, the social rank and metabolic coupling modeling module comprises: A foraging restriction compensation unit is used for identifying a low social status individual, generating a compensation instruction for increasing unit feed energy density and amino acid concentration by calculating a restricted foraging window period of the individual, and making the individual ingest sufficient nutrients in a short time; A dominant individual restricted feeding unit is used for identifying a high social status individual, and generating a formula instruction for reducing energy density but increasing satiety fiber content to prevent excessive deposition of body fat of the individual; The population evenness simulation unit is used to simulate the population weight dispersion over several days under the current nutrition strategy, in order to adjust the average nutritional baseline of the entire population.

[0009] As a further aspect of the present invention, the predictive nutrition formulation generation module includes: The growth trajectory prediction unit is used to input historical growth data into the long short-term memory network to predict the net energy requirement for growth of an individual within a preset future period. The stress response matching unit combines weather forecast data and vaccination schedules to generate a pre-protective formula rich in immune enhancers in advance before the expected stress occurs. The dynamic amino acid balance unit is used to adjust the ratio of lysine to energy in real time based on the predicted lean meat growth rate, breaking the ratio limit of a fixed stage.

[0010] As a further aspect of the present invention, the micro-precision control execution module includes: The dual-base material variable frequency mixing unit includes a high-energy, high-protein base material bin and a low-energy, high-fiber base material bin, which are used to achieve stepless proportional mixing of the two base materials through a variable frequency auger to construct the formulation framework; The microfluidic nozzle addition unit, located at the feed inlet, contains multiple independent storage tanks that store different liquid enzyme preparations, probiotic liquids, or liquid vitamins, and is used to precisely spray nano- or micro-level additives onto the surface of each feed. The pulse-type feeding control unit is used to intermittently feed in a pulse manner according to the individual's feeding rhythm, preventing waste and pollution caused by feed accumulation.

[0011] As a further aspect of the present invention, the digestion and absorption efficiency evaluation module also includes an intestinal flora imbalance early warning unit, which is used to: determine intestinal microecological imbalance when abnormal fecal pH value or specific spectral shift in color is continuously detected, and trigger the targeted delivery instruction of probiotics in the micro-precision regulation execution module, rather than administering medication to the entire population.

[0012] As a further aspect of the present invention, the social hierarchy and metabolic coupling modeling module is also connected to a dynamic reorganization suggestion unit for pens, which is used to output an automatic pens reorganization scheme based on the similarity of body size and feeding behavior when the social hierarchy difference in a certain pen is greater than a preset threshold, causing the compensation strategy to fail.

[0013] As a further aspect of the present invention, the micro-precision control execution module also includes a targeted drug administration identification unit, which is used to: in the electronic access control lock state, use RFID or visual recognition to confirm whether the currently feeding individual is on the treatment list. If so, start an independent drug spray nozzle to spray the treatment drug onto the feed, thereby realizing individualized drug administration through feed mixing and avoiding the ingestion of drugs by healthy individuals.

[0014] As a further aspect of the present invention, the livestock and poultry health breeding nutrition management system adopts an edge computing and cloud collaborative architecture. The part of the predictive nutrition formula generation module involving model training runs on a cloud server, while the digestion and absorption efficiency evaluation module and the micro-precision regulation execution module run on a local edge computing gateway. Its purpose is to ensure that even when the network is disconnected, the livestock and poultry health breeding nutrition management system can still make millisecond-level feeding adjustments based on real-time fecal feedback and behavioral feedback.

[0015] This invention provides a livestock and poultry health breeding nutrition management system adapted to different growth stages. By constructing a digestive feedback and social hierarchy compensation mechanism, combined with predictive control, it generates dynamic formulas suitable for individual animals. This effectively solves the problem of nutrient mismatch, improves feed conversion rate and herd growth uniformity, while reducing drug dependence, achieving a synergy between healthy breeding and cost reduction and efficiency improvement. Attached Figure Description

[0016] Figure 1 This is a main structure diagram of a livestock and poultry health breeding nutrition management system adapted to different growth stages.

