Equine concentrate feed formula regulation method, device, equipment and storage medium

Abstract

The present application relates to the technical field of concentrate formula regulation, and discloses a horse concentrate formula regulation method, device, equipment and storage medium, a horse concentrate formula basic database is established, a formula regulation target function determined by the lowest cost and a nutrition standard, and constraint conditions composed of raw material proportion constraints, nutrition demand constraints and total proportion relaxation constraints, an EXCEL programming solution method is used in programming solution, dynamic iterative optimization of the solution algorithm, quantitative processing of no solution problems and residual error verification of the solution result are performed, formula adjustment is performed through nutrition index determination and data verification, and finally a horse concentrate formula is obtained. Therefore, the present application uses dynamic iterative steps, no solution problem quantification and determination adjustment, solves the problems of low solution efficiency, solution failure and uncontrollable nutrition deviation of the traditional EXCEL programming method, and proposes a horse concentrate formula regulation scheme with high precision, high efficiency and low cost.

Description

Technical Field

[0001] This invention relates to the field of concentrated feed formulation control technology, and in particular to a method, apparatus, equipment and storage medium for controlling concentrated feed formulations for equines. Background Technology

[0002] China's horse breeding industry has a long history of over 5,000 years. Its industrial structure has gradually shifted from traditional agricultural and draft purposes (such as agricultural labor and military transportation) to modern equestrian sports, horse racing, leisure riding, and mare's milk and meat production, reflecting societal development. Currently, it is in the initial stage of this transition from traditional to modern horse breeding, with a promising market prospect. The healthy growth, reproductive efficiency, and exercise / production potential of equines (such as foals and adult horses) depend heavily on a scientifically formulated concentrate feed. Concentrate supplements, as a key source of nutrition for herbivores (horses, cattle, sheep, etc.), are formulated in specific proportions from energy feeds (such as corn and corn germ meal), protein feeds (such as soybean meal and whey powder), mineral feeds (such as limestone powder and dicalcium phosphate), and feed additives (such as L-lysine hydrochloride and salt). Their nutritional balance directly affects the production performance of equines and the quality and safety of subsequent animal-derived foods.

[0003] The existing concentrated feed formulation design technology has the following limitations: (1) Traditional feed formulation design relies on professional software (such as formulation optimization system), which is expensive and difficult for small and medium-sized farms, grassroots research institutions and researchers in schools to afford; (2) Although some studies have used EXCEL linear programming (planning solution function) to design formulations, it has problems such as low solution efficiency (time-consuming in scenarios with multiple raw materials and multiple constraints), easy to have no solution (single raw material nutrition or improper constraint setting), large deviation between theoretical value and measured value (not considering raw material nutrition fluctuations); (3) Some formulation designs lack standardized nutritional index determination (such as crude protein, acid detergent fiber ADF, neutral detergent fiber NDF, calcium, phosphorus, lysine, methionine) and data verification process (such as SPSS variance analysis), making it difficult to ensure the nutritional uniformity of the formulation. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for regulating the formulation of concentrated feed for equines, aiming to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides a method for regulating the formulation of concentrated feed for equines, comprising the following steps:

[0006] Establish a basic database of concentrated feed formulations for equines; wherein, the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters;

[0007] The formula control objective function is set based on the minimum cost and nutritional target, and the constraints consist of raw material ratio constraints, nutritional requirement constraints and total ratio relaxation constraints.

[0008] After preprocessing the raw material nutrition data, the algorithm is dynamically iteratively optimized, unsolvable problems are quantified, and the residuals of the solution results are verified based on the EXCEL Solver module to obtain the initial raw material formula output by the EXCEL Solver module.

[0009] The initial raw material formula output by EXCEL programming solution was used to determine the feed nutrient index and verify the data. The uniformity and accuracy of the formula nutrition were verified through sample preparation, index detection and statistical analysis.

[0010] The formula was adjusted based on the results of nutritional index testing to determine the final formula for equine concentrated feed.

[0011] Optionally, the steps for establishing a basic database of concentrated feed formulations for equines include:

[0012] Referring to relevant standard documents on feed formulation for equines, the core nutritional standards for the target equines are set; wherein, the core nutritional standards include the DM parameters of crude protein, neutral detergent fiber, acid detergent fiber, calcium, phosphorus, lysine and methionine.

[0013] Collect several raw materials for the preparation of concentrated feed formula, verify them using the raw material nutritional parameters and feed composition and nutritional value reference documents, and record the unit price, nutritional content and nutritional fluctuation data of each raw material to form feed raw material parameters;

[0014] Construct a structured Excel spreadsheet, and write the core nutritional standards and feed ingredient parameters into the Excel spreadsheet to form a basic database of concentrated feed formulations for equines.

[0015] Optionally, the following steps are included: setting the objective function for formula control based on minimum cost and nutritional target achievement, and the constraint conditions consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints:

[0016] The expression for the objective function of formulation regulation is as follows:

[0017] ;

[0018] ;

[0019] Indicates the total amount of raw materials. This indicates the proportion of raw material i in the formula. This indicates the actual total content of nutrient index j in the formula. This represents the standard value of nutritional indicator j. , Indicates the weighting coefficient. This represents the dry matter mean of the j-th nutrient indicator in the i-th raw material. This represents the dry matter content of the i-th raw material;

[0020] The expression for the raw material ratio constraint is as follows:

[0021] ;

[0022] The expression for the nutritional requirement constraint is as follows:

[0023] ;

[0024] ;

[0025] The expression for the total proportional relaxation constraint is as follows:

[0026] ;

[0027] In the formula, denoted as the coefficient of variation, used to measure the relative fluctuation of nutrient index j in the i-th raw material, and configured as the ratio of the standard deviation of nutrient index j in the i-th raw material to the mean of the dry matter basis.

[0028] Optionally, the raw material nutrient data preprocessing step specifically includes:

[0029] The measured nutrient values ​​of each raw material are uniformly corrected to a dry matter basis, thus achieving dry matter correction of the raw material nutrient data; wherein, the expression for the dry matter correction is specifically:

[0030] ;

[0031] In the formula, This represents the dry matter content of nutrient index j in the i-th raw material. This represents the measured content of nutrient index j in the i-th raw material. This represents the dry matter content of the i-th raw material;

[0032] Based on the results of three parallel tests of each raw material's nutritional indicators, outlier handling is performed using the 3σ principle to detect and remove outliers in the raw material's nutritional data.

