A precise breeding management method for improving growth performance and meat quality of pigs

Abstract

The application discloses a kind of precise breeding management methods for improving pig growth performance and meat quality, comprising the following steps: step one: multi-source data real-time acquisition, obtain pig group temperature data by infrared thermal imaging array, and collect pig motion trajectory and posture video stream by top-mounted vision sensor, while collecting pig growth stage data and pig house environment data;Ultrasonic wave drive avoidance array and LED guiding device are arranged in pig house passage.The application identifies pig individual information by RFID ear tag, combines growth stage data, and real-time monitors feed intake by trough weight sensor, and the system dynamically adjusts feed formula according to growth stage: automatically matches 20-22% crude protein feed in rearing period, switches to 16-18% crude protein formula in fattening later period, and calculates feeding amount according to individual body weight (single error ≤8%), which makes feed composition and pig metabolism demand highly consistent, and the actual application verification shows that feed conversion rate is improved by 15-20%, and growth cycle is shortened by 7-10 days.

Description

Technical Field

[0001] This invention belongs to the field of breeding, specifically relating to a precision breeding management method for improving the growth performance and meat quality of pigs. Background Technology

[0002] Pig breeding is the technology of creating genetic variations and improving genetic characteristics to cultivate superior new pig breeds. The core goal of the pig farming industry is to simultaneously optimize growth performance (weight gain rate, feed conversion rate) and meat quality traits (intramuscular fat content, tenderness, meat color, etc.). With the upgrading of consumption, the market's requirements for pork quality have increased significantly, and the traditional breeding model that only pursues growth rate can no longer meet the industry's needs.

[0003] Currently, in terms of nutrition, general-purpose feed formulas do not meet the needs of pigs at different growth stages, resulting in reduced feed conversion rates and prolonged growth cycles. In terms of environmental control, pig farms have extensive environmental conditions, with unstable temperature, humidity, and stocking density, which affect pig growth and meat quality. In terms of health management, common diseases occur frequently, existing disease prevention measures lack precision, and the immunization effect is poor, making it impossible to quickly remove diseased pigs from the pen to prevent the spread of the disease to other pigs. Further improvements are needed. Summary of the Invention

[0004] To overcome the problems of reduced feed conversion rate and prolonged growth cycle caused by the use of general-purpose feed formulas in the current nutritional supply, which do not meet the needs of pigs at different growth stages; unstable temperature, humidity and stocking density in environmental control affecting pig growth and meat quality; and frequent occurrence of common diseases in health management with a lack of precision in disease prevention measures and the inability to quickly cull sick pigs to prevent infection, a precision breeding management method to improve pig growth performance and meat quality is proposed.

[0005] The technical solution of this invention is: a precision breeding management method for improving pig growth performance and meat quality, comprising the following steps: Step 1: Real-time acquisition of multi-source data The system acquires the group temperature data of pigs through an infrared thermal imaging array, and collects video streams of pig movement trajectories and postures through a top-mounted visual sensor. It also collects data on the growth stage of pigs and the environment of the pigsty. An ultrasonic repellency array and LED guidance device are installed in the pigsty passage. Step Two: Joint Diagnosis of Abnormal Behavior Input body temperature data and behavioral data into a trained disease risk prediction model, and output an individual disease probability value P_d; Step 3: Dynamically Isolate and Execute When P_d≥0.95, the isolation channel control system is activated to guide the target pig into the isolation area, with a response time ≤5 seconds; Step 4: Dynamic Nutritional Regulation During the Growth Stage Based on the growth stage data collected in step one, the corresponding feed formula is matched and the feed is precisely administered. Step 5: Environmental Adaptive Regulation Based on the pigsty environmental data collected in step one, adjust the pigsty temperature, humidity, and stocking density in real time.

