Calculus bovis quality improving method based on natural pasture environment optimization

Abstract

The invention discloses a bezoar quality improving method based on natural pasture environment optimization. According to the method, the internal quality of the calculus bovis is remarkably improved through multi-dimensional cooperative regulation and control. A natural feed basis with balanced nutrition is provided for cattle from soil environment baseline investigation to precise pasture proportioning, and by combining directional cultivation and implantation of genetic engineering strains, the metabolic activity of rumen microorganisms is effectively enhanced, and accumulation of key components for synthesis of calculus bovis is promoted. The linkage application of the intelligent monitoring system and the environment regulation and control device optimizes the living environment of cattle in real time, and cooperates with innovative intervention means such as lavender essential oil and music therapy, so that the stress reaction of cattle herds is remarkably reduced, and the efficient operation of liver metabolic pathways is guaranteed. The directional feeding of the functional lick bricks further enhances the conversion efficiency of the calculus bovis precursor substance, the whole-process data tracing ensures the controllability of the quality formation process, and finally the improvement of the content of effective active ingredients in the calculus bovis and the enhancement of the quality stability are realized.

Description

Technical Field

[0001] This invention belongs to the field of bezoar production technology, specifically a method for improving bezoar quality based on the optimization of natural pasture environment. Background Technology

[0002] Bezoar, a gallstone found in the gallbladder of bovine animals, is a traditional and valuable Chinese medicinal material. It possesses properties such as clearing heat and detoxifying, opening the orifices and resolving phlegm, and calming wind and stopping spasms. It is widely used in the treatment of stroke, convulsions, and sore throat. Due to its scarcity and high price, natural bezoar is largely produced through artificial cultivation or in vitro culture techniques. Improving the quality of bezoar is primarily achieved through optimizing bovine feeding and management, inducing gallstone formation, improving extraction processes, and standardizing processing methods. Modern technologies, combined with high-performance liquid chromatography and mass spectrometry, allow for precise control of the content of effective components such as bilirubin, bile acids, and cholesterol in bezoar, enhancing its efficacy, stability, and safety. Furthermore, advancements in genetic engineering and biosynthesis technologies have opened new avenues for the efficient production of bezoar substitutes, promoting the sustainable utilization of bezoar resources and the modernization of traditional Chinese medicine.

[0003] However, existing technologies often suffer from fragmented processes in improving the quality of bezoar, focusing on single factors such as feed formulation or breeding environment. Furthermore, data collection relies on manual recording, resulting in poor traceability and difficulty in achieving dynamic optimization. At the same time, stress management methods are limited, relying mainly on drugs or physical isolation, neglecting the linkage between bioactive substances and environmental signals, leading to insufficient metabolic stability in the herd and significant quality fluctuations. Summary of the Invention

[0004] The purpose of this invention is to provide a method for improving the quality of bezoar based on the optimization of natural pasture environment in order to solve the problems mentioned above.

[0005] The technical solution adopted in this invention is as follows: A method for improving the quality of bezoar based on natural pasture environment optimization, the method comprising the following steps:

[0006] S1: Conduct baseline surveys of heavy metals, organic matter, and microbial diversity in pasture soils to delineate core grazing areas, forage planting areas, and ecological buffer zones, providing basic data for subsequent forage planting and cattle activity planning.

[0007] S2: Based on the soil analysis results of S1, high-protein alfalfa, tannin-regulating saxaul and prebiotic-rich chicory were selected for mixed sowing. The forage nutrient composition was sampled and analyzed regularly and transmitted to the formulation system of S3 simultaneously.

[0008] S3: Based on the real-time nutritional data of forage from S2, dynamic feed formulations are developed in combination with the key element requirements for bezoar synthesis. Daily formulation parameters are pushed to the microbial cultivation system of S4 via IoT devices.

[0009] S4: Collect rumen fluid samples from healthy cattle to construct a genetically engineered strain library. Use the feed composition data provided in S3 to induce the strains to produce active peptides, which are then implanted into the rumen of cattle using sustained-release capsule technology.

[0010] S5: A smart collar with integrated sensors for body temperature, heart rate, and rumination behavior is worn on the ears of cattle. The monitoring data is transmitted in real time to a cloud platform and correlated with the bacterial activity data of S4 for analysis.

