Donkey accurate feeding daily ration preparation method based on online table interlocking and linear programming optimization

Abstract

The invention provides a donkey accurate feeding daily ration preparation method based on online table interlocking and linear programming optimization, and relates to the technical field of feeding management. According to the method, a raw material database set containing roughage, concentrated feed and premix and a nutrition requirement database with the body weight interval of 200-700 jin and the daily gain interval of 300-700 g / day as joint indexes are established, and automatic matching of target nutrition requirements is achieved. And after receiving the target body weight, the daily gain and the localized raw material set, the online tabular batching platform calls the database to obtain nutritional requirements, constructs a cost-minimized linear programming model according to raw material nutritional ingredients and prices, and solves to obtain the lowest-cost raw material proportion. And the platform further calculates the daily ration nutrition supply quantity, compares the daily ration nutrition supply quantity with the target demand on line, and outputs a formula satisfaction result and a preparation scheme, so that the whole process of demand matching, cost optimization and nutrition checking of the donkey daily ration is online, and the accuracy and economical efficiency of formula formulation are improved.

Description

Technical Field

[0001] This invention relates to the field of feeding management technology, and in particular to a method for precise feeding ration formulation for donkeys based on online table interlocking and linear programming optimization. Background Technology

[0002] The formulation of fattening rations for donkeys is generally done by farmers based on the nutritional composition and price of concentrates, roughage, and premixes, combined with the donkey's weight and daily weight gain target, either manually or with the help of general formulation software. Current formulation methods mostly rely on raw material nutrient content tables and static feeding standards to establish nutritional constraints and cost targets. The minimum cost formulation is then solved offline using methods such as linear programming, and the nutrient supply of protein, energy, calcium, phosphorus, and salt in the ration is calculated manually.

[0003] With the improvement of large-scale fattening and refined feeding management of donkeys, formula formulation is developing towards online, standardized and closed-loop optimization: on the one hand, it is necessary to unify the nutritional and price information of roughage, concentrate, and premix into a database that can be updated online; on the other hand, it is necessary to digitize the nutritional requirements standards corresponding to different weights of donkeys and daily weight gain targets of 300-700 grams, and make them automatically indexable; furthermore, through an online tabular platform, an integrated service can be realized that "input weight and weight gain target - automatically provide nutritional requirements - select local raw materials - solve for the optimal cost in real time - compare nutritional supply and demand online - output formula and cost".

[0004] Existing general formulation technologies still exist in donkey fattening scenarios:

[0005] 1) Raw material information is scattered, and there is a lack of a mechanism for building and interlocking databases for concentrate feed, roughage feed, and premix feed according to the same field system, which leads to repeated manual conversion after local raw material selection;

[0006] 2) Donkey feeding standards are mostly static tables, which cannot automatically match specific requirements according to weight range and daily weight gain target, making it easy for the formula target to deviate;

[0007] 3) The offline solution and manual calculation are disconnected, lacking online comparison and verification of the daily diet nutrition (dry matter, protein, digestible energy, calcium, phosphorus, salt, calcium-phosphorus ratio, etc.) with the standards, as well as the linkage output of formula cost, batch concentrate formulation and weight gain benefits, resulting in low formula efficiency, lagging cost control and unstable nutritional satisfaction. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method for formulating precise donkey feed rations based on online table interlocking and linear programming optimization. This method realizes the online linkage of the entire process of donkey ration formulation, from automatic matching of nutritional requirements to cost optimization and nutritional verification, which significantly improves the accuracy and economy of the formulation.

[0009] To achieve the above objectives, the present invention provides the following solution:

[0010] A method for precise donkey feeding ration formulation based on online table interlocking and linear programming optimization includes:

[0011] Establish a database set of donkey ration ingredients; the database set includes a roughage ingredient database, a concentrate ingredient database, and a premix ingredient database; each ingredient database stores the nutritional composition indicators and ingredient prices of the corresponding ingredient.

[0012] A donkey nutrition requirement database is established; the database uses a donkey weight range and a target daily weight gain range as a joint index, where the donkey weight range covers 200-700 catties and the target daily weight gain range covers 300-700 g / day; the database stores the set of target nutrient requirements corresponding to each joint index.

[0013] After receiving the target donkey weight and target daily weight gain using an online tabular feed platform, the donkey nutritional requirement database is called according to the database interlocking rules, and the corresponding target nutritional requirement set is output.

[0014] The online tabular feed formulation platform receives the localized raw material set and usage boundary parameters selected by the user from the donkey ration raw material database. It reads the nutritional components and raw material prices of the localized raw material set, constructs and solves a linear programming model with the goal of minimizing the total cost of the ration and the constraint that the ration nutrient supply set formed based on the nutritional components of the localized raw material set satisfies the target nutrient requirement set. The lowest cost donkey ration raw material ratio is obtained.