[0006] As a preferred option, step one also includes: Acoustic sensors are installed at the drinking water inlets to monitor the daily drinking frequency of each pig. If the drinking frequency decreases by 50%, it is marked as a behavioral abnormality. At the same time, weight sensors are installed in the feed troughs to monitor the daily feed intake of each pig. Combined with the standard feed intake for the growth stage, if the feed intake is lower than the standard value by 30%, it is marked as an abnormal feeding behavior. Temperature and humidity sensors are evenly distributed in the pigsty, and the temperature and humidity data of the pigsty are collected every 10 minutes, with the collection accuracy being ±0.5℃ and ±2%, respectively. The number of pigs in a single area is counted by image recognition, and the stocking density is calculated by combining the area area.

[0007] Preferably, the disease risk prediction model in step two includes: (a) Abnormal body temperature module: When the body temperature is >40.5℃ for 2 consecutive hours, a level 1 alarm is triggered; (b) Behavioral abnormality module: Identifying cough frequency >10 times / minute or lying down time >80% using a convolutional neural network; (c) Fusion decision layer: When either behavior abnormality in (a) and (b) is met simultaneously, output P_d=0.95.

[0008] As a preferred option, step two also includes an environmental stress compensation rule: When the temperature in the pigsty exceeds 30℃, the threshold for abnormal body temperature is raised to 41.0℃; During the feed change period (within 3 days), the lying-down time threshold is relaxed to 90%. When the temperature is greater than 28°C and the humidity is greater than 70%, the cough frequency threshold drops to 8 times / minute, and environmental and behavioral data are fused together with a weight ratio of 1:3.

[0009] As a preferred option, the dynamic isolation execution process in step three specifically includes: Sub-step 1: Path planning The coordinates of the target pig are obtained based on UWB positioning tags, and the shortest path to the quarantine area is generated using the A* algorithm. Sub-step 2: Channel guidance LED guide strips are set at intervals along the generated path, and the strips light up sequentially in the direction of the path to form a dynamic light flow guide; at the same time, ultrasonic repelling devices on both sides of the path are activated, emitting directional sound waves (frequency 20-30kHz) to form an acoustic barrier to restrict the pigs' walking range. Sub-step 3: Channel Control The pneumatic doors in the buffer channel are opened according to the path sequence, with a path update frequency of ≥1Hz; if the target pig deviates from the path by more than 0.5 meters, the flashing frequency of the light strip is increased to 5 times / second, and the intensity of the repelling sound wave is increased by 30%.

[0010] As a preferred option, the dynamic nutrient regulation during the growth stage in step four specifically includes: (a) Growth stage division: The growth cycle of pigs is divided into nursery period (21-45 days old), early fattening period (46-90 days old), and late fattening period (91 days old to slaughter). (b) Dynamic adjustment of feed formula: crude protein content of feed during the nursery period is 20-22% and lysine content is 1.3-1.5%; crude protein content of feed during the early fattening period is 18-20% and lysine content is 1.1-1.3%; crude protein content of feed during the late fattening period is 16-18% and lysine content is 0.9-1.1%. (c) Precise feeding: Pigs are identified by RFID ear tags, and the daily feeding amount is calculated according to individual weight and growth stage. They are fed 3-4 times at fixed times, with a single feeding amount error of ≤8%.

[0011] As a preferred embodiment, step five, environmental adaptive regulation, specifically includes: (a) Temperature and humidity control: During the nursery period, the temperature of the pig house should be maintained at 26-28℃ and the relative humidity at 60-65%; during the fattening period, the temperature should be maintained at 20-24℃ and the relative humidity at 55-60%; when the temperature and humidity deviate from the set range by ±1℃ or ±5%, the ventilation system or heating / humidification equipment should be started, with a response time of ≤30 seconds. (b) Stocking density control: Each pig should occupy an area of ​​≥0.3㎡ during the nursery period, ≥0.8㎡ during the early fattening period, and ≥1.2㎡ during the later fattening period; a pre-set standard density threshold database should be established; when the density exceeds the upper limit, a dynamic isolation channel should be activated to guide the pigs to be diverted.