[0011] S6: Based on the physiological indicators of the cattle herd collected by S5, the shading net lifting system and the spray cooling device are linked and controlled. When the herd stress index exceeds the threshold, the intervention program of S7 is automatically activated.

[0012] S7: Lavender essential oil slow-release devices are installed at key points in the pasture. When S6 detects abnormal cattle activity, the cattle are guided into a quiet area equipped with music therapy through odor signals.

[0013] S8: Based on the microbial metabolic data of S4 and the stress relief effect of S7, liver metabolic pathways were enhanced by targeted feeding of functional licks rich in bile acid precursors at specific times of the day.

[0014] S9: After collecting bezoar samples, the entire process data from S1 to S8 is linked through blockchain technology. Near-infrared spectroscopy analysis is used to establish a quality prediction model and provide feedback to optimize the forage ratio in S2 and the strain cultivation parameters in S4.

[0015] In a preferred embodiment, during step S1, when conducting a baseline survey of the pasture soil environment, sampling grids of 20m × 20m should be set up in the core area, transition area, and edge area respectively. A five-point sampling method should be used to collect surface soil samples from the 0-20cm layer, with at least 30 mixed samples collected from each area. Inductively coupled plasma mass spectrometry (ICP-MS) is used to detect the content of heavy metals such as lead, arsenic, and mercury. The potassium dichromate oxidation method is used to determine the organic matter content, and 16S rRNA gene sequencing is used to analyze the microbial community structure. Based on the test results, the pasture is divided into three functional zones. The core grazing area requires heavy metal content to be lower than the screening value of the "Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard," organic matter content to be maintained at 25-35g / kg, and a microbial diversity index not lower than 3.5. The pasture planting area needs to ensure that the soil pH is maintained between 6.5 and 7.5. The ecological buffer zone should be at least 50 meters wide and laid out along contour lines to prevent soil erosion. The survey data should be compiled into electronic archives as the basis for pasture selection in S2 and quality traceability in S9.

[0016] In a preferred embodiment, in step S2, the forage mixed sowing scheme uses high-protein alfalfa, tannin-regulating saprolegnia, and prebiotic-rich chicory in a 5:3:2 ratio for row sowing, with a sowing depth controlled at 2-3 cm and a row spacing of 30 cm. During the forage growth cycle, aboveground samples are collected every two weeks, and the crude protein, crude fiber, tannin, and prebiotic contents are analyzed using a near-infrared spectroscopy instrument. The data is transmitted in real-time to the dynamic formulation system database in S3 via a LoRa wireless transmission module. Soil fertility is replenished twice a year, in spring and autumn, with nitrogen-phosphorus-potassium (NPK) compound fertilizer applied according to the forage growth, at a NPK ratio of 2:1:1, and an application rate controlled at 150-200 kg / ha. The forage is harvested at a height of 30-40 cm, with a stubble height of no less than 5 cm to ensure regeneration capacity. The nutritional data of the harvested forage must be updated synchronously to the S3 system.

[0017] In a preferred embodiment, in step S3, the dynamic feed formulation system automatically generates a feed formulation daily based on the forage nutrient data and the key element requirement model for bezoar synthesis transmitted in S2. The system database contains thresholds for 28 key elements required for bezoar synthesis. When the content of a certain element in the forage is below 20% of the threshold, a supplementation mechanism is automatically activated. Daily formulation parameters (including concentrate supplementation amount, mineral addition ratio, etc.) are pushed to the microbial cultivation system in S4 via an IoT gateway at 3:00 AM daily. The formulation adjustment follows a gradual principle, with a single nutrient change not exceeding 10% to avoid stress on the cattle's digestive system. The system performs algorithm optimization quarterly, adjusting the element requirement model parameters based on the quality feedback data from S9.

