An intelligent feeding method and system based on an AI dynamic formula engine

Abstract

This invention proposes an intelligent feeding method and system based on an AI dynamic formulation engine, comprising: constructing a digital twin model of the animal, mapping the animal's physiological parameters to a unified physiological state vector; inputting the physiological state vector into a quantum-inspired neural network to obtain a real-time nutrient requirement spectrum and a nutrient deficiency risk assessment matrix, and generating an optimal feed formulation based on these two; controlling the feeding equipment to feed the animal according to the optimal feed formulation; monitoring the animal's feeding behavior and uneaten feed, and transmitting the feeding feedback data back to the AI ​​engine to form a closed-loop control system; storing the formulation data, feeding records, and animal response data for each feeding using blockchain technology to construct a complete feeding traceability chain; and quantitatively evaluating the effectiveness of the feeding program by comparing and analyzing historical data with current growth indicators. This invention can improve feed conversion rate and formulation adaptability, reduce feed costs, reduce human intervention, and improve the level of automation in animal husbandry.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to an intelligent feeding method and system based on an AI dynamic formula engine. Background Technology

[0002] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, digitalization and intelligentization have become an inevitable trend in the development of the aquaculture industry. In the aquaculture process, feed costs account for a large proportion of the total cost, and feed utilization directly affects the economic and environmental benefits of farming. However, existing feeding methods have problems in terms of feed conversion rate, feed formulation specificity, feeding costs, and automation level. Summary of the Invention

[0003] Based on the above-mentioned problems, this invention proposes an intelligent feeding method and system based on an AI dynamic formulation engine. Through this invention, feed conversion rate can be improved, formulation adaptability can be significantly enhanced, feed costs can be reduced, human intervention can be reduced, the level of feeding automation can be improved, and a reliable basis can be provided for food safety and quality control.

[0004] In view of this, one aspect of the present invention proposes an intelligent feeding method based on an AI dynamic formula engine, comprising: By deploying a multi-sensor array in the breeding environment, real-time collection of physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency is carried out. A digital twin model of each individual animal is constructed by combining these physiological parameters. The digital twin model maps multi-source heterogeneous data into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

[0005] Optionally, the step of inputting the physiological state vector into a quantum-inspired neural network, wherein the quantum-inspired neural network uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals, and combines prior knowledge of animal breed, growth stage, and health status to output an individualized real-time nutritional requirement spectrum and generate a nutritional deficiency risk assessment matrix, includes: The physiological state vector is converted into a quantum state input format through normalization. At the same time, a prior knowledge encoding matrix is ​​constructed based on animal species, growth stage, and health status. The physiological data and prior knowledge are fused and mapped to obtain the first fused input data. Based on the five major nutritional categories of protein, fat, carbohydrate, vitamin, and mineral, a quantum entanglement network is constructed. The quantum superposition state is used to represent the three relationship states between different nutrients: synergistic promotion, mutual antagonism, and independent action, forming a dynamic nutrient association topology. The first fused input data is transmitted in multiple hidden layers of a quantum-inspired neural network. Each neuron updates the nutrient association weights based on the quantum entanglement mechanism. At the same time, the impact of the current physiological state on nutrient absorption efficiency is combined to achieve hierarchical reasoning of nutrient requirements. Based on the calculation results of the output layer of the quantum-inspired neural network, an individualized nutritional requirement spectrum is generated, which includes the specific requirements of various nutrients, the optimal intake time window, and the nutrient ratio relationship. The individualized nutritional requirement spectrum is dynamically adjusted according to the real-time physiological state of the individual. Based on a comparative analysis of individualized nutritional requirements and current feed supply, a nutritional deficiency risk assessment matrix is ​​constructed. This matrix quantifies the probability, severity, and potential impact on growth performance of each nutrient deficiency, providing risk warning information for subsequent formula optimization.

[0006] Optionally, based on the nutritional requirement spectrum and the nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is activated to dynamically generate the optimal feed formula while satisfying nutritional balance, cost control, and raw material supply chain constraints. This formula optimization process incorporates a fuzzy logic controller to handle uncertainties such as raw material price fluctuations, inventory status, and transportation delays, including the following steps: Based on the aforementioned nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-dimensional optimization problem model is constructed with nutritional balance, cost-effectiveness, and supply chain stability as objectives. At the same time, nutrient content boundary constraints, raw material ratio restrictions, and production process constraints are set as hard constraints. The fuzzy logic controller is activated to monitor uncertain parameters such as raw material price fluctuations, inventory status, and transportation delays in real time. These continuously changing uncertain parameters are converted into discrete fuzzy linguistic variables through fuzzification, and then processed by the fuzzy inference rule base to output uncertainty adjustment factors for dynamically adjusting optimization weights. An initial formula population is generated based on the types and proportions of available raw materials. Each individual uses a real number to represent the proportion of each raw material. At the same time, the uncertainty adjustment factor output by the fuzzy logic controller is incorporated into the individual's fitness evaluation function to ensure that the population evolution direction adapts to the current market environment. The formulation population is iteratively evolved through genetic operations such as selection, crossover, and mutation. During the evolution process, the weight relationship between nutritional objectives, cost objectives, and supply chain objectives is dynamically balanced. The Pareto optimal solution set is screened using a non-dominated sorting method to form multiple candidate formulation schemes. The formula with the highest comprehensive score is selected from the Pareto optimal solution set as the current optimal solution. At the same time, a dynamic adjustment mechanism for the formula is established. When the fuzzy logic controller detects a significant change in the market environment, it automatically triggers the formula re-optimization process to ensure that the formula solution always adapts to the real-time changing external conditions.