[0033] Optionally, the algorithm is dynamically iteratively optimized based on the Excel Solver module, and unsolvable problems are quantified and the residuals of the solution results are verified. Specifically, this includes:

[0034] The iterative step size of the raw material ratio is dynamically adjusted based on the constraint satisfaction. A three-round solution strategy of coarse adjustment, fine adjustment and verification is adopted. The formula is optimized by combining the sensitivity report output by EXCEL planar solver.

[0035] The expression for the iteration step size is as follows:

[0036] ;

[0037] In the formula, This represents the iteration step size of raw material i in the (t+1)th round. This represents the iteration step size of raw material i in the t-th round. Indicates the adjustment factor. This represents the constraint satisfaction rate in round t, which is the ratio of the number of satisfied constraints to the total number of constraints.

[0038] When no solution is found, the gap value of each nutritional indicator is calculated to determine the amount of supplementary ingredients to be added.

[0039] The specific expressions for calculating the deficit values ​​of each nutritional indicator are as follows:

[0040] ;

[0041] In the formula, This represents the deficit value of nutrient indicator j. This indicates the upper limit of the proportion of raw material i;

[0042] Calculate the residual between the theoretical nutrient value and the nutrient standard value output by the solution, and evaluate the goodness of fit between the solution and the nutrient standard by the coefficient of determination;

[0043] The expressions for the residuals and coefficients of determination between the theoretical nutrient values ​​and the standard nutrient values ​​are as follows:

[0044] ;

[0045] ;

[0046] In the formula, Represents the residual. The coefficient of determination is represented by the coefficient of determination. This represents the average nutritional standard.

[0047] Optionally, the initial raw material formula output by the EXCEL solver is subjected to feed nutrient index determination and data verification. The steps of sample preparation, index detection, and statistical analysis are used to verify the nutritional uniformity and accuracy of the formula. Specifically, these steps include:

[0048] According to the raw material ratio in the initial raw material formula output by EXCEL programming solution, weigh 1000g of raw materials, crush the raw materials to a particle size of 1-2mm using a pulverizer, mix them for 20 minutes using a mixer, and take 3 parallel samples from the mixed material using the five-point sampling method.

[0049] The crude protein content of the parallel samples was determined by the Kjeldahl method, the calcium content was determined by the EDTA titration method, the phosphorus content was determined by the vanadium molybdenum yellow colorimetric method, and the lysine and methionine content of the parallel samples were determined by high performance liquid chromatography.

[0050] One-way ANOVA P-analysis was performed on the test results of three parallel samples using SPSS 27.0 software. If P>0.05, the feed nutritional uniformity was deemed qualified. The deviation rate Dr between the measured and theoretical values ​​of each indicator was calculated. If Dr≤10%, the formula verification was deemed qualified.

[0051] Optionally, the formula can be adjusted based on the results of nutritional index testing to determine the final formula for equine concentrate feed, specifically including:

[0052] For nutritional indicators that fail formula validation, manual inspection and deviation analysis are performed. If the discrepancy is due to fluctuations in the nutritional content of the raw materials, the raw materials are re-verified. The database is updated; if the problem is due to uneven mixing, the mixing time in the mixer is extended and the sample is prepared again for testing; if the problem is due to deviations in the theoretical formula design, the process proceeds to the ratio fine-tuning stage.

[0053] When entering the ratio fine-tuning stage, the ratio of corresponding raw materials or additives is adjusted based on the preset standard. The sample is then re-prepared according to the fine-tuned ratio, and feed nutrient index determination and data verification are performed. If all indicators meet Dr≤10% and P>0.05, the formula is determined to be the final equine concentrated feed formula; otherwise, the above steps are repeated until the standard is met.

[0054] Furthermore, to achieve the above objectives, the present invention also provides a device for regulating the formulation of concentrated feed for equines, comprising:

[0055] A module is established to create a basic database of concentrated feed formulations for equines; wherein the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters;

[0056] The setting module is used to set the formula control objective function determined by the lowest cost and nutritional standards, as well as the constraints consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints.

[0057] The solver module is used to perform dynamic iterative optimization of the solver algorithm, quantitative processing of unsolvable problems, and residual verification of the solver results based on the EXCEL solver module after preprocessing the raw material nutrient data, so as to obtain the initial raw material formula output by the EXCEL solver module.

[0058] The verification module is used to determine and verify the feed nutrient indexes of the initial raw material formula output by EXCEL programming solution. It verifies the uniformity and accuracy of the formula nutrition through sample preparation, index detection and statistical analysis.

[0059] The determination module is used to adjust the formula based on the results of nutritional index testing to determine the final concentrate feed formula for equines.

[0060] In addition, to achieve the above objectives, the present invention also provides a device for regulating the formulation of concentrated feed for equines, the device comprising: a memory, a processor, and a program for regulating the formulation of concentrated feed for equines stored in the memory and executable on the processor, wherein when the program for regulating the formulation of concentrated feed for equines is executed by the processor, the steps of the method for regulating the formulation of concentrated feed for equines as described above are implemented.

[0061] In addition, to achieve the above objectives, the present invention also provides a storage medium storing an equine concentrate feed formulation control program, which, when executed by a processor, implements the steps of the equine concentrate feed formulation control method described above.

[0062] The beneficial effects of this invention are as follows: It proposes a method, device, equipment, and storage medium for regulating the formulation of concentrated feed for equines. By establishing a basic database of concentrated feed formulations for equines, and using a formulation regulation objective function determined by minimum cost and nutritional compliance, along with constraints consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints, the invention employs an EXCEL programming algorithm. During the programming process, dynamic iterative optimization of the algorithm, quantification of unsolvable problems, and residual verification of the solution results are performed. Furthermore, the formulation is adjusted through nutritional index measurement and data verification to obtain the final concentrated feed formulation for equines. Therefore, this invention, by employing dynamic iterative step size, quantification of unsolvable problems, and measurement and adjustment, solves the problems of low efficiency, solution failure, and uncontrollable nutritional deviation in the traditional EXCEL programming method, proposing a high-precision, high-efficiency, and low-cost scheme for regulating the formulation of concentrated feed for equines. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0064] Figure 2This is a flowchart illustrating an embodiment of the method for regulating the formulation of concentrated feed for equines according to the present invention.