[0015] The nutritional supply set of the donkey diet is calculated based on the ratio of raw materials in the donkey diet, and compared online with the target nutritional requirement set to output the formula satisfaction result and the donkey diet formulation scheme.

[0016] Preferably, each raw material database in the donkey diet raw material database set establishes a field record for each raw material; each field record includes the raw material name, raw material price, and moisture content, protein content, energy content, calcium content, phosphorus content, and salt content used to characterize the nutritional components.

[0017] Preferably, after receiving the localized raw material set, the online tabular feed platform calculates the nutritional contribution of each raw material based on the daily feeding amount and corresponding nutrient index of each raw material in the localized raw material set, and weights and summarizes the nutritional contribution of each raw material according to the ratio of raw materials in the donkey's daily ration to form the set of daily ration nutritional supply.

[0018] Preferably, the online tabular feed distribution platform calculates the daily feed intake of each raw material based on the moisture content of each raw material in the localized raw material set, obtains the daily dry matter intake, and incorporates the daily dry matter intake into the daily nutrient supply set.

[0019] Preferably, the online tabular ingredient distribution platform calculates the ratio of dietary calcium supply to dietary phosphorus supply based on the set of dietary nutrient supplies to obtain the calcium-phosphorus ratio, and compares the calcium-phosphorus ratio with the corresponding indicators in the set of target nutrient requirements online to output the calcium-phosphorus ratio satisfaction result.

[0020] Preferably, after obtaining the donkey ration ingredient ratio, the online tabular ingredient platform categorizes the localized ingredient set into roughage ingredients, concentrate ingredients, and premix ingredients according to the database type to which the ingredients belong, and calculates the roughage cost, concentrate cost, and premix cost respectively, and outputs the ration cost breakdown results.

[0021] Preferably, after receiving the number of donkeys, the online tabular feed platform calculates the daily feeding amount of each raw material corresponding to the donkey ration ingredient ratio by multiplying it by the number of donkeys, outputs the total daily feeding amount, and outputs the unit daily cost based on the total daily feeding amount and the raw material price.

[0022] Preferably, after receiving the batch formulation weight parameters, the online tabular feed formulation platform extracts the raw material ratios belonging to the concentrate feed raw material database and the premix feed raw material database from the donkey daily ration raw material ratio, performs proportional conversion according to the batch formulation weight parameters, and outputs a suggested concentrate feed formulation weight for batch formulation.

[0023] Preferably, after receiving the live donkey price parameters, the online tabular feed platform calculates the cost and revenue per unit of daily weight gain based on the target daily weight gain, the ratio of raw materials in the donkey's daily feed, and the price of the raw materials, and outputs the economic benefit analysis results of the weight gain.

[0024] Preferably, when the online tabular feed platform detects changes in the target donkey weight, the target daily weight gain, the localized raw material set, the usage boundary parameters, or the raw material price, it automatically re-invokes the donkey nutritional requirement database and re-solves the linear programming model to update the donkey ration ingredient ratio, the ration nutrient supply set, and the formula satisfaction result.

[0025] The present invention discloses the following technical effects:

[0026] This invention establishes a donkey ration ingredient database consisting of a roughage ingredient database, a concentrate ingredient database, and a premix ingredient database. Each ingredient is recorded with fields containing nutritional information and its price, achieving structured and unified management of ingredient information. Compared to existing technologies where ingredient nutritional data is scattered and requires repeated manual calculations, this invention allows all ingredient information to be automatically read, linked, and accessed within a single platform. This avoids the accumulation of errors caused by switching between multiple external tables, improving the accuracy and operability of donkey ration formulation.

[0027] This invention constructs a donkey nutritional requirement database with a combined index of body weight range (200-700 catties) and daily weight gain range (300-700 g / d), achieving automatic matching between target donkey body weight and target daily weight gain. This transforms the determination of nutritional requirements from traditional manual table lookup to automatic system derivation. Compared to the shortcomings of prior art, such as "dispersed feeding standards, inability to automatically match, and easy deviation of formula from growth requirements," this invention can call upon the corresponding set of nutritional requirements in real time based on input, ensuring the scientific nature and consistency of the diet optimization target.

[0028] The online tabular ingredient platform constructed by this invention automatically links the raw material database and the nutritional requirement database, enabling weight input, nutritional requirement generation, raw material selection, and formula calculation to be completed within a single interface. Compared to the fragmented process of "manual input—offline calculation—manual verification" in existing technologies, this invention forms a continuous data chain, achieving "one-time input, full-process linkage," significantly improving the real-time performance and visualization capabilities of formula calculation.