[0018] In a preferred embodiment, in step S4, when collecting rumen fluid samples from healthy cattle, healthy bulls aged 3-5 years and weighing 500-600 kg are selected. 500 ml of rumen fluid is collected via a rumen fistula before morning feeding and immediately stored in a 37°C incubator. Strains are isolated using a serial dilution method, and a genetically engineered strain library containing five genera, including lactobacilli and bifidobacteria, is constructed using 16S rRNA gene sequencing. Using the feed composition data provided in S3, the strains are directionally induced to produce bioactive peptides under anaerobic culture conditions at 37°C and pH 6.8 for 72 hours. The concentration of bioactive peptides is monitored by high-performance liquid chromatography, and induction is stopped when the concentration reaches 1.2 mg / mL. The cultured strains are then formulated into sustained-release capsules, each containing 1 × 10^9 CFU of bioactive strains. These capsules are implanted into the rumen of cattle using rumen cannulation technology, with three capsules implanted per cow over a period of 90 days.

[0019] In a preferred embodiment, in step S5, a smart collar is worn on the cow's ear. The collar integrates a three-axis accelerometer, an infrared body temperature sensor, and a heart rate monitoring module, with a sampling frequency of once every 5 minutes. Sensor data is transmitted in real-time to a cloud platform via an NB-IoT network. The platform performs correlation analysis with the bacterial activity data from S4 to establish a correlation model between rumination behavior and bacterial metabolic activity. If a cow's rumination time is less than 20 minutes / hour for three consecutive times, the system automatically marks it as an abnormal state and triggers the environmental intervention mechanism in S6. The collar's battery life is no less than 6 months, and its weight is controlled to within 150 grams to minimize the impact on the cow's activities.

[0020] In a preferred embodiment, in step S6, the environmental control system automatically activates the shading net lifting system and the spray cooling device when the average body temperature of the herd exceeds 39°C or the heart rate exceeds 80 beats / minute, based on the herd physiological indicators collected in S5. The shading net is made of polyethylene with a 70% shading rate, and the lifting response time does not exceed 5 minutes; the spray system maintains an operating pressure of 0.3-0.5 MPa, a droplet diameter of 50-100 micrometers, and a spray volume of 0.5 liters / minute per square meter. The system sets three stress index thresholds. When the first threshold (stress index 1.2-1.5) is reached, the spray cooling is activated; when the second threshold (1.5-1.8) is reached, both the shading net and the spray are activated simultaneously; and when the third threshold (>1.8) is exceeded, the intervention procedure in S7 is automatically activated. Environmental parameters are recorded hourly to form an environmental control log.

[0021] In a preferred embodiment, in step S7, one lavender essential oil slow-release device is installed every 500 square meters within the pasture. The device is made of microporous ceramic material, and the essential oil evaporation rate is controlled at 0.5 ml / hour. When abnormal cattle activity (such as frequent restlessness or reduced feed intake) is detected in S6, the slow-release device in the abnormal area is triggered via the ZigBee wireless communication protocol to release 0.1% lavender essential oil. Simultaneously, classical music with a frequency of 60-80 Hz is automatically played in the quiet area, with the volume controlled below 60 decibels, for a duration of 2 hours. The quiet area is no less than 100 square meters and equipped with an automatic drinking water device; the water quality meets the "Livestock and Poultry Drinking Water Quality" standard. The intervention effect is evaluated using sensor data from S5, and the intervention is stopped when the cattle stress index drops below 1.0.

[0022] In a preferred embodiment, in step S8, the functional lick block is composed of bile acid precursors (30%), calcium (20%), phosphorus (10%), magnesium (5%), and a carrier (35%), and is made into a block weighing 5 kg. Based on the microbial metabolic data (such as active peptide concentration) from S4 and the stress relief effect (such as heart rate recovery time) from S7, targeted feeding is carried out daily between 16:00 and 17:00. The daily licking amount per cow is controlled at 100-150 grams, and individual licking data is recorded using a smart feeder and correlated with the physiological indicators in S5. The lick block is replaced every 7 days to ensure the freshness of the ingredients, and is replaced when the remaining weight of the lick block is less than 20% of the initial weight.