[0012] As a preferred option, the disease risk prediction model in step two integrates environmental compensation factors. When the temperature in the pigsty is >28℃ and the humidity is >70%, the cough frequency threshold is automatically reduced to 8 times / minute, and the weighted fusion calculation of environmental data and behavioral abnormalities is initiated, with a weight ratio of environment:behavior = 1:3.

[0013] As a preferred option, the isolation area is equipped with an independent environmental control system that automatically executes upon the entry of the target pigs. (a) The temperature should be maintained 0.5-1.5℃ higher than the original pigsty, with a maximum of 28℃; humidity should be reduced by 5%±2%; (b) Start millimeter-wave radar respiratory monitoring; trigger secondary diagnosis when respiratory rate >40 breaths / minute. (c) Feed the animal a special feed containing 5% electrolyte supplement during the quarantine period.

[0014] As a preferred approach, a growth-environment-health correlation model is established, and the control parameters are dynamically optimized using the following formula: Nutritional compensation coefficient K_n = 1 + 0.1 × (T_actual - T_standard) + 0.05 × (D_actual - D_standard) Where T is temperature (°C) and D is stocking density (heads / m²), the standard values ​​are obtained from environmental adaptive regulation; When K_n>1.25, the crude protein content of the feed increases by 0.5 percentage points, and the feeding frequency increases by 1 time per day.

[0015] The beneficial effects of this invention are: 1. This invention solves the problem of poor adaptability of general-purpose feed formulations: By identifying individual pig information through RFID ear tags and combining growth stage data (21-45 days old nursery period, 46-90 days old early fattening period, etc.), the feed trough weight sensor monitors feed intake in real time. The system dynamically adjusts the feed formulation according to the growth stage—automatically matching 20-22% crude protein feed during the nursery period, and switching to 16-18% crude protein formula in the later fattening period, and calculating the feeding amount according to individual body weight (single error ≤8%). This precise control makes the feed composition highly compatible with the metabolic needs of pigs. Practical application verification shows that the feed conversion rate is increased by 15-20% and the growth cycle is shortened by 7-10 days.

[0016] 2. This invention solves the problem of unstable environmental temperature, humidity, and stocking density: Environmental data is collected every 10 minutes in the pigsty by temperature and humidity sensors (accuracy ±0.5℃, ±2%), and a top-mounted visual sensor counts the number of pigs and calculates the stocking density. When the temperature deviates from the range of 26-28℃ ±1℃ during the nursery period, ventilation or heating equipment is activated within 30 seconds. If the density exceeds 1.2㎡ / head in the later stage of fattening, the UWB positioning system immediately plans a diversion path, and LED light strips dynamically guide pigs through pneumatic doors into the vacant area. Stable environmental conditions reduce stress response in pigs by 40%, increase intramuscular fat content by 2-3 percentage points, and significantly improve meat color uniformity. 3. This invention solves the problem of insufficient precision in disease prevention: an infrared thermal imaging array monitors the group's temperature in real time, and a convolutional neural network identifies abnormal behaviors such as coughing (>10 times / minute) and lying down (>80%) through video streams. When the body temperature is >40.5℃ for 2 consecutive hours and accompanied by any abnormal behavior, the system outputs P_d=0.95 and triggers isolation. After the A* algorithm plans the path, LED strips along the path light up sequentially to form a light flow guide, and ultrasonic devices (20-30kHz) on both sides form an acoustic barrier, driving sick pigs into the isolation area within 5 seconds. Millimeter-wave radar in the isolation area monitors the respiratory rate, and with the help of special electrolyte feed, disease transmission is avoided (the infection rate is reduced by more than 90%), and targeted care is provided for sick pigs, increasing the group's health compliance rate to over 95%. Detailed Implementation

[0017] The present invention will be further described below with reference to embodiments.

