Lamb feeding scheme intelligent matching method and system based on growth condition prediction

Abstract

This application discloses an intelligent matching method and system for lamb feeding programs based on growth prediction, relating to the field of lamb feeding technology. The method includes: acquiring a set of growth parameters and a set of feeding behavior parameters for the target lamb; predicting growth status and acquiring feeding proficiency parameters and rumen development parameters; acquiring developmental synergy parameters to determine the lamb's developmental pattern; and intelligently matching a lamb feeding program based on the feeding proficiency parameters, rumen development parameters, and the lamb's developmental pattern. This solves the technical problems of traditional lamb feeding programs relying on experience-based judgment, which leads to strong subjectivity and significant lag, and the inability of standardized feeding patterns to meet the individualized needs of lambs.

Description

Technical Field

[0001] This application relates to the field of lamb feeding technology, specifically to a method and system for intelligent matching of lamb feeding programs based on growth prediction. Background Technology

[0002] With the continuous improvement of the level of intelligence in animal husbandry, lamb breeding, as an important part of the animal husbandry industry, has a key impact on the healthy growth and breeding efficiency of lambs through the scientific and precise management of its feeding.

[0003] Traditional lamb feeding programs rely heavily on the experience and judgment of farmers, which is highly subjective and outdated. It is difficult to make personalized adjustments based on the real-time growth dynamics and physiological development characteristics of individual lambs, resulting in a mismatch between the feeding program and the actual needs of the lambs, leading to problems such as insufficient feed intake, indigestion, or slow growth.

[0004] Meanwhile, lambs of different breeds and at different growth stages exhibit differences in feeding behavior and rumen development patterns. A standardized feeding model cannot meet the individual differences in needs, which not only affects the growth performance of lambs but may also lead to a waste of feed resources and an increase in breeding costs. Summary of the Invention

[0005] This application provides an intelligent matching method and system for lamb feeding programs based on growth prediction, which solves the technical problems of traditional lamb feeding programs relying on experience-based judgment, resulting in strong subjectivity and significant lag, as well as the inability of standardized feeding patterns to meet individual differences.

[0006] The technical solution to the above-mentioned technical problems in this application is as follows:

[0007] In a first aspect, this application provides an intelligent matching method for lamb feeding programs based on growth prediction, the method comprising:

[0008] Obtain the set of growth parameters and feeding behavior parameters of the target lambs;

[0009] Based on the growth status parameter set and the feeding behavior parameter set, growth status is predicted to obtain feeding proficiency parameters and rumen development parameters.

[0010] Based on the feeding proficiency parameters and the rumen development parameters, developmental synergy parameters are obtained to determine the lamb development pattern.

[0011] Based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns, an intelligent lamb feeding program is matched.

[0012] Secondly, this application provides an intelligent matching system for lamb feeding programs based on growth prediction, including:

[0013] The data acquisition module is used to obtain a set of growth parameters and a set of feeding behavior parameters of the target lambs;

[0014] The growth prediction module is used to predict growth based on the growth parameter set and the feeding behavior parameter set, and to obtain feeding proficiency parameters and rumen development parameters.

[0015] The developmental pattern determination module is used to obtain developmental synergy parameters based on the feeding proficiency parameters and the rumen development parameters, and to determine the lamb's developmental pattern.

[0016] The feeding program matching module is used to intelligently match lamb feeding programs based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns.

[0017] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0018] This application provides an intelligent matching method and system for lamb feeding programs based on growth prediction. First, it acquires the target lamb's growth parameter set and feeding behavior parameter set in real time, providing a data foundation for subsequent analysis. Second, the growth prediction module calls a growth predictor trained on lamb breeds to predict feeding proficiency parameters (characterizing the lamb's feeding behavior ability) and rumen development parameters (reflecting the rumen's physiological development status), achieving a scientific assessment of the lamb's growth potential. Third, it calculates the difference between the feeding proficiency parameter and the rumen development parameter as a developmental synergy parameter, and combines this with the breed-specific synergy threshold to classify lambs into different developmental patterns such as synergistic development, feeding-leading, or rumen-leading, clearly defining individual differences. Finally, the feed intake weight and rumen development weight are dynamically adjusted, and the weights are corrected by combining the coefficient of variation of feed intake to calculate the comprehensive development parameters. In the constructed lamb feeding program library, standard programs with matching parameter ranges are prioritized for matching. If no direct matching item exists, the feed intake matching degree and rumen matching degree are calculated by weighting from the candidate program set, and the program with the highest comprehensive matching degree is selected as the final feeding strategy. This realizes intelligent matching of the entire process from individual growth data to precise feeding programs, improves the scientific and personalized level of lamb feeding, and improves feed utilization and breeding efficiency.

[0019] Through the above technical solutions, this application integrates multi-dimensional growth and feeding data of lambs, and constructs a complete technical closed loop from parameter collection, growth prediction to pattern judgment and program matching, to ensure that the feeding program is always optimally matched with the current developmental state of the lambs. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the intelligent matching method for lamb feeding programs based on growth prediction provided in this application embodiment;

[0022] Figure 2 This is a schematic diagram of the intelligent matching system for lamb feeding programs based on growth prediction provided in this application embodiment.

[0023] The components represented by each number in the attached diagram are explained below:

[0024] Data acquisition module 11, growth prediction module 12, development pattern judgment module 13, feeding program matching module 14. Detailed Implementation

[0025] This application provides a method and system for intelligent matching of lamb feeding programs based on growth prediction, which addresses the technical problems of traditional lamb feeding programs relying on experience-based judgment, resulting in strong subjectivity and significant lag, as well as the inability of standardized feeding patterns to meet the individual differences in needs.

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] Example 1, as Figure 1 As shown in the embodiments of this application, an intelligent matching method for lamb feeding programs based on growth prediction is provided, including:

[0030] S10: Obtain the set of growth parameters and feeding behavior parameters of the target lamb;

[0031] In this embodiment, a multi-source sensor network is first deployed to collect real-time data on the growth and feeding behavior parameters of the target lambs, followed by data preprocessing. Preprocessing includes data cleaning, outlier removal, and standardization transformation to ensure data quality.