[0017] Figure 2 This is a structural diagram of a multidimensional biometric data acquisition module in a livestock and poultry health breeding nutrition management system adapted to different growth stages.

[0018] Figure 3 This is a structural diagram of a digestive and absorption efficiency assessment module in a livestock and poultry health breeding nutrition management system adapted to different growth stages.

[0019] Figure 4 This is a structural diagram of a social hierarchy and metabolic coupling modeling module in a livestock and poultry health breeding nutrition management system adapted to different growth stages.

[0020] Figure 5 This is a structural diagram of a predictive nutrition formula generation module in a livestock and poultry health breeding nutrition management system adapted to different growth stages.

[0021] Figure 6 This is a structural diagram of a micro-precision regulation execution module in a livestock and poultry health breeding nutrition management system adapted to different growth stages. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0024] The present invention provides a livestock and poultry health breeding nutrition management system adapted to the growth stage, which solves the technical problems in the background art.

[0025] like Figure 1 The diagram shown is a main structural diagram of a livestock and poultry health breeding nutrition management system adapted to different growth stages, provided in an embodiment of the present invention. The system is applied to a group-rearing environment equipped with independent intelligent feeding stations and electronic access control, and is designed for medium to large-sized livestock individuals wearing electronic ear tags. The livestock and poultry health breeding nutrition management system adapted to different growth stages includes: The multidimensional biometrics acquisition module 100 is used to construct digital vital signs files for individual livestock and poultry based on RFID radio frequency identification technology. It uses sensors deployed in the intelligent feeding station channel to collect external morphological data of individual livestock and poultry, and monitors the excrement characteristics data of individual livestock and poultry as well as feeding competition behavior data outside the feeding station. The digestion and absorption efficiency assessment module 200 is connected to the multidimensional biological feature imaging acquisition module and is used to infer the current intestinal health and nutrient absorption rate of livestock and poultry based on the excrement characteristics, and generate digestion correction factors. The social hierarchy and metabolic coupling modeling module 300 is used to analyze the social status index of livestock and poultry in a group-housed environment, and to establish a dynamic metabolic model that includes compensation for social competition pressure in combination with growth stages. The predictive nutrition formula generation module 400, based on the dynamic metabolic model, uses a model predictive control algorithm to predict the growth trajectory for a future time step and generates a dynamic formula containing macronutrients and microecological preparations. The micro-precision control execution module 500 is used in the intelligent feeding station to perform real-time mixing and differentiated feeding of basic feed and functional additives after identifying the electronic ear tag of a specific individual and the electronic access control is locked.

[0026] In this embodiment, the solution breaks through the traditional linear management model of breeding that relies solely on age or weight, establishing a dynamic feedback mechanism based on a complex biological-environment-social interaction system. Firstly, the system is applied to a group-rearing environment equipped with independent intelligent feeding stations and electronic access control. The medium to large-sized livestock (such as pigs and beef cattle) all wear unique RFID electronic ear tags on their ears or necks. The independent intelligent feeding station includes: a single feeding channel with physical partitions on both sides; an anti-tailgating electronic access control (such as a pneumatic or electric pig gate) at the channel entrance; an RFID reader and multi-dimensional sensor group (including depth cameras) inside the channel; and an intelligent feed trough at the end of the channel.

[0027] Secondly, during the data acquisition and identity verification phase, the multi-dimensional biometric imaging acquisition module 100 performs spatially isolated data acquisition based on the aforementioned hardware. When individual livestock animals are queuing outside the feeding station, wide-angle vision sensors and auxiliary RFID antennas deployed outside the station continuously monitor their queuing, physical collisions, and feeding competition behaviors (driving and being driven away), using this data as the basis for assessing the individual's social status index. When an individual livestock animal enters the intelligent feeding station channel, the RFID reader inside the channel instantly reads its electronic ear tag. After confirming its identity, the system immediately controls the electronic access control at the entrance to lock (i.e., lower the barrier or close the access control), forming a closed and safe feeding space that can only accommodate a single animal. In this locked state, a multi-view depth camera at the top of the channel performs an undisturbed three-dimensional point cloud scan to obtain high-precision body size data.

