A method for quantifying health benefits of pure green ammonia water and a dynamic authentication system

Abstract

This invention discloses a method for quantifying the health benefits of pure green ammonia water, including obtaining the heavy metal residue in forage grass, where the forage grass is grown after application of pure green ammonia water; calculating the health benefit value based on the heavy metal residue in the forage grass using a pre-set health benefit quantification model, where the health risk reduction rate = 1 - (heavy metal residue in forage grass / 0.01 mg / kg)²; and generating and outputting health certification information for the corresponding end-product livestock based on the health benefit value. The invention also discloses a dynamic certification system for quantifying the health benefits of pure green ammonia water. This invention transforms health benefits from a vague concept into a calculable and verifiable quantitative indicator, realizing the quantification of health value across the entire chain from raw materials to end products. Blockchain storage ensures data immutability, directly supporting premium sales of end products and forming a closed loop of "technology-certification-commerce." This method can be widely applied in green livestock farming, healthy agricultural product certification, and premium trading.

Description

Technical Field

[0001] This invention relates to a method for quantifying the health benefits of pure green ammonia water and a dynamic certification system, belonging to the technical field of green agricultural production and livestock product health certification. Background Technology

[0002] Green ammonia is gradually being used in agriculture as a clean energy carrier, but existing green ammonia preparation technologies only focus on production efficiency and basic purity control, without establishing a quantitative correlation between green ammonia purity and the health benefits of livestock products. For example, although traditional green ammonia preparation can achieve basic pollution-free production, it lacks precise threshold definitions for heavy metal residues (such as lead and cadmium), making it impossible to scientifically assess its health impact on forage and end livestock products.

[0003] In the field of agricultural product certification, existing technologies mainly remain at the level of basic data recording. For example, the digital testing system for agricultural products developed in Anqiu City, Shandong Province, records agricultural product testing information through "quality and safety codes + ecological origin codes," allowing consumers to scan the codes to view product information and testing records. The "Sunshine Agricultural Safety" pilot project in Suzhou, Jiangsu Province, applies technologies such as high-definition video surveillance, artificial intelligence, 5G, and blockchain to achieve transparency in the production process and intelligent quality supervision. However, these existing certification technologies only focus on data recording and process transparency, failing to address the issue of "quantitative correlation between technical parameters and health benefits." They can only record static data such as "heavy metal residues in forage ≤0.1mg / kg," and cannot reflect the actual degree of reduction in health risks.

[0004] Regarding the integration of blockchain and IoT technologies, existing research has proposed blockchain-based sustainable agricultural product certification solutions. These solutions integrate IoT sensors to continuously monitor crop, soil, and environmental conditions, ensuring the transparency and traceability of food certification. However, these solutions primarily address data authenticity and traceability issues, failing to establish a quantifiable path for health benefits from raw materials to finished products. This leaves the concept of "purity" merely a promotional gimmick, unable to support premium pricing.

[0005] The main shortcomings of existing technologies are concentrated in three aspects: First, at the technical level, there is a lack of a mathematical model linking the purity of green ammonia to its health benefits, making it impossible to quantify the contribution of "improved purity" to "reduced health risks." Second, at the certification level, health certification only records forage testing data and does not generate a health risk index for end-product livestock, making it difficult for consumers to intuitively understand the product's health advantages. Third, at the commercial level, the health premium lacks data support, and companies cannot convert certification results into calculable revenue. These deficiencies have resulted in the application of green ammonia in the field of healthy animal husbandry remaining at the "zero-carbon" concept level, failing to realize its core value of "purity," and severely restricting the large-scale development of the healthy livestock product market. Summary of the Invention

[0006] This invention aims to address the core technical deficiency in existing technologies where there is a lack of quantitative correlation between pure green ammonia water and its health benefits. It provides a method for quantifying the health benefits of pure green ammonia water and a dynamic certification system for the health benefits of pure green ammonia water. By constructing a quantitative health benefit model and a dynamic certification system, a closed loop of health value from raw materials to end products is achieved.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for quantifying the health benefits of pure green ammonia water, comprising the following steps: S1. Obtain the heavy metal residue in the forage grass, wherein the forage grass is grown after applying pure green ammonia water, and the pure green ammonia water is obtained by a water electrolysis ammonia generator after being processed by a three-stage filtration unit, and its heavy metal residue is ≤0.001mg / kg. S2. Based on the heavy metal residue in the forage, calculate the health benefit value using a pre-set health benefit quantification model. The health benefit quantification model is: Health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². S3. Based on the health benefit value, generate and output the corresponding health certification information for the terminal livestock products.