[0029] This invention achieves automatic optimization of raw material ratios by constructing a linear programming model with the objective of minimizing total daily feed costs and the constraint of ensuring that the daily feed nutrient supply meets the target nutrient requirements. Compared with existing technologies that rely on manual experience or rough comparisons for formula adjustments, this invention can obtain the lowest-cost feasible formula through mathematical optimization under the premise of clearly defined nutritional requirements, ensuring both nutritional standards are met and feeding costs are minimized, thereby improving the economic benefits of donkey fattening.

[0030] This invention, after obtaining the raw material ratio of donkey rations, automatically calculates the set of nutrient supplies in the rations and compares it online with the target nutrient requirement set. Simultaneously, it outputs the cost of roughage, the cost of concentrate, the total feed cost, and the ration formulation plan. Compared to existing technologies that require manual recalculation of nutrient content and cannot perform real-time cost analysis, this invention establishes a closed-loop mechanism for nutrient verification and cost assessment. This allows the rationality, cost-effectiveness, and suitability of the formulation to be verified and demonstrated simultaneously, providing donkey farms with a more reliable and economical basis for feed formulation decisions. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the online tabular interlocking calculation interface of the online analysis platform for nutrient composition of donkey precision feeding rations provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the daily feeding feed sheet and cost breakdown output interface generated based on the lowest cost donkey ration ingredient ratio, provided in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of the output interface for the weight gain economic benefit analysis results and batch feed formulation suggestions provided in the embodiments of the present invention.

[0036] Figure 5 A schematic diagram illustrating the economic benefits of weight gain provided in an embodiment of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] The purpose of this invention is to provide a method for formulating precise donkey feed rations based on online table interlocking and linear programming optimization, which realizes the coordinated linkage of donkey ration nutritional requirements acquisition, raw material data retrieval and minimum cost optimization, making the formulation generation process more precise and efficient.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization, comprising:

[0041] Step 100: Establish a database of donkey ration ingredients; the database includes a roughage ingredient database, a concentrate ingredient database, and a premix ingredient database; each ingredient database stores the corresponding nutrient composition indicators and ingredient prices.

[0042] Step 200: Establish a donkey nutrition requirement database; the donkey nutrition requirement database uses the donkey's weight range and the target daily weight gain range as a joint index, with the target daily weight gain range covering 300-700 grams per day; the donkey nutrition requirement database stores the set of target nutrient requirements corresponding to each joint index.

[0043] Step 300: After receiving the target donkey weight and target daily weight gain using the online tabular feed platform, call the donkey nutrition requirement database according to the database interlocking rules and output the corresponding target nutrition requirement set;

[0044] Step 400: Utilize the online tabular feed formulation platform to receive the localized raw material set and usage boundary parameters selected by the user from the donkey ration raw material database. Read the nutritional component indicators and raw material prices of the localized raw material set, construct and solve a linear programming model with the goal of minimizing the total ration cost and the constraint that the ration nutrient supply set formed based on the nutritional component indicators of the localized raw material set meets the target nutrient requirement set, and obtain the lowest cost donkey ration raw material ratio.

[0045] Step 500: Calculate the set of nutrient supply for the donkey diet based on the ratio of raw materials, compare it online with the set of target nutrient requirements, and output the formula satisfaction results and the donkey diet formulation plan.

[0046] In step 100 of this embodiment, the scope of the donkey feed ingredient database set is first determined, and the meaning of "field record" is uniformly defined: a field record is used to represent a data row of a certain feed ingredient in the database, and is used to store the name, price, and various nutritional component indicators of the ingredient. Based on the previously determined nutritional needs of donkeys and the list of commonly used ingredients at the breeding site, this embodiment selects commonly used roughage, concentrate, and premix as the input objects. Roughage includes commonly used straw, silage, and hay ingredients; concentrate includes commonly used energy and protein ingredients; and premix includes commercially available donkey supplements and vitamin and mineral premixes, ensuring that the diet formulation needs of donkeys with a weight of approximately 200 to 700 catties and a daily weight gain of approximately 300 to 700 grams can be covered. In one embodiment, it is preferable to pre-set no fewer than fifteen kinds of concentrate ingredients and no fewer than twenty kinds of roughage ingredients, so that each type of database has sufficient candidate ingredients for linear programming solutions, and each field record contains no fewer than nine data items.