[0065] Figure 3 This is a schematic diagram illustrating the analysis of feed nutrient indicators according to the present invention;

[0066] Figure 4 This is a structural block diagram of a concentrated feed formulation control device for equines according to an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0068] like Figure 1 As shown, Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0069] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0070] Those skilled in the art will understand that Figure 1 The structure of the device shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0071] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a feed formulation control program for equines.

[0072] exist Figure 1In the terminal shown, network interface 1004 is mainly used to connect to the backend server and communicate data with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate data with it; while processor 1001 can be used to call the equine animal concentrate feed formula control program stored in memory 1005 and perform the following operations:

[0073] Establish a basic database of concentrated feed formulations for equines; wherein, the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters;

[0074] The formula control objective function is set based on the minimum cost and nutritional target, and the constraints consist of raw material ratio constraints, nutritional requirement constraints and total ratio relaxation constraints.

[0075] After preprocessing the raw material nutrition data, the algorithm is dynamically iteratively optimized, unsolvable problems are quantified, and the residuals of the solution results are verified based on the EXCEL Solver module to obtain the initial raw material formula output by the EXCEL Solver module.

[0076] The initial raw material formula output by EXCEL programming solution was used to determine the feed nutrient index and verify the data. The uniformity and accuracy of the formula nutrition were verified through sample preparation, index detection and statistical analysis.

[0077] The formula was adjusted based on the results of nutritional index testing to determine the final formula for equine concentrated feed.

[0078] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the method for regulating the formulation of concentrated feed for equines described below, and will not be repeated here.

[0079] This invention provides a method for regulating the formulation of concentrated feed for equines, referring to... Figure 2 , Figure 2 This is a schematic flowchart illustrating an embodiment of the method for regulating the formulation of concentrated feed for equines according to the present invention.

[0080] In this embodiment, a method for regulating the formulation of concentrated feed for equines includes the following steps:

[0081] S100: Establish a basic database of concentrated feed formulations for equines; wherein, the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters;

[0082] S200: Set the formula control objective function determined by the lowest cost and nutritional standards, as well as the constraints consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints.

[0083] S300: After preprocessing the raw material nutrition data, the algorithm is dynamically iteratively optimized, unsolvable problems are quantified, and the residuals of the solution results are verified based on the EXCEL Solver module to obtain the initial raw material formula output by the EXCEL Solver module.

[0084] S400: Perform feed nutrient index determination and data verification on the initial raw material formula output by EXCEL programming solution, and verify the nutritional uniformity and accuracy of the formula through sample preparation, index detection and statistical analysis.

[0085] S500: Adjust the formula based on the results of nutritional index testing to determine the final formula for equine concentrated feed.

[0086] It should be noted that the existing concentrated feed formulation design technology has the following limitations: (1) Traditional feed formulation design relies on professional software (such as formulation optimization system), which is expensive and difficult for small and medium-sized farms, grassroots research institutions and researchers in schools to afford; (2) Although some studies have used EXCEL linear programming (planning and solving function) to design formulations, it has problems such as low solution efficiency (time-consuming in scenarios with multiple raw materials and multiple constraints), easy to have no solution (single raw material nutrition or improper constraint setting), large deviation between theoretical value and measured value (not considering raw material nutrition fluctuations); (3) Some formulation designs lack standardized nutritional index determination (such as crude protein, acid detergent fiber ADF, neutral detergent fiber NDF, calcium, phosphorus, lysine, methionine) and data verification process (such as SPSS variance analysis), making it difficult to ensure the nutritional uniformity of the formulation.

[0087] To address the aforementioned issues, this embodiment establishes a basic database of equine concentrate feed formulations. It uses a formulation control objective function determined by minimum cost and nutritional compliance, along with constraints comprised of raw material ratio constraints, nutritional requirement constraints, and overall ratio relaxation constraints. The EXCEL programming algorithm is employed for dynamic iterative optimization, quantification of unsolvable problems, and residual verification of the solution results. Further formulation adjustments are made through nutritional index measurement and data verification to obtain the final equine concentrate feed formulation. Therefore, this invention, by employing dynamic iterative step size, quantification of unsolvable problems, and measurement-based adjustments, solves the problems of low efficiency, solution failure, and uncontrollable nutritional deviations associated with traditional EXCEL programming methods. It proposes a high-precision, high-efficiency, and low-cost scheme for regulating equine concentrate feed formulations.

[0088] In a preferred embodiment, the step of establishing a basic database of concentrated feed formulations for equines specifically includes:

[0089] S110: Refer to the relevant standard documents for feed formulation of equines and set the core nutritional standards for the target equines; wherein, the core nutritional standards include the DM parameters of crude protein, neutral detergent fiber, acid detergent fiber, calcium, phosphorus, lysine and methionine.

[0090] S120: Collect several raw materials for the preparation of concentrated feed formula, verify them with the raw material nutritional parameters and feed composition and nutritional value reference documents, and record the unit price, nutritional content and nutritional fluctuation data of each raw material to form feed raw material parameters;

[0091] S130: Construct a structured EXCEL data table, and write the core nutritional standards and feed ingredient parameters into the EXCEL data table to form a basic database of concentrated feed formulations for equines.

[0092] In this embodiment, by clearly defining target nutritional standards, the formulation design is ensured to be targeted and adapted to the needs of equines at specific growth stages. Then, by collecting and verifying raw material parameters and combining them with authoritative data tables (the 35th edition of the "Chinese Feed Composition and Nutritional Value Table"), the authenticity of the data is guaranteed. Simultaneously, nutritional fluctuation data is recorded to provide a basis for subsequent preprocessing. Then, by constructing a structured Excel table, orderly storage and rapid retrieval of data are achieved, laying the foundation for data reading in the Excel solver module. The final output database can be directly used as the input source for the solver model, reducing errors during data conversion and providing prerequisite support for subsequent accurate solutions. Specifically, the execution process includes the following:

[0093] (1) Determine nutritional standards:

[0094] Referring to the "Technical Specifications for the Formulation and Feeding of Supplementary Feed for Donkey Foals" (DB15 / T 3428—2024), taking a 6-month-old donkey foal as an example, the nutritional requirements are set as follows: crude protein 18%, neutral detergent fiber 15%, acid detergent fiber 6%, calcium 0.9%, phosphorus 0.6%, lysine 0.9%, and methionine 0.4%. See Table 1 for details.