[0028] During the formula generation and locked-down feeding phase, absolute physical delivery of a unique formula for each animal is achieved. During the electronic gate locking protection period, the system's backend digestibility and absorption efficiency assessment module 200, social hierarchy and metabolic coupling modeling module 300, and predictive nutrition formula generation module 400 complete cloud computing and edge computing within milliseconds to generate customized dynamic formula instructions for that specific individual. Subsequently, the micro-precision control execution module 500 is activated at the feeding trough, mixing the basic feed and microecological preparations in proportion and administering the feed. Because the electronic gate is physically locked at this time, individuals with a dominant social position are blocked outside the station by the physical anti-tailgating gate, absolutely unable to enter the passage to interfere or steal. After the current individual finishes feeding and exits the passage, the sensor confirms that the passage is empty, and only then does the electronic gate unlock and reopen, allowing the next animal to enter.

[0029] Using a multi-dimensional biometrics imaging module 100 as the sensing front end, the system goes beyond simple visual monitoring, delving into excrement spectral analysis and microscopic behavioral tracking to establish a digital holographic profile encompassing individual physiological metabolism and psychological stress (competitive behavior). Subsequently, the digestive and absorption efficiency assessment module 200 uses biochemical principles to reverse-engineer intestinal function, addressing the blind spot of traditional farming where feeding is the primary concern, but absorption is not. The core computing layer consists of a social hierarchy and metabolic coupling modeling module 300 and a predictive nutrient formulation generation module 400. The former introduces sociobiological parameters to quantify the impact of competition on energy allocation in a gregarious environment, while the latter introduces a model predictive control algorithm to transform lagging feedback regulation into proactive predictive regulation. Finally, the micro-precision control execution module 500 achieves physical implementation, completing the transformation from data flow to material flow. Through this five-in-one collaborative approach, the system achieves a leap from macro-group management to micro-individual precision control, ensuring that each animal receives optimal nutrition tailored to its current physiological and social state, thereby significantly improving feed conversion rate and reducing farming costs.

[0030] like Figure 2 As shown, in another preferred embodiment of the present invention, the multidimensional biometric imaging acquisition module 100 includes: The hyperspectral excrement analysis unit 101 is arranged under the slatted floor or in the cleaning area to acquire hyperspectral images of livestock and poultry manure and analyze the residual characteristics of undigested protein, water and specific digestive enzymes in it. The body size dynamic reconstruction unit 102 uses a multi-view depth camera arranged in the restricted passage at the entrance of the intelligent feeding station to reconstruct the three-dimensional point cloud model of livestock and poultry in real time when the individual is stationary or moving slowly, and calculates the body surface area to volume ratio to assess the current metabolic heat production type. The competitive behavior quantification unit 103 is used to track the queuing time, physical collision and fighting frequency, and active retreat frequency of individuals in front of the feeding station using RFID card readers and visual assistance.