[0047] In this embodiment, when establishing the donkey ration ingredient database, the roughage ingredient database, concentrate ingredient database, and premix ingredient database are modeled using the same field structure to ensure that the three types of ingredients can be uniformly accessed in the same online tabular feed dispensing platform. For each ingredient, this embodiment creates a field record, which includes at least an ingredient name field, an ingredient price field, and fields for characterizing nutritional components such as moisture content, protein content, energy content, calcium content, phosphorus content, and salt content. These fields are written into the corresponding ingredient database in a fixed column order. In specific implementation, this embodiment can form structured field records by inputting the nutritional component data of ingredients from existing feeding standards, the nutritional indicators indicated on donkey feed labels, and the test results of some local ingredients into the corresponding fields. For example, in the roughage ingredient database, a field record is created for a certain hay ingredient, where the ingredient name field records the name of the hay, the ingredient price field records the current purchase price of the hay, and the other nutritional fields record the content of moisture, protein, energy, calcium, phosphorus, and salt, respectively. This ensures that the nutritional and cost information of each ingredient in the database is clear and traceable.

[0048] After establishing the field records, this embodiment performs consistency checks and prepares for subsequent calls to the field content of each raw material database in the donkey ration raw material database set to ensure that the database can support the needs of online table interlocking and linear programming modeling. This embodiment sets up table columns in the online tabular feed batching platform that correspond one-to-one with the field records, allowing field records in the roughage raw material database, concentrate raw material database, and premix raw material database to be directly referenced in the subsequent localized raw material set. Thus, when any raw material is selected, its price field and various nutrient fields can be simultaneously obtained, serving as the basis for calculating the ration nutrient supply set and ration cost. In one implementation, when the total number of raw materials entered in the database reaches more than thirty and each field record has complete nutrient fields, this embodiment can support online formulation solving for single batches containing multiple roughage and concentrate combinations, meeting the common batching scenarios of small and medium-sized donkey farms.

[0049] In step 200 of this embodiment, a donkey nutritional requirement database is established, and a "joint index" is defined as an index structure jointly determined by the donkey's weight range and the target daily weight gain range, used to uniquely point to the corresponding nutritional requirement record. To cover the common weight range of fattening donkeys, this embodiment divides the donkey's weight into a continuous range of approximately 200-700 catties, and further segments each range with a span of approximately 50-100 catties, ensuring that there are no fewer than 6 weight ranges. To match different fattening goals, this embodiment sets the target daily weight gain range to 300-700 grams per day, and divides it into at least 3 weight gain levels. After combining the weight range and the daily weight gain range, no fewer than 12 joint indexes are formed to ensure that the database can support the calling needs of the online feed mixing platform at different growth stages.

[0050] In this embodiment, the "target nutrient requirement set" is defined as the daily nutrient requirement data set of donkeys under the corresponding composite index, which serves as the basis for subsequent nutrient constraints in ration optimization. Based on donkey feeding standards and online feed formulation requirements, this embodiment sets at least six essential nutrient fields for each nutrient record, including dry matter intake, protein requirement, energy requirement, calcium requirement, phosphorus requirement, and salt requirement. Dry matter intake is generally estimated at 2%–3% of the donkey's body weight; for example, a donkey weighing 400 catties corresponds to approximately 8–12 catties of dry matter per day. Protein requirement increases with daily weight gain, reaching approximately 1 catties of protein per day at a daily weight gain of about 700 grams. Energy, calcium, phosphorus, and salt values ​​are entered separately according to different weight and weight gain combinations based on feeding standards. By maintaining a consistent order of these fields, each nutrient requirement record contains no fewer than six nutrient indicators and can be directly referenced by the online tabular feed formulation platform.

[0051] This embodiment standardizes the database field format after establishing all nutrient requirement records, arranging nutrient fields such as dry matter intake, protein, energy, calcium, phosphorus, and salt in a fixed column order to ensure that the platform can accurately locate the corresponding composite index by inputting weight and daily weight gain. This embodiment sets the database size to no fewer than 12 composite index records, enabling automatic matching of the online table within the weight range of 200–700 catties and the daily weight gain range of 300–700 g. Through the standardization of field formats and the consistency of data types, the set of dietary nutrient supplies can be directly compared with the target nutrient requirement set, ensuring that nutrient constraints are calculable, verifiable, and traceable, thereby meeting the subsequent processing needs of linear programming optimization and nutrient matching.