[0095] Table 1 Nutritional Standards

[0096]

[0097] (2) Collect raw material parameters:

[0098] We purchased raw materials from the market, including ordinary corn (2.15 yuan / kg, crude protein 9.4% DM, dry matter 87.6%) and ordinary soybean meal (3.55 yuan / kg, crude protein 44.3% DM, dry matter 89.0%) (specific parameters are shown in Table 2.4). We verified the nutritional composition according to the "Chinese Feed Composition and Nutritional Value Table" (35th edition) and recorded the unit price of the raw materials and nutritional fluctuation data (e.g., crude protein fluctuation of soybean meal ±1.8%). See Table 2 for details.

[0099] Table 2 Feed Prices and Nutritional Value

[0100]

[0101] (3) Create a table in EXCEL containing fields such as raw material name, unit price, dry matter (DM%), CP%DM, NDF%DM, ADF%DM, Ca%DM, P%DM, Lys%DM, and Met%DM. Enter the parameters collected by S12 to form a callable raw material-nutrient-price related database.

[0102] In a preferred embodiment, the steps of setting the formula control objective function determined by minimum cost and nutritional target achievement, and the constraint conditions consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints, are as follows:

[0103] The expression for the objective function of formulation regulation is as follows:

[0104] ;

[0105] ;

[0106] Indicates the total amount of raw materials. This indicates the proportion of raw material i in the formula. This indicates the actual total content of nutrient index j in the formula. This represents the standard value of nutritional indicator j. , Indicates the weighting coefficient. This represents the dry matter mean of the j-th nutrient indicator in the i-th raw material. This represents the dry matter content of the i-th raw material;

[0107] The expression for the raw material ratio constraint is as follows:

[0108] ;

[0109] The expression for the nutritional requirement constraint is as follows:

[0110] ;

[0111] ;

[0112] The expression for the total proportional relaxation constraint is as follows:

[0113] ;

[0114] In the formula, denoted as the coefficient of variation, used to measure the relative fluctuation of nutrient index j in the i-th raw material, and configured as the ratio of the standard deviation of nutrient index j in the i-th raw material to the mean of the dry matter basis.

[0115] In this embodiment, by setting a formula control objective function that determines the lowest cost and nutritional compliance, and by setting constraints consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints, it is possible to plan a formula with the lowest cost and nutritional compliance under the following conditions: satisfying the setting constraints of each raw material ratio (set by production experience, for example, an excessively high corn ratio will burden digestion), satisfying the nutritional requirement constraints (considering the fluctuation of raw material nutrition, setting a lower limit of fluctuation to ensure that the actual nutrition is not lower than the standard, and setting an upper limit of fluctuation to avoid nutritional excess), and the total ratio relaxation constraints (breaking through the traditional 100% fixed value, setting a ratio of 90%-110%, allowing subsequent fine-tuning of the additive ratio, and reducing the probability of no solution).

[0116] In a preferred embodiment, the raw material nutrient data preprocessing step specifically includes:

[0117] S310: The measured values ​​of the nutritional components of each raw material are uniformly corrected to the dry matter basis, thereby achieving dry matter correction of the raw material nutritional data; wherein, the expression for the dry matter correction is specifically:

[0118] ;

[0119] In the formula, This represents the dry matter content of nutrient index j in the i-th raw material. This represents the measured content of nutrient index j in the i-th raw material. This represents the dry matter content of the i-th raw material;

[0120] S320: Based on the results of three parallel tests of each raw material's nutritional indicators, outlier handling is performed using the 3σ principle to achieve outlier detection and removal in the raw material's nutritional data.

[0121] In this embodiment, considering the significant differences in dry matter content among different raw materials (e.g., 87.6% dry matter in corn and 97.2% dry matter in whey powder), directly using fresh weight-based nutritional data would lead to inconsistent calculation benchmarks for nutritional contributions among different raw materials. Dry matter correction unifies the data dimensions, ensuring the accuracy of nutritional contribution calculations. Simultaneously, outliers may arise during raw material testing due to operational errors. The 3σ principle effectively eliminates extreme interference data, preventing outliers from causing the solution model output to deviate from actual requirements. By first unifying the data benchmark through correction and then purifying data quality through outlier processing, the final standardized nutritional data can be directly input into the solution model, reducing deviations between theoretical and measured values ​​caused by inherent data defects and providing high-quality data input for subsequent efficient solutions.

[0122] In a preferred embodiment, the steps of dynamic iterative optimization of the solution algorithm, quantization of unsolvable problems, and residual verification of the solution results based on the EXCEL Solver module specifically include:

[0123] S330: The iterative step size of the raw material ratio is dynamically adjusted based on the constraint satisfaction. A three-round solution strategy of coarse adjustment, fine adjustment and verification is adopted, and the formula is optimized by combining the sensitivity report output by EXCEL planning and solving.

[0124] The expression for the iteration step size is as follows:

[0125] ;

[0126] In the formula, This represents the iteration step size of raw material i in the (t+1)th round. This represents the iteration step size of raw material i in the t-th round. Indicates the adjustment factor. This represents the constraint satisfaction rate in round t, which is the ratio of the number of satisfied constraints to the total number of constraints.