[0008] The aforementioned method, wherein the three-stage filtration unit comprises a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence.

[0009] In the aforementioned method, the pore size of the nano-ceramic membrane filtration unit is 0.0005μm to 0.001μm, the amount of resin used in the ion exchange resin unit is 5-8kg, and the activated carbon particle size of the activated carbon adsorption column unit is 0.5-1.0mm, with a carbon content of 2-4kg.

[0010] The aforementioned method includes at least one of health risk value, product identification, or health premium coefficient.

[0011] In the aforementioned method, the health risk value is calculated based on the amount of heavy metal residue in the forage and the type of the end livestock product.

[0012] A dynamic certification system for the health benefits of pure green ammonia water includes: The pure green ammonia water preparation module is used to prepare pure green ammonia water. The data acquisition module is used to acquire the amount of heavy metal residue in the forage grass, which is grown after applying the pure green ammonia water prepared by the pure green ammonia water preparation module. A health benefit calculation module, connected to the data acquisition module, is used to calculate the health benefit value based on the heavy metal residue in the forage through a preset health benefit quantification model. The health benefit quantification model is: health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². The certification information generation module is connected to the health benefit calculation module and is used to generate and output the health certification information of the corresponding terminal livestock products based on the health benefit value.

[0013] The aforementioned system, wherein the pure green ammonia water preparation module includes an electrolytic water ammonia generator and a three-stage filtration unit, wherein the output end of the electrolytic water ammonia generator is connected to the three-stage filtration unit, and the heavy metal residue of the pure green ammonia water is ≤0.001mg / kg.

[0014] The aforementioned system includes a three-stage filtration unit comprising a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence. The nano-ceramic membrane filtration unit has a pore size of 0.0005 μm to 0.001 μm, the ion exchange resin unit uses 5 kg to 8 kg of resin, and the activated carbon adsorption column unit has an activated carbon particle size of 0.5-1.0 mm and a carbon content of 3 kg.

[0015] In the aforementioned system, the health certification information includes at least one of health risk value, product identification, or health premium coefficient.

[0016] In the aforementioned system, the health risk value is calculated based on the amount of heavy metal residues in the forage and the type of the end livestock product.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention transforms health benefits from a vague concept into a calculable and verifiable quantitative indicator by defining a mathematical model that links heavy metal residue thresholds to health risk reduction rates. This achieves a precise mapping between technical parameters and health benefits, unlike existing technologies that only define purity.

[0018] This invention uses an AI-driven dynamic health certification system to automatically match product labels, enabling consumers to intuitively understand the health advantages of products and overcoming the shortcomings of traditional certification that only records static data.

[0019] This invention automatically calculates the premium coefficient based on health risk values ​​through a health premium calculation module, transforming certification data into commercial revenue for the first time and forming a closed loop of "technology-certification-commerce".

[0020] This invention uses an AI model to automatically adjust the weights of different livestock product types, enabling "one system for multiple products" certification capabilities and making the certification system universally applicable. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the present invention.

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0023] 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 for illustrative purposes only and are not intended to limit the invention.

[0024] First, this invention provides a method for quantifying the health benefits of pure green ammonia water, comprising the following steps: S1. Obtain the heavy metal residue in the forage grass. The forage grass was grown after applying pure green ammonia water. The pure green ammonia water was produced by an electrolytic water ammonia generator after being processed by a three-stage filtration unit. Its heavy metal residue was ≤0.001mg / kg. S2. Based on the heavy metal residue in forage, calculate the health benefit value through a pre-set health benefit quantification model. The health benefit quantification model is: Health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². S3. Based on the health benefit value, generate and output the corresponding health certification information for the end livestock products.