[0052] In step 300 of this embodiment, a target donkey weight input unit and a target daily weight gain input unit are first set up in the online tabular feed distribution platform to receive the individual donkey weight and planned daily weight gain data input by the breeder. This embodiment interlocks the composite index field in the donkey nutritional requirements database established in step 200 with the above input units, so that when the weight input value and the daily weight gain input value fall within a certain weight range and a certain target daily weight gain range, the platform can automatically locate the corresponding nutritional requirement record. For example, when the weight input is 400 jin (approximately 200 catties) and the target daily weight gain input is 500 grams per day, the online tabular feed distribution platform automatically selects the index set "350-450 jin, 400-600 grams per day" based on the composite index, and calls the nutritional requirement records under this index set as the current target nutritional requirement set.

[0053] To ensure the visualization and verifiability of the above-mentioned invocation process, this embodiment sets up a target nutrient display area in the online tabular feed distribution platform. The target nutrient requirements set located by the joint index are output item by item in tabular form, including fields such as dry matter intake, protein requirement, energy requirement, calcium requirement, phosphorus requirement, and salt requirement. Preferably, this embodiment displays the corresponding weight range and target daily weight gain range simultaneously in the display area, making it easier for farmers to verify whether the currently adopted feeding standards are consistent with the production stage. For example, with a body weight of 400 catties and a daily weight gain of 500 g, the platform can output data such as a dry matter intake of approximately 10-12 catties and a protein requirement of approximately 0.8-1.0 catties, providing a clear reference in terms of the order of magnitude of the target nutrient requirements set.

[0054] In step 400 of this embodiment, an online tabular feed formulation platform is used to receive the set of localized feed ingredients selected by the farmer from the donkey ration ingredient database, along with the corresponding usage boundary parameters. This embodiment treats the daily feeding amount of each ingredient in the localized feed ingredient set as a formula variable to be determined, and the usage boundary parameters as the upper and lower limits of this formula variable. By reading the pre-set nutritional component indicators and ingredient prices from step 100, the supply contribution of each ingredient (protein, energy, calcium, phosphorus, salt, etc.) and its unit price information are imported into a linear programming modeling unit. For example, this embodiment can limit the daily feeding amount of a certain roughage ingredient to no more than 10 jin (5 catties), and limit the daily feeding amount of a certain high-priced concentrate ingredient to the range of 1-3 jin (5-1.5 catties), to reflect on-site feeding habits and economic constraints.

[0055] In this embodiment, when constructing the linear programming model, the optimization objective is set as "minimizing the total daily feed cost." The nutritional contributions of the localized raw material set are weighted and summed according to the daily feeding amount of each raw material to form a set of daily feed nutrient supplies. This set of daily feed nutrient supplies is required to meet or approach the target nutrient requirement set output in step 300 in terms of protein, energy, calcium, phosphorus, and salt. This embodiment sets conditions such as the total daily feed cost not exceeding a certain reference cost upper limit, the daily feeding amount of each raw material not being negative, and the daily feeding amount of each raw material not exceeding the usage boundary parameters. This ensures that the solution meets nutritional requirements without violating on-site material constraints. For example, in this embodiment, if the unit price of roughage is concentrated in the range of 0.5 to 1.5 yuan per kilogram, and the unit price of concentrate is concentrated in the range of 1.5 to 4.0 yuan per kilogram, the linear programming solution will automatically find the raw material ratio with the lowest price combination under the premise of satisfying nutritional constraints.

[0056] After solving the linear programming problem, this embodiment uses the resulting daily feed combinations of various raw materials as the raw material ratio for donkey rations. Furthermore, based on the type of raw material database to which the raw materials belong, the localized raw material set is categorized into roughage raw materials, concentrate raw materials, and premix raw materials. The cost breakdown of each of these three types of raw materials in the ration is calculated. For example, in a certain solution, the cost of roughage raw materials may account for 40%–60% of the total ration cost, concentrate raw material costs may account for 30%–50%, and premix raw material costs may account for 5%–15%. This embodiment displays these breakdown costs along with the total ration cost in an online tabular feed formulation platform, facilitating farmers' intuitive assessment of the cost structure of the formulation and the direction of adjustments.

[0057] To adapt to dynamic parameter changes during production, this embodiment sets up automatic update trigger rules in the online tabular feed formulation platform. When changes are detected in the target donkey's weight, target daily weight gain, localized raw material set, usage boundary parameters, or raw material prices, the process of re-retrieving nutrient requirements records and resolving the linear programming problem is triggered. This embodiment can define the change judgment as follows: any input value changes by at least one unit of body weight; the target daily weight gain changes by at least 50 grams per day; the price of a single raw material changes by at least 10%; or any raw material is added to or removed from the localized raw material set. When the trigger conditions are met, this embodiment automatically relocates the joint index according to the new weight and daily weight gain inputs, updates the target nutrient requirement set, and resolves the new donkey ration ingredient ratios and cost breakdowns based on the latest raw material prices and usage boundary parameters, ensuring that the formula always matches the current production conditions.