[0007] Optionally, the step of controlling the intelligent feeding device to feed according to a feeding plan including time window, feeding amount, and feeding frequency based on the optimal feed formula includes: The optimal feed formula is converted into control commands that can be recognized by the intelligent feeding equipment. Based on the ratio requirements of each raw material component in the formula and the characteristics of nutrient release sequence, the feeding task is decomposed into sub-tasks with different time windows and assigned to the corresponding feeding units for execution. Based on the animal's physiological rhythms, feeding habits, and nutrient absorption characteristics, the optimal feeding time window is dynamically planned, and the daily feeding plan is divided into multiple time periods. Within each time period, the specific feeding time and duration are determined according to the urgency of nutrient needs and digestion and absorption efficiency. The intelligent weighing system and flow control device are activated to accurately measure the feeding amount of various feed components according to the formula requirements. The multi-channel synchronous control mechanism ensures that different nutrients are added at the same time or in sequence according to the preset ratio, avoiding nutrient separation and uneven distribution. Based on the animal's current feeding status and digestive capacity, the feeding frequency and single feeding amount are dynamically adjusted. While ensuring that the total nutrient intake remains unchanged, the feeding frequency is optimized to improve nutrient absorption efficiency, and the load is balanced among multiple feeding points. During the feeding process, key parameters such as equipment operating status, feed flow rate, and feeding accuracy are monitored in real time. When abnormal situations such as equipment failure, feed blockage, or metering deviation are detected, emergency procedures are immediately activated to ensure the continuity and accuracy of the feeding task through backup feeding channels or by adjusting feeding parameters.

[0008] Optionally, the step of monitoring animal feeding behavior and uneaten feed in real time using image recognition technology, and transmitting feeding feedback data back to the AI ​​engine to form a closed-loop control system of prediction-execution-monitoring-adjustment includes: By deploying a camera array in the feeding area, multi-angle image sequences of the animal feeding process are collected in real time. Deep learning algorithms are used to extract feeding behavior features such as the animal's head posture, mouth movements, and body position from the images, while also identifying the distribution and quantity changes of leftover feed in the feed trough. Based on feeding behavior characteristics, the animal feeding process is divided into different stages such as approach, sniffing, feeding, chewing, and leaving using a behavior pattern recognition algorithm. The duration, frequency, and intensity of each stage are quantitatively analyzed, and the volume and weight changes of leftover food are measured using image segmentation technology. The actual feeding data obtained by image recognition is compared and analyzed with the feeding plan to calculate key indicators such as feeding rate, feeding speed, and nutrient intake completion. Deviations such as abnormal feeding behavior, excessive feed leftovers, and insufficient nutrient intake are identified, and a detailed feeding effect evaluation report is generated. Feeding behavior data, leftover feed status, and effect evaluation results are converted into a standardized feedback data format through data preprocessing and feature engineering to obtain feedback data, which is then transmitted back in real time to the quantum-inspired neural network and the multi-objective genetic algorithm optimization engine to update the learning parameters and decision weights of the AI ​​model. Based on the analysis results of the feedback data, the AI ​​engine automatically determines whether it is necessary to adjust the quantum-inspired neural network, re-optimize the feed formula, or modify the feeding execution strategy. When a systematic deviation is detected, the entire process is recalibrated. When a local deviation is detected, targeted fine-tuning is performed, forming a complete prediction-execution-monitoring-adjustment closed-loop control mechanism.

[0009] Optionally, the step of storing the formula data, feeding records, and animal response data for each feeding in an immutable manner using blockchain technology to construct a complete breeding traceability chain includes: The feed formulation data, feeding execution records, and feedback data are standardized and converted into a format. Each data block is assigned a unique digital identity and bound and encapsulated with the corresponding animal individual identifier, timestamp, and geographic location information. The packaged feeding data is used to generate a data fingerprint using the SHA-256 hash algorithm. A tamper-proof identifier is created for each data block using digital signature technology. At the same time, metadata such as data source, operator, and equipment information is encrypted to ensure the integrity and authenticity of the data. The encrypted feeding data is organized into data blocks according to the blockchain protocol specifications. Consensus verification is performed by multiple nodes in the distributed network. Proof-of-work or proof-of-authority mechanisms are used to ensure the legality of the data writing. The verified data blocks are linked in chronological order to form an immutable feeding record chain. Deploy smart contract programs to monitor key event nodes in the feeding process. When important events such as formula adjustment, feeding abnormalities, and changes in health status occur, the relevant data association index is automatically triggered to build a complete traceability graph from raw material procurement to the final product. Establish a standardized traceability data query interface to support data retrieval based on several dimensions, including individual animals, time range, feeding stage, and formula type. Display the complete feeding process trajectory through a visual interface, including information on the entire life cycle such as nutrient intake curves, changes in health status, and the impact of environmental factors.

[0010] Optionally, the step of quantitatively evaluating the effectiveness of the feeding program by comparing and analyzing historical data with current growth indicators, and providing data support for subsequent formula optimization, includes: Historical feeding records are extracted from the constructed blockchain traceability database. The data is stratified according to the animal breed, growth stage, and feeding environment. Data mining algorithms are used to identify excellent feeding cases and construct a multi-dimensional benchmark indicator system including growth rate, feed conversion rate, health status, and behavioral performance. Real-time collection of growth indicators such as animal weight changes, body size growth, health check results, and feeding behavior data during the current feeding cycle; and extraction of feature parameters corresponding to historical benchmark indicators through data cleaning and feature engineering to ensure consistency of data format and evaluation dimensions. The current growth indicators are compared with historical benchmark data item by item to identify areas for improvement and shortcomings in growth performance. The degree of deviation and significance level of each indicator are calculated through statistical analysis methods. At the same time, the time points and possible causes of the deviations are analyzed. Establish a comprehensive evaluation model, convert the comparative analysis results of each dimension into quantitative scores, set the weight coefficients of key evaluation indicators such as growth efficiency, nutrient utilization, health level and economic benefits, calculate the comprehensive effectiveness score of the current feeding program, and generate a detailed evaluation report. Based on the quantitative assessment results, targeted formula optimization suggestions and feeding management improvement measures are automatically generated. The assessment results and optimization suggestions are fed back to the quantum-inspired neural network and multi-objective genetic algorithm to update the knowledge base and decision-making model of the AI ​​system, providing empirical data support for the formulation of subsequent feeding programs.