[0025] Specifically, the three-stage filtration unit includes a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence. The pore size of the nano-ceramic membrane filtration unit is 0.0005μm to 0.001μm, the resin dosage of the ion exchange resin unit is 5-8kg, and the activated carbon particle size of the activated carbon adsorption column unit is 0.5-1.0mm with a carbon content of 3kg. Furthermore, the health certification information includes at least one of the following: health risk value, product label, or health premium coefficient. The health risk value can be calculated by a pre-set AI model based on the heavy metal residue in the forage and the type of end livestock product.

[0026] This invention also provides a dynamic certification system for the health benefits of pure green ammonia water, comprising: The pure green ammonia water preparation module is used to prepare pure green ammonia water. The data acquisition module is used to acquire the amount of heavy metal residue in the forage grass, which is grown after applying the pure green ammonia water prepared by the pure green ammonia water preparation module. A health benefit calculation module, connected to the data acquisition module, is used to calculate the health benefit value based on the heavy metal residue in the forage through a preset health benefit quantification model. The health benefit quantification model is: health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². The certification information generation module is connected to the health benefit calculation module and is used to generate and output the health certification information of the corresponding terminal livestock products based on the health benefit value.

[0027] Specifically, the pure green ammonia water preparation module includes an electrolytic water ammonia generation device and a three-stage filtration unit. The output of the electrolytic water ammonia generation device is connected to the three-stage filtration unit, resulting in pure green ammonia water with a heavy metal residue of ≤0.001 mg / kg. The three-stage filtration unit comprises a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence. The pore size of the nano-ceramic membrane filtration unit is 0.0005 μm to 0.001 μm. The resin dosage of the ion exchange resin unit is 5 kg to 8 kg. The activated carbon particle size of the activated carbon adsorption column unit is 0.5-1.0 mm, and the activated carbon dosage is 3 kg.

[0028] Furthermore, the health certification information includes at least one of health risk value, product identification, or health premium coefficient. The health risk value is calculated by a pre-set AI model based on the amount of heavy metal residue in the forage and the type of the end livestock product.

[0029] Specifically, the data acquisition module of this invention serves as the data entry point for the dynamic authentication system, responsible for converting physical world detection data into system-recognizable digital signals. This module's implementation includes: an on-site detection terminal, which can utilize a handheld heavy metal detector alongside laboratory-grade SGS testing standards. Multiple fixed detection points are set up in the ranch's planting area, while multiple mobile detection terminals are provided for inspection personnel. Its detection accuracy reaches 0.001 mg / kg, meeting the heavy metal residue threshold detection requirements of this invention. An industrial-grade IoT gateway with built-in data cache storage is deployed in the ranch control room to prevent data loss due to network interruptions. The detection terminal transmits data to the gateway via Bluetooth 5.0 or USB-C wired connection, where the gateway performs preliminary data verification.

[0030] When the pasture grows to a height of 30cm (predictable via a growth cycle model), the system automatically pushes a sampling task to the inspector's mobile app. During sampling, the app's GPS positioning function is activated, and the latitude and longitude coordinates of the sampling point are recorded (accuracy ±1 meter) to ensure spatial traceability of the data. Each sample generates a unique QR code label; after scanning the QR code, the app automatically records the sampling time, sampler, and sample number. After the handheld detector completes the test, the test results are transmitted to the app in real time via Bluetooth. The app automatically overlays the sampling coordinates and uploads the results to the cloud. After the samples are sent to the SGS laboratory, the inspectors log in to the system's web interface and manually upload a PDF test report. The system automatically extracts key data from the report using OCR technology, such as the test items, test results, and test date, and associates them with the sampling QR code.

[0031] Specifically, the health benefit calculation module is the core algorithm unit of this invention. Deployed on a cloud server, it provides API interfaces in a microservice manner. It is responsible for converting the heavy metal residue data in forage provided by the heavy metal dynamic monitoring module into quantifiable health benefit indicators, providing data support for subsequent certification and premium pricing. This module consists of a data preprocessing unit, a core algorithm unit, an AI inference unit, and a result encapsulation unit. Input data includes: heavy metal residue in forage (mg / kg), heavy metal element type, sampling time, pasture ID, and end-product type (milk / beef / mutton). Optional inputs include soil pH and forage growth cycle. Output data includes: health risk reduction rate, health risk value, product identification level, premium coefficient, and confidence level.

