Nutrition control method, system and equipment for feed processing and storage medium

Abstract

The invention discloses a nutrition control method, system and equipment for feed processing and a storage medium. The method comprises the following steps: after feed raw materials are crushed, arranging an online near-infrared spectrometer before the feed raw materials enter a proportioning bin, collecting spectral data of the raw materials in real time, and analyzing to obtain the content of key nutritional ingredients such as crude protein; establishing a corresponding relationship between the raw material detection data and the feeding time and weight of the batching bin; based on a set feed formula and a standard nutrition target value, in combination with real-time detection data, the proportion of main raw materials is dynamically adjusted through an optimization algorithm, so that accurate control over nutritional ingredients of the compound feed is achieved. The system comprises an online detection module, a data processing and analysis module, a formula optimization module, an ingredient execution control module and the like. Compared with the prior art, the control precision of nutritional ingredients of the feed can be remarkably improved, formula deviation caused by nutrient fluctuation of the raw materials is reduced, the utilization efficiency of the raw materials is improved, and good practicability and popularization value are achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of feed processing, and particularly relates to a nutrition control method, system, device and storage medium for feed processing. BACKGROUND

[0002] The nutritional composition of animal feed is directly related to the growth rate of animals, feed conversion efficiency, health status, and the quality of the final product. In order to maximize the production potential of animals, the feed formula is usually designed according to the physiological needs of animals at different growth stages, and the energy, protein, fat, cellulose, minerals and vitamins are accurately set as multiple nutritional indicators. However, this ideal nutritional setting is often difficult to effectively guarantee in the actual processing and production process, resulting in significant nutritional deviation between the theoretical formula and the finished feed.

[0003] In the feed production link, the fluctuation of raw material composition is one of the key factors that make it difficult to control nutrition. Even in the same kind of raw material, different batches, different places of origin or different processing techniques will cause significant differences in nutritional composition, especially protein content. Such differences are difficult to perceive in time in traditional processes, so the potential deviation can only be compensated by increasing the design margin. This "redundant safety design" ensures a certain level of nutrition, but also directly leads to waste of raw material resources and rise in production costs.

[0004] On the other hand, the uncertainty of the feed mixing process will also cause the discrete distribution of the nutritional level of the finished feed. The batching process is often affected by many factors such as batching accuracy, material flowability, raw material particle size difference, equipment operation fluctuation, etc. Even if the formula proportioning is accurate, the distribution of actual components in the mixture may also deviate, further amplifying the uncertainty of nutrition control. In addition, the rapidization of production rhythm and the continuous switching between batches also make it difficult for the traditional control mode relying on static formula to respond to raw material and process changes in real time.

[0005] Current feed nutrition control systems mostly take static formula as the core, and raw materials are fed by pre-setting the average nutritional value of the raw materials. However, this method ignores the reality of raw material fluctuations in the production process and cannot identify and adjust the real-time nutritional differences of raw materials during the processing process. This mode leads to unpredictable deviation between formula execution and actual products, ultimately affecting the nutritional consistency of finished feed and the feeding effect of animals. SUMMARY

[0006] In view of the problems of low control accuracy of nutritional components, difficulty in real-time response to raw material fluctuations, and lag of traditional formula adjustment in the current feed processing process, the present application provides a nutrition control method, system, device and storage medium for feed processing.

[0007] Specifically, the technical scheme provided by the present application is as follows: A nutrition control method for feed processing, comprising the steps of: obtaining a raw formula of feed and theoretical values of contents of various nutrients of each raw material referred to by the raw formula; real-time obtaining actual values of contents of various nutrients of each raw material through near-infrared spectrum analysis; correcting the raw material ratio of the raw formula according to differences between the actual values and the theoretical values of the contents of various nutrients of each raw material; preparing finished feed with consistent total contents of various nutrients with the raw formula according to the corrected formula ratio.

[0008] Preferably, the near-infrared spectrum sampling data of each raw material after being crushed are obtained at certain time intervals, and corresponding values of the contents of various nutrients are obtained through spectrum analysis, and the average value of the content of the same nutrient obtained from multiple sampling data of the same raw material within a certain time is taken as the actual value of the content of the nutrient in the raw material.

[0009] Further, it is assumed that the raw formula contains n kinds of raw materials, and the raw material ratio is as follows: ; The theoretical values of contents of m kinds of nutrients contained in each raw material referred to by the raw formula are as follows: ; The actual values of contents of various nutrients of each raw material obtained in real time are as follows: ; Therefore, the corrected raw material ratio should be as follows: ; Wherein, represents a raw ratio vector composed of the raw material ratio of the n kinds of raw materials in the raw formula, represents a corrected ratio vector composed of the corrected raw material ratio of each raw material; represents a theoretical content matrix composed of the theoretical values of contents of the m kinds of nutrients contained in the n kinds of raw materials, represents the theoretical value of the mth nutrient contained in the nth raw material; represents an actual content matrix composed of the actual values of contents of the m kinds of nutrients contained in the n kinds of raw materials, represents the actual value of the mth nutrient contained in the nth raw material; if , , is the inverse matrix of , otherwise the generalized inverse matrix of is taken to minimize the cost of raw feed or the change of formula.