[0023] In a preferred embodiment, in step S9, sterile surgical instruments are used when collecting bezoar samples, the sample weight is not less than 5 grams, and the samples are immediately placed in an ultra-low temperature freezer at -80℃ for storage. Using blockchain technology, the soil data from S1, the forage nutrient data from S2, the feed formulation from S3, the bacterial strain activity data from S4, the physiological indicators from S5, the environmental parameters from S6, the intervention records from S7, and the feeding data from S8 are linked to the bezoar sample number and uploaded to the blockchain, forming an immutable end-to-end data chain. Near-infrared spectroscopy is used to detect the bezoar samples, with a scanning range of 1000-2500 nm and a resolution of 4 cm^-1. Each sample is scanned three times and the average value is taken. A quality prediction model including indicators such as bilirubin, cholesterol, and calcium content is established. When the model's prediction accuracy reaches over 90%, the optimized parameters are fed back to the forage ratio system in S2 (adjusting the grass seed ratio) and the bacterial strain cultivation system in S4 (optimizing the induction temperature and time), achieving closed-loop optimization of the entire process.

[0024] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0025] 1. This invention significantly enhances the intrinsic quality of bezoar through multi-dimensional synergistic regulation. From baseline soil environmental surveys to precise forage formulation, a nutritionally balanced natural feed base is provided for cattle. Combined with the targeted cultivation and implantation of genetically engineered strains, the metabolic activity of rumen microorganisms is effectively enhanced, promoting the accumulation of key components in bezoar synthesis. The coordinated application of an intelligent monitoring system and environmental control devices optimizes the cattle's living environment in real time. In conjunction with innovative interventions such as lavender essential oil and music therapy, stress responses in the herd are significantly reduced, ensuring the efficient operation of liver metabolic pathways. Targeted feeding of functional licks further enhances the conversion efficiency of bezoar precursor substances, while full-process data traceability ensures the controllability of the quality formation process, ultimately achieving an increase in the content of effective active ingredients in bezoar and enhanced quality stability.

[0026] 2. This invention integrates data from soil to finished product through blockchain technology, forming a traceable and verifiable quality management model. The combination of near-infrared spectroscopy analysis and a feedback mechanism continuously optimizes forage ratios and microbial cultivation parameters, driving the entire production process towards precision and intelligence. Data interaction and coordinated control between each stage not only improve the implementation effect of individual steps but also maximize the overall system efficiency, enhancing the scientific nature and replicability of the method. This end-to-end collaborative optimization model ensures high-quality bezoar production and provides innovative ideas for standardized production in the field of natural medicine cultivation, contributing to the industry's development towards a green, efficient, and sustainable direction. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the process principle of the present invention. Detailed Implementation

[0028] 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.

[0029] Example:

[0030] Reference Figure 1 A method for improving the quality of bezoar based on natural pasture environment optimization, the method comprising the following steps:

[0031] S1: Conduct baseline surveys of heavy metals, organic matter, and microbial diversity in pasture soils to delineate core grazing areas, forage planting areas, and ecological buffer zones, providing basic data for subsequent forage planting and cattle activity planning.

[0032] S2: Based on the soil analysis results of S1, high-protein alfalfa, tannin-regulating saxaul and prebiotic-rich chicory were selected for mixed sowing. The forage nutrient composition was sampled and analyzed regularly and transmitted to the formulation system of S3 simultaneously.

[0033] S3: Based on the real-time nutritional data of forage from S2, dynamic feed formulations are developed in combination with the key element requirements for bezoar synthesis. Daily formulation parameters are pushed to the microbial cultivation system of S4 via IoT devices.

[0034] S4: Collect rumen fluid samples from healthy cattle to construct a genetically engineered strain library. Use the feed composition data provided in S3 to induce the strains to produce active peptides, which are then implanted into the rumen of cattle using sustained-release capsule technology.

[0035] S5: A smart collar with integrated sensors for body temperature, heart rate, and rumination behavior is worn on the ears of cattle. The monitoring data is transmitted in real time to a cloud platform and correlated with the bacterial activity data of S4 for analysis.

[0036] S6: Based on the physiological indicators of the cattle herd collected by S5, the shading net lifting system and the spray cooling device are linked and controlled. When the herd stress index exceeds the threshold, the intervention program of S7 is automatically activated.

[0037] S7: Lavender essential oil slow-release devices are installed at key points in the pasture. When S6 detects abnormal cattle activity, the cattle are guided into a quiet area equipped with music therapy through odor signals.

[0038] S8: Based on the microbial metabolic data of S4 and the stress relief effect of S7, liver metabolic pathways were enhanced by targeted feeding of functional licks rich in bile acid precursors at specific times of the day.