[0032] The module has a built-in formula for quantifying health benefits: Health risk reduction rate = 1 − (Cgrass 0.01)2 Health risk reduction rate = 1 − (0.01Cgrass) 2 Where C represents the residue level (mg / kg) of a specific heavy metal element in the forage, and 0.01 mg / kg is the baseline residue level (corresponding to the traditional organic certification limit). The formula is based on a simplified dose-response model of toxicology, reflecting that health risk is proportional to the square of the residue concentration.

[0033] To establish an accurate mapping between "forage residues and health risks of end products," the module integrates a regression prediction model based on multilayer perceptron (MLP). The model is trained using five consecutive years of measured data (800 paired samples) from a cooperative ranch in Xilingol League, Inner Mongolia. The model automatically learns the bioaccumulation differences of different livestock products, and the weights can also be manually set.

[0034] Specifically, the certification information generation module is the output unit of this invention. It is responsible for converting the health risk value output by the health benefit calculation module into a visualized certification mark, a tradable premium coefficient, and a traceable blockchain record, providing consumers with an intuitive basis for health identification and providing production enterprises with data support for premium sales.

[0035] This module consists of four parts: an identifier matching unit, a premium calculation unit, a blockchain notarization unit, and a QR code generation unit. Deployed on a cloud server, it interfaces with e-commerce platforms, traceability mini-programs, and other front-end applications. The system has a built-in rule engine that matches identifier levels based on product type and health risk value. Based on the matched identifier level and product type, it queries the corresponding premium coefficient from the rule table and outputs it to the e-commerce platform for final product pricing. The system packages authentication information (sample ID, risk value, identifier level, premium coefficient, authentication time, etc.), calculates the SHA-256 hash value, and writes it to the FISCO BCOS consortium blockchain, returning the transaction ID and block height to ensure data immutability. A QR code containing the consumer's page URL is generated for packaging printing or embedding on e-commerce platforms. Consumers can scan the code to view: health identifier level (S / A / B / C and corresponding color), health risk value and risk reduction rate, comparison with regular products (e.g., "risk reduced by 97.5%"), blockchain notarization certificate (transaction ID, block height), original test report, and ranch traceability information.

[0036] This invention comprises three core components: the preparation of pure green ammonia water, dynamic monitoring of heavy metal residues in forage and calculation of health benefits, and blockchain-driven dynamic certification and health premium conversion, forming a closed-loop value chain of "technology-certification-commerce". This invention integrates a heavy metal dynamic monitoring module into an electrolytic water ammonia production device, constructing a full-chain dynamic certification system based on a health benefit quantification formula, thereby realizing the quantification and premium conversion of health value from raw materials to end products.

[0037] First, the pure green ammonia water preparation process uses a 200kW electrolytic cell (working voltage 48V) to treat Xilingol League groundwater (lead content 0.05mg / L), and through precise control of a three-stage filtration unit, green ammonia water with heavy metal residue ≤0.0008mg / kg is produced.

[0038] Secondly, in the monitoring of heavy metal residues in forage, stem and leaf samples were collected 10cm above the ground when the forage was 30cm tall. After testing by SGS, the health risk reduction rate was calculated using a health benefit quantification formula, as follows: Health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)² Finally, the blockchain dynamic authentication process automatically matches health risk values ​​with identification levels based on an AI model. For example, a risk value of 0.02 matches an "A-level" identification, and a premium coefficient is calculated based on the health risk value. For instance, a premium of 15% is applied when the risk value is 0.02, thus making health benefits quantifiable and marketable. This solution breaks through the limitations of existing technologies that only focus on basic purity by accurately quantifying the relationship between "technical parameters" and "health benefits," transforming health benefits from a "concept" into a "calculable and marketable" asset.

[0039] In one embodiment of the present invention, the health risk value is a quantitative indicator calculated by a pre-set AI model. This calculation process integrates the normalization of multi-dimensional input features, the forward propagation operation of the neural network, and the post-processing of the model output, ultimately generating a dimensionless value between 0 and 1 to characterize the health risk level of the end livestock product.

[0040] The calculation of health risk values ​​consists of four main stages: data preparation and input feature extraction, feature normalization, neural network forward propagation, and model output and risk value generation. The entire process is completed automatically in the health benefit calculation module.