[0058] After obtaining the donkey ration ingredient ratio obtained in step 400, this embodiment first calculates the nutritional contribution of each ingredient based on its daily feeding amount and corresponding nutrient indicators in the donkey ration ingredient database. In this embodiment, "nutritional contribution" is defined as the actual supply value of a certain ingredient to nutrients such as protein, energy, calcium, phosphorus, salt, and dry matter under the current feeding amount. For example, if the feeding amount of a certain concentrate ingredient in the formula is 3 jin (1.5 catties), and its protein content is recorded as 12%, then the protein contribution is approximately 0.36 jin (0.13 catties). By calculating and summing the nutritional contributions of roughage, concentrate, and premix, this embodiment forms a complete set of daily ration nutrient supplies, with no fewer than 6 nutrient items, which can meet the needs of subsequent comparisons.

[0059] To ensure the uniformity and traceability of the calculated nutrient supply set, this embodiment sets up a weighted aggregation logic throughout the entire process in the online tabular feed formulation platform: the nutrient contribution of each raw material is proportionally added according to the proportion of raw materials in the donkey's daily ration, so that the final calculated nutrient supply set directly corresponds to the overall nutritional level of the actual daily ration. For example, in a certain formulation, roughage contributes 60%–70% of dry matter, concentrate contributes 50%–60% of protein, and premix contributes 10%–20% of minerals; this embodiment weights each nutrient item according to the above distribution to ensure that the final calculated nutrient supply result is consistent with the actual feeding structure.

[0060] This embodiment incorporates the method of dry matter intake formation into the calculation process of the dietary nutrient supply set. The online tabular feed formulation platform converts the feeding amount into dry matter mass based on the moisture content of each ingredient and sums them to obtain the complete dietary dry matter intake. During the fattening stage when the animal weighs approximately 400 catties, the dry matter intake in this embodiment typically falls within the range of 8-12 catties. Simultaneously, this embodiment further calculates the calcium-to-phosphorus ratio based on the ratio of calcium to phosphorus supply in the dietary nutrient supply set and compares it with the required range in the target nutrient requirement set obtained in step 300. If the ratio deviates from the target range, for example, below 1.5 or above 2.5, this embodiment marks the deviation as a nutrient deficiency and prompts the user to adjust the formulation.

[0061] This embodiment compares each nutrient item in the daily nutrient supply set with the target nutrient requirement set item by item to form a "formula satisfaction result". This embodiment defines "satisfaction result" as providing a judgment of whether each nutrient indicator is satisfied or not, and can display the difference to guide farmers in determining whether the current formula meets the target. When all nutrient items meet or slightly exceed the target requirements, this embodiment marks the formula as a satisfactory solution and automatically generates a donkey ration formulation plan, including the daily feeding amount of each raw material, the cost breakdown of the three types of raw materials (usually roughage accounts for 40%–60%, concentrate accounts for 30%–50%, and premix accounts for 5%–15%), and the total ration cost, allowing farmers to intuitively understand the formula structure.

[0062] After formulating the donkey ration, this embodiment can also implement various extended functions based on the obtained nutrient supply set and raw material ratio. For example, when receiving 50 donkeys, this embodiment multiplies the daily feeding amount per donkey by 50 to form the total daily feeding amount; when receiving a batch of 1000 catties, this embodiment converts the ratio of concentrate and premix raw materials proportionally according to this weight to generate batch feeding suggestions. In addition, when the weight input, target daily weight gain, or raw material price change exceeds the set threshold, such as a weight change exceeding 50 catties, a daily weight gain change exceeding 50 grams per day, or a raw material price increase exceeding 10%, this embodiment will automatically recalculate the nutrient supply set of the ration and re-output the satisfaction result to ensure that the formula always conforms to the latest production conditions.

[0063] This embodiment belongs to a data-driven diet optimization method. The linear programming model itself is a conventional mathematical tool in this field. This embodiment has fully disclosed the source of the objective function, the source of constraints, and the meaning of variables. Those skilled in the art can construct the corresponding model based on this without creative effort.