[0127] In this embodiment, considering the time-consuming nature and susceptibility to local optima inherent in traditional fixed-step iterations, a dynamic step-size adjustment is implemented. The step-size is optimized in real-time based on constraint satisfaction: increasing the step-size when constraint satisfaction is low quickly narrows the infeasible region; decreasing the step-size when constraint satisfaction is high avoids missing the optimal solution due to excessively large step-sizes, achieving efficient iteration for rapid exploration and precise approximation. Through multiple rounds of solving and sensitivity feedback, combined with Lagrange multipliers to identify raw materials with minimal cost impact, cost control is prioritized while ensuring nutritional compliance. Simultaneously, accuracy is progressively improved through three rounds of solution iteration to ensure the final ratio satisfies both nutritional constraints and cost requirements. Ultimately, this achieves dual optimization of solution efficiency and result quality, avoiding the time-consuming or biased problems common in traditional single-step solutions.

[0128] Specifically, when dynamically adjusting the iteration step size, the traditional fixed step size is abandoned, and the iteration step size is dynamically adjusted based on the constraint satisfaction degree to improve solution efficiency. A value less than 0.6 indicates low constraint satisfaction. In this case, increasing the step size (e.g., step size × 1.2) speeds up the exploration of the feasible region. When the value is greater than 0.9, the constraint satisfaction is high. In this case, reduce the step size (e.g., step size × 0.8) to approximate the optimal solution more precisely.

[0129] Following this, a three-round solution strategy of coarse adjustment, fine adjustment, and verification was adopted, combined with sensitivity reports (Lagrange multipliers) to optimize the formula:

[0130] Round 1 (Coarse Adjustment): Set a loose precision (ε=0.01) to quickly screen out the raw material ratio range that meets the core nutrient constraints (CP, Ca, P) and output the preliminary ratio. ;

[0131] Second round (fine-tuning): Based on the results of the first round, the Excel sensitivity report is used to extract key constraints (such as the Lagrange multiplier of P = 0.8 yuan / %, meaning that for every 1% increase in demand for P, the cost increases by 0.8 yuan). Raw materials with minimal cost impact are prioritized for adjustment (such as corn germ meal, Lagrange multiplier = 0.1 yuan / %), improving the precision to ε = 0.005, and the optimized ratio is output. ;

[0132] Third round (verification): Calculate the optimization ratio Corresponding total nutritional contribution value If all satisfy ( If the result is within the nutritional standard value, proceed to the next step; otherwise, return to the second round for readjustment.

[0133] S340: When no solution is found, calculate the gap value of each nutrient indicator to determine the amount of supplementary ingredients to be added;

[0134] The specific expressions for calculating the deficit values ​​of each nutritional indicator are as follows:

[0135] ;

[0136] In the formula, This represents the deficit value of nutrient indicator j. This indicates the upper limit of the proportion of raw material i.

[0137] In this embodiment, to systematically solve unsolvable problems in Excel's Solver algorithm and avoid the blind reliance on manual trial and error in traditional methods, a nutrient gap quantification calculation is used to accurately pinpoint the key nutrient indicators leading to the unsolvable problem, avoiding errors caused by judging gaps solely based on experience. Furthermore, additive supplementation calculations are performed to precisely derive the minimum additive dosage based on the gap value, satisfying nutritional needs while avoiding cost increases or nutritional imbalances caused by excessive additives. This ensures that, without deviating from core nutritional requirements, the unsolvable state can be quickly transformed into a feasible solution, avoiding the time-consuming problem of repeated trial and error in traditional manual adjustments.

[0138] Furthermore, after obtaining the deficit values ​​for each nutrient indicator, the minimum amount of additive required is calculated for the deficient nutrient. Taking limestone powder (calcium source, 35% DM Ca content) to supplement the calcium deficit as an example, the formula is as follows:

[0139] ;

[0140] In the formula, (Calcium content of dry matter in stone powder). (Dry matter content of stone powder), for example, ,but This means that an additional 0.55% stone powder needs to be added, while adjusting the proportion of other raw materials (the total proportion should still be controlled within 110%).

[0141] S350: Calculate the residual between the theoretical nutrient value and the nutrient standard value output by the solution, and evaluate the goodness of fit between the solution and the nutrient standard by the coefficient of determination;

[0142] The expressions for the residuals and coefficients of determination between the theoretical nutrient values ​​and the standard nutrient values ​​are as follows:

[0143] ;

[0144] ;

[0145] In the formula, Represents the residual. The coefficient of determination is represented by the coefficient of determination. This represents the average nutritional standard.

[0146] In this embodiment, to ensure that the output theoretical formula closely matches the nutritional standards and avoid formula deviations caused by errors in the solution model, residual calculation is used to quantify the degree of deviation between the theoretical and standard values ​​of each nutritional indicator, intuitively identifying indicators with large deviations; and the coefficient of determination is used... To evaluate the overall fit of the solution results: The closer the value is to 1, the higher the degree to which the theoretical formula conforms to the nutritional standards, and the stronger the reliability of the solution; if If the value is too low, it indicates that there is room for optimization in the solution model, and further optimization is needed by adjusting the iteration parameters to avoid directly applying the poorly fitted formula to actual production. It is essential to ensure that the solution results meet both the deviation requirements of individual indicators and the overall nutritional coordination needs, providing a high-quality theoretical formula foundation for subsequent experimental verification.

[0147] For example, if A value ≥0.9 indicates that the solution has a high degree of fit with the standard and can proceed to subsequent verification. If the result is less than 0.9, return to adjust the iteration parameters (such as reducing the step size) and solve again.

[0148] It should be noted that when using Excel's Solver feature, you can load the "Solver" module in the Excel software interface (File → Options → Add-ins → check Solver), select the objective function cell (cost calculation cell) and the variable cell (raw material ratio cell) in the parameter interface, enter the determined constraints, select the "Nonlinear GRG" method, enable "Make constraint variables non-negative", execute the solver, and output the calculation result report and sensitivity report.

[0149] In a series of experiments, when no solution is found after solving the programming problem, Excel can generate an "error report," the details of which are shown in Table 3.

[0150] Table 3 Error Report

[0151]

[0152] Results analysis: It can be seen that when the proportions of all feed ingredients reach the upper limit of the set proportions, the calculated calcium content still cannot meet the nutritional requirements. The calcium content can be supplemented by exogenous additives, such as limestone powder or dicalcium phosphate, to make up for the calcium content that cannot be met by conventional feed.