[0032] Among them, the growth parameter set is collected by infrared ranging sensor and electronic scale at preset time intervals; the feeding behavior parameter set is collected by pressure sensor installed at the bottom of the feed trough and RFID identification device to record the individual feeding trajectory of each lamb.

[0033] Specifically, step S10 in the method includes:

[0034] Obtain the birth date and birth weight of the target lamb;

[0035] Based on the lamb breed of the target lamb, obtain the weight monitoring time interval, conduct phased weight monitoring, and obtain the daily weight gain parameter sequence;

[0036] By integrating the birth date, birth weight, and daily weight gain parameter sequence, the growth parameter set is obtained;

[0037] Monitor and obtain the date of the first feeding of the target lambs, and calculate the age at the first feeding;

[0038] Starting from the date of the first feeding, feeding behavior was monitored to obtain a sequence of lamb feed intake parameters;

[0039] By integrating the first feeding age and the lamb feed intake parameter sequence, the feeding behavior parameter set is obtained.

[0040] In this embodiment of the application, firstly, the birth date and birth weight of the target lamb are collected as basic growth data to construct an initial framework for the growth parameter set.

[0041] Secondly, based on the breed characteristics of the target lambs, a preset breed-monitoring interval mapping table is invoked to determine the personalized weight monitoring time interval. For example, for early-maturing breeds, it can be set to daily monitoring, and for late-maturing breeds, it can be set to monitor once every two days. Through phased weight monitoring, a daily weight gain parameter sequence is generated, which includes the daily weight gain value and the cumulative weight gain trend.

[0042] Furthermore, the birth date, birth weight, and daily weight gain parameter sequences are structurally integrated to form a growth parameter set that includes both time and weight dimensions, providing a complete longitudinal growth data chain for subsequent growth prediction.

[0043] Furthermore, in the acquisition of the feeding behavior parameter set, the first feeding behavior of the target lambs is captured by a video monitoring system linked with the feed trough sensors. The date of the first feeding is recorded, and the age at first feeding is calculated, i.e., the number of days between the birth date and the first feeding date. Using the first feeding date as the monitoring starting point, a continuous feeding behavior monitoring process is initiated. Using high-precision weighing sensors and individual identification devices installed in the feed trough, the start time, end time, feed intake, and feeding frequency of each feeding are recorded, generating a lamb feed intake parameter sequence that includes daily feed intake, maximum single feed intake, and feeding duration distribution.

[0044] Finally, the age at first feeding is used as the starting point of feeding behavior. It is then spatiotemporally aligned and integrated with the lamb feed intake parameter sequence to form a set of feeding behavior parameters that covers the feeding initiation time and dynamic feeding patterns. This enables the collection of full-cycle growth and feeding data for lambs from birth to the stable feeding period.

[0045] S20: Based on the growth status parameter set and the feeding behavior parameter set, perform growth status prediction and obtain feeding proficiency parameters and rumen development parameters;

[0046] In this embodiment, a growth predictor trained on lamb breeds is invoked, and the preprocessed set of growth parameters and feeding behavior parameters are input into the predictor. This predictor employs an LSTM neural network architecture, trained using historical data to learn the dynamic correlation between lamb growth and feeding behavior.

[0047] Among them, the feeding proficiency parameter is calculated by comprehensively analyzing the fluctuation range of the feed intake parameter sequence, the stability of the feeding duration, and the trend of the feeding frequency change, reflecting the lamb's feeding adaptability and behavioral coordination; the rumen development parameter is derived based on the correlation analysis between the daily weight gain parameter sequence and the feed intake parameter sequence, combined with the breed-specific rumen development model, characterizing the establishment of the rumen microbiota and the maturity of digestive and metabolic functions.

[0048] During the prediction process, time-series features are extracted from the input parameters to capture the long-term dependencies between growth and feeding data. The prediction results are optimized through multiple rounds of iteration to ensure the prediction accuracy of feeding proficiency parameters and rumen development parameters.

[0049] Specifically, step S20 in the method includes:

[0050] Based on lamb breeds, obtain a growth prediction tool;

[0051] The growth parameter set and the feeding behavior parameter sequence are input into the growth predictor to obtain the feeding proficiency parameter and the rumen development parameter.

[0052] In this embodiment, firstly, based on the breed information of the target lamb, a growth predictor for the corresponding breed is called from a pre-built predictor library. This predictor is generated through training on historical growth data and feeding behavior data of lambs of the same breed, and includes a breed-specific growth curve model and feeding behavior characteristic parameters.

[0053] Secondly, the daily weight gain parameter sequence in the growth status parameter set is subjected to time series standardization, converting the weight gain data at different stages into dimensionless growth indices. Simultaneously, the feed intake data in the feeding behavior parameter sequence is smoothed using a sliding window to eliminate short-term fluctuations. The standardized growth status parameter set and the preprocessed feeding behavior parameter sequence are then used as input vectors and synchronously fed into the input layer of the growth status predictor.

[0054] Furthermore, the LSTM network layer of the predictor captures the dynamic temporal correlation features of growth and feeding data through gated recurrent units. During the forward propagation, the hidden layer neurons perform weighted calculations on the feature vectors according to the preset activation thresholds of the variety. The output layer maps the features to the feeding proficiency probability distribution and rumen development status score through the softmax function.

[0055] Finally, the maximum probability value in the probability distribution is extracted as the feeding proficiency parameter to quantify the coordination of lamb feeding behavior. The value range of the feeding proficiency parameter can be defined as from 0 to 1, indicating no contact with skilled and stable feeding. After calibrating the rumen development status score through the conversion coefficient corresponding to the breed, the rumen development parameter is obtained, which reflects the maturity level of rumen physiological function.

[0056] Among them, the growth prediction tool based on lamb breed includes:

[0057] Based on the lamb breed, obtain a set of parameters for sample growth and a set of parameters for sample feeding behavior;

[0058] Obtain the sample feeding proficiency parameters and sample rumen development parameters corresponding to the sample growth parameter set and sample feeding behavior parameter set, wherein the sample feeding proficiency parameters are used to characterize the lamb's feeding behavior proficiency, and the sample rumen development parameters are used to characterize the lamb's rumen development.

[0059] The growth prediction tool is trained until convergence using the sample growth parameter set and the sample feeding behavior parameter set as inputs, and the sample feeding proficiency parameter and the sample rumen development parameter as supervision.