[0018] Example 1: A precision breeding management method for improving pig growth performance and meat quality, comprising the following steps: Step 1: Real-time acquisition of multi-source data The system acquires the group temperature data of pigs through an infrared thermal imaging array, and collects video streams of pig movement trajectories and postures through a top-mounted visual sensor. It also collects data on the growth stage of pigs and the environment of the pigsty. An ultrasonic repellency array and LED guidance device are installed in the pigsty passage. Step Two: Joint Diagnosis of Abnormal Behavior Input body temperature data and behavioral data into a trained disease risk prediction model, and output an individual disease probability value P_d; Step 3: Dynamically Isolate and Execute When P_d≥0.95, the isolation channel control system is activated to guide the target pig into the isolation area, with a response time ≤5 seconds; Step 4: Dynamic Nutritional Regulation During the Growth Stage Based on the growth stage data collected in step one, the corresponding feed formula is matched and the feed is precisely administered. Step 5: Environmental Adaptive Regulation Based on the pigsty environmental data collected in step one, adjust the pigsty temperature, humidity, and stocking density in real time.

[0019] As a preferred option, step one also includes: Acoustic sensors are installed at the drinking water inlets to monitor the daily drinking frequency of each pig. If the drinking frequency decreases by 50%, it is marked as a behavioral abnormality. At the same time, weight sensors are installed in the feed troughs to monitor the daily feed intake of each pig. Combined with the standard feed intake for the growth stage, if the feed intake is lower than the standard value by 30%, it is marked as an abnormal feeding behavior. Temperature and humidity sensors are evenly distributed in the pigsty, and the temperature and humidity data of the pigsty are collected every 10 minutes, with the collection accuracy being ±0.5℃ and ±2%, respectively. The number of pigs in a single area is counted by image recognition, and the stocking density is calculated by combining the area area.

[0020] Preferably, the disease risk prediction model in step two includes: (a) Abnormal body temperature module: When the body temperature is >40.5℃ for 2 consecutive hours, a level 1 alarm is triggered; (b) Behavioral abnormality module: Identifying cough frequency >10 times / minute or lying down time >80% using a convolutional neural network; (c) Fusion decision layer: When either behavior abnormality in (a) and (b) is met simultaneously, output P_d=0.95.

[0021] As a preferred option, step two also includes an environmental stress compensation rule: When the temperature in the pigsty exceeds 30℃, the threshold for abnormal body temperature is raised to 41.0℃; During the feed change period (within 3 days), the lying-down time threshold is relaxed to 90%. When the temperature is greater than 28°C and the humidity is greater than 70%, the cough frequency threshold drops to 8 times / minute, and environmental and behavioral data are fused together with a weight ratio of 1:3.

[0022] As a preferred option, the dynamic isolation execution process in step three specifically includes: Sub-step 1: Path planning The coordinates of the target pig are obtained based on UWB positioning tags, and the shortest path to the quarantine area is generated using the A* algorithm. Sub-step 2: Channel guidance LED guide strips are set at intervals along the generated path, and the strips light up sequentially in the direction of the path to form a dynamic light flow guide; at the same time, ultrasonic repelling devices on both sides of the path are activated, emitting directional sound waves (frequency 20-30kHz) to form an acoustic barrier to restrict the pigs' walking range. Sub-step 3: Channel Control The pneumatic doors in the buffer channel are opened according to the path sequence, with a path update frequency of ≥1Hz; if the target pig deviates from the path by more than 0.5 meters, the flashing frequency of the light strip is increased to 5 times / second, and the intensity of the repelling sound wave is increased by 30%.

[0023] As a preferred option, the dynamic nutrient regulation during the growth stage in step four specifically includes: (a) Growth stage division: The growth cycle of pigs is divided into nursery period (21-45 days old), early fattening period (46-90 days old), and late fattening period (91 days old to slaughter). (b) Dynamic adjustment of feed formula: crude protein content of feed during the nursery period is 20-22% and lysine content is 1.3-1.5%; crude protein content of feed during the early fattening period is 18-20% and lysine content is 1.1-1.3%; crude protein content of feed during the late fattening period is 16-18% and lysine content is 0.9-1.1%. (c) Precise feeding: Pigs are identified by RFID ear tags, and the daily feeding amount is calculated according to individual weight and growth stage. They are fed 3-4 times at fixed times, with a single feeding amount error of ≤8%.