[0011] Optionally, the physiological state vector is constructed using a multi-sensor data spatiotemporal fusion algorithm, which calculates a comprehensive physiological state index based on the following formula:

[0012] Where PSI represents the physiological state index; M is the total number of sensors; The weighting coefficient for the m-th sensor is dynamically adjusted using an adaptive Kalman filter. Let be the measurement value of the m-th sensor at time t; This is the health baseline value for the m-th sensor; Let m be the standard deviation of the measurement value of the m-th sensor; The coefficient of sensitivity to rate of change; It is the time derivative of the sensor measurement, reflecting the trend of physiological parameter changes.

[0013] Optionally, the nutrient requirement prediction of the quantum-inspired neural network uses a quantum entanglement nutrient correlation matrix, which is calculated using the following formula:

[0014]

[0015] in, This represents the quantum entanglement state between the p-th and q-th nutrients; The angle of quantum entanglement between nutrients p and q; The phase angle reflects the directionality of synergistic or antagonistic effects of nutrients; and These are the quantum ground states, representing the independent nutrient states and the fully correlated nutrient states, respectively. This is a function for the strength of the nutrient association. R is a nonlinear adjustment parameter; R is the number of neurons in the hidden layer. and These are the connection weights from nutrients p and q to the r-th hidden layer neuron, respectively. It is an activation function based on a physiological state index.

[0016] Another aspect of the present invention provides an intelligent feeding system based on an AI dynamic formula engine for executing an intelligent feeding method based on an AI dynamic formula engine, comprising: a control platform and a multi-sensor array deployed in the breeding environment; The multi-sensor array is configured to collect physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency in real time. The control platform is configured as follows: A digital twin model of each individual animal is constructed by combining the physiological parameters. The digital twin model maps multi-source heterogeneous data into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

[0017] The intelligent feeding method based on an AI dynamic formulation engine, employing the technical solution of this invention, includes: real-time collection of physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency using a multi-sensor array deployed in the breeding environment; constructing a digital twin model for each individual animal based on these physiological parameters; mapping multi-source heterogeneous data into a unified physiological state vector using a time-series data fusion algorithm; inputting the physiological state vector into a quantum-inspired neural network; the quantum-inspired neural network using a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals; combining prior knowledge of animal breed, growth stage, and health status; outputting an individualized real-time nutritional requirement spectrum; and generating a nutritional deficiency risk assessment matrix. Based on the aforementioned nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is activated to dynamically generate the optimal feed formula while meeting the constraints of nutritional balance, cost control, and raw material supply chain. According to the optimal feed formula, intelligent feeding equipment is controlled to feed animals according to a feeding plan that includes time windows, feeding amounts, and feeding frequencies. Image recognition technology is used to monitor animal feeding behavior and uneaten feed in real time, and the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology, constructing a complete feeding traceability chain. By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program is quantitatively evaluated, providing data support for subsequent formula optimization. Personalized nutrition management for individual animals is achieved through digital twin modeling, improving feed conversion rate; quantum-inspired neural networks can capture nonlinear relationships between nutrients in real time, reducing formula adjustment response time to the minute level and significantly improving adaptability; multi-objective optimization algorithms reduce feed costs by comprehensively considering cost and supply chain factors while ensuring nutritional needs are met; real-time feedback mechanisms ensure continuous optimization of feeding programs, reduce human intervention, and improve the level of feeding automation; blockchain technology ensures the integrity and traceability of feeding data, providing a reliable basis for food safety and quality control. Attached Figure Description

[0018] Figure 1 This is a flowchart of an intelligent feeding method based on an AI dynamic formula engine provided in one embodiment of the present invention; Figure 2 This is a schematic block diagram of an intelligent feeding system based on an AI dynamic formula engine provided in one embodiment of the present invention. Detailed Implementation

[0019] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0021] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] The following reference Figures 1 to 2 This invention describes an intelligent feeding method and system based on an AI dynamic formula engine, provided by some embodiments of the present invention.

[0024] like Figure 1 As shown, one embodiment of the present invention provides an intelligent feeding method based on an AI dynamic formula engine, comprising: By deploying a multi-sensor array in the breeding environment, real-time collection of physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency is carried out. Combined with these physiological parameters, a digital twin model of each individual animal is constructed. The digital twin model maps multi-source heterogeneous data (i.e., the collected physiological parameters) into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Understandably, the formula optimization process incorporates a fuzzy logic controller to handle uncertainties such as raw material price fluctuations, inventory status, and transportation delays. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

[0025] The technical solution adopted in this embodiment enables personalized nutrition management for individual animals through digital twin modeling, thereby improving feed conversion rate; quantum-inspired neural networks can capture nonlinear correlations between nutrients in real time, reducing the response time for formula adjustments to the minute level and significantly improving adaptability; multi-objective optimization algorithms, while ensuring nutritional needs, comprehensively consider cost and supply chain factors to reduce feed costs; real-time feedback mechanisms ensure continuous optimization of feeding programs, reduce human intervention, and improve the level of feeding automation; blockchain technology ensures the integrity and traceability of feeding data, providing a reliable basis for food safety and quality control.

[0026] In some possible embodiments of the present invention, the step of inputting the physiological state vector into a quantum-inspired neural network, wherein the quantum-inspired neural network uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals, and combines prior knowledge of animal breed, growth stage, and health status to output an individualized real-time nutritional requirement spectrum and generate a nutritional deficiency risk assessment matrix, includes: The physiological state vector is converted into a quantum state input format through normalization. At the same time, a prior knowledge encoding matrix is ​​constructed based on animal species, growth stage, and health status. The physiological data and prior knowledge are fused and mapped to obtain the first fused input data. Based on the five major nutritional categories of protein, fat, carbohydrate, vitamin, and mineral, a quantum entanglement network is constructed. The quantum superposition state is used to represent the three relationship states between different nutrients: synergistic promotion, mutual antagonism, and independent action, forming a dynamic nutrient association topology. The first fused input data is transmitted in multiple hidden layers of a quantum-inspired neural network. Each neuron updates the nutrient association weights based on the quantum entanglement mechanism. At the same time, the impact of the current physiological state on nutrient absorption efficiency is combined to achieve hierarchical reasoning of nutrient requirements. Based on the calculation results of the output layer of the quantum-inspired neural network, an individualized nutritional requirement spectrum is generated, which includes the specific requirements of various nutrients, the optimal intake time window, and the nutrient ratio relationship. The individualized nutritional requirement spectrum is dynamically adjusted according to the real-time physiological state of the individual. Based on a comparative analysis of individualized nutritional requirements and current feed supply, a nutritional deficiency risk assessment matrix is ​​constructed. This matrix quantifies the probability, severity, and potential impact on growth performance of each nutrient deficiency, providing risk warning information for subsequent formula optimization.