[0153] Conventional feeds often contain limited or no nutrients, such as calcium or lysine. Therefore, exogenous additives are needed to supplement these nutrients. For example, calcium can be supplemented with limestone powder, and lysine can be supplemented with L-lysine hydrochloride. Furthermore, feed formulations need to be modified based on the specific nutritional requirements of different horse breeds and growth stages. For instance, whey powder may be added to foal feed. In Excel, the amount of this ingredient can be adjusted by setting upper and lower limits in the formula table.

[0154] In a set of experiments, if a solution is found after solving the programming problem, three reports can be generated using Excel: "Calculation Results Report," "Sensitivity Report," and "Limit Value Report." Tables 4 and 5 are the "Calculation Results Report," which show the calculation results for the target cell and variable cells, i.e., the price per kilogram of feed and the proportion of raw materials. Table 6 shows the nutritional components of the formula and the values ​​of the components and the limit values, i.e., the conditions under which the constraints are met.

[0155] Table 4 Calculation Result Report (Target Cell)

[0156]

[0157] Table 5 Calculation Result Report (Variable Cells)

[0158]

[0159] Table 6 Calculation Result Report (Constraints)

[0160]

[0161] Table 6 shows that the lowest cost feed formula that meets nutritional requirements is 55% corn, 30% soybean meal, 10% corn germ meal, 10% wheat bran, 2.46% whey powder, 4.5% premix, and 2% limestone powder, with a cost of 2.9 yuan per kilogram. Based on production experience, salt and other substances need to be added to the feed, so the final modified feed formula is shown in Table 7.

[0162] Table 7 Formula for Refined Supplement

[0163]

[0164] In a preferred embodiment, the initial raw material formula output by the EXCEL programming solver is subjected to feed nutrient index determination and data verification. The verification of the formula's nutritional uniformity and accuracy is achieved through sample preparation, index detection, and statistical analysis. Specifically, this includes:

[0165] S410: Based on the raw material ratio in the initial raw material formula output by EXCEL programming solution, weigh 1000g of raw materials, crush the raw materials to a particle size of 1-2mm using a pulverizer, mix them for 20 minutes using a mixer, and take 3 parallel samples from the mixed material using the five-point sampling method.

[0166] S420: The crude protein index of the parallel samples was determined by the Kjeldahl method, the calcium index was determined by the EDTA titration method, the phosphorus index was determined by the vanadium molybdenum yellow colorimetric method, and the lysine and methionine indexes of the parallel samples were determined by high performance liquid chromatography.

[0167] S430: Use SPSS 27.0 software to perform one-way ANOVA P analysis on the test results of 3 parallel samples. If P>0.05, the feed nutrition uniformity is deemed qualified. Calculate the deviation rate Dr between the measured value and the theoretical value of each indicator. If Dr≤10%, the formula verification is deemed qualified.

[0168] In this embodiment, the theoretical formula output by the solution is correlated with the actual feed performance to verify the feasibility and accuracy of the formula, avoiding a disconnect between theoretical and practical applications. Standardized sample preparation controls particle size, mixing time, and sampling methods to reduce errors during sample preparation and ensure the representativeness of parallel samples. Standardized testing methods are used to determine core nutritional indicators according to national or industry standards, ensuring the objectivity and comparability of test results. Data validation evaluates the formula from two dimensions: uniformity (analysis of variance) and accuracy (deviation rate). Passing uniformity indicates that the feed is mixed evenly without localized nutritional imbalances; passing accuracy indicates that the theoretical formula closely matches the actual nutritional level and can be directly used in production. Ultimately, this achieves the conversion of theoretical formulas into practically usable formulas, avoiding the problem of theoretically meeting standards but actual performance failing due to lack of validation.

[0169] In a series of experiments, the experimental feed ingredients were selected three times (mixed evenly), and all experiments were performed in triplicate. One-way ANOVA and Duncan's multiple comparisons were conducted using SPSS 27.0 software to assess feed nutrient levels. Results are expressed as mean ± standard deviation. P < 0.05 indicated a significant difference, and P > 0.05 indicated no significant difference. Experimental results are shown in Table 8, and analytical results are as follows: Figure 3 It can be seen that, except for Lys, which showed a significant difference, the other values ​​did not show a significant difference.

[0170] Table 8 Measured values ​​of the indicators

[0171]

[0172] The theoretical feed nutrient indicators are shown in Table 9. Compared with the measured feed indicators, it is clear that there is a difference in the nutrient content of the feed. For example, the crude protein content in the homemade feed is 12.66% on average, but the theoretical value is 17.74%, indicating insufficient nutrient level.

[0173] Table 9 Theoretical feed content values

[0174]

[0175] In a preferred embodiment, the step of adjusting the formula based on the nutritional index measurement results to determine the final equine concentrate feed formula specifically includes:

[0176] S510: For nutritional indicators that fail formula validation, perform manual inspection and deviation analysis. If the deviation is due to fluctuations in the nutritional content of raw materials, then re-verify the nutritional content of those raw materials. The database is updated; if the problem is due to uneven mixing, the mixing time in the mixer is extended and the sample is prepared again for testing; if the problem is due to deviations in the theoretical formula design, the process proceeds to the ratio fine-tuning stage.

[0177] S520: When entering the ratio fine-tuning stage, adjust the ratio of corresponding raw materials or additives based on the preset standard, re-prepare the sample according to the fine-tuned ratio, and perform feed nutrition index determination and data verification. If all indicators meet Dr≤10% and P>0.05, then the formula is determined to be the final equine concentrated feed formula; otherwise, repeat the above steps until the standard is met.

[0178] For example, if a certain indicator deviates by more than 10% (e.g., Lys measured 0.69%, standard 0.90%), then:

[0179] Calculate the deviation rate ;

[0180] Adjust the L-lysine hydrochloride ratio (original ratio 0.6%), new ratio ;

[0181] Verify the adjusted total proportion (still ≤100%) and finally determine the formula (e.g., 52% ordinary corn, 29.64% soybean meal, 0.78% L-lysine salt, etc.).