[0024] As a preferred embodiment, step five, environmental adaptive regulation, specifically includes: (a) Temperature and humidity control: During the nursery period, the temperature of the pig house should be maintained at 26-28℃ and the relative humidity at 60-65%; during the fattening period, the temperature should be maintained at 20-24℃ and the relative humidity at 55-60%; when the temperature and humidity deviate from the set range by ±1℃ or ±5%, the ventilation system or heating / humidification equipment should be started, with a response time of ≤30 seconds. (b) Stocking density control: Each pig should occupy an area of ​​≥0.3㎡ during the nursery period, ≥0.8㎡ during the early fattening period, and ≥1.2㎡ during the later fattening period; a pre-set standard density threshold database should be established; when the density exceeds the upper limit, a dynamic isolation channel should be activated to guide the pigs to be diverted.

[0025] As a preferred option, the disease risk prediction model in step two integrates environmental compensation factors. When the temperature in the pigsty is >28℃ and the humidity is >70%, the cough frequency threshold is automatically reduced to 8 times / minute, and the weighted fusion calculation of environmental data and behavioral abnormalities is initiated, with a weight ratio of environment:behavior = 1:3.

[0026] As a preferred option, the isolation area is equipped with an independent environmental control system that automatically executes upon the entry of the target pigs. (a) The temperature should be maintained 0.5-1.5℃ higher than the original pigsty, with a maximum of 28℃; humidity should be reduced by 5%±2%; (b) Start millimeter-wave radar respiratory monitoring; trigger secondary diagnosis when respiratory rate >40 breaths / minute. (c) Feed the animal a special feed containing 5% electrolyte supplement during the quarantine period.

[0027] As a preferred approach, a growth-environment-health correlation model is established, and the control parameters are dynamically optimized using the following formula: Nutritional compensation coefficient K_n = 1 + 0.1 × (T_actual - T_standard) + 0.05 × (D_actual - D_standard) Where T is temperature (°C) and D is stocking density (heads / m²), the standard values ​​are obtained from environmental adaptive regulation; When K_n>1.25, the crude protein content of the feed increases by 0.5 percentage points, and the feeding frequency increases by 1 time per day.

[0028] Example 2: The difference from Example 1 is that this example adds a physiological indicator dimension to the data collection and disease diagnosis process. The specific technical solution is as follows: Step 1: Real-time acquisition of multi-source data Based on Example 1, a pig saliva sample collection module was added (collected once a day by an automatic sampling device) to detect cortisol (stress indicator) and immunoglobulin content; at the same time, a near-infrared spectroscopy sensor was added to the feed trough to analyze the actual nutritional composition of the feed (actual content of crude protein and lysine) in real time and synchronize it to the nutrition regulation system.

[0029] Step Two: Joint Diagnosis of Abnormal Behavior The disease risk prediction model adds a "physiological indicator module": when the salivary cortisol content is >50ng / mL (stress threshold) or the immunoglobulin content is lower than 20% of the standard value, a level 2 alarm is triggered; the fusion decision layer is adjusted to: if any one of abnormal body temperature (a) + abnormal behavior (b) + any one of abnormal physiological indicators is met, the output P_d=0.92; if only the physiological indicators are abnormal, the output P_d=0.6 and it is marked as "potential risk individual".

[0030] The other steps remain consistent with those in Example 1.

[0031] This embodiment integrates physiological indicators with behavioral and body temperature data to identify potentially infected pigs 12-24 hours earlier, reducing the risk of group infection by 15%.