[0027] This embodiment enables highly accurate and personalized prediction of nutritional needs. The quantum entanglement mechanism effectively captures the complex nonlinear correlations between nutrients, significantly improving the scientificity and accuracy of nutrient ratios compared to traditional linear prediction models. At the same time, the introduction of the risk assessment matrix enables proactive early warning of nutritional management, effectively avoiding the negative impact of nutritional deficiencies on animal health and production performance.

[0028] In some possible embodiments of the present invention, the step of activating a multi-objective genetic algorithm optimization engine based on the nutritional requirement spectrum and the nutritional deficiency risk assessment matrix to dynamically generate the optimal feed formula under the premise of satisfying nutritional balance, cost control, and raw material supply chain constraints, and the step of introducing a fuzzy logic controller to handle uncertainties such as raw material price fluctuations, inventory status, and transportation delays, includes: Based on the aforementioned nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-dimensional optimization problem model is constructed with nutritional balance, cost-effectiveness, and supply chain stability as objectives. At the same time, nutrient content boundary constraints, raw material ratio restrictions, and production process constraints are set as hard constraints. The fuzzy logic controller is activated to monitor uncertain parameters such as raw material price fluctuations, inventory status, and transportation delays in real time. These continuously changing uncertain parameters are converted into discrete fuzzy linguistic variables through fuzzification, and then processed by a fuzzy inference rule base (a corresponding fuzzy inference rule base is pre-established for dynamic adjustment of optimization weights). The output is an uncertainty adjustment factor for dynamically adjusting optimization weights. An initial formula population is generated based on the types and proportions of available raw materials. Each individual uses a real number to represent the proportion of each raw material. At the same time, the uncertainty adjustment factor output by the fuzzy logic controller is incorporated into the individual's fitness evaluation function to ensure that the population evolution direction adapts to the current market environment. The formulation population is iteratively evolved through genetic operations such as selection, crossover, and mutation. During the evolution process, the weight relationship between nutritional objectives, cost objectives, and supply chain objectives is dynamically balanced. The Pareto optimal solution set is screened using a non-dominated sorting method to form multiple candidate formulation schemes. The formula with the highest comprehensive score is selected from the Pareto optimal solution set as the current optimal solution. At the same time, a dynamic adjustment mechanism for the formula is established. When the fuzzy logic controller detects a significant change in the market environment, it automatically triggers the formula re-optimization process to ensure that the formula solution always adapts to the real-time changing external conditions.

[0029] This embodiment enables intelligent optimization decision-making for feed formulations in complex and ever-changing market environments. The fuzzy logic controller effectively handles uncertainties that are difficult to quantify in traditional optimization methods, significantly improving the practicality and robustness of the formulation scheme. The multi-objective genetic algorithm ensures the coordinated balance of multiple constraints such as nutrition, cost, and supply chain, and can obtain more comprehensive and practical formulation solutions compared to single-objective optimization methods.

[0030] In some possible embodiments of the present invention, the step of controlling the intelligent feeding device to feed according to a feeding plan including time windows, feeding amounts, and feeding frequencies based on the optimal feed formula includes: The optimal feed formula is converted into control commands that can be recognized by the intelligent feeding equipment. Based on the ratio requirements of each raw material component in the formula and the characteristics of nutrient release sequence, the feeding task is decomposed into sub-tasks with different time windows and assigned to the corresponding feeding units for execution. Based on the animal's physiological rhythms, feeding habits, and nutrient absorption characteristics, the optimal feeding time window is dynamically planned, and the daily feeding plan is divided into multiple time periods. Within each time period, the specific feeding time and duration are determined according to the urgency of nutrient needs and digestion and absorption efficiency. The intelligent weighing system and flow control device are activated to accurately measure the feeding amount of various feed components according to the formula requirements. The multi-channel synchronous control mechanism ensures that different nutrients are added at the same time or in sequence according to the preset ratio, avoiding nutrient separation and uneven distribution. Based on the animal's current feeding status and digestive capacity, the feeding frequency and single feeding amount are dynamically adjusted. While ensuring that the total nutrient intake remains unchanged, the feeding frequency is optimized to improve nutrient absorption efficiency, and the load is balanced among multiple feeding points. During the feeding process, key parameters such as equipment operating status, feed flow rate, and feeding accuracy are monitored in real time. When abnormal situations such as equipment failure, feed blockage, or metering deviation are detected, emergency procedures are immediately activated to ensure the continuity and accuracy of the feeding task through backup feeding channels or by adjusting feeding parameters.

[0031] This embodiment enables high-precision automated control of the feed feeding process. The dynamic planning of the time window significantly improves nutrient absorption efficiency and animal welfare. The multi-component synchronous feeding technology ensures the integrity of the formula and nutritional balance. The adaptive frequency adjustment mechanism effectively adapts to the feeding characteristics of different individuals. The abnormal handling mechanism ensures the stable and reliable operation of the feeding system. Compared with the traditional timed and quantitative feeding method, it achieves truly precise and intelligent feeding management.