[0182] In this embodiment, the unqualified formula is precisely optimized to ensure that the final formula fully meets the actual production needs. Deviation analysis accurately identifies the root cause of deviations, avoiding blind adjustments: deviations caused by raw material fluctuations require data updates, deviations caused by uneven mixing require process optimization, and deviations caused by formula design require ratio adjustments, achieving targeted adjustments. Through ratio fine-tuning, the precise amount of raw material adjustment is calculated based on the deviation rate, avoiding over-adjustment that could lead to new deviations. Final verification ensures that the fine-tuned formula meets the standards in terms of uniformity and accuracy, forming a closed loop of analysis, adjustment, and verification. Unqualified formulas are gradually optimized into qualified formulas, ensuring that the final output formula not only meets the nutritional needs of equines but also adapts to actual production conditions and can be directly applied to farming scenarios.

[0183] The proposed method for regulating equine concentrate feed formulations in this embodiment establishes a basic database of equine concentrate feed formulations. It uses a formulation regulation objective function determined by minimum cost and nutritional compliance, along with constraints comprised of raw material ratio constraints, nutritional requirement constraints, and overall ratio relaxation constraints. The method employs an Excel programming algorithm, dynamically iteratively optimizing the algorithm, quantifying unsolvable problems, and verifying residuals during the programming process. Finally, nutritional index measurements and data verification are used to adjust the formulation, resulting in the final equine concentrate feed formulation. Therefore, this invention, by employing dynamic iterative step size, quantification of unsolvable problems, and measurement-based adjustments, solves the problems of low efficiency, solution failure, and uncontrollable nutritional deviations associated with traditional Excel programming methods. It proposes a high-precision, high-efficiency, and low-cost scheme for regulating equine concentrate feed formulations.

[0184] Reference Figure 4 , Figure 4 This is a structural block diagram of an embodiment of the equine concentrated feed formulation control device of the present invention.

[0185] like Figure 4 As shown, the equine concentrated feed formulation control device proposed in this embodiment of the invention includes:

[0186] Module 10 is used to establish a basic database of concentrated feed formulations for equines; wherein, the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters;

[0187] The setting module 20 is used to set the formula control objective function determined by the lowest cost and nutritional standards, as well as the constraint conditions consisting of raw material ratio constraints, nutritional requirement constraints and total ratio relaxation constraints.

[0188] The solver module 30 is used to perform dynamic iterative optimization of the solver algorithm, quantitative processing of unsolvable problems, and residual verification of the solver results based on the EXCEL solver module after preprocessing the raw material nutrient data, so as to obtain the initial raw material formula output by the EXCEL solver module.

[0189] The verification module 40 is used to determine the feed nutrient index and verify the data of the initial raw material formula output by EXCEL programming solution. The formula nutrient uniformity and accuracy are verified through sample preparation, index detection and statistical analysis.

[0190] The determination module 50 is used to adjust the formula based on the results of nutritional index testing to determine the final formula for equine concentrated feed.

[0191] Other embodiments or specific implementations of the equine concentrated feed formulation control device of the present invention can refer to the above-described method embodiments, and will not be repeated here.

[0192] Furthermore, the present invention also proposes an equine concentrated feed formulation control device, which includes: a memory, a processor, and an equine concentrated feed formulation control program stored in the memory and executable on the processor. When the equine concentrated feed formulation control program is executed by the processor, it implements the steps of the equine concentrated feed formulation control method described above.

[0193] The specific implementation method of the equine concentrated feed formulation control device of this application is basically the same as the above-mentioned embodiments of the equine concentrated feed formulation control method, and will not be repeated here.

[0194] Furthermore, this invention also proposes a readable storage medium, comprising a computer-readable storage medium storing a feed formulation control program for equines. The readable storage medium may be... Figure 1 The memory 1005 in the terminal can also be at least one of ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc. The readable storage medium includes several instructions to cause a processor-equine concentrate feed formulation control device to execute the equine concentrate feed formulation control method described in various embodiments of the present invention.

[0195] The specific implementation methods in the readable storage medium of this application are basically the same as those in the above-described embodiments of the method for regulating the formulation of concentrated feed for equines, and will not be repeated here.

[0196] It is understood that in the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Nth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0197] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0198] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0199] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0200] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for regulating the formulation of concentrated feed for equines, characterized in that, Includes the following steps: Establish a basic database of concentrated feed formulations for equines; wherein, the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters; The formula control objective function is set based on the minimum cost and nutritional target, and the constraints consist of raw material ratio constraints, nutritional requirement constraints and total ratio relaxation constraints. The expression for the formulation control objective function is as follows: ； ； Indicates the total amount of raw materials. This indicates the proportion of raw material i in the formula. This indicates the actual total content of nutrient index j in the formula. This represents the standard value of nutritional indicator j. , Indicates the weighting coefficient. This represents the dry matter mean of the j-th nutrient indicator in the i-th raw material. This represents the dry matter content of the i-th raw material; The expression for the raw material ratio constraint is as follows: ； The expression for the nutritional requirement constraint is as follows: ； ； The expression for the total proportional relaxation constraint is as follows: ； In the formula, denoted as the coefficient of variation, used to measure the relative fluctuation of nutrient index j in the i-th raw material, and configured as the ratio of the standard deviation of nutrient index j in the i-th raw material to the mean of the dry matter basis. After preprocessing the raw material nutrition data, the algorithm is dynamically iteratively optimized, unsolvable problems are quantified, and the residuals of the solution results are verified based on the EXCEL Solver module to obtain the initial raw material formula output by the EXCEL Solver module. The initial raw material formula output by EXCEL programming solution was used to determine the feed nutrient index and verify the data. The uniformity and accuracy of the formula nutrition were verified through sample preparation, index detection and statistical analysis. The formula was adjusted based on the results of nutritional index testing to determine the final formula for equine concentrated feed.

2. The method for regulating the formulation of concentrated feed for equines as described in claim 1, characterized in that, The steps for establishing a basic database of concentrated feed formulations for equines include: Referring to relevant standard documents on feed formulation for equines, the core nutritional standards for the target equines are set; wherein, the core nutritional standards include the DM parameters of crude protein, neutral detergent fiber, acid detergent fiber, calcium, phosphorus, lysine and methionine. Collect several raw materials for the preparation of concentrated feed formula, verify them with the raw material nutritional parameters and feed composition and nutritional value reference documents, and record the unit price, nutritional content and nutritional fluctuation data of each raw material to form feed raw material parameters; Construct a structured Excel spreadsheet, and write the core nutritional standards and feed ingredient parameters into the Excel spreadsheet to form a basic database of concentrated feed formulations for equines.