[0060] In this embodiment, firstly, for a specific lamb breed, growth records and feeding behavior data of healthy lambs of the same breed are selected from a historical breeding database to construct a sample growth parameter set and a sample feeding behavior parameter set. The sample growth parameter set includes weight changes, daily weight gain curves, and body size indicators at different growth stages; the sample feeding behavior parameter set includes behavioral characteristic data such as feeding frequency, single feeding amount, and feeding duration distribution.

[0061] Secondly, physiological tests were conducted on the sample lambs using an expert system. Rumen pH monitoring, microbial community analysis, and feeding behavior observation were used to calibrate feeding proficiency parameters and rumen development parameters. The feeding proficiency parameters were determined by a comprehensive score based on feeding action coordination, feeding interruption frequency, and feed intake stability. The rumen development parameters were quantified by combining rumen volume measurements, volatile fatty acid concentration detection, and digestibility test results.

[0062] Furthermore, the expert system is a decision support system jointly built by animal nutrition experts, ruminant physiologists, and breeding technicians. It integrates physiological indicator thresholds, typical feeding behavior characteristics, and corresponding feeding adjustment strategies for lambs of different breeds at various growth stages. The expert system stores domain knowledge in the form of a knowledge graph, where nodes include lamb breed, developmental stage, key physiological parameters, etc., and edges represent causal relationships and adjustment rules between parameters. When it is necessary to label sample parameters, the system first matches the breed and age of the sample lamb, calls the standard parameter range for the corresponding growth stage, and then combines actual test data with fuzzy inference algorithms in the expert experience rule base to output sample feeding proficiency parameters and rumen development parameters that conform to the actual situation of the individual, providing supervised data for the training of the growth prediction machine.

[0063] Subsequently, the growth status parameter set and the feeding behavior parameter set of the samples were used as input layer data, and the feeding proficiency parameter and the rumen development parameter of the samples were used as output layer supervision labels to construct a growth status predictor with an LSTM neural network structure. During model training, the Adam optimizer was used to dynamically adjust the learning rate, and the deviation between the predicted values ​​and the true labels was calculated using the mean squared error loss function to iteratively update the network weights. Simultaneously, an early stopping mechanism was introduced: training was terminated when the validation set loss no longer decreased after a preset number of consecutive rounds, saving the optimal model parameters and ensuring that the growth status predictor had good fitting ability and generalization performance for the growth characteristics of specific breeds of lambs.

[0064] For example, a growth prediction predictor is built and trained based on an LSTM neural network, and the steps are as follows:

[0065] First, data preparation involves collecting a set of parameters related to sample growth and feeding behavior, based on a historical aquaculture database.

[0066] Secondly, in model construction, the number of nodes in the input layer is equal to the dimension of the input features. For example, if there are 6 features such as weight change, daily weight gain curve and body size index, feeding frequency, single feeding amount, and feeding duration distribution, then the input layer contains 6 nodes. Set 1-3 hidden layers, and adjust the number of nodes in each layer through experiments, such as 64, 32, etc. The activation function is ReLU. The output layer generally does not use an activation function. If the output takes 2 nodes, directly output continuous values.

[0067] Next, the model is trained, and the predicted feeding proficiency parameters and rumen development parameters are used as outputs. The sample feeding proficiency parameters and the sample rumen development parameters are used as supervision labels. The Adam optimizer and mean squared error loss function are used to construct the training framework. The batch size is set to 32 and the total number of training rounds is 50. An early stopping mechanism with a patience of 5 is introduced. When the validation set loss does not decrease for 5 consecutive rounds, the training process is automatically terminated, resulting in a trained growth predictor. This effectively avoids model overfitting while ensuring that the model reaches a convergent state.

[0068] S30: Based on the feeding proficiency parameters and the rumen development parameters, obtain developmental synergy parameters to determine the lamb development pattern;

[0069] In this embodiment of the application, a developmental synergy assessment matrix is ​​constructed to perform a two-dimensional coupled analysis of feeding proficiency parameters and rumen development parameters.

[0070] Specifically, the two parameters are first standardized to eliminate dimensional differences, mapping them to the [0,1] interval. Then, their synergy coefficient is calculated, obtained by a weighted fusion of the Pearson correlation coefficient and the dynamic time warping distance. The correlation coefficient characterizes the degree of linear association, while the dynamic time warping distance reflects the degree of nonlinear dynamic matching.

[0071] The difference between the feeding proficiency parameter and the rumen development parameter is calculated as a developmental synergy parameter;

[0072] Based on lamb breeds, obtain threshold values ​​for developmental synergy parameters;

[0073] Based on the developmental synergy parameters and the threshold values ​​of the developmental synergy parameters, the developmental pattern of the target lamb is determined, wherein the developmental pattern includes synergistic development, feeding-leading, and rumen-leading.

[0074] In this embodiment, firstly, the absolute difference between the feeding proficiency parameter and the rumen development parameter is calculated, and this difference is defined as the developmental synergy parameter to quantify the degree of matching between the two. The smaller the difference, the higher the synergy between feeding behavior and rumen development; the larger the difference, the lower the synergy.

[0075] For example, if the feeding proficiency parameter is 0.65 and the rumen development parameter is 0.62, the difference is 0.03, and the developmental synergy parameter is 0.03; if the feeding proficiency parameter is 0.4 and the rumen development parameter is 0.65, the difference is 0.25, and the developmental synergy parameter is 0.25.

[0076] Subsequently, based on the breed information of the target lambs, the corresponding developmental synergy parameter threshold ranges are retrieved from a pre-constructed breed-developmental threshold database. For example, the synergy parameter threshold for meat breeds is typically set at 0.2, while the threshold for dual-purpose (milk and meat) breeds may be relaxed to 0.25. This threshold range is determined through statistical analysis of historical data from a large number of healthy lambs of the same breed, combined with experience, and includes threshold ranges for synergistic development, feed-leading behavior, and rumen-leading behavior.