[0032] Example 3: The difference from Example 1 is that this example upgrades nutritional regulation from "stage matching" to "trend prediction". The specific technical solution is as follows: Step 4: Dynamic Nutritional Regulation During the Growth Stage (a) Growth trend prediction: Based on the 30-day growth data (weight and feed intake) collected in step one, the weight gain curve for the next 15 days is predicted by an LSTM neural network, and the "expected growth rate" is output. (b) Dynamic adjustment of formula: If the expected growth rate is 10% lower than the standard value of the same stage, the lysine content will be increased by 0.1 percentage points on the basis of the original stage formula, and 0.2% probiotics will be added (to regulate intestinal absorption); if the expected growth rate is 10% higher than the standard value, the upper limit of crude protein in the later stage of fattening will be reduced to 17% (to avoid insufficient fat deposition). (c) Precise feeding: Set "floating feeding amount" based on the prediction curve - when the expected growth rate is fast, the single feeding amount is increased by 5% of the standard value (the error is still ≤8%), and one additional night feeding is added (the amount is 60% of the single day feeding).

[0033] Step 5: Environmental Adaptive Regulation When the K_n calculated by the growth-environment-health correlation model is greater than 1.2, temperature and humidity pre-regulation should be started 2 hours in advance (e.g., the temperature during the fattening period should be pre-adjusted from 22℃ to 23℃) to reduce the impact of environmental fluctuations on nutrient absorption.

[0034] The other steps remain consistent with those in Example 1.

[0035] This embodiment improves the matching degree between feed nutrition and growth requirements by 20% and increases the uniformity of individual body weight by 12% by predicting growth trends.

[0036] Example 4: The difference from Example 1 is that the environmental control in this example uses "the actual comfort level of the pigs" as the core feedback. The specific technical solution is as follows: Step 1: Real-time acquisition of multi-source data The top-mounted visual sensor adds a "comfort behavior recognition" function: it uses the YOLO algorithm to identify behaviors such as "clustering (3 or more heads gathering together)," "lying on the ground with the abdomen close to the ground when lying down," and "frequently licking the skin," and counts the percentage of such behaviors in a single area (updated every 5 minutes).

[0037] Step 5: Environmental Adaptive Regulation (a) Temperature and humidity control: Based on the threshold of Example 1, if the proportion of comfort behavior is >30%, the "fine-tuning mode" will be automatically triggered - the temperature during the nursery period will be adjusted by ±0.5℃ based on the original setting (such as cooling down by 0.5℃ when huddled together), and the humidity will be adjusted by ±3% simultaneously; (b) Stocking density control: Dynamically adjust based on individual pig weight (estimated by visual sensors). When a single pig weighs >100kg, the upper limit of density in the later stage of fattening is adjusted to 1.3㎡ / head. At the same time, "fighting behavior" (≥3 times per day) is identified by visual recognition. If it occurs, smaller pigs are preferentially diverted to low-density areas.

[0038] Step Two: Joint Diagnosis of Abnormal Behavior If the proportion of comfort behaviors caused by environmental discomfort is greater than 40%, the threshold for abnormal behavior modules will be temporarily relaxed (e.g., the proportion of lying down time will be relaxed to 85%) to avoid misjudgment.

[0039] The other steps remain consistent with those in Example 1.

[0040] This embodiment improves the accuracy of environmental control by 30% and reduces the incidence of fighting behavior by 40% by using feedback from pig behavior.

[0041] Example 5: The difference from Example 1 is that this example strengthens the linkage between rehabilitation monitoring and herd immunity after isolation of sick pigs. The specific technical solution is as follows: Step 3: Dynamically Isolate and Execute A new "recovery assessment unit" has been added to the quarantine area: After the target pigs enter the quarantine area, their body temperature, respiratory rate (millimeter-wave radar), and feed intake (dedicated feed trough sensor) data are collected daily to generate a "recovery index" (30 points for normal body temperature (38.5-39.5℃), 30 points for respiratory rate ≤30 breaths / minute, and 40 points for feed intake recovering to 80% of the standard value).

[0042] Step Two: Joint Diagnosis of Abnormal Behavior (New Linkage) When the recovery index of pigs in the isolation area is ≥80 for two consecutive days, the "recovery probability P_r" is output: P_r=0.9+0.1×(actual feed intake / standard feed intake); if P_r≥0.95, "return guidance" is initiated - the pigs are guided to the transition area (a buffer area independent of the original group) through LED light strips. After being raised in the transition area for 3 days without any abnormalities, they are then guided back to the original group.