[0041] Before calculating the risk value, the following six types of input data need to be collected. These data come from the heavy metal dynamic monitoring module, the environmental monitoring system, and user presets, respectively: The first type is the heavy metal residue in forage, specifically lead and cadmium residues. Lead residue is obtained through handheld detectors or SGS laboratory testing, with units of mg / kg, typically ranging from 0.0001 to 0.1 mg / kg. Cadmium residue is also obtained through testing, with values ​​ranging from 0.0001 to 0.05 mg / kg. The second type is soil environmental parameters, specifically soil pH. This value is obtained through soil sensors or laboratory measurements, typically ranging from 5.5 to 8.5, reflecting the acidity or alkalinity of the forage growth environment. The third type is forage growth parameters, specifically the forage growth cycle. This value is recorded by the planting management system, indicating the number of days from sowing to sampling, typically ranging from 30 to 90 days. The fourth type is the type of end-product livestock, including milk, beef, and mutton. This parameter is selected by the user based on the actual breed or preset by the system, and is represented by a One-Hot encoding method: milk corresponds to the code [1,0,0], beef corresponds to the code [0,1,0], and mutton is the base type corresponding to the code [0,0,1] (that is, when the codes for milk and beef are both 0, it is automatically identified as mutton).

[0042] Since the dimensions and value ranges of the above input features are different, they cannot be directly input into the neural network. Normalization is necessary to map all features to the interval between 0 and 1. The normalization formula and specific calculation example are as follows: For lead residue in forage, the normalization formula is the input value divided by 0.1. 0.1 mg / kg is the traditional upper limit for heavy metal residues in forage for organic certification, and this is used as a benchmark. For example, in Example 1, the lead residue in forage is 0.008 mg / kg, which, after normalization, is 0.008 divided by 0.1 equals 0.08. For cadmium residue in forage, the normalization formula is the input value divided by 0.05. 0.05 mg / kg is a common upper limit for cadmium residue. For example, in Example 1, the cadmium residue in forage is 0.0003 mg / kg, which, after normalization, is 0.0003 divided by 0.05 equals 0.006. For soil pH, the normalization formula is the input value minus 5.5, then divided by 3. 5.5 is the lower limit of pH suitable for forage growth, and 3 is the pH range (8.5 minus 5.5). For example, in Example 1, the soil pH is 6.8. After normalization, 6.8 minus 5.5 equals 1.3, then divided by 3 equals 0.433. For the pasture growth cycle, the normalization formula is the input number of days minus 30, then divided by 60. 30 days is the starting point for rapid pasture growth, and 60 days is the range of variation during the main growth period (90 minus 30). For example, in Example 1 below, the growth cycle is 45 days. After normalization, 45 minus 30 equals 15, then divided by 60 equals 0.25.

[0043] For livestock product types, no additional normalization is required; the One-Hot encoding values ​​are used directly: milk is [1,0,0], beef is [0,1,0], and mutton is [0,0,1].

[0044] The AI ​​model used in this invention is a multilayer perceptron regression model, whose network structure consists of an input layer, two hidden layers, and an output layer.

[0045] The input layer contains 6 neurons, which receive the following six normalized feature values: normalized value of lead residue in forage, normalized value of cadmium residue in forage, normalized value of soil pH, normalized value of forage growth cycle, milk code value, and beef code value.

[0046] The first hidden layer contains 64 neurons, with ReLU activation. Each neuron in this layer receives 6 feature values ​​from the input layer, which are multiplied by their corresponding weights, summed, and then a bias term is added before activation by the ReLU function. The ReLU function is defined as follows: the output equals the input value when the input value is greater than 0, and the output is 0 when the input value is less than or equal to 0.

[0047] The second hidden layer contains 32 neurons, and the activation function is also ReLU. This layer receives 64 output values ​​from the first hidden layer and performs similar weighted summation and activation operations.

[0048] The output layer contains one neuron with the sigmoid activation function. The sigmoid function is defined as the output being equal to 1 divided by 1 plus e raised to the power of the negative input. This function maps any real number to the interval between 0 and 1, making it suitable for representing risk values.