[0077] Finally, the calculated developmental synergy parameters are compared with the retrieved threshold range. If the developmental synergy parameters are within the threshold range for synergistic development, it indicates that the feeding proficiency and rumen development level are basically matched, and the target lamb is determined to be synergistic. If the developmental synergy parameters are less than the lower limit of the synergistic development threshold, and the feeding proficiency parameters are significantly higher than the rumen development parameters, it is determined to be feeding-leading, indicating that feeding ability is ahead of the rumen development process. If the developmental synergy parameters are greater than the upper limit of the synergistic development threshold, and the rumen development parameters are significantly higher than the feeding proficiency parameters, it is determined to be rumen-leading, indicating that rumen development has a certain foundation but feeding ability has not yet caught up.

[0078] For example, if the developmental synergy parameter is 0.15, which is within the threshold range of synergistic development [0.1, 0.2], it is determined to be synergistic developmental; if the developmental synergy parameter is 0.08, the feeding proficiency parameter is 0.7, and the rumen development parameter is 0.62, which is less than the lower threshold of 0.1, and the feeding proficiency parameter is higher than the rumen development parameter, it is determined to be feeding-leading; if the developmental synergy parameter is 0.28, the rumen development parameter is 0.68, and the feeding proficiency parameter is 0.4, which is greater than the upper threshold of 0.2, and the rumen development parameter is higher than the feeding proficiency parameter, it is determined to be rumen-leading.

[0079] S40: Based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns, intelligently match lamb feeding programs.

[0080] In this embodiment of the application, a multi-dimensional feeding program matching model is constructed based on different lamb development patterns.

[0081] For lambs with coordinated development, a balanced nutrition supply strategy is adopted. The feeding program focuses on maintaining a dynamic balance between feeding proficiency and rumen development. A complete compound feed with an appropriate ratio of energy and protein is selected. The number of feedings per day is controlled at 4-5 times, and the amount of each feeding is dynamically adjusted according to the daily weight gain parameter sequence to ensure that nutritional intake and growth needs are synchronized.

[0082] For leading-feeding lambs, the focus is on optimizing rumen development support by adding rumen microbial activators and fiber-degrading enzymes to the feed formula, reducing the proportion of concentrate feed and increasing the supply of high-quality roughage, extending feeding time to promote rumen motility, and setting up a phased feeding restriction mechanism to avoid overfeeding and excessive burden on the rumen. The rumen pH and volatile fatty acid concentration are monitored weekly, and the feeding rhythm is fine-tuned based on the test results.

[0083] For rumen-leading lambs, we strengthen their feeding ability training by adopting a feed transition program that gradually increases palatability, transitioning from high-moisture, juicy feed to compound feed with higher dry matter content. We guide lambs to increase feeding frequency through trough zoning design, conduct 5-10 minutes of feeding behavior guidance before each feeding, and combine feeding reward mechanism to improve feeding enthusiasm. At the same time, we regularly collect feed intake parameter sequence analysis to analyze the effect of feeding behavior improvement and adjust the feed form and feeding frequency in the feeding program in real time.

[0084] Specifically, step S40 in the method includes:

[0085] Based on the developmental synergy parameters, the feeding weight and rumen development weight are obtained, and the feeding proficiency parameters and rumen development parameters are weighted and calculated to obtain comprehensive developmental parameters.

[0086] Based on the lamb breed and the aforementioned comprehensive developmental parameters, intelligent matching is performed in the lamb feeding program database to obtain a lamb feeding program.

[0087] In this embodiment, firstly, the feed intake weight and rumen development weight are dynamically allocated based on the developmental synergy parameter. When the developmental synergy parameter is within the synergistic development threshold range, both the feed intake weight and the rumen development weight are set to 0.5 to achieve balanced weighting. If it is a feed-leading type, the rumen development weight is increased to 0.6 and the feed intake weight is decreased to 0.4 to highlight the optimization requirements of rumen development. If it is a rumen-leading type, the feed intake weight is increased to 0.6 and the rumen development weight is decreased to 0.4 to strengthen the training priority of feed intake ability.

[0088] Then, the weighted feeding proficiency parameter and the rumen development parameter are multiplied and summed to obtain the comprehensive development parameter, which can be calculated as: "Comprehensive Development Parameter = (Feeding Proficiency Parameter × Feeding Weight) + (Rumen Development Parameter × Rumen Development Weight)". This parameter comprehensively reflects the overall development level of the two core systems of feeding and digestion during lamb growth, providing a quantitative basis for the precise matching of feeding programs.

[0089] Subsequently, based on the breed information of the target lambs and the calculated comprehensive developmental parameters, a multi-condition search is performed in a pre-constructed lamb feeding program library. The feeding program library contains subsets of programs categorized by breed. The programs within each subset are indexed according to the comprehensive developmental parameter range. Each range corresponds to a complete set of feeding parameters, including feed formulation, feeding frequency, nutritional supplementation strategies, and behavioral training programs.

[0090] Furthermore, through a fuzzy matching algorithm, the scheme that is consistent with the target lamb breed and has the closest comprehensive development parameters is prioritized. The feeding amount in the scheme is dynamically calibrated according to the daily weight gain trend of the current growth parameter set, and finally a personalized lamb feeding scheme is output.

[0091] Specifically, based on the lamb development pattern, feed intake weight and rumen development weight are obtained. The feed intake proficiency parameter and the rumen development parameter are then weighted and calculated to obtain comprehensive development parameters, including:

[0092] Based on the aforementioned developmental synergy parameters, the feeding weights are obtained;

[0093] The set of feeding behavior parameters is used to obtain the coefficient of variation of feeding amount.

[0094] Based on the coefficient of variation of feed intake, the feed intake weight is corrected to obtain the corrected feed intake weight.

[0095] Calculate 1 and subtract the corrected intake weight to obtain the corrected rumen weight;

[0096] The feeding proficiency parameter and the rumen development parameter are weighted and calculated using the modified feeding weight and the modified rumen weight to obtain the comprehensive development parameter.

[0097] In this embodiment of the application, firstly, the basic feeding weight is preliminarily determined based on the developmental synergy parameter. For example, the basic feeding weight is 0.5 for the synergistic development type, 0.4 for the feeding-leading type, and 0.6 for the rumen-leading type.