[0031] In this embodiment, the hyperspectral excrement analysis unit 101 incorporates spectral imaging technology. Utilizing the differences in absorption rates of different organic substances (such as undigested proteins and residual enzymes) to specific wavelengths of light, it can generate real-time chemical composition maps of feces without sampling or disturbing livestock, providing molecular-level data support for subsequent digestibility assessments. The body size dynamic reconstruction unit 102 utilizes point cloud reconstruction technology from the field of machine vision to overcome the interference of livestock posture changes on measurements, accurately calculating the body surface area to volume ratio. This ratio is a key parameter for assessing biological thermoregulation capacity and basal metabolic rate, directly affecting the accuracy of net energy maintenance calculations. The competitive behavior quantification unit 103 focuses on behavioral analysis, recording the micro-behavior of individuals at the feeding trough—a key resource point—through a time-series tracking algorithm. This includes tracking whether individuals are pushed away when it's time to eat or whether they monopolize the trough for extended periods, quantifying the ambiguous feeding status into a calculable feeding urgency index. These three units together constitute the system's multi-dimensional "eagle eye," ensuring that subsequent decision-making models are built on a comprehensive and accurate data foundation.

[0032] like Figure 3 As shown, in another preferred embodiment of the present invention, the digestion and absorption efficiency evaluation module 200 includes: The fecal characteristics classification unit 201 is used to classify the collected fecal images into four levels: normal, watery diarrhea, feed feces and dry feces using a convolutional neural network. The nutrient loss inversion unit 202 is used to calculate the specific nutrient loss rate during the feed conversion process based on the spectral characteristic peaks of nitrogen and phosphorus in the hyperspectral data. The enzymatic hydrolysis requirement calculation unit 203 is used to calculate the dosage of a specific exogenous enzyme required to improve the current feed conversion rate based on the deviation value obtained from the nutrient loss inversion unit when feed fecal characteristics or undigested protein are detected. The dosage is then converted into part of the digestion correction factor.

[0033] In this embodiment, the fecal morphology classification unit 201 employs a convolutional neural network algorithm from deep learning. Through training on a large number of labeled samples, it acquires the diagnostic capabilities of a seasoned veterinarian, capable of distinguishing between watery diarrhea (pathological state), feed-induced feces (due to insufficient digestive enzymes or excessively rapid intestinal peristalsis), and normal formed feces within milliseconds. The nutrient loss inversion unit 202 performs a mathematical analysis of hyperspectral data, inverting the spectral reflectance curve into specific nitrogen and phosphorus loss rates, directly reflecting the degree of waste of protein and minerals in the feed. Based on the above judgment, when the system detects overfeeding, it calculates and generates a digestibility correction factor. The specific calculation logic is as follows: the digestibility correction factor is a dimensionless scalar with a default value of 1.0; when the nutrient loss inversion unit detects that the residual rate of a specific protein in the excrement exceeds a preset threshold (e.g., 15%), the digestibility correction factor is adjusted to 0.85. At this point, the system links with the formula execution module to multiply the proportion of high-protein base feed in the basic formula by the correction factor (i.e., reduce by 15%), and simultaneously calculates the required dose of exogenous enzymes (e.g., add 500U of protease per kilogram of feed). This is sent to the regulation module in the form of a digestion correction factor, transforming traditional empirical feeding into data-driven enzymatic regulation. This allows for intervention through nutritional means in the early stages of mild digestive disorders in animals, preventing the deterioration of a sub-healthy state into a disease state.

[0034] like Figure 4 As shown, in another preferred embodiment of the present invention, the social hierarchy and metabolic coupling modeling module 300 includes: The restricted feeding compensation unit 301 is used to identify individuals with low social status and generate compensation instructions to increase the energy density and amino acid concentration per unit of feed by calculating their restricted feeding window, so that they can ingest sufficient nutrition in a short period of time. The dominant individual restricted feeding unit 302 is used to identify individuals with high social status and generate formulation instructions that reduce energy density but increase satiety fiber content to prevent excessive body fat deposition. The population evenness simulation unit 303 is used to simulate the population weight dispersion over several days under the current nutrition strategy, in order to adjust the average nutritional baseline of the entire population.