[0032] In some possible embodiments of the present invention, the step of monitoring animal feeding behavior and uneaten feed in real time using image recognition technology, and transmitting feeding feedback data back to the AI ​​engine to form a closed-loop control system of prediction-execution-monitoring-adjustment includes: By deploying a camera array in the feeding area (such as above and to the side), multi-angle image sequences of the animal feeding process are collected in real time. Deep learning algorithms are used to extract feeding behavior features such as the animal's head posture, mouth movements, and body position from the images, while also identifying the distribution and quantity changes of leftover feed in the feed trough. Based on feeding behavior characteristics, the animal feeding process is divided into different stages such as approach, sniffing, feeding, chewing, and leaving using a behavior pattern recognition algorithm. The duration, frequency, and intensity of each stage are quantitatively analyzed, and the volume and weight changes of leftover food are measured using image segmentation technology. The actual feeding data obtained by image recognition is compared and analyzed with the feeding plan to calculate key indicators such as feeding rate, feeding speed, and nutrient intake completion. Deviations such as abnormal feeding behavior, excessive feed leftovers, and insufficient nutrient intake are identified, and a detailed feeding effect evaluation report is generated. Feeding behavior data, leftover feed status, and effect evaluation results are converted into a standardized feedback data format through data preprocessing and feature engineering to obtain feedback data, which is then transmitted back in real time to the quantum-inspired neural network and the multi-objective genetic algorithm optimization engine to update the learning parameters and decision weights of the AI ​​model. Based on the analysis results of the feedback data, the AI ​​engine automatically determines whether it is necessary to adjust the quantum-inspired neural network (including the nutrient requirement prediction model), re-optimize the feed formula, or modify the feeding execution strategy. When a systematic deviation is detected, the entire process is recalibrated. When a local deviation is detected, targeted fine-tuning is performed, forming a complete prediction-execution-monitoring-adjustment closed-loop control mechanism.

[0033] This embodiment enables the feeding management system to learn and continuously optimize itself. Image recognition technology provides an objective and accurate means of monitoring feeding behavior, effectively avoiding the subjectivity and limitations of manual observation. The real-time feedback mechanism ensures that the AI ​​system can quickly respond to changes in the actual needs of animals. The closed-loop control strategy significantly improves the adaptability and accuracy of the feeding program. Compared with the open-loop control method, it can continuously improve the feeding effect and animal welfare level, realizing the intelligent and automated upgrade of feeding management.

[0034] In some possible embodiments of the present invention, the step of storing the formula data, feeding records, and animal response data for each feeding in an immutable manner using blockchain technology to construct a complete breeding traceability chain includes: The feed formulation data, feeding execution records, and feedback data are standardized and converted into a format. Each data block is assigned a unique digital identity and bound and encapsulated with the corresponding animal individual identifier, timestamp, and geographic location information. The packaged feeding data is used to generate a data fingerprint using the SHA-256 hash algorithm. A tamper-proof identifier is created for each data block using digital signature technology. At the same time, metadata such as data source, operator, and equipment information is encrypted to ensure the integrity and authenticity of the data. The encrypted feeding data is organized into data blocks according to the blockchain protocol specifications. Consensus verification is performed by multiple nodes in the distributed network. Proof-of-work or proof-of-authority mechanisms are used to ensure the legality of the data writing. The verified data blocks are linked in chronological order to form an immutable feeding record chain. Deploy smart contract programs to monitor key event nodes in the feeding process. When important events such as formula adjustment, feeding abnormalities, and changes in health status occur, the relevant data association index is automatically triggered to build a complete traceability graph from raw material procurement to the final product. Establish a standardized traceability data query interface to support data retrieval based on several dimensions, including individual animals, time range, feeding stage, and formula type. Display the complete feeding process trajectory through a visual interface, including information on the entire life cycle such as nutrient intake curves, changes in health status, and the impact of environmental factors.

[0035] This embodiment can build a completely transparent and tamper-proof traceability system for the breeding process. Blockchain technology ensures the authenticity and integrity of the breeding data, effectively preventing the risks of data fraud and human tampering. The distributed storage mechanism improves the security and reliability of the data. The automated association function of smart contracts simplifies the complexity of traceability management. The visual query interface provides a convenient information access channel for regulatory authorities, consumers, and breeding enterprises. Compared with traditional paper records or centralized database storage methods, it significantly improves the efficiency and credibility of food safety supervision.

[0036] In some possible embodiments of the present invention, the step of quantitatively evaluating the effectiveness of the feeding program by comparing and analyzing historical data with current growth indicators, and providing data support for subsequent formula optimization, includes: Historical feeding records are extracted from the constructed blockchain traceability database. The data is stratified according to the animal breed, growth stage, and feeding environment. Data mining algorithms are used to identify excellent feeding cases and construct a multi-dimensional benchmark indicator system including growth rate, feed conversion rate, health status, and behavioral performance. Real-time collection of growth indicators such as animal weight changes, body size growth, health check results, and feeding behavior data during the current feeding cycle; and extraction of feature parameters corresponding to historical benchmark indicators through data cleaning and feature engineering to ensure consistency of data format and evaluation dimensions. The current growth indicators are compared with historical benchmark data item by item to identify areas for improvement and shortcomings in growth performance. The degree of deviation and significance level of each indicator are calculated through statistical analysis methods. At the same time, the time points and possible causes of the deviations are analyzed. Establish a comprehensive evaluation model, convert the comparative analysis results of each dimension into quantitative scores, set the weight coefficients of key evaluation indicators such as growth efficiency, nutrient utilization, health level and economic benefits, calculate the comprehensive effectiveness score of the current feeding program, and generate a detailed evaluation report. Based on the quantitative assessment results, targeted formula optimization suggestions and feeding management improvement measures are automatically generated. The assessment results and optimization suggestions are fed back to the quantum-inspired neural network and multi-objective genetic algorithm to update the knowledge base and decision-making model of the AI ​​system, providing empirical data support for the formulation of subsequent feeding programs.