[0049] The model's weight matrix and bias terms were learned during training. Training used 800 sets of measured data from the Hezuo Ranch in Xilingol League, Inner Mongolia, spanning five consecutive years from 2019 to 2023, with mean squared error as the loss function and the Adam optimizer employed for optimization. The trained model achieved a prediction accuracy of 0.0025 and a coefficient of determination of 0.92 on the test set.

[0050] The health risk value output by the model is a dimensionless numerical value between 0 and 1. This value is transmitted to the blockchain dynamic authentication process, where a smart contract automatically matches an identification level and premium coefficient according to preset rules. For example, a risk value of 0.02 is identified as below 0.05, matching an A-level label and a 15% premium coefficient for milk products; a risk value of 0.005 is identified as below 0.01, matching an S-level label and a 25% premium coefficient; and a risk value of 0.03 matches a B-level label and a 12% premium coefficient for beef products. These matching rules are hard-coded in the smart contract, ensuring the transparency and immutability of the authentication process. Consumers can scan the QR code on the product packaging to see the risk value and its corresponding health label, intuitively understanding the product's health advantages.

[0051] Through the detailed calculation process and examples described above, those skilled in the art can clearly understand how the health risk value in this invention is calculated based on the heavy metal residue in forage and the type of end livestock product, and can fully reproduce the calculation process according to the description in this specification.

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, a further detailed description is provided. Based on the actual scenario and process flow of the core pastoral area in Inner Mongolia, a dynamic monitoring module for heavy metals is integrated into an electrolytic water ammonia production device to construct a quantitative and dynamic certification system for the health benefits of pure green ammonia water. The following embodiments are all based on a cooperative ranch (500 mu of pasture planting area) in Xilingol League, Inner Mongolia, and all parameters are local measured values.

[0053] Example 1: This example is based on the actual scenario of a cooperative ranch (500 mu of pasture planting area) in Xilingol League, Inner Mongolia. It quantifies the health benefits of pure green ammonia water through standard processes. The core is to embed the health benefit calculation formula (health risk reduction rate = 1 - (pasture heavy metal residue / 0.01mg / kg)²) into the certification system to ensure that the entire chain from preparation to end is quantifiable and verifiable.

[0054] The specific implementation steps are as follows: The ammonia production device uses a 200kW electrolytic cell (working voltage 48V), and the influent is groundwater from Xilingol League (lead content 0.05mg / L); the three-stage filtration unit is strictly configured in sequence: nano-ceramic membrane (pore size 0.001μm) → H-type ion exchange resin (5kg) → activated carbon adsorption column (particle size 0.5-1.0mm, carbon content 3kg); the produced green ammonia water was tested by SGS and found to have heavy metal residue of 0.0008mg / kg, and was applied to forage grass at a standard of 10kg per acre. When the forage grass grows to a height of 30cm, stem and leaf samples are collected at 10cm above the ground and sent to the SGS laboratory for lead residue testing. The measured lead residue value of the forage was 0.0008 mg / kg (meeting the threshold of ≤0.001 mg / kg). The health risk reduction rate was calculated as 1 - (0.008 / 0.01)² = 0.36 (i.e., a reduction of 64%). The lead risk value of the milk at the terminal was output as 0.02 by the AI ​​model (below the threshold of 0.05 for Grade A label), and the "Pure and Healthy Grade A" label was automatically matched. The selling price of the milk at the terminal increased by 15% accordingly, resulting in an annual premium of 2 million yuan.

[0055] Example 2: Building upon Example 1, this example achieves a breakthrough in health benefits by precisely controlling the filter pore size to 0.0005 μm and optimizing fertilization parameters. It innovatively addresses the industry pain point of the "non-linear relationship between purity threshold and health benefits" in existing technologies. The core innovation lies in fine-tuning the pore size of the nano-ceramic membrane from 0.001 μm to 0.0005 μm (precise value) and simultaneously increasing the amount of ion exchange resin to 8 kg. This reduces heavy metal residues in forage to 0.0005 mg / kg, achieving a health risk reduction rate exceeding 99.75%, significantly higher than the 64% reduction in Example 1.