[0039] S9: After collecting bezoar samples, the entire process data from S1 to S8 is linked through blockchain technology. Near-infrared spectroscopy analysis is used to establish a quality prediction model and provide feedback to optimize the forage ratio in S2 and the strain cultivation parameters in S4.

[0040] In step S1, when conducting a baseline survey of the pasture soil environment, sampling grids of 20m × 20m should be set up in the core area, transition area, and edge area. A five-point sampling method should be used to collect topsoil samples from the 0-20cm layer, with at least 30 mixed samples collected from each area. Inductively coupled plasma mass spectrometry (ICP-MS) is used to detect the content of heavy metals such as lead, arsenic, and mercury. The potassium dichromate oxidation method is used to determine the organic matter content, and 16S rRNA gene sequencing is used to analyze the microbial community structure. Based on the test results, the pasture is divided into three functional zones. The core grazing area requires heavy metal content to be lower than the screening value of the "Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard," organic matter content to be maintained at 25-35g / kg, and a microbial diversity index not lower than 3.5. The pasture planting area must ensure that the soil pH is maintained between 6.5 and 7.5. The ecological buffer zone should be at least 50 meters wide and laid out along contour lines to prevent soil erosion. The survey data should be compiled into electronic archives as the basis for pasture selection in S2 and quality traceability in S9.

[0041] In step S2, the mixed forage sowing scheme uses high-protein alfalfa, tannin-regulating saprolegnia, and prebiotic-rich chicory in a 5:3:2 ratio for row sowing, with a sowing depth of 2-3 cm and a row spacing of 30 cm. During the forage growth cycle, aboveground samples are collected every two weeks, and the crude protein, crude fiber, tannin, and prebiotic contents are analyzed using a near-infrared spectroscopy system. The data is transmitted in real-time to the dynamic formulation system database in S3 via a LoRa wireless transmission module. Soil fertility is replenished twice a year, in spring and autumn, with NPK compound fertilizer applied according to the forage growth, at a NPK ratio of 2:1:1, and an application rate controlled at 150-200 kg / ha. The forage is harvested at a height of 30-40 cm, with a stubble height of no less than 5 cm to ensure regeneration capacity. The nutritional data of the harvested forage must be updated synchronously to the S3 system.

[0042] In step S3, the dynamic feed formulation system automatically generates a feed formula daily based on the forage nutrient data and the key element requirement model for bezoar synthesis transmitted in S2. The system database contains threshold values ​​for 28 key elements required for bezoar synthesis. When the content of a certain element in the forage falls below 20% of the threshold, a supplementation mechanism is automatically activated. Daily formula parameters (including concentrate supplementation amount, mineral addition ratio, etc.) are pushed to the microbial cultivation system in S4 via an IoT gateway at 3:00 AM daily. Formula adjustments follow a gradual principle, with single nutrient component changes not exceeding 10% to avoid stress on the cattle's digestive system. The system undergoes algorithm optimization quarterly, adjusting the element requirement model parameters based on quality feedback data from S9.

[0043] In step S4, when collecting rumen fluid samples from healthy cattle, healthy bulls aged 3-5 years and weighing 500-600 kg were selected. 500 ml of rumen fluid was collected via a rumen fistula before morning feeding and immediately stored in a 37°C incubator. Strains were isolated using a serial dilution method, and a genetically engineered strain library containing five genera, including lactobacilli and bifidobacteria, was constructed using 16S rRNA gene sequencing. Using the feed composition data provided in S3, the strains were directionally induced to produce bioactive peptides under anaerobic culture conditions at 37°C and pH 6.8 for 72 hours. The concentration of bioactive peptides was monitored by high-performance liquid chromatography, and induction was stopped when the concentration reached 1.2 mg / mL. The cultured strains were then formulated into sustained-release capsules, each containing 1 × 10^9 CFU of bioactive strains. These capsules were implanted into the rumen of cattle using rumen cannulation technology, with three capsules implanted per cow over a 90-day period.