[0064] like Figure 2 As shown, the online tabular feed distribution platform interface of this embodiment includes a basic information input area, a raw material selection and calculation area, and a nutrient summary and comparison area. The basic information input area is used to enter key parameters such as ranch name, donkey breed, current weight, expected weight, number of donkeys, body condition score, and target daily weight gain. In the example, the current weight is 300 jin (150 catties), the target daily weight gain is 500 grams per day, and the number of donkeys is 16. After receiving the above input, the platform automatically calls the donkey nutrient requirement database according to the database interlocking rules, outputs the target nutrient requirement set corresponding to the weight and daily weight gain on the right side of the interface, and simultaneously provides prompts for the feeding structure of roughage and concentrate. For example, in the example, the roughage feeding amount is 5.3 jin (2.5 catties), the concentrate feeding amount is 2.6 jin (1.3 catties), and the total feeding amount is 7.9 jin (3.4 catties), corresponding to a dry matter ratio of approximately 68% and 32%.

[0065] Figure 2The raw material selection and calculation area lists the localized raw material set selected by the user from the donkey ration raw material database in tabular form. Each row corresponds to fields such as raw material name, moisture content, unit price, daily feeding amount, percentage ratio, dry matter intake, protein content, energy, calcium content, phosphorus content, and salt content. Examples include wheat straw, wheat bran, peanut vines, corn, soybean meal, fermented feed, H-stage growth supplement feed, and 2% compound premix for young donkeys. The platform automatically generates a set of daily nutrient supply based on the nutritional components of each raw material and the daily feeding amount. It then provides the key nutrient supply results and a comparison with the target nutrient requirement set in the nutrient summary and comparison area at the bottom. For example, the dry matter intake in the example is 3.7 kg / day, the protein supply is 15.2%, the energy supply is 9.8 MJ / kg, the calcium supply is 0.3%, the phosphorus supply is 0.5%, and the salt supply is 1%. It also displays the calcium-to-phosphorus ratio of 0.6 and the corresponding feeding standard of "300 catties @ 500 g / day" as a reference row, which is used to intuitively judge the formula's sufficiency.

[0066] like Figure 3 As shown, after solving the linear programming problem and obtaining the feed ingredient ratio for donkeys, this embodiment outputs a daily feeding schedule. Taking the "Dezhou Donkey 300 jin (body weight) Feed Schedule" as an example, the schedule lists the amount of feed per meal and per day for each donkey, item by item. In the example, wheat bran is 39.1 jin / meal, 78.1 jin / day; peanut vines are 3.4 jin / meal, 6.9 jin / day; corn is 6.0 jin / meal, 12.1 jin / day; soybean meal is 6.3 jin / meal, 12.7 jin / day; cottonseed meal is 7.9 jin / meal, 15.7 jin / day; baking soda is 0.6 jin / meal, 1.3 jin / day; and salt is 0.05 jin / meal, 0.1 jin / day. The summary at the bottom of the feed schedule shows a total of 63.5 jin per meal and a total of 126.9 jin per day, used to guide farms in implementing actual feeding by meal or day.

[0067] Figure 3 The right side is the cost breakdown output area. In this example, the localized raw material set is categorized into roughage raw materials and concentrate raw materials according to the database type to which the raw materials belong, and the costs of the two categories are calculated separately. In the example, the total roughage is 85 catties, the total cost of roughage is 20 yuan / day, and the unit price of roughage is 0.23 yuan / catties; the total concentrate is 42 catties, the total cost of concentrate is 55 yuan / day, and the unit price of concentrate is 1.31 yuan / catties. The platform provides a summary of the total daily feed cost at the bottom, which is 75 yuan / day in the example. This allows farmers to obtain the lowest cost daily feed ratio, the breakdown of costs, and the total cost conclusion at the same time, making it easier to adjust for cost-sensitive raw materials without changing nutritional satisfaction.

[0068] like Figure 4As shown in the lower part, this embodiment outputs a batch preparation suggestion for concentrated feed after receiving the batch preparation weight parameters. In the example, the batch preparation weight is set to 1000.0 catties. The platform extracts the raw material ratios belonging to the concentrated feed raw material database and the premix raw material database from the donkey daily ration raw material ratio and performs proportional conversion to obtain the weight of each raw material used for one-time preparation of concentrated feed. In the example, corn is 288.8 catties, soybean meal is 302.9 catties, cottonseed meal is 375.8 catties, baking soda is 30.3 catties, and salt is 2.1 catties, totaling 1000.0 catties. This batch preparation suggestion corresponds to the daily feeding feed sheet, enabling farms to complete the large-scale premixing of concentrated feed and the daily feeding according to the same lowest cost ratio.