[0043] Added "herd immunity adjustment" step: If the proportion of isolated pigs in a single batch is greater than 5% of the population, the remaining pigs are tagged with RFID ear tags, and saliva samples are collected within 24 hours to test antibody levels. For pigs with antibody levels below the threshold, targeted vaccines (such as porcine circovirus vaccines) are given priority, and 0.1% vitamin C is added to the feed (to enhance immunity).

[0044] Feed adjustments in the isolation area: When the recovery index is <60, the electrolyte supplement content is increased to 8%, and 0.5% glucose is added (to supplement energy).

[0045] The other steps remain consistent with those in Example 1.

[0046] This embodiment uses closed-loop management for rehabilitation to shorten the recovery period of sick pigs by 2-3 days and reduce the secondary infection rate of the group to below 5%.

Claims

1. A precision breeding management method for improving pig growth performance and meat quality, characterized in that, Includes the following steps: Step 1: Real-time acquisition of multi-source data; The system acquires the group temperature data of pigs through an infrared thermal imaging array, and collects video streams of pig movement trajectories and postures through a top-mounted visual sensor. It also collects data on the growth stage of pigs and the environment of the pigsty. An ultrasonic repellency array and LED guidance device are installed in the pigsty passage. Step Two: Joint Diagnosis of Abnormal Behaviors; Input body temperature data and behavioral data into a trained disease risk prediction model, and output an individual disease probability value P_d; Step 3: Dynamically isolate and execute; When P_d≥0.95, the isolation channel control system is activated to guide the target pig into the isolation area, with a response time ≤5 seconds; Step 4: Dynamic nutritional regulation during the growth stage; Based on the growth stage data collected in step one, the corresponding feed formula is matched and the feed is precisely administered. Step 5: Environmental adaptive regulation; Based on the pigsty environmental data collected in step one, adjust the pigsty temperature, humidity and stocking density in real time. Step three, the dynamic isolation execution process, specifically includes: Sub-step 1: Path planning; The coordinates of the target pig are obtained based on UWB positioning tags, and the shortest path to the quarantine area is generated using the A* algorithm. Sub-step two: Channel guidance; LED guide light strips are set at intervals along the generated path. The light strips are lit sequentially in the direction of the path to form a dynamic light flow guide. At the same time, the ultrasonic repelling devices on both sides of the path are activated, emitting directional sound waves with a frequency of 20-30kHz to form an acoustic barrier to restrict the pigs' walking range. Sub-step 3: Channel control; The pneumatic doors in the buffer channel are opened according to the path sequence, with a path update frequency of ≥1Hz; if the target pig deviates from the path by more than 0.5 meters, the flashing frequency of the light strip is increased to 5 times / second, and the intensity of the repelling sound wave is increased by 30%.

2. The precision breeding management method for improving pig growth performance and meat quality according to claim 1, characterized in that, Step one also includes: Acoustic sensors are installed at the drinking water inlets to monitor the daily drinking frequency of each pig. If the drinking frequency decreases by 50%, it is marked as a behavioral abnormality. At the same time, weight sensors are installed in the feed troughs to monitor the daily feed intake of each pig. Combined with the standard feed intake for the growth stage, if the feed intake is lower than the standard value by 30%, it is marked as an abnormal feeding behavior. Temperature and humidity sensors are evenly distributed in the pigsty, and the temperature and humidity data of the pigsty are collected every 10 minutes, with the collection accuracy being ±0.5℃ and ±2%, respectively. The number of pigs in a single area is counted by image recognition, and the stocking density is calculated by combining the area area.