[0037] This embodiment enables objective quantitative evaluation and continuous improvement of the feeding program's effectiveness. In-depth mining of historical data provides a reliable reference benchmark for evaluation. Multi-dimensional comparative analysis comprehensively reflects the actual effect of the feeding program. The quantitative scoring mechanism eliminates the bias of subjective judgment and effectively identifies key influencing factors in the feeding process. Automated optimization suggestion generation significantly improves the efficiency and scientific nature of program improvement. Continuous updates to the knowledge base ensure that the AI ​​system's learning ability and decision-making level are constantly improved. Compared with traditional experience-based evaluation methods, this embodiment realizes data-driven and intelligent upgrades in feeding management.

[0038] In some possible embodiments of the present invention, the physiological state vector is constructed using a multi-sensor data spatiotemporal fusion algorithm, which calculates a comprehensive physiological state index based on the following formula:

[0039] Where PSI represents the physiological state index; M is the total number of sensors; The weighting coefficient for the m-th sensor is dynamically adjusted using an adaptive Kalman filter. Let be the measurement value of the m-th sensor at time t; This is the health baseline value for the m-th sensor; Let m be the standard deviation of the measurement value of the m-th sensor; The coefficient of sensitivity to rate of change; It is the time derivative of the sensor measurement, reflecting the trend of physiological parameter changes.

[0040] This embodiment quantifies the deviation of each sensor's data from the health baseline using a Gaussian kernel function, while introducing a time derivative term to capture the dynamic changes in physiological state, thereby achieving a precise quantitative assessment of the animal's health status.

[0041] In some possible embodiments of the present invention, the nutrient requirement prediction of the quantum-inspired neural network employs a quantum entanglement nutrient correlation matrix, the formula for which this matrix is ​​calculated is:

[0042]

[0043] in, This represents the quantum entanglement state between the p-th and q-th nutrients; The angle of quantum entanglement between nutrients p and q; The phase angle reflects the directionality of synergistic or antagonistic effects of nutrients; and These are the quantum ground states, representing the independent nutrient states and the fully correlated nutrient states, respectively. This is a function for the strength of the nutrient association. R is a nonlinear adjustment parameter; R is the number of neurons in the hidden layer. and These are the connection weights from nutrients p and q to the r-th hidden layer neuron, respectively. It is an activation function based on a physiological state index.

[0044] This embodiment utilizes quantum superposition to simulate the complex nonlinear relationships between nutrients. Compared with traditional linear correlation models, it can capture the dynamic interactions of nutrients under different physiological states and improve the accuracy of nutrient demand prediction.

[0045] In some possible embodiments of the present invention, the recipe optimization of the multi-objective genetic algorithm adopts a dynamic penalty function and a supply chain risk assessment model, and its fitness function is:

[0046]

[0047]

[0048] in, The overall fitness function; , , The weighting coefficients for nutrition, cost, and environmental objectives are respectively adjusted dynamically by a fuzzy logic controller. The function is the nutritional balance. It is a cost-benefit function; It is an environmental friendliness function; To constrain violations and penalties; This is the supply chain risk assessment function; N is the total number of nutrient types. and These represent the required quantity and actual supply of the nth nutrient, respectively; S represents the number of raw material suppliers. Let the risk weight be that of the s-th supplier; The reliability degradation factor for supplier s; This is a historical stability indicator of cooperation with supplier S. The raw material price fluctuation coefficient of supplier S; This refers to the time delay in transportation.

[0049] This embodiment introduces a supply chain risk quantification model to effectively avoid supply chain disruption risks while ensuring nutritional balance. Compared with traditional static optimization methods, it can improve the actual executability and stability of the formulation scheme.

[0050] Please refer to Figure 2Another embodiment of the present invention provides an intelligent feeding system based on an AI dynamic formula engine for executing an intelligent feeding method based on an AI dynamic formula engine, comprising: a control platform and a multi-sensor array deployed in the breeding environment; The multi-sensor array is configured to collect physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency in real time. The control platform is configured as follows: A digital twin model of each individual animal is constructed by combining the physiological parameters. The digital twin model maps multi-source heterogeneous data into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

[0051] It should be known that, Figure 2 The block diagram of the intelligent feeding system based on an AI dynamic formula engine shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of this invention. The intelligent feeding system based on an AI dynamic formula engine provided in this embodiment can be used to execute various embodiments of the corresponding intelligent feeding method based on an AI dynamic formula engine. For specific implementation details, please refer to the descriptions of the respective method embodiments, which will not be repeated here.

[0052] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0053] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0054] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0055] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0056] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0057] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0059] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0060] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A smart feeding method based on an AI dynamic formula engine, characterized in that, include: By deploying a multi-sensor array in the breeding environment, real-time collection of physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency is carried out. A digital twin model of each individual animal is constructed by combining these physiological parameters. The digital twin model maps multi-source heterogeneous data into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

2. The intelligent feeding method based on an AI dynamic formula engine according to claim 1, characterized in that, The step of inputting the physiological state vector into a quantum-inspired neural network, which uses quantum entanglement to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals, and combines prior knowledge of animal breed, growth stage, and health status to output an individualized real-time nutritional requirement spectrum and generate a nutritional deficiency risk assessment matrix, includes: The physiological state vector is converted into a quantum state input format through normalization. At the same time, a prior knowledge encoding matrix is ​​constructed based on animal species, growth stage, and health status. The physiological data and prior knowledge are fused and mapped to obtain the first fused input data. Based on the five major nutritional categories of protein, fat, carbohydrate, vitamin, and mineral, a quantum entanglement network is constructed. The quantum superposition state is used to represent the three relationship states between different nutrients: synergistic promotion, mutual antagonism, and independent action, forming a dynamic nutrient association topology. The first fused input data is transmitted in multiple hidden layers of a quantum-inspired neural network. Each neuron updates the nutrient association weights based on the quantum entanglement mechanism. At the same time, the impact of the current physiological state on nutrient absorption efficiency is combined to achieve hierarchical reasoning of nutrient requirements. Based on the calculation results of the output layer of the quantum-inspired neural network, an individualized nutritional requirement spectrum is generated, which includes the specific requirements of various nutrients, the optimal intake time window, and the nutrient ratio relationship. The individualized nutritional requirement spectrum is dynamically adjusted according to the real-time physiological state of the individual. Based on a comparative analysis of individualized nutritional requirements and current feed supply, a nutritional deficiency risk assessment matrix is ​​constructed. This matrix quantifies the probability, severity, and potential impact on growth performance of each nutrient deficiency, providing risk warning information for subsequent formula optimization.