[0010] Further, by adjusting the proportion of two raw materials in the original formula to make the total content of a certain nutrient component of the finished feed consistent with the original formula: Suppose the proportions of n raw materials in the original formula are respectively: … , The theoretical values of the content of a certain nutrient component of the n raw materials are respectively: … , The actual values of the content of the nutrient component of the n raw materials are respectively: … , If the proportions of the first two raw materials in the formula are adjusted, the displacement amount is calculated: , The adjusted proportions of the n raw materials are: 、 、 … .

[0011] A nutrient control system based on the above method, comprising an online detection module, the online detection module comprising a plurality of near-infrared spectrometers respectively installed between each raw material grinder and the batching bin, for obtaining spectral sampling data of each raw material after grinding at a certain time interval, and further comprising a spectral analysis system for obtaining the content of each nutrient component according to the spectral sampling data.

[0012] Further, it further comprises a formula optimization module, the formula optimization module comprising a data processing module and a formula correction module, the data processing module being used for calculating the mean value of the content of each nutrient component of each raw material obtained by near-infrared spectrum analysis, and taking it as the actual value of the content of the nutrient component of the raw material, and further being used for calculating the actual proportion of each raw material according to the difference between the actual value of the content of each nutrient component of each raw material and the theoretical value of the content of each nutrient component of each raw material referred to by the original formula, to correct the original formula.

[0013] Further, it further comprises a central control system, the central control system being used for controlling the execution of the feed preparation process according to the corrected formula proportion, the central control system being configured with a database for storing the original formula of the feed, the theoretical value of the content of each nutrient component of each raw material referred to by the original formula, and the corrected formula and the value of the content of each nutrient component of each raw material referred to by the corrected formula.

[0014] An electronic device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the steps of the above method when executing the program.

[0015] A computer readable storage medium stores a computer program, when the computer program is executed by a processor, the processor executes the steps of the above method.

[0016] The present application breaks through the problems of raw material waste and nutritional deviation caused by static formula and nutritional redundancy design in traditional feed production. By introducing real-time nutritional data acquisition, dynamic analysis and formula correction mechanism in the feed processing flow, the present application realizes precise regulation based on actual raw material nutritional fluctuation, so that the nutritional level of the finished feed is closer to the theoretical formula requirement, effectively reducing the gap between the formula target value and the actual value.

[0017] Unlike the past, which only relies on the nutritional database in the formula design stage and presets the average value of the raw material nutrition, the present application uses real-time spectral data collected by a near-infrared spectrometer, combined with multi-point continuous detection and statistical mean extraction method, to overcome the problems of large single-point detection error and poor sampling representativeness. Especially, the spectrometer is installed on the conveying path of the crushed material, which not only ensures that the detected raw material is uniform in particle size and stable in state, but also facilitates precise matching of time and flow with the batching system, ensuring that the detection data is highly consistent with the actual feed quantity.

[0018] Based on the above high-quality raw material nutritional detection data, the present application further constructs a formula dynamic optimization model, analyzes the difference between the actual detection value and the theoretical nutritional requirement, and adjusts the ratio of main raw materials in real time through an optimization algorithm that minimizes raw material cost or minimizes formula variation, so as to accurately control the nutritional indicators of the finished feed.

[0019] Compared with the prior art, the present application not only improves the consistency and controllability of the nutritional of the finished feed, significantly reduces the raw material waste and cost burden caused by excessive nutritional design, but also has good modularity and scalability, can be widely applied to intelligent upgrading and reconstruction of various feed factories, has high feasibility and industry promotion potential, and provides key technical support for realizing efficient, economical and green feed production system. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application.

[0021] Figure 1 is a technical framework schematic diagram of the nutritional control method provided by an embodiment of the present application; Figure 2 is a crude protein content spectrum detection curve diagram before and after the batching bin provided by an embodiment of the present application; Figure 3 is a schematic diagram of an independent deployment of the nutritional control system provided by an embodiment of the present application; Figure 4 This is a schematic diagram of the integrated deployment of a nutrition control system provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, other embodiments obtained by those skilled in the art without creative effort are all within the scope of protection of the present invention.

[0023] Example 1 This embodiment provides a nutritional control method for feed processing.