[0056] Specific implementation steps: The ammonia production device uses a 200kW electrolytic cell (operating voltage 48V) and Xilingol League groundwater (lead content 0.05mg / L); the three-stage filtration unit is strictly configured as follows: nano-ceramic membrane (pore size 0.0005μm) → H-type ion exchange resin (8kg) → activated carbon adsorption column (particle size 0.5-1.0mm, carbon content 3kg); the produced green ammonia water was tested by SGS and found to have heavy metal residue of 0.0005mg / kg, and was applied to forage grass at a standard of 8kg per acre (concentration 1.2%). When the forage grass grows to a height of 30cm, stem and leaf samples are collected at 10cm above the ground and sent to SGS for testing. The measured lead residue in the forage was 0.0005 mg / kg (meeting the threshold of ≤0.001 mg / kg). The health risk reduction rate was calculated as 1 - (0.0005 / 0.01)² = 0.9975 (i.e., a reduction of 99.75%). The lead risk value of the finished beef was output as 0.005 by the AI ​​model (below the S-level label threshold of 0.01), and it was automatically matched with the "Pure and Healthy S-level" label. The selling price of the finished beef increased by 25%, resulting in an annual premium of 3 million yuan.

[0057] Compared to Example 1, this example achieves a health risk reduction rate of nearly 100% for the first time through precise control of the 0.0005μm pore size, directly demonstrating the exponential increase in premium due to threshold optimization, forming a strong correlation between "technical parameters and health benefits", which is completely different from the limitation of existing patents that only focus on basic purity.

[0058] Example 3: This example innovatively extends the health benefit quantification system to a dynamic certification scenario for multiple livestock products, addressing the industry pain point that existing technologies cannot support differentiated health assessments for different livestock products, and achieving certification capabilities for "one system for multiple products." The core innovation lies in the AI ​​model automatically adjusting the health risk calculation weights based on the type of livestock product (such as milk and beef), establishing for the first time a multi-dimensional mapping relationship between "heavy metal residues in pastures and health risks in end products," making the certification system universally applicable.

[0059] Specific implementation steps: Green ammonia water (lead residue 0.0008 mg / kg, tested by SGS) from Example 1 was applied at a standard of 10 kg per acre to the forage of dairy farms (100 head) and beef cattle farms (50 head); when the forage grew to a height of 30 cm, stem and leaf samples were collected at 10 cm above the ground and sent to SGS for testing, confirming that the lead residue in the forage was 0.008 mg / kg; the AI ​​model was input into this data and automatically output a health risk value of 0.02 for dairy farms (below the A-level threshold of 0.05, matching the "Pure and Healthy A-level" label, with a premium of 15%) and a health risk value of 0.03 for beef cattle farms (below the B-level threshold of 0.05, matching the "Pure and Healthy B-level" label, with a premium of 12%).

[0060] Compared to traditional methods (without this system, milk lead residue was 0.08 mg / kg, health risk value was 0.8, with no labeling or premium), this embodiment demonstrates that the system can adapt to different livestock product types, increasing the health premium by 12-15%. Data is sourced from actual measurements at ranches in Xilingol League, Inner Mongolia. AI model parameters (such as weight coefficients of 0.75 for milk and 0.65 for beef) are specific point values ​​without range descriptions; the dynamic calculation process has been running stably on the ranches without any technical obstacles.

[0061] Comparative Example: This comparative example uses measured data from pastures in Xilingol League, Inner Mongolia, to rigorously compare the technical differences between traditional health certification schemes and Example 1 of this patent, verifying the inventiveness and irreplaceability of this patent. Traditional health certification schemes (such as GB / T 19630) only require that heavy metal residues in forage be ≤0.1mg / kg, but they do not establish a quantitative model for health benefits, resulting in the inability of end products to achieve risk assessment and premium conversion.

[0062] Specific implementation: In the same pasture (500 mu of forage planting area), using conventional green ammonia production process (200kW electrolytic cell, 48V voltage, lead content in influent 0.05mg / L, no three-stage filtration unit), the measured lead residue in the forage was 0.08mg / kg (compliant with GB / T standards), and the lead residue in the finished milk was 0.08mg / kg. The health risk value was calculated as 0.8 (without applying the formula of this patent), there was no health label, and the product price had no premium (premium rate 0%).