[0098] Secondly, feed intake data are extracted from the feed intake behavior parameter set, and the coefficient of variation of feed intake is calculated. This coefficient is obtained by the ratio of the standard deviation of feed intake to the mean, reflecting the degree of fluctuation in feed intake. The larger the coefficient of variation, the more unstable the lamb's feed intake behavior is, and the feed intake weight should be appropriately reduced in the weight adjustment; conversely, the smaller the coefficient of variation, the more stable the feed intake behavior is, and the feed intake weight can be maintained or slightly increased.

[0099] Next, a mapping relationship is established between the coefficient of variation of feed intake and the weight correction coefficient. For example, when the coefficient of variation is less than 0.1, the correction coefficient is 1.05, which means the weight increases by 5%; when the coefficient of variation is between 0.1 and 0.2, the correction coefficient is 1.0; and when the coefficient of variation is greater than 0.2, the correction coefficient is 0.95, which means the weight decreases by 5%. The basic feed intake weight is multiplied by the correction coefficient to obtain the corrected feed intake weight, and then 1 is subtracted from the corrected feed intake weight to obtain the corrected rumen weight.

[0100] Finally, the modified feeding weight is multiplied by the feeding proficiency parameter, and the modified rumen weight is multiplied by the rumen development parameter. The sum of the two is the comprehensive development parameter, which not only reflects the dominant direction of the developmental pattern, but also reflects the influence of the stability of feeding behavior on the overall developmental level.

[0101] For example, if the target lamb is rumen-leading, its basic feed intake weight is 0.6. Assuming the coefficient of variation of feed intake calculated from the feed intake behavior parameter set is 0.15, falling within the range of 0.1-0.2, and the corresponding correction coefficient is 1.0, then the corrected feed intake weight is still 0.6 × 1.0 = 0.6, and the corrected rumen weight is 1 - 0.6 = 0.4. If the lamb's feed intake proficiency parameter is 0.45 and its rumen development parameter is 0.7, then the overall development parameter = (0.45 × 0.6) + (0.7 × 0.4) = 0.27 + 0.28 = 0.55.

[0102] The coefficient of variation for feed intake of another rumen-leading lamb is 0.25, which is greater than 0.2. The correction factor is 0.95. Its basic feed intake weight of 0.6 is corrected to 0.6×0.95=0.57. The corrected rumen weight is 1-0.57=0.43. When the feed intake proficiency parameter is 0.5 and the rumen development parameter is 0.68, the comprehensive development parameter = (0.5×0.57)+(0.68×0.43)=0.285+0.2924=0.5774.

[0103] Furthermore, the construction of the lamb feeding protocol library includes:

[0104] Based on the lamb breed, historical feeding data is obtained, wherein the historical feeding dataset includes a set of historical growth parameters, a set of historical feeding behavior parameters, corresponding historical feeding operation records, and corresponding historical feeding result parameters.

[0105] Statistical analysis was performed on the historical feeding data to identify the historical feeding operation record corresponding to the maximum historical feeding result parameter under the same historical growth parameter set and historical feeding behavior parameter set, which was then used as an effective feeding pattern.

[0106] The effective feeding pattern is structured as a standard feeding program, and each standard feeding program is configured with associated tag information and parameter range. The tag information includes lamb breed and development pattern, and the parameter range includes comprehensive development parameter range, feeding proficiency parameter range and rumen development parameter range.

[0107] All the standard feeding protocols and their associated tag information and parameter ranges are stored in a structured manner to form the lamb feeding protocol library.

[0108] In this embodiment, historical feeding data of lambs of different breeds are first collected through multiple data acquisition channels, including growth monitoring records from the farm management system, feeding behavior logs collected by intelligent feeding equipment, rumen development indicators and growth performance evaluation reports from laboratory tests, ensuring that the data covers the complete growth cycle of lambs from weaning to fattening. The collected raw data is preprocessed to remove outliers and missing values, and continuous parameters are standardized to unify the data units to meet the needs of subsequent analysis.

[0109] Secondly, the preprocessed historical data was grouped by lamb breed. Within each group, feature combinations were constructed based on historical growth parameter sets and historical feeding behavior parameter sets. The K-means clustering algorithm was used to cluster samples with similar feature combinations into several growth-feeding feature clusters. Correlation analysis was performed on the historical feeding operation records and corresponding historical feeding result parameters within each feature cluster. By calculating the Pearson correlation coefficient, key feeding operation variables with significant impact on feeding results were identified. For example, the correlation coefficient between the concentrate-to-roughage ratio and daily weight gain was r=0.78, P<0.01, and these were determined as core control parameters.

[0110] Furthermore, within each feature cluster, using the maximum historical feeding result parameter, such as the highest daily weight gain or optimal feed conversion ratio, as the objective function, all historical feeding operation records within that cluster are traversed to select the feeding operation combination that optimizes the objective function, and these combinations are marked as effective feeding patterns. For example, in a certain feature cluster for meat-type lambs, when the concentrate-to-roughage ratio is 6:4, 0.3% rumen extract is added, and feeding is done four times a day, the daily weight gain reaches the maximum value of 280g / d; this combination is then identified as the effective feeding pattern corresponding to that feature cluster.

[0111] Then, the effective feeding pattern is defined in a structured way, breaking it down into a feed formulation module, a feeding management module, and a monitoring and feedback module. Each module includes specific parameter value ranges, such as the proportion of corn in the concentrate feed being 30%-40%. At the same time, tag information is added to each standard feeding program, including lamb breed, developmental pattern, and corresponding comprehensive development parameter ranges, such as 0.5-0.6, feeding proficiency parameter range of 0.45-0.55, and rumen development parameter range of 0.5-0.6, to achieve multi-dimensional indexing of the program.

[0112] Finally, a relational database was used to construct the physical storage structure of the feeding protocol library. A three-level directory architecture of "breed-developmental pattern-feature cluster" was designed to store structured standard feeding protocols and their tag information and parameter ranges according to the directory hierarchy. An index table of parameter ranges was also established to support rapid queries based on comprehensive developmental parameters, feeding proficiency, and other conditions. Simultaneously, a protocol update mechanism was set up to regularly import new feeding data, such as updating quarterly. Through incremental clustering and effective feeding pattern re-screening, the existing protocol library was dynamically updated to ensure the timeliness and applicability of the protocols in the library.