[0044] In step S5, a smart collar is fitted to the cow's ear. The collar integrates a three-axis accelerometer, an infrared body temperature sensor, and a heart rate monitoring module, sampling every 5 minutes. Sensor data is transmitted in real-time to a cloud platform via an NB-IoT network. The platform correlates this data with the bacterial activity data from step S4 to establish a correlation model between rumination behavior and bacterial metabolic activity. If a cow ruminates for three consecutive times at a time less than 20 minutes per hour, the system automatically flags it as an abnormal state and triggers the environmental intervention mechanism in step S6. The collar's battery life is at least 6 months, and its weight is controlled to be less than 150 grams to minimize impact on the cow's activities.

[0045] In step S6, the environmental control system automatically activates the shading net lifting system and the spray cooling device when the average body temperature of the herd exceeds 39℃ or the heart rate exceeds 80 beats / minute, based on the physiological indicators of the cattle collected in S5. The shading net is made of polyethylene with a 70% shading rate, and the lifting response time does not exceed 5 minutes. The spray system maintains an operating pressure of 0.3-0.5 MPa, a droplet diameter of 50-100 micrometers, and a spray volume of 0.5 liters / minute per square meter. The system sets three stress index thresholds. When the first threshold (stress index 1.2-1.5) is reached, the spray cooling is activated; when the second threshold (1.5-1.8) is reached, both the shading net and the spray are activated simultaneously; and when the third threshold (>1.8) is exceeded, the intervention procedure in S7 is automatically activated. Environmental parameters are recorded hourly to form an environmental control log.

[0046] In step S7, a lavender essential oil slow-release device is installed every 500 square meters within the pasture. The device is made of microporous ceramic material, and the essential oil evaporation rate is controlled at 0.5 ml / hour. When abnormal cattle activity (such as frequent restlessness and reduced feed intake) is detected in S6, the slow-release device in the abnormal area is triggered via the ZigBee wireless communication protocol to release 0.1% lavender essential oil. Simultaneously, classical music with a frequency of 60-80 Hz is automatically played in the quiet area, with the volume controlled below 60 decibels, for 2 hours. The quiet area is no less than 100 square meters and equipped with an automatic drinking water device; the water quality meets the "Livestock and Poultry Drinking Water Quality" standard. The intervention effect is evaluated using sensor data from S5, and the intervention is stopped when the cattle stress index drops below 1.0.

[0047] In step S8, the functional lick block is composed of bile acid precursors (30%), calcium (20%), phosphorus (10%), magnesium (5%), and a carrier (35%), and is made into a block weighing 5 kg. Based on the microbial metabolic data (such as active peptide concentration) from S4 and the stress relief effect (such as heart rate recovery time) from S7, targeted feeding is carried out daily between 16:00 and 17:00. The daily licking amount per cow is controlled at 100-150 grams, and individual licking data is recorded using a smart feeder and correlated with the physiological indicators in S5. The lick block is replaced every 7 days to ensure the freshness of the ingredients, and is replaced when the remaining weight of the lick block is less than 20% of the initial weight.

[0048] In step S9, sterile surgical instruments are used to collect bezoar samples, with a sample weight of no less than 5 grams, and the samples are immediately placed in an ultra-low temperature freezer at -80℃ for storage. Using blockchain technology, the soil data from S1, the forage nutrient data from S2, the feed formulation from S3, the bacterial strain activity data from S4, the physiological indicators from S5, the environmental parameters from S6, the intervention records from S7, and the feeding data from S8 are linked to the bezoar sample number and uploaded to the blockchain, forming an immutable end-to-end data chain. Near-infrared spectroscopy is used to detect the bezoar samples, with a scanning range of 1000-2500 nm and a resolution of 4 cm^-1. Each sample is scanned three times, and the average value is taken. A quality prediction model including indicators such as bilirubin, cholesterol, and calcium content is established. When the model's prediction accuracy reaches over 90%, the optimized parameters are fed back to the forage ratio system in S2 (adjusting the grass seed ratio) and the bacterial strain cultivation system in S4 (optimizing the induction temperature and time), achieving closed-loop optimization of the entire process.