3. The intelligent feeding method based on an AI dynamic formula engine according to claim 2, characterized in that, Based on the nutritional requirement spectrum and the nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is activated to dynamically generate the optimal feed formula while satisfying nutritional balance, cost control, and raw material supply chain constraints. This formula optimization process incorporates a fuzzy logic controller to handle uncertainties such as raw material price fluctuations, inventory status, and transportation delays, including the following steps: Based on the aforementioned nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-dimensional optimization problem model is constructed with nutritional balance, cost-effectiveness, and supply chain stability as objectives. At the same time, nutrient content boundary constraints, raw material ratio restrictions, and production process constraints are set as hard constraints. The fuzzy logic controller is activated to monitor uncertain parameters such as raw material price fluctuations, inventory status, and transportation delays in real time. These continuously changing uncertain parameters are converted into discrete fuzzy linguistic variables through fuzzification, and then processed by the fuzzy inference rule base to output uncertainty adjustment factors for dynamically adjusting optimization weights. An initial formula population is generated based on the types and proportions of available raw materials. Each individual uses a real number to represent the proportion of each raw material. At the same time, the uncertainty adjustment factor output by the fuzzy logic controller is incorporated into the individual's fitness evaluation function to ensure that the population evolution direction adapts to the current market environment. The formulation population is iteratively evolved through genetic operations such as selection, crossover, and mutation. During the evolution process, the weight relationship between nutritional objectives, cost objectives, and supply chain objectives is dynamically balanced. The Pareto optimal solution set is screened using a non-dominated sorting method to form multiple candidate formulation schemes. The formula with the highest comprehensive score is selected from the Pareto optimal solution set as the current optimal solution. At the same time, a dynamic adjustment mechanism for the formula is established. When the fuzzy logic controller detects a significant change in the market environment, it automatically triggers the formula re-optimization process to ensure that the formula solution always adapts to the real-time changing external conditions.

4. The intelligent feeding method based on an AI dynamic formula engine according to claim 3, characterized in that, The step of controlling the intelligent feeding device to feed according to a feeding plan including time windows, feeding amounts, and feeding frequencies based on the optimal feed formula includes: The optimal feed formula is converted into control commands that can be recognized by the intelligent feeding equipment. Based on the ratio requirements of each raw material component in the formula and the characteristics of nutrient release sequence, the feeding task is decomposed into sub-tasks with different time windows and assigned to the corresponding feeding units for execution. Based on the animal's physiological rhythms, feeding habits, and nutrient absorption characteristics, the optimal feeding time window is dynamically planned, and the daily feeding plan is divided into multiple time periods. Within each time period, the specific feeding time and duration are determined according to the urgency of nutrient needs and digestion and absorption efficiency. The intelligent weighing system and flow control device are activated to accurately measure the feeding amount of various feed components according to the formula requirements. The multi-channel synchronous control mechanism ensures that different nutrients are added at the same time or in sequence according to the preset ratio, avoiding nutrient separation and uneven distribution. Based on the animal's current feeding status and digestive capacity, the feeding frequency and single feeding amount are dynamically adjusted. While ensuring that the total nutrient intake remains unchanged, the feeding frequency is optimized to improve nutrient absorption efficiency, and the load is balanced among multiple feeding points. During the feeding process, key parameters such as equipment operating status, feed flow rate, and feeding accuracy are monitored in real time. When abnormal situations such as equipment failure, feed blockage, or metering deviation are detected, emergency procedures are immediately activated to ensure the continuity and accuracy of the feeding task through backup feeding channels or by adjusting feeding parameters.

5. The intelligent feeding method based on an AI dynamic formula engine according to claim 4, characterized in that, The steps of using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, and transmitting feeding feedback data back to the AI ​​engine to form a closed-loop control system of prediction-execution-monitoring-adjustment include: By deploying a camera array in the feeding area, multi-angle image sequences of the animal feeding process are collected in real time. Deep learning algorithms are used to extract feeding behavior features such as the animal's head posture, mouth movements, and body position from the images, while also identifying the distribution and quantity changes of leftover feed in the feed trough. Based on feeding behavior characteristics, the animal feeding process is divided into different stages such as approach, sniffing, feeding, chewing, and leaving using a behavior pattern recognition algorithm. The duration, frequency, and intensity of each stage are quantitatively analyzed, and the volume and weight changes of leftover food are measured using image segmentation technology. The actual feeding data obtained by image recognition is compared and analyzed with the feeding plan to calculate key indicators such as feeding rate, feeding speed, and nutrient intake completion. Deviations such as abnormal feeding behavior, excessive feed leftovers, and insufficient nutrient intake are identified, and a detailed feeding effect evaluation report is generated. Feeding behavior data, leftover feed status, and effect evaluation results are converted into a standardized feedback data format through data preprocessing and feature engineering to obtain feedback data, which is then transmitted back in real time to the quantum-inspired neural network and the multi-objective genetic algorithm optimization engine to update the learning parameters and decision weights of the AI ​​model. Based on the analysis results of the feedback data, the AI ​​engine automatically determines whether it is necessary to adjust the quantum-inspired neural network, re-optimize the feed formula, or modify the feeding execution strategy. When a systematic deviation is detected, the entire process is recalibrated. When a local deviation is detected, targeted fine-tuning is performed, forming a complete prediction-execution-monitoring-adjustment closed-loop control mechanism.