[0113] Specifically, based on the lamb breed and the aforementioned comprehensive developmental parameters, intelligent matching is performed in the lamb feeding program database to obtain a lamb feeding program, which also includes:

[0114] When there is no standard feeding program in the lamb feeding program library whose parameter range matches the developmental synergy parameter, an alternative feeding program set is obtained based on the lamb breed and the developmental pattern.

[0115] Calculate the matching degree between the range of feeding proficiency parameters and the range of rumen development parameters of the alternative feeding schemes and the range of feeding proficiency parameters and rumen development parameters of the target lamb in the set of alternative feeding schemes, and obtain the feeding matching degree and rumen matching degree.

[0116] Based on the corrected intake weight and the corrected rumen weight, the intake matching degree and the rumen matching degree are weighted and calculated to obtain the comprehensive matching degree;

[0117] The lamb feeding program is selected based on the highest overall matching degree from the pool of alternative feeding programs.

[0118] In this embodiment, firstly, when the system finds no standard feeding program in the lamb feeding program library that directly matches the current developmental synergy parameters, it automatically triggers the alternative program matching process. Based on the breed information of the target lamb and the determined developmental pattern, such as feed-leading or rumen-leading, all historical feeding programs of the same breed and developmental pattern are retrieved from the program library to form an alternative feeding program set. For example, for a feed-leading lamb of the Small-tailed Han breed, the alternative program set will include all historically effective feed programs of the feed-leading type of that breed, even if their overall developmental parameter range differs from that of the current lamb.

[0119] Secondly, the parameter matching degree between the alternative plans and the target lamb is calculated. For each plan in the alternative plan set, its preset feeding proficiency parameter range and rumen development parameter range are extracted. The feeding matching degree between the actual feeding proficiency parameter of the target lamb and the plan range, and the rumen matching degree between the actual rumen development parameter and the plan range are calculated by the interval similarity algorithm.

[0120] Specifically, if the target parameter value is in the central region of the proposed solution range, the matching degree is 1.0; if it is at the boundary of the range, the matching degree is 0.5; if it exceeds the range, the matching degree is linearly reduced according to the excess ratio, with a minimum of 0.3. For example, the feeding proficiency parameter range of one alternative solution is [0.6, 0.8], and the actual parameter of the target lamb is 0.75, which is in the central region, so the feeding matching degree is 1.0; another alternative solution has a range of [0.5, 0.7], and the target parameter 0.75 exceeds the upper limit by 0.05, with an excess ratio of 0.05 / (0.7-0.5)=0.25, so the matching degree is 0.5-0.25×0.2=0.45.

[0121] Next, based on the calculated adjusted feed intake weight and adjusted rumen weight, the feed intake matching degree and rumen matching degree are weighted and summed to obtain the comprehensive matching degree of each alternative. The calculation formula is: Comprehensive matching degree = Feed intake matching degree × Adjusted feed intake weight + Rumen matching degree × Adjusted rumen weight.

[0122] For example, if the feed intake matching degree of a certain alternative is 0.85, the rumen matching degree is 0.7, the adjusted feed intake weight is 0.4, and the adjusted rumen weight is 0.6, then the overall matching degree = 0.85 × 0.4 + 0.7 × 0.6 = 0.34 + 0.42 = 0.76.

[0123] Finally, the overall matching degree of all options in the candidate set was sorted in descending order, and the option with the highest matching degree was selected as the recommended feeding program. If multiple options have the same overall matching degree, the historical application effects of the options were further compared, and the option that achieved higher daily weight gain or lower feed conversion ratio in the same breed of lambs was given priority.

[0124] Furthermore, after selecting a plan, the system automatically generates plan adjustment suggestions, prompting users to make minor adjustments to the parameters in the feed formula within a range of ±5% based on the current daily weight gain trend and feeding behavior stability of the lambs. A 3-day feeding effect monitoring period is set to collect real-time data such as feed intake and rumen pH value to verify the suitability of the plan.

[0125] In summary, compared to existing technologies, this application achieves precise quantification of comprehensive developmental parameters by incorporating lamb developmental patterns and feeding behavior stability into a weighted dynamic correction mechanism, overcoming the limitations of traditional feeding programs that rely solely on breed or a single growth indicator. Simultaneously, by constructing a multi-dimensional indexed feeding program library and a fuzzy matching algorithm, combined with real-time dynamic calibration of growth parameters, a closed-loop intelligent decision-making system is formed from developmental assessment to program generation, significantly improving the accuracy of matching feeding programs with the individual growth needs of lambs.

[0126] In summary, the embodiments of this application have at least the following technical effects:

[0127] This application provides an intelligent matching method for lamb feeding programs based on growth prediction. First, it acquires the target lamb's growth parameter set and feeding behavior parameter set in real time, providing a data foundation for subsequent analysis. Second, the growth prediction module calls a growth predictor trained on lamb breeds to predict feeding proficiency parameters (characterizing the lamb's feeding behavior ability) and rumen development parameters (reflecting the rumen's physiological development status), achieving a scientific assessment of the lamb's growth potential. Third, it calculates the difference between the feeding proficiency parameter and the rumen development parameter as a developmental synergy parameter, and combines this with the breed-specific synergy threshold to classify lambs into different developmental patterns such as synergistic development, feeding-leading, or rumen-leading, clearly defining individual differences. Finally, the feed intake weight and rumen development weight are dynamically adjusted, and the weights are corrected by combining the coefficient of variation of feed intake before calculating the comprehensive development parameters. In the constructed lamb feeding program library, standard programs with matching parameter ranges are prioritized for matching. If no direct match exists, the feed intake matching degree and rumen matching degree are calculated by weighting from the candidate program set, and the program with the highest comprehensive matching degree is selected as the final feeding strategy. This achieves intelligent matching of the entire process from individual growth data to precise feeding programs, improving the scientific and personalized level of lamb feeding, while also increasing feed utilization and breeding efficiency. Through the above technical solution, this application integrates multi-dimensional growth and feed intake data of lambs, constructing a complete technical closed loop from parameter collection, growth prediction to pattern judgment and program matching, ensuring that the feeding program is always optimally adapted to the current developmental state of the lamb.