[0049] From the above, we can conclude that:

[0050] This invention significantly enhances the intrinsic quality of bezoar through multi-dimensional synergistic regulation. From baseline soil environmental surveys to precise forage formulation, a nutritionally balanced natural feed foundation is provided for cattle. Combined with the targeted cultivation and implantation of genetically engineered strains, the metabolic activity of rumen microorganisms is effectively enhanced, promoting the accumulation of key components in bezoar synthesis. The coordinated application of an intelligent monitoring system and environmental control devices optimizes the cattle's living environment in real time. In addition, innovative interventions such as lavender essential oil and music therapy significantly reduce stress responses in the herd and ensure the efficient operation of liver metabolic pathways. Targeted feeding of functional licks further enhances the conversion efficiency of bezoar precursors, while full-process data traceability ensures the controllability of the quality formation process, ultimately achieving an increase in the content of effective active ingredients in bezoar and enhanced quality stability.

[0051] This invention integrates data from soil to finished product through blockchain technology, forming a traceable and verifiable quality management model. The combination of near-infrared spectroscopy analysis and a feedback mechanism continuously optimizes forage ratios and microbial cultivation parameters, driving the entire production process towards precision and intelligence. Data interaction and coordinated control between each stage not only improve the effectiveness of individual steps but also maximize the overall system efficiency, enhancing the scientific rigor and replicability of the method. This end-to-end collaborative optimization model ensures high-quality bezoar production and provides innovative ideas for standardized production in the field of natural medicine cultivation, contributing to the industry's green, efficient, and sustainable development.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the quality of bezoar based on natural pasture environment optimization, characterized in that: The method includes the following steps: S1: Conduct baseline surveys of heavy metals, organic matter, and microbial diversity in pasture soils to delineate core grazing areas, forage planting areas, and ecological buffer zones, providing basic data for subsequent forage planting and cattle activity planning; S2: Based on the soil analysis results of S1, high-protein alfalfa, tannin-regulating saxaul and prebiotic-rich chicory were selected for mixed sowing. The forage nutrient composition was sampled and analyzed regularly and transmitted to the formulation system of S3 simultaneously. S3: Based on the real-time nutritional data of forage from S2, dynamic feed formulations are developed in combination with the key element requirements for bezoar synthesis. Daily formulation parameters are pushed to the microbial cultivation system of S4 via IoT devices. S4: Collect rumen fluid samples from healthy cattle to construct a genetically engineered strain library. Use the feed composition data provided by S3 to induce the strains to produce active peptides, which are then implanted into the rumen of cattle using sustained-release capsule technology. S5: A smart collar with integrated sensors for body temperature, heart rate and rumination behavior is worn on the ears of cattle. The monitoring data is transmitted to the cloud platform in real time and correlated with the bacterial activity data of S4 for analysis. S6: Based on the physiological indicators of the cattle herd collected by S5, the shading net lifting system and the spray cooling device are linked and controlled. When the herd stress index exceeds the threshold, the intervention program of S7 is automatically activated. S7: Lavender essential oil slow-release devices are set up at key points in the pasture. When S6 detects abnormal cattle activity, the cattle are guided into a quiet area equipped with music therapy through odor signals. S8: Based on the microbial metabolic data of S4 and the stress relief effect of S7, liver metabolic pathways were enhanced by targeted feeding of functional licks rich in bile acid precursors at specific times of the day. S9: After collecting bezoar samples, the entire process data from S1 to S8 is linked through blockchain technology. Near-infrared spectroscopy analysis is used to establish a quality prediction model and provide feedback to optimize the forage ratio in S2 and the strain cultivation parameters in S4.

2. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S1, when conducting a baseline survey of the pasture soil environment, a 20m × 20m sampling grid should be set up in the core area, transition area and edge area respectively. A five-point sampling method should be used to collect surface soil samples from 0-20cm, and at least 30 mixed samples should be collected from each area. Inductively coupled plasma mass spectrometry should be used to detect the content of heavy metals such as lead, arsenic and mercury, and the organic matter content should be determined by potassium dichromate oxidation method. The microbial community structure should be analyzed by 16S rRNA gene sequencing.

3. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S2, the forage mixed sowing scheme uses high-protein alfalfa, tannin-regulating saxaul, and prebiotic-rich chicory in a 5:3:2 ratio for row sowing, with the sowing depth controlled at 2-3 cm and the row spacing maintained at 30 cm. During the forage growth cycle, aboveground samples are collected every two weeks, and the crude protein, crude fiber, tannin, and prebiotic content are analyzed using a near-infrared spectroscopy instrument. The data is transmitted in real time to the dynamic formulation system database in S3 via a LoRa wireless transmission module.

4. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S3, the dynamic feed formulation system automatically generates a feed formulation daily based on the pasture nutrition data and the key element requirement model for bezoar synthesis transmitted in S2. The system database contains thresholds for 28 key elements required for bezoar synthesis. When the content of a certain element in the pasture is lower than 20% of the threshold, the supplementation mechanism is automatically activated. The daily formulation parameters are pushed to the microbial cultivation system in S4 through the Internet of Things gateway at 3:00 AM every day.

5. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S4, when collecting rumen fluid samples from healthy cattle, healthy bulls aged 3-5 years and weighing 500-600 kg are selected. 500 ml of rumen fluid is collected through a rumen fistula before morning feeding and immediately placed in a 37℃ incubator for preservation. Strains are isolated using a gradient dilution method, and a genetically engineered strain library containing five genera, including lactic acid bacteria and bifidobacteria, is constructed by 16S rRNA gene sequencing. Using the feed composition data provided in S3, the strains are directionally induced to produce bioactive peptides under anaerobic culture conditions at 37℃ and pH 6.8 for 72 hours. The concentration of bioactive peptides is monitored by high-performance liquid chromatography, and induction is stopped when the concentration reaches 1.2 mg / mL.

6. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S5, a smart collar is worn on the ears of the cattle. The collar integrates a three-axis accelerometer, an infrared body temperature sensor, and a heart rate monitoring module, with a sampling frequency of once every 5 minutes. The sensor data is transmitted to the cloud platform in real time via the NB-IoT network. The platform performs correlation analysis with the bacterial activity data in S4 to establish a correlation model between rumination behavior and bacterial metabolic activity.

7. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S6, the environmental control system automatically activates the shading net lifting system and the spray cooling device when the average body temperature of the herd exceeds 39°C or the heart rate exceeds 80 beats / minute, based on the physiological indicators of the cattle herd collected in S5. The shading net is made of polyethylene with a 70% shading rate, and the lifting response time does not exceed 5 minutes. The spray system maintains an operating pressure of 0.3-0.5 MPa, controls the droplet diameter at 50-100 micrometers, and sprays 0.5 liters / minute per square meter. The system sets three stress index thresholds. When the first threshold is reached, the spray cooling is activated. When the second threshold is reached, both the shading net and the spray are activated simultaneously. When the third threshold is exceeded, the intervention program in S7 is automatically activated. Environmental parameters are recorded once per hour to form an environmental control log.

8. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S7, a lavender essential oil slow-release device is installed every 500 square meters in the pasture. The device is made of microporous ceramic material, and the essential oil evaporation is controlled at 0.5 ml / hour. When abnormal cattle activity is detected in S6, the slow-release device in the abnormal area is triggered through the ZigBee wireless communication protocol to release lavender essential oil at a concentration of 0.1%. At the same time, classical music with a frequency of 60-80 Hz is automatically played in the quiet area, with the volume controlled below 60 decibels, for a duration of 2 hours. The quiet area has an area of ​​not less than 100 square meters and is equipped with an automatic drinking water device.

9. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S8, the functional lick brick is composed of bile acid precursors, calcium, phosphorus, magnesium, and a carrier, and is made into a block weighing 5 kg. Based on the microbial metabolism data in S4 and the stress relief effect in S7, targeted feeding is carried out during the period of 16:00-17:00 every day. The daily licking amount per cow is controlled at 100-150 grams. Individual licking data is recorded by a smart feeder and correlated with the physiological indicators in S5. The lick brick is replaced every 7 days to ensure the freshness of the ingredients. It is replaced when the remaining weight of the lick brick is less than 20% of the initial weight.

10. The method for improving the quality of bezoar based on natural pasture environment optimization as described in claim 1, characterized in that: In step S9, sterile surgical instruments are used when collecting bezoar samples. The sample weight is not less than 5 grams, and the samples are immediately placed in an ultra-low temperature freezer at -80℃ for storage. The soil data of S1, the pasture nutrition data of S2, the feed formula of S3, the bacterial activity data of S4, the physiological indicators of S5, the environmental parameters of S6, the intervention records of S7, and the feeding data of S8 are linked to the bezoar sample number and uploaded to the blockchain to form an immutable full-process data chain.

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