[0035] In this embodiment, the low-status individual is specifically defined as: an individual who is chased away by other individuals more than 5 times consecutively during the effective feeding window of a single day, or whose average feeding time per session is less than 30 seconds and whose total feed intake is less than 80% of the group average; while the high-status individual is defined as: an individual who occupies the feed trough for more than 150% of the group average and whose frequency of initiating aggressive behavior ranks in the top 10% of the group. For these two extreme groups, the feeding restriction compensation unit 301 implements a specific compensation strategy: that is, through the micro-regulation module, a highly concentrated fast food package with 20% higher energy density and 15% higher amino acid concentration is generated for the low-status individual to ensure that it can still ingest all the nutrients required for growth during the limited short feeding time; at the same time, the dominant individual feeding restriction unit 302 provides the high-status individual with a high-fiber, low-energy-density satiety formula, using cellulose to fill the stomach volume and physically inhibit fat deposition caused by excessive energy intake. The herd uniformity simulation unit 303 then simulates the effect of this differentiation strategy in a virtual environment, dynamically adjusting the baseline of the entire herd, with the goal of reducing the dispersion of herd weight and achieving uniform slaughter.

[0036] like Figure 5 As shown, in another preferred embodiment of the present invention, the predictive nutrition formulation generation module 400 includes: The growth trajectory prediction unit 401 is used to input historical growth data into the long short-term memory network to predict the net energy demand for growth of an individual in a future preset period. The stress response matching unit 402 is used to combine weather forecast data and vaccination schedules to generate a pre-protective formula rich in immune enhancers in advance before the expected stress occurs. The dynamic amino acid balance unit 403 is used to adjust the ratio of lysine to energy in real time according to the predicted lean meat growth rate, breaking the ratio limit of a fixed stage.

[0037] In this embodiment, the growth trajectory prediction unit 401 utilizes a long short-term memory network to accurately predict the net energy requirement for growth over the next 24 to 72 hours. This feedforward control allows the system to prepare nutritionally before the animal actually feels hungry or experiences accelerated growth. The stress response matching unit 402 connects to a weather API and a disease prevention plan database, proactively increasing anti-stress factors (such as vitamin C and electrolytes) in the formula during cold waves or pre-vaccination windows to build a biological defense barrier. The dynamic amino acid balance unit 403 breaks away from the conventional setting of a fixed lysine / energy ratio, adjusting the amino acid supply in real time based on the predicted lean meat deposition rate.

[0038] like Figure 6 As shown, in another preferred embodiment of the present invention, the micro-precision control execution module 500 includes: The dual-base material variable frequency mixing unit 501 includes a high-energy high-protein base material bin and a low-energy high-fiber base material bin, which are used to achieve stepless proportional mixing of the two base materials through a variable frequency auger to construct the formula framework; The microfluidic nozzle addition unit 502 is located at the feed inlet and includes multiple independent storage tanks that store different liquid enzyme preparations, probiotic liquids or liquid vitamins, for precisely spraying nano- or micro-level additives onto the surface of each feed. The pulse feeding control unit 503 is used to intermittently feed in a pulse manner according to the individual's feeding rhythm, to prevent waste and pollution caused by feed accumulation.

[0039] In this embodiment, the dual-base variable frequency mixing unit 501 employs a macro-level coarse adjustment strategy. Utilizing two base materials with drastically different properties—high-energy, high-protein and low-energy, high-fiber—it achieves arbitrary-ratio physical mixing through variable frequency motor speed control, constructing a basic nutritional framework tailored to individual needs. Based on this, the microfluidic nozzle addition unit 502 performs micro-level fine adjustment, borrowing from inkjet printing principles to precisely spray nanoliter or microliter-level liquid additives (enzymes, vitamins, probiotics) onto the surface of each falling feed pellet. This design avoids the grading and segregation problems of traditional premixes during transportation, achieving true real-time synthesis. The pulse-type feeding control unit focuses on the feeding rhythm, monitoring the swallowing and feeding rhythm of individuals through infrared sensors at the bottom of the trough, simulating intermittent feeding behavior in nature. By controlling the start and stop frequency of feeding, it prevents feed accumulation in the trough. Crucially, all the differentiated feeding and precise control processes described in this invention are executed after the livestock enter the intelligent feeding station and the electronic access control on their backs is physically locked. This physical barrier completely isolates the target weak individuals from the competition and interference from external dominant individuals, ensuring that the customized formula generated by the system can be fully absorbed by the target weak individuals, thus ensuring the absolute reliability of the project implementation.