3. The precision breeding management method for improving pig growth performance and meat quality according to claim 1, characterized in that, The disease risk prediction model in step two includes: Abnormal body temperature module: If body temperature is >40.5℃ for 2 consecutive hours, a Level 1 alarm will be triggered; Behavioral abnormality module: Identifies cough frequency > 10 times / minute or lying down time > 80% using a convolutional neural network; Fusion Decision Layer: When either the abnormal body temperature module or the abnormal behavior module is satisfied simultaneously, output P_d=0.

95.

4. The precision breeding management method for improving pig growth performance and meat quality according to claim 3, characterized in that, Step two also includes environmental stress compensation rules: When the temperature in the pigsty is greater than 30℃, the threshold for abnormal body temperature is raised to 41.0℃; During feed changeover periods, the lying-down time threshold is relaxed to 90%. When the temperature is greater than 28°C and the humidity is greater than 70%, the cough frequency threshold drops to 8 times / minute, and environmental and behavioral data are fused together with a weight ratio of 1:

3.

5. The precision breeding management method for improving pig growth performance and meat quality according to claim 1, characterized in that, Step four, dynamic nutrient regulation during the growth stage, specifically includes: Growth stages are divided into three phases: the nursery period (21-45 days old), the early fattening period (46-90 days old), and the late fattening period (91 days old to slaughter). Dynamic adjustments to the feed formula: During the nursery period, the crude protein content should be 20-22% and lysine 1.3-1.5%; in the early fattening stage, the crude protein content should be 18-20% and lysine 1.1-1.3%; in the later fattening stage, the crude protein content should be 16-18% and lysine 0.9-1.1%. Precise feeding: Pigs are identified by RFID ear tags, and the daily feeding amount is calculated according to individual weight and growth stage. Feeding is done in 3-4 timed sessions, with a single feeding amount error of ≤8%.

6. The precision breeding management method for improving pig growth performance and meat quality according to claim 5, characterized in that, Step five, environmental adaptive regulation, specifically includes: Temperature and humidity control: During the nursery period, the temperature of the pig house should be maintained at 26-28℃ and the relative humidity at 60-65%; during the fattening period, the temperature should be maintained at 20-24℃ and the relative humidity at 55-60%; when the temperature and humidity deviate from the set range by ±1℃ or ±5%, the ventilation system or heating / humidification equipment should be activated, with a response time of ≤30 seconds. Stocking density control: During the nursery period, each pig should have a space of ≥0.3m². 2 ≥0.8m in the early fattening stage 2 ≥1.2m in the later stages of fattening 2 A pre-set standard density threshold database is used; when the density exceeds the upper limit, a dynamic isolation channel is activated to guide the pigs to be diverted.

7. The precision breeding management method for improving pig growth performance and meat quality according to claim 6, characterized in that, In step two, the disease risk prediction model integrates environmental compensation factors. When the temperature in the pigsty is >28℃ and the humidity is >70%, the cough frequency threshold is automatically reduced to 8 times / minute, and the weighted fusion calculation of environmental data and behavioral abnormalities is initiated, with a weight ratio of environment:behavior = 1:

3.

8. The precision breeding management method for improving pig growth performance and meat quality according to claim 3, characterized in that, The quarantine area is equipped with an independent environmental control system, which automatically executes upon the entry of the target pig: The temperature should be maintained 0.5-1.5℃ higher than the original pigsty, with a maximum of 28℃; humidity should be reduced by 5%±2%. Activate millimeter-wave radar respiratory monitoring; trigger secondary diagnosis when respiratory rate > 40 breaths / minute. During the quarantine period, feed them special feed containing 5% electrolyte supplement.

9. The precision breeding management method for improving pig growth performance and meat quality according to claim 7, characterized in that, Establish a growth-environment-health relationship model and dynamically optimize control parameters using the following formula: Nutritional compensation coefficient K_n = 1 + 0.1 × (T_actual - T_standard) + 0.05 × (D_actual - D_standard) Where T is temperature, D is stocking density, and the standard values ​​are obtained from environmental adaptive regulation. When K_n > 1.25, the crude protein content of the feed increases by 0.5 percentage points, and the feeding frequency increases by 1 time per day.