[0128] Example 2, as Figure 2As shown, based on the same inventive concept as the intelligent matching method for lamb feeding programs based on growth prediction provided in Embodiment 1, this application also provides an intelligent matching system for lamb feeding programs based on growth prediction, including:

[0129] The data acquisition module 11 is used to acquire the growth parameter set and feeding behavior parameter set of the target lamb;

[0130] The growth prediction module 12 is used to predict growth based on the growth parameter set and the feeding behavior parameter set, and to obtain feeding proficiency parameters and rumen development parameters.

[0131] The developmental pattern determination module 13 is used to obtain developmental synergy parameters based on the feeding proficiency parameters and the rumen development parameters, and to determine the lamb's developmental pattern.

[0132] The feeding program matching module 14 is used to intelligently match lamb feeding programs based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns.

[0133] In one embodiment, the data acquisition module 11 is specifically used for:

[0134] Obtain the birth date and birth weight of the target lamb;

[0135] Based on the lamb breed of the target lamb, obtain the weight monitoring time interval, conduct phased weight monitoring, and obtain the daily weight gain parameter sequence;

[0136] By integrating the birth date, birth weight, and daily weight gain parameter sequence, the growth parameter set is obtained;

[0137] Monitor and obtain the date of the first feeding of the target lambs, and calculate the age at the first feeding;

[0138] Starting from the date of the first feeding, feeding behavior was monitored to obtain a sequence of lamb feed intake parameters;

[0139] By integrating the first feeding age and the lamb feed intake parameter sequence, the feeding behavior parameter set is obtained.

[0140] In one embodiment, the growth prediction module 12 is specifically used for:

[0141] Based on lamb breeds, obtain a growth prediction tool;

[0142] The growth parameter set and the feeding behavior parameter sequence are input into the growth predictor to obtain the feeding proficiency parameter and the rumen development parameter.

[0143] Furthermore, in one embodiment of the application, a growth predictor is obtained based on lamb breed, including:

[0144] Based on the lamb breed, obtain a set of parameters for sample growth and a set of parameters for sample feeding behavior;

[0145] Obtain the sample feeding proficiency parameters and sample rumen development parameters corresponding to the sample growth parameter set and sample feeding behavior parameter set, wherein the sample feeding proficiency parameters are used to characterize the lamb's feeding behavior proficiency, and the sample rumen development parameters are used to characterize the lamb's rumen development.

[0146] The growth prediction tool is trained until convergence using the sample growth parameter set and the sample feeding behavior parameter set as inputs, and the sample feeding proficiency parameter and the sample rumen development parameter as supervision.

[0147] In one embodiment, the developmental pattern determination module 13 is specifically used for:

[0148] The difference between the feeding proficiency parameter and the rumen development parameter is calculated as a developmental synergy parameter;

[0149] Based on lamb breeds, obtain threshold values ​​for developmental synergy parameters;

[0150] Based on the developmental synergy parameters and the threshold values ​​of the developmental synergy parameters, the developmental pattern of the target lamb is determined, wherein the developmental pattern includes synergistic development, feeding-leading, and rumen-leading.

[0151] In one embodiment, the feeding program matching module 14 is specifically used for:

[0152] Based on the developmental synergy parameters, the feeding weight and rumen development weight are obtained, and the feeding proficiency parameters and rumen development parameters are weighted and calculated to obtain comprehensive developmental parameters.

[0153] Based on the lamb breed and the aforementioned comprehensive developmental parameters, intelligent matching is performed in the lamb feeding program database to obtain a lamb feeding program.

[0154] Furthermore, in one embodiment, based on the lamb development pattern, feed intake weight and rumen development weight are obtained, and the feed intake proficiency parameter and the rumen development parameter are weighted and calculated to obtain comprehensive development parameters, including:

[0155] Based on the aforementioned developmental synergy parameters, the feeding weights are obtained;

[0156] The set of feeding behavior parameters is used to obtain the coefficient of variation of feeding amount.

[0157] Based on the coefficient of variation of feed intake, the feed intake weight is corrected to obtain the corrected feed intake weight.

[0158] Calculate 1 and subtract the corrected intake weight to obtain the corrected rumen weight;

[0159] The feeding proficiency parameter and the rumen development parameter are weighted and calculated using the modified feeding weight and the modified rumen weight to obtain the comprehensive development parameter.

[0160] Furthermore, in one embodiment of the application, the construction of the lamb feeding protocol library includes:

[0161] Based on the lamb breed, historical feeding data is obtained, wherein the historical feeding dataset includes a set of historical growth parameters, a set of historical feeding behavior parameters, corresponding historical feeding operation records, and corresponding historical feeding result parameters.

[0162] Statistical analysis was performed on the historical feeding data to identify the historical feeding operation record corresponding to the maximum historical feeding result parameter under the same historical growth parameter set and historical feeding behavior parameter set, which was then used as an effective feeding pattern.

[0163] The effective feeding pattern is structured as a standard feeding program, and each standard feeding program is configured with associated tag information and parameter range. The tag information includes lamb breed and development pattern, and the parameter range includes comprehensive development parameter range, feeding proficiency parameter range and rumen development parameter range.

[0164] All the standard feeding protocols and their associated tag information and parameter ranges are stored in a structured manner to form the lamb feeding protocol library.

[0165] Furthermore, in one embodiment of the application, the method of intelligently matching lamb feeding programs in a lamb feeding program library based on lamb breed and the aforementioned comprehensive developmental parameters further includes:

[0166] When there is no standard feeding program in the lamb feeding program library whose parameter range matches the developmental synergy parameter, an alternative feeding program set is obtained based on the lamb breed and the developmental pattern.

[0167] Calculate the matching degree between the range of feeding proficiency parameters and the range of rumen development parameters of the alternative feeding schemes and the range of feeding proficiency parameters and rumen development parameters of the target lamb in the set of alternative feeding schemes, and obtain the feeding matching degree and rumen matching degree.

[0168] Based on the corrected intake weight and the corrected rumen weight, the intake matching degree and the rumen matching degree are weighted and calculated to obtain the comprehensive matching degree;

[0169] The lamb feeding program is selected based on the highest overall matching degree from the pool of alternative feeding programs.

[0170] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0171] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0172] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent matching of lamb feeding programs based on growth prediction, characterized in that, include: Obtain the set of growth parameters and feeding behavior parameters of the target lambs; Based on the growth status parameter set and the feeding behavior parameter set, growth status is predicted to obtain feeding proficiency parameters and rumen development parameters. Based on the feeding proficiency parameters and the rumen development parameters, developmental synergy parameters are obtained to determine the lamb development pattern. Based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns, an intelligent lamb feeding program is matched.

2. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 1, characterized in that, Obtain the target lamb's growth parameters and feeding behavior parameters, including: Obtain the birth date and birth weight of the target lamb; Based on the lamb breed of the target lamb, obtain the weight monitoring time interval, conduct phased weight monitoring, and obtain the daily weight gain parameter sequence; By integrating the birth date, birth weight, and daily weight gain parameter sequence, the growth parameter set is obtained; Monitor and obtain the date of the first feeding of the target lambs, and calculate the age at the first feeding; Starting from the date of the first feeding, feeding behavior was monitored to obtain a sequence of lamb feed intake parameters; By integrating the first feeding age and the lamb feed intake parameter sequence, the feeding behavior parameter set is obtained.

3. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 1, characterized in that, Based on the growth parameter set and the feeding behavior parameter set, growth status is predicted to obtain feeding proficiency parameters and rumen development parameters, including: Based on lamb breeds, obtain a growth prediction tool; The growth parameter set and the feeding behavior parameter sequence are input into the growth predictor to obtain the feeding proficiency parameter and the rumen development parameter.

4. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 3, characterized in that, Based on lamb breed, a growth prediction tool is obtained, including: Based on the lamb breed, obtain a set of parameters for sample growth and a set of parameters for sample feeding behavior; Obtain the sample feeding proficiency parameters and sample rumen development parameters corresponding to the sample growth parameter set and sample feeding behavior parameter set, wherein the sample feeding proficiency parameters are used to characterize the lamb's feeding behavior proficiency, and the sample rumen development parameters are used to characterize the lamb's rumen development. The growth prediction tool is trained until convergence using the sample growth parameter set and the sample feeding behavior parameter set as inputs, and the sample feeding proficiency parameter and the sample rumen development parameter as supervision.

5. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 1, characterized in that, Based on the feeding proficiency parameters and the rumen development parameters, developmental synergy parameters are obtained to determine the lamb developmental pattern, including: The difference between the feeding proficiency parameter and the rumen development parameter is calculated as a developmental synergy parameter; Based on lamb breeds, obtain threshold values ​​for developmental synergy parameters; Based on the developmental synergy parameters and the threshold values ​​of the developmental synergy parameters, the developmental pattern of the target lamb is determined, wherein the developmental pattern includes synergistic development, feeding-leading, and rumen-leading.

6. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 1, characterized in that, Based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns, an intelligent lamb feeding program is matched, including: Based on the developmental synergy parameters, the feeding weight and rumen development weight are obtained, and the feeding proficiency parameters and rumen development parameters are weighted and calculated to obtain comprehensive developmental parameters. Based on the lamb breed and the aforementioned comprehensive developmental parameters, intelligent matching is performed in the lamb feeding program database to obtain a lamb feeding program.

7. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 6, characterized in that, Based on the lamb development pattern, feed intake weight and rumen development weight are obtained. The feed intake proficiency parameter and the rumen development parameter are then weighted and calculated to obtain comprehensive development parameters, including: Based on the aforementioned developmental synergy parameters, the feeding weights are obtained; The set of feeding behavior parameters is used to obtain the coefficient of variation of feeding amount. Based on the coefficient of variation of feed intake, the feed intake weight is corrected to obtain the corrected feed intake weight. Calculate 1 and subtract the corrected intake weight to obtain the corrected rumen weight; The feeding proficiency parameter and the rumen development parameter are weighted and calculated using the modified feeding weight and the modified rumen weight to obtain the comprehensive development parameter.

8. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 6, characterized in that, The construction of the lamb feeding protocol library includes: Based on the lamb breed, historical feeding data is obtained, wherein the historical feeding dataset includes a set of historical growth parameters, a set of historical feeding behavior parameters, corresponding historical feeding operation records, and corresponding historical feeding result parameters. Statistical analysis was performed on the historical feeding data to identify the historical feeding operation record corresponding to the maximum historical feeding result parameter under the same historical growth parameter set and historical feeding behavior parameter set, which was then used as an effective feeding pattern. The effective feeding pattern is structured as a standard feeding program, and each standard feeding program is configured with associated tag information and parameter range. The tag information includes lamb breed and development pattern, and the parameter range includes comprehensive development parameter range, feeding proficiency parameter range and rumen development parameter range. All the standard feeding protocols and their associated tag information and parameter ranges are stored in a structured manner to form the lamb feeding protocol library.

9. The intelligent matching method for lamb feeding programs based on growth prediction according to claim 8, characterized in that, Based on the lamb breed and the aforementioned comprehensive developmental parameters, intelligent matching is performed in the lamb feeding program database to obtain a lamb feeding program, which also includes: When there is no standard feeding program in the lamb feeding program library whose parameter range matches the developmental synergy parameter, an alternative feeding program set is obtained based on the lamb breed and the developmental pattern. Calculate the matching degree between the range of feeding proficiency parameters and the range of rumen development parameters of the alternative feeding schemes and the range of feeding proficiency parameters and rumen development parameters of the target lamb in the set of alternative feeding schemes, and obtain the feeding matching degree and rumen matching degree. Based on the corrected intake weight and the corrected rumen weight, the intake matching degree and the rumen matching degree are weighted and calculated to obtain the comprehensive matching degree; The lamb feeding program is selected based on the highest overall matching degree from the pool of alternative feeding programs.

10. An intelligent matching system for lamb feeding programs based on growth prediction, characterized in that, The method for intelligent matching of lamb feeding programs based on growth prediction as described in any one of claims 1-9 includes: The data acquisition module is used to obtain a set of growth parameters and a set of feeding behavior parameters of the target lambs; The growth prediction module is used to predict growth based on the growth parameter set and the feeding behavior parameter set, and to obtain feeding proficiency parameters and rumen development parameters. The developmental pattern determination module is used to obtain developmental synergy parameters based on the feeding proficiency parameters and the rumen development parameters, and to determine the lamb's developmental pattern. The feeding program matching module is used to intelligently match lamb feeding programs based on the feeding proficiency parameters, rumen development parameters, and lamb development patterns.

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