[0069] like Figure 5 As shown, this embodiment, based on the output donkey daily feed formulation plan, further calculates the economic benefits of weight gain by combining live donkey price parameters and target daily weight gain, and displays the results in the "Donkey Weight Gain Economic Benefit Analysis" area. In the example, the live donkey price is 46.0 yuan / jin, and the target daily weight gain is 500 grams per day. The platform calculates the weight gain cost and benefit based on the feed cost per unit day and the daily weight gain target. In the example, the feed cost per unit day is 4.67 yuan / head, corresponding to a weight gain cost of 4.67 yuan / jin, and a weight gain benefit of 41.3 yuan / day. This result is used to provide farmers with a basis for judging the economics of the current formula under a given price and weight gain target.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0071] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for precise donkey feeding ration formulation based on online table interlocking and linear programming optimization, characterized in that, include: Establish a database set of donkey ration ingredients; the database set of donkey ration ingredients includes a roughage ingredient database, a concentrate ingredient database, and a premix ingredient database. Each raw material database stores the nutritional components and prices of the corresponding raw materials; A donkey nutrition requirement database is established; the donkey nutrition requirement database uses the donkey weight range and the target daily weight gain range as a joint index, the donkey weight range covers 200-700 catties, and the target daily weight gain range covers 300-700 g / day; the donkey nutrition requirement database stores the set of target nutrient requirements corresponding to each joint index; After receiving the target donkey weight and target daily weight gain using an online tabular feed platform, the donkey nutritional requirement database is called according to the database interlocking rules, and the corresponding target nutritional requirement set is output. The online tabular feed formulation platform receives the localized raw material set and usage boundary parameters selected by the user from the donkey ration raw material database. It reads the nutritional components and raw material prices of the localized raw material set, constructs and solves a linear programming model with the goal of minimizing the total cost of the ration and the constraint that the ration nutrient supply set formed based on the nutritional components of the localized raw material set satisfies the target nutrient requirement set. The lowest cost donkey ration raw material ratio is obtained. The nutritional supply set of the donkey diet is calculated based on the ratio of raw materials in the donkey diet, and compared online with the target nutritional requirement set to output the formula satisfaction result and the donkey diet formulation scheme.

2. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, Each raw material database in the donkey ration raw material database set establishes a field record for each raw material; each field record includes the raw material name, raw material price, and moisture content, protein content, energy content, calcium content, phosphorus content, and salt content used to characterize the nutritional components.

3. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, After receiving the localized raw material set, the online tabular feed platform calculates the nutritional contribution of each raw material based on the daily feeding amount and corresponding nutrient index of each raw material in the localized raw material set, and weights and summarizes the nutritional contribution of each raw material according to the ratio of raw materials in the donkey's daily ration to form the set of daily ration nutritional supply.

4. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, The online tabular feed distribution platform calculates the daily feed intake of each raw material based on its moisture content in the localized raw material set, thereby obtaining the daily dry matter intake, and incorporates the daily dry matter intake into the daily nutrient supply set.

5. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, The online tabular ingredient distribution platform calculates the ratio of dietary calcium supply to dietary phosphorus supply based on the set of dietary nutrient supplies to obtain the calcium-phosphorus ratio, and compares the calcium-phosphorus ratio with the corresponding indicators in the set of target nutrient requirements online to output the calcium-phosphorus ratio satisfaction result.

6. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, After obtaining the donkey ration ingredient ratio, the online tabular feed formulation platform categorizes the localized ingredient set into roughage ingredients, concentrate ingredients, and premix ingredients according to the database type to which the ingredients belong, and calculates the cost of roughage, concentrate, and premix respectively, and outputs the ration cost breakdown results.

7. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, After receiving the headcount parameter, the online tabular feed distribution platform calculates the daily feed amount of each ingredient corresponding to the donkey's daily feed ingredient ratio by multiplying it by the headcount parameter, outputs the total daily feed amount, and outputs the unit head daily cost based on the total daily feed amount and the ingredient price.

8. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, After receiving the batch formulation weight parameters, the online tabular feed formulation platform extracts the ingredient ratios belonging to the concentrate feed ingredient database and the premix feed ingredient database from the donkey daily ration ingredient ratio, performs proportional conversion according to the batch formulation weight parameters, and outputs a suggested concentrate feed ingredient weight for batch formulation.

9. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, After receiving the live donkey price parameters, the online tabular feed platform calculates the cost and revenue per unit of daily weight gain based on the target daily weight gain, the ratio of raw materials in the donkey's daily feed, and the price of the raw materials, and outputs the economic benefit analysis results of weight gain.

10. The method for formulating precise donkey feeding rations based on online table interlocking and linear programming optimization according to claim 1, characterized in that, When the online tabular feed platform detects changes in the target donkey weight, target daily weight gain, localized raw material set, usage boundary parameters, or raw material prices, it automatically re-invokes the donkey nutritional requirement database and re-solves the linear programming model to update the donkey ration ingredient ratio, the ration nutrient supply set, and the formula satisfaction result.

Images