[0040] As another preferred embodiment of the present invention, the digestion and absorption efficiency evaluation module 200 further includes an intestinal flora imbalance early warning unit 204, which is used to determine intestinal microecological imbalance when abnormal fecal pH value or specific spectral shift in color is continuously detected, and trigger the targeted delivery instruction of probiotics in the micro-precision regulation execution module 500, rather than administering drugs to the entire population.

[0041] In this embodiment, when the system continuously monitors that the pH value of the feces of a particular individual or pen deviates from the normal range (for example, a significant decrease in the pH value of pig feces may indicate carbohydrate over-fermentation, while an increase may involve protein putrefaction), the intestinal flora imbalance early warning unit 204 determines that the intestinal flora is disordered. At this time, the microfluidic nozzle addition unit 502 is directly triggered to immediately spray a specific probiotic preparation (such as Lactobacillus or Bacillus subtilis) or acidifier into the feed. This allows for correction of flora imbalance through nutritional means at an early stage, greatly reducing the need for antibiotics.

[0042] In another preferred embodiment of the present invention, the social hierarchy and metabolism coupling modeling module 300 is also connected to a dynamic reorganization suggestion unit 304, which is used to output an automatic reorganization scheme based on the similarity of body size and feeding behavior when the social hierarchy difference in a certain pen is greater than a preset threshold, causing the compensation strategy to fail.

[0043] In this embodiment, the specific quantitative standard for social hierarchy differences exceeding a preset threshold is set as follows: within the same pen, the coefficient of variation in body weight exceeds 12%, and the total frequency of aggression and victimization behaviors monitored in a single day exceeds three times the average for each individual. When this threshold is reached, it indicates that simply adjusting feed concentration is no longer sufficient to compensate for the growth disadvantage of weaker individuals. At this point, the dynamic reorganization suggestion unit 304 will generate an automatic reorganization scheme. The specific implementation logic of this scheme is: using a clustering algorithm, all livestock and poultry in the farm are regrouped according to the principle of similar body weight and complementary behavioral characteristics (for example, separating aggressive individuals from defensive individuals, or grouping all slow-feeding, mild-mannered individuals together), and a specific pen adjustment operation instruction table is output. Through the reconstruction of physical space, extreme social oppression is eliminated, creating a fair environment for lagging individuals to catch up and grow.

[0044] As another preferred embodiment of the present invention, the micro-precision control execution module 500 further includes a targeted drug delivery identification unit 504, which is used to: use RFID or visual recognition to confirm whether the current feed-eating individual is on the treatment list. If so, an independent drug spray nozzle is activated to spray the treatment drug onto the feed, thereby realizing individualized drug delivery through feed mixing and avoiding the ingestion of drugs by healthy individuals.

[0045] In this embodiment, the targeted drug delivery identification unit 504 utilizes RFID electronic ear tags or facial recognition technology to confirm the identity of the individual currently feeding within milliseconds. The system backend compares the ID in real time to see if it is on the treatment list. If it is a sick individual, an independent drug nozzle will activate the moment it starts feeding, precisely spraying the therapeutic dose of drug onto its designated feed; if it is a healthy individual, the nozzle remains closed. This achieves individual drug delivery in a group feeding environment, ensuring that the drug only enters the target individual's body. This not only significantly reduces veterinary drug costs but also greatly protects the liver and kidney function and meat safety of healthy livestock and poultry.