3. The method for regulating the formulation of concentrated feed for equines as described in claim 1, characterized in that, The preprocessing steps for raw material nutrient data specifically include: The measured nutrient values ​​of each raw material are uniformly corrected to a dry matter basis, thus achieving dry matter correction of the raw material nutrient data; wherein, the expression for the dry matter correction is specifically: ； In the formula, This represents the dry matter content of nutrient index j in the i-th raw material. This represents the measured content of nutrient index j in the i-th raw material. This represents the dry matter content of the i-th raw material; Based on the results of three parallel tests of each raw material's nutritional indicators, outlier handling is performed using the 3σ principle to detect and remove outliers in the raw material's nutritional data.

4. The method for regulating the formulation of concentrated feed for equines as described in claim 3, characterized in that, The steps for dynamic iterative optimization of the solution algorithm based on the Excel Solver module, quantization of unsolvable problems, and residual verification of the solution results specifically include: The iterative step size of the raw material ratio is dynamically adjusted based on the constraint satisfaction. A three-round solution strategy of coarse adjustment, fine adjustment and verification is adopted. The formula is optimized by combining the sensitivity report output by EXCEL planar solver. The expression for the iteration step size is as follows: ； In the formula, This represents the iteration step size of raw material i in the (t+1)th round. This represents the iteration step size of raw material i in the t-th round. Indicates the adjustment factor. This represents the constraint satisfaction rate in round t, which is the ratio of the number of satisfied constraints to the total number of constraints. When no solution is found, the gap value of each nutritional indicator is calculated to determine the amount of supplementary ingredients to be added. The specific expressions for calculating the deficit values ​​of each nutritional indicator are as follows: ； In the formula, This represents the deficit value of nutrient indicator j. This indicates the upper limit of the proportion of raw material i; Calculate the residual between the theoretical nutrient value and the nutrient standard value output by the solution, and evaluate the goodness of fit between the solution and the nutrient standard by the coefficient of determination; The expressions for the residuals and coefficients of determination between the theoretical nutrient values ​​and the standard nutrient values ​​are as follows: ； ； In the formula, Represents the residual. The coefficient of determination is represented by the coefficient of determination. This represents the average nutritional standard.

5. The method for regulating the formulation of concentrated feed for equines as described in claim 1, characterized in that, The initial feed formula output from the Excel solver was subjected to feed nutrient index determination and data verification. The verification of the formula's nutritional uniformity and accuracy was achieved through sample preparation, index testing, and statistical analysis. Specifically, this included: According to the raw material ratio in the initial raw material formula output by EXCEL Scheme, weigh 1000g of raw materials, crush the raw materials to a particle size of 1-2mm using a pulverizer, mix them for 20 minutes using a mixer, and take 3 parallel samples from the mixed raw materials using the five-point sampling method. The crude protein content of the parallel samples was determined by the Kjeldahl method, the calcium content was determined by the EDTA titration method, the phosphorus content was determined by the vanadium molybdenum yellow colorimetric method, and the lysine and methionine content of the parallel samples were determined by high performance liquid chromatography. One-way ANOVA P-analysis was performed on the test results of three parallel samples using SPSS 27.0 software. If P>0.05, the feed nutritional uniformity was deemed qualified. The deviation rate Dr between the measured and theoretical values ​​of each indicator was calculated. If Dr≤10%, the formula verification was deemed qualified.

6. The method for regulating the formulation of concentrated feed for equines as described in claim 3, characterized in that, The steps for adjusting the formula based on the nutritional index test results to determine the final concentrated feed formula for equines include: For nutritional indicators that fail formula validation, manual inspection and deviation analysis are performed. If the discrepancy is due to fluctuations in the nutritional content of the raw materials, the raw materials are re-verified. The database is updated; if the problem is due to uneven mixing, the mixing time in the mixer is extended and the sample is prepared again for testing; if the problem is due to deviations in the theoretical formula design, the process proceeds to the ratio fine-tuning stage. When entering the ratio fine-tuning stage, the ratio of corresponding raw materials or additives is adjusted based on the preset standard. The sample is then re-prepared according to the fine-tuned ratio, and feed nutrient index determination and data verification are performed. If all indicators meet Dr≤10% and P>0.05, the formula is determined to be the final equine concentrated feed formula; otherwise, the above steps are repeated until the standard is met.

7. A device for regulating the formulation of concentrated feed for equines, characterized in that, The method for regulating the formulation of concentrated feed for equines as described in any one of claims 1 to 6 includes: A module is established to create a basic database of concentrated feed formulations for equines; wherein the basic database is configured as a structured EXCEL data table containing nutritional standards for equines and feed ingredient parameters; The setting module is used to set the formula control objective function determined by the lowest cost and nutritional standards, as well as the constraints consisting of raw material ratio constraints, nutritional requirement constraints, and total ratio relaxation constraints. The solver module is used to perform dynamic iterative optimization of the solver algorithm, quantitative processing of unsolvable problems, and residual verification of the solver results based on the EXCEL solver module after preprocessing the raw material nutrient data, so as to obtain the initial raw material formula output by the EXCEL solver module. The verification module is used to determine and verify the feed nutrient indexes of the initial raw material formula output by EXCEL programming solution. It verifies the uniformity and accuracy of the formula nutrition through sample preparation, index detection and statistical analysis. The determination module is used to adjust the formula based on the results of nutritional index testing to determine the final concentrate feed formula for equines.

8. A device for regulating the formulation of concentrated feed for equines, characterized in that, The equine concentrated feed formulation control device includes: a memory, a processor, and an equine concentrated feed formulation control program stored in the memory and executable on the processor. When the equine concentrated feed formulation control program is executed by the processor, it implements the steps of the equine concentrated feed formulation control method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a program for regulating the formulation of concentrated feed for equines, which, when executed by a processor, implements the steps of the method for regulating the formulation of concentrated feed for equines as described in any one of claims 1 to 6.

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