[0063] This data is completely consistent with the "actual measurement of traditional pastures" (lead residue 0.08 mg / kg). In contrast, Example 1 of this patent uses the health benefit formula (health risk reduction rate = 1 - (heavy metal residue in pasture / 0.01 mg / kg)²) to convert the lead residue of pasture of 0.008 mg / kg into a health risk value of 0.02, matching the Grade A label, with a premium of 15%.

[0064] This comparative example directly demonstrates that traditional methods only record static data (forage residue ≤0.1mg / kg), while this invention achieves a closed loop of "technology-health-commerce" through formulaic quantification, solving the industry's core pain point—the inability to convert health value into economic benefits. Its innovation lies in the fact that this invention is the first to embed a mathematical model into a certification system, transforming health benefits from a "concept" into a "calculable and marketable" asset. Traditional methods, lacking a quantification mechanism, remain merely at the level of "zero-carbon" promotion.

[0065] Table 1: Quantitative Measured Data on the Health Benefits of Standard Pure Green Ammonia Water in Example 1

[0066] Table 2: Measured data on health benefits enhanced by threshold optimization in Example 2

[0067] Table 3: Measured Data of Dynamic Calculation of Health Risks of Multiple Livestock Products in Example 3

[0068] Table 4: Comparison between traditional health certification schemes and Example 1 of this patent .

Claims

1. A method for quantifying the health benefits of pure green ammonia water, characterized in that, Includes the following steps: S1. Obtain the heavy metal residue in the forage grass, wherein the forage grass is grown after applying pure green ammonia water, and the pure green ammonia water is obtained by a water electrolysis ammonia generator after being processed by a three-stage filtration unit, and its heavy metal residue is ≤0.001mg / kg. S2. Based on the heavy metal residue in the forage, calculate the health benefit value using a pre-set health benefit quantification model. The health benefit quantification model is: Health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². S3. Based on the health benefit value, generate and output the corresponding health certification information for the terminal livestock products.

2. The method according to claim 1, characterized in that, The three-stage filtration unit includes a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence.

3. The method according to claim 2, characterized in that, The pore size of the nano-ceramic membrane filtration unit is 0.0005μm to 0.001μm, the amount of resin used in the ion exchange resin unit is 5-8kg, and the activated carbon particle size of the activated carbon adsorption column unit is 0.5-1.0mm, with a carbon content of 2-4kg.

4. The method according to claim 1, characterized in that, The health certification information includes at least one of the following: health risk value, product identification, or health premium coefficient.

5. The method according to claim 4, characterized in that, The health risk value is calculated based on the amount of heavy metal residue in the forage and the type of the end livestock product.

6. A dynamic certification system for the health benefits of pure green ammonia water, characterized in that, include: The pure green ammonia water preparation module is used to prepare pure green ammonia water. The data acquisition module is used to acquire the heavy metal residue levels in forage grass, which is grown after being treated with the pure green ammonia water prepared by the pure green ammonia water preparation module; A health benefit calculation module, connected to the data acquisition module, is used to calculate the health benefit value based on the heavy metal residue in the forage through a preset health benefit quantification model. The health benefit quantification model is: health risk reduction rate = 1 - (heavy metal residue in forage / 0.01 mg / kg)². The certification information generation module is connected to the health benefit calculation module and is used to generate and output the health certification information of the corresponding terminal livestock products based on the health benefit value.

7. The system according to claim 6, characterized in that, The pure green ammonia water preparation module includes an electrolytic water ammonia generator and a three-stage filtration unit. The output end of the electrolytic water ammonia generator is connected to the three-stage filtration unit, and the heavy metal residue of the pure green ammonia water is ≤0.001mg / kg.

8. The system according to claim 7, characterized in that, The three-stage filtration unit includes a nano-ceramic membrane filtration unit, an ion exchange resin unit, and an activated carbon adsorption column unit connected in sequence. The pore size of the nano-ceramic membrane filtration unit is 0.0005μm to 0.001μm, the amount of resin used in the ion exchange resin unit is 5kg to 8kg, and the activated carbon particle size of the activated carbon adsorption column unit is 0.5-1.0mm, with a carbon content of 3kg.

9. The system according to claim 6, characterized in that, The health certification information includes at least one of the following: health risk value, product identification, or health premium coefficient.

10. The system according to claim 9, characterized in that, The health risk value is calculated based on the amount of heavy metal residue in the forage and the type of the end livestock product.

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