6. The intelligent feeding method based on an AI dynamic formula engine according to claim 5, characterized in that, The steps of storing the formula data, feeding records, and animal response data for each feeding in an immutable manner using blockchain technology to build a complete breeding traceability chain include: The feed formulation data, feeding execution records, and feedback data are standardized and converted into a format. Each data block is assigned a unique digital identity and bound and encapsulated with the corresponding animal individual identifier, timestamp, and geographic location information. The packaged feeding data is used to generate a data fingerprint using the SHA-256 hash algorithm. A tamper-proof identifier is created for each data block using digital signature technology. At the same time, metadata such as data source, operator, and equipment information is encrypted to ensure the integrity and authenticity of the data. The encrypted feeding data is organized into data blocks according to the blockchain protocol specifications. Consensus verification is performed by multiple nodes in the distributed network. Proof-of-work or proof-of-authority mechanisms are used to ensure the legality of the data writing. The verified data blocks are linked in chronological order to form an immutable feeding record chain. Deploy smart contract programs to monitor key event nodes in the feeding process. When important events such as formula adjustment, feeding abnormalities, and changes in health status occur, the relevant data association index is automatically triggered to build a complete traceability graph from raw material procurement to the final product. Establish a standardized traceability data query interface to support data retrieval based on several dimensions, including individual animals, time range, feeding stage, and formula type. Display the complete feeding process trajectory through a visual interface, including information on the entire life cycle such as nutrient intake curves, changes in health status, and the impact of environmental factors.

7. The intelligent feeding method based on an AI dynamic formula engine according to claim 6, characterized in that, The steps of quantitatively evaluating the effectiveness of the feeding program by comparing and analyzing historical data with current growth indicators, and providing data support for subsequent formula optimization, include: Historical feeding records are extracted from the constructed blockchain traceability database. The data is stratified according to the animal breed, growth stage, and feeding environment. Data mining algorithms are used to identify excellent feeding cases and construct a multi-dimensional benchmark indicator system including growth rate, feed conversion rate, health status, and behavioral performance. Real-time collection of growth indicators such as animal weight changes, body size growth, health check results, and feeding behavior data during the current feeding cycle; through data cleaning and feature engineering, extracting feature parameters corresponding to historical benchmark indicators to ensure consistency of data format and evaluation dimensions. The current growth indicators are compared with historical benchmark data item by item to identify areas for improvement and shortcomings in growth performance. The degree of deviation and significance level of each indicator are calculated through statistical analysis methods. At the same time, the time points and possible causes of the deviations are analyzed. Establish a comprehensive evaluation model, convert the comparative analysis results of each dimension into quantitative scores, set the weight coefficients of key evaluation indicators such as growth efficiency, nutrient utilization, health level and economic benefits, calculate the comprehensive effectiveness score of the current feeding program, and generate a detailed evaluation report. Based on the quantitative assessment results, targeted formula optimization suggestions and feeding management improvement measures are automatically generated. The assessment results and optimization suggestions are fed back to the quantum-inspired neural network and multi-objective genetic algorithm to update the knowledge base and decision-making model of the AI ​​system, providing empirical data support for the formulation of subsequent feeding programs.

8. The intelligent feeding method based on an AI dynamic formula engine according to claim 7, characterized in that, The physiological state vector is constructed using a multi-sensor data spatiotemporal fusion algorithm, which calculates the comprehensive physiological state index based on the following formula: Where PSI represents the physiological state index; M is the total number of sensors; The weighting coefficient for the m-th sensor is dynamically adjusted using an adaptive Kalman filter. Let be the measurement value of the m-th sensor at time t; This is the health baseline value for the m-th sensor; Let m be the standard deviation of the measurement value of the m-th sensor; The coefficient of sensitivity to rate of change; It is the time derivative of the sensor measurement, reflecting the trend of physiological parameter changes.

9. The intelligent feeding method based on an AI dynamic formula engine according to claim 8, characterized in that, The quantum-inspired neural network predicts nutritional needs using a quantum entanglement-based nutritional correlation matrix, which is calculated using the following formula: in, This represents the quantum entanglement state between the p-th and q-th nutrients; The angle of quantum entanglement between nutrients p and q; The phase angle reflects the directionality of synergistic or antagonistic effects of nutrients; and These are the quantum ground states, representing the independent nutrient states and the fully correlated nutrient states, respectively. This is a function for the strength of the nutrient association. R is a nonlinear adjustment parameter; R is the number of neurons in the hidden layer. and These are the connection weights from nutrients p and q to the r-th hidden layer neuron, respectively. It is an activation function based on a physiological state index.

10. An intelligent feeding system based on an AI dynamic formula engine, used to execute the intelligent feeding method based on an AI dynamic formula engine as described in any one of claims 1 to 9, characterized in that, include: Control platform and multi-sensor array deployed in the aquaculture environment; The multi-sensor array is configured to collect physiological parameters such as animal weight, body temperature, activity level, feeding behavior, and excretion frequency in real time. The control platform is configured as follows: A digital twin model of each individual animal is constructed by combining the physiological parameters. The digital twin model maps multi-source heterogeneous data into a unified physiological state vector based on a time-series data fusion algorithm. The physiological state vector is input into a quantum-inspired neural network, which uses a quantum entanglement mechanism to simulate the complex relationships between nutrients including proteins, fats, carbohydrates, vitamins, and minerals. Combined with prior knowledge of animal breed, growth stage, and health status, it outputs an individualized real-time nutritional requirement spectrum and generates a nutritional deficiency risk assessment matrix. Based on the nutritional requirement spectrum and nutritional deficiency risk assessment matrix, a multi-objective genetic algorithm optimization engine is launched to dynamically generate the optimal feed formula under the premise of meeting nutritional balance, cost control and raw material supply chain constraints. Based on the optimal feed formula, the intelligent feeding equipment is controlled to feed according to a feeding plan that includes time windows, feeding amount, and feeding frequency; By using image recognition technology to monitor animal feeding behavior and uneaten feed in real time, the feeding feedback data is transmitted back to the AI ​​engine, forming a closed-loop control system of prediction-execution-monitoring-adjustment. The formula data, feeding records, and animal response data for each feeding are stored immutably using blockchain technology to build a complete breeding traceability chain; By comparing and analyzing historical data with current growth indicators, the effectiveness of the feeding program can be quantitatively evaluated, providing data support for subsequent formula optimization.

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