[0046] As another preferred embodiment of the present invention, the livestock and poultry health breeding nutrition management system adopts an edge computing and cloud collaborative architecture. The part of the predictive nutrition formula generation module 400 involving model training runs on a cloud server, while the digestion and absorption efficiency evaluation module 200 and the micro-precision regulation execution module 500 run on a local edge computing gateway. Its purpose is to ensure that even when the network is disconnected, the livestock and poultry health breeding nutrition management system can still make millisecond-level feeding adjustments based on real-time fecal feedback and behavioral feedback.

[0047] In this embodiment, the system employs a two-tier architecture combining edge computing and cloud collaboration. The cloud server, possessing unlimited computing power and massive amounts of historical data, is responsible for running complex deep learning model training and continuously optimizing algorithm parameters. Meanwhile, the local edge computing gateway is deployed locally on the farm, carrying the inference model that has been compressed and optimized from the cloud. Even if the external internet is completely disconnected, the local gateway can still independently process fecal image analysis, behavior recognition, and microsecond-level nozzle control commands from high-definition cameras. This leverages the powerful intelligence of the cloud while ensuring absolutely low latency and high reliability for on-site control.

[0048] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0049] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0050] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A livestock and poultry health breeding nutrition management system adapted to different growth stages, characterized in that, The system is applied to group-housed environments equipped with independent intelligent feeding stations and electronic access control, and is designed for medium to large-sized livestock individuals wearing electronic ear tags. The system includes: The multidimensional biometrics acquisition module is used to construct digital vital signs files for individual livestock and poultry based on RFID radio frequency identification technology. It uses sensors deployed in the intelligent feeding station channel to collect external morphological data of individual livestock and poultry, and monitors the excrement characteristics data and feeding competition behavior data of individual livestock and poultry outside the feeding station. The digestion and absorption efficiency assessment module is connected to the multidimensional biometrics acquisition module and is used to infer the current intestinal health and nutrient absorption rate of livestock and poultry based on the excrement characteristics, and generate digestion correction factors. The social hierarchy and metabolic coupling modeling module is used to analyze the social status index of livestock and poultry in a group-housed environment, and to establish a dynamic metabolic model that includes compensation for social competitive pressure by combining growth stages. The predictive nutrition formula generation module, based on the dynamic metabolic model, uses a model predictive control algorithm to predict the growth trajectory for a future time step and generate a dynamic formula containing macronutrients and microecological agents. The micro-precision control execution module is used in the intelligent feeding station to perform real-time mixing and differentiated feeding of basic feed and functional additives after identifying the electronic ear tag of a specific individual and the electronic access control is locked.

2. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 1, characterized in that, The multidimensional biometric imaging acquisition module includes: The hyperspectral excrement analysis unit is located under the slatted floor or in the cleaning area to acquire hyperspectral images of livestock and poultry manure and analyze the residual characteristics of undigested protein, water and specific digestive enzymes. The body size dynamic reconstruction unit uses multi-view depth cameras arranged in the restricted passage at the entrance of the intelligent feeding station to reconstruct the three-dimensional point cloud model of livestock and poultry in real time when the individual is stationary or moving slowly, and calculates the body surface area to volume ratio to assess the current metabolic heat production type. The competitive behavior quantification unit is used to track an individual's queuing time, frequency of physical collisions and scrambling, and frequency of voluntary retreat in front of the feeding station using RFID readers and visual assistance.

3. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 1, characterized in that, The digestion and absorption efficiency assessment module includes: The fecal characteristics classification unit is used to classify the collected fecal images into four levels: normal, watery diarrhea, feed feces, and dry feces using a convolutional neural network. The nutrient loss inversion unit is used to calculate the specific nutrient loss rate during feed conversion based on the spectral characteristic peaks of nitrogen and phosphorus in hyperspectral data. The enzymatic hydrolysis requirement calculation unit is used to calculate the dosage of a specific exogenous enzyme required to improve the current feed conversion rate based on the deviation value obtained from the nutrient loss inversion unit when feed fecal characteristics or undigested protein are detected. The dosage is then converted into part of the digestibility correction factor.

4. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 1, characterized in that, The social hierarchy and metabolic coupling modeling module includes: The restricted feeding compensation unit is used to identify individuals with low social status. By calculating their restricted feeding window, it generates compensation instructions to increase the energy density and amino acid concentration per unit of feed, enabling them to ingest sufficient nutrients in a short period of time. The dominant individual restricted feeding unit is used to identify individuals with high social status and generate formulation instructions that reduce energy density but increase satiety fiber content to prevent excessive body fat deposition. The population evenness simulation unit is used to simulate the population weight dispersion over several days under the current nutrition strategy, in order to adjust the average nutritional baseline of the entire population.

5. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 1, characterized in that, The predictive nutrition formulation generation module includes: The growth trajectory prediction unit is used to input historical growth data into the long short-term memory network to predict the net energy requirement for growth of an individual within a preset future period. The stress response matching unit combines weather forecast data and vaccination schedules to generate a pre-protective formula rich in immune enhancers in advance before the expected stress occurs. The dynamic amino acid balance unit is used to adjust the ratio of lysine to energy in real time based on the predicted lean meat growth rate, breaking the ratio limit of a fixed stage.

6. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 1, characterized in that, The micro-precision control execution module includes: The dual-base material variable frequency mixing unit includes a high-energy, high-protein base material bin and a low-energy, high-fiber base material bin, which are used to achieve stepless proportional mixing of the two base materials through a variable frequency auger to construct the formulation framework; The microfluidic nozzle addition unit, located at the feed inlet, contains multiple independent storage tanks that store different liquid enzyme preparations, probiotic liquids, or liquid vitamins, and is used to precisely spray nano- or micro-level additives onto the surface of each feed. The pulse-type feeding control unit is used to intermittently feed in a pulse manner according to the individual's feeding rhythm, preventing waste and pollution caused by feed accumulation.

7. The livestock and poultry health breeding nutrition management system adapted to the growth stage according to claim 3, characterized in that, The digestive and absorptive efficiency assessment module also includes a gut microbiota dysbiosis early warning unit, the purpose of which is: When abnormal fecal pH or specific spectral shifts in color are continuously detected, an imbalance in the gut microbiota is determined, triggering a targeted probiotic delivery instruction in the micro-precision regulation module, rather than administering medication to the entire gut.

8. The livestock and poultry health breeding nutrition management system adapted to the growth stage according to claim 4, characterized in that, The social hierarchy and metabolic coupling modeling module is also connected to a column-based dynamic reorganization suggestion unit, the purpose of which is: When the social hierarchy difference within a certain pen exceeds a preset threshold, causing the compensation strategy to fail, an automatic pen reorganization scheme based on the similarity of body size and feeding behavior is output.

9. The livestock and poultry health breeding nutrition management system adapted to the growth stage as described in claim 6, characterized in that, The micro-precision regulation execution module also includes a targeted drug delivery identification unit, the purpose of which is: When the electronic access control is locked, RFID or visual recognition is used to confirm whether the current feed-eating individual is on the treatment list. If so, an independent medicine nozzle is activated to spray the treatment medicine onto the feed, realizing individualized administration of medicine by mixing feed and avoiding the ingestion of medicine by healthy individuals.

10. The livestock and poultry health breeding nutrition management system adapted to the growth stage according to claim 1, characterized in that, The livestock and poultry health breeding nutrition management system adopts an edge computing and cloud collaborative architecture. The model training portion of the predictive nutrition formula generation module runs on a cloud server, while the digestibility and absorption efficiency evaluation module and the micro-precision regulation execution module run on a local edge computing gateway. Their purpose is as follows: Even when the network is disconnected, the livestock and poultry health breeding nutrition management system can still make millisecond-level feeding adjustments based on real-time fecal and behavioral feedback.

Images