[0024] like Figure 1 As shown, animal feed production lines generally include crushing, batching, mixing, conditioning, pelleting, cooling and packaging. Large-scale feed mills are generally equipped with near-infrared spectrometers for sampling and testing the moisture content and crude protein content of materials. Figure 2 The crude protein content curves were obtained by continuous sampling of crushed rapeseed meal as it entered and exited the feed silo, using an offline near-infrared spectroscopy instrument. The samples were from the same batch of material and obtained through continuous sampling within 10 minutes; however, the test data still showed significant fluctuations, indicating that conventional closed-loop nutrient control based on online detection lacks a mature technical solution for feed management.

[0025] The nutrient control method provided in this embodiment involves installing an online near-infrared spectrometer between the grinder and the feed hopper to detect the content of nutrients such as crude protein in the ground raw materials. Then, based on the feed formula, the reference values ​​of the nutrient content of the raw materials used in the formula design, and the actual values ​​of the nutrient content of the raw materials detected online, the feed formula is optimized so that the nutritional level of the finished feed product is closer to the theoretical formula requirements.

[0026] Installing an online near-infrared spectrometer between the crusher and the batching bin to detect the content of crude protein and other nutrients in the crushed raw materials has the following advantages: the crushed material has a smaller particle size and more stable distribution than the uncrushed material, resulting in higher detection accuracy; feed formulations are diverse, and it is difficult and inaccurate to calibrate mixed feeds with an online near-infrared spectrometer, while the detection accuracy of a single material is much higher.

[0027] During the process of material entering the batching bin, the spectral data of the material are continuously and uninterruptedly detected by the online near-infrared spectrometer, and the contents of the nutritional components such as crude protein of the material are analyzed by spectrum analysis; during batching, the material enters the mixing machine in batches for batch mixing, and a large amount of online near-infrared detection data of each kind of main raw material in each mixing batch is obtained, and the mean value of a large amount of data instead of one measurement value can significantly improve the accuracy of the data (assuming that the deviation of the sensor detection data is ±1, the deviation of the mean value of 100 data is ±0.1, and the detection accuracy will be improved by one order of magnitude).

[0028] Assuming that there are n kinds of main raw materials in the feed formula, the mass proportion of each raw material is (1) During formula design, the reference values (raw material database) of the m kinds of nutritional components contained in the n kinds of main raw materials (2) The actual contents of the m kinds of nutritional components contained in the n kinds of main raw materials detected online are (3) The mass proportion of each raw material contained in the formula optimized based on the online detection data is (4) If , then there is a unique solution; otherwise, the generalized inverse matrix is selected to minimize the feed raw material cost or the formula change, and then is obtained.

[0029] If only the crude protein content is balanced, the protein content in the finished feed can be controlled by fine-tuning the proportion of the main raw materials in the feed formula, that is, by replacing the proportions of a plurality of main raw materials with different protein content in equal amounts to control the crude protein content in the finished feed.

[0030] For example, the protein balance is achieved by adjusting the contents of the first two main raw materials, and the algorithm is as follows: The proportions of the n kinds of main raw materials in the feed formula are: … , The theoretical crude protein contents of the n kinds of main raw materials in the feed formula are: … , The measured crude protein contents of the n kinds of main raw materials in the feed formula are: … , The first two main raw materials are replaced in equal amounts, and the replacement amount is (5) The proportion of n main raw materials in the adjusted feed formula: , , … .

[0031] Table 1 Reference crude protein content and measured crude protein content of raw materials of a certain feed formula

[0032] As shown in Table 1, the reference protein content and the measured protein content of each raw material of a certain feed formula, and the proportion of each raw material before and after formula optimization. Through calculation, the total theoretical crude protein content of the formula feed is 15.51%, if the formula is not adjusted, the actual crude protein content of the finished feed is 14.97%. In order to keep the crude protein content of the finished feed consistent with the theoretical value of the formula, the proportion of some raw materials in the original formula needs to be corrected.

[0033] Assuming that the proportion of second-class corn and rapeseed meal is adjusted, the replacement amount calculated by formula (5) is-1.64%, then the proportion of second-class corn in the corrected formula should be 67.00%+(-1.64%) = 65.36%, and the proportion of rapeseed meal should be 15.00%-(-1.64%) = 16.64%, and the proportions of other raw materials remain unchanged. At this time, according to the corrected formula, the finished feed with a crude protein content of 15.51% can be obtained.

[0034] Example two Based on the above method, the present embodiment provides a nutrition control system for feed processing. As shown in Figure 3 and Figure 4 , it mainly includes an online detection module, a formula optimization module and a batching execution control module (central control system).

[0035] The online detection module includes a plurality of near-infrared spectrometers respectively installed between each raw material pulverizer and the batching bin, which is used to obtain the spectral data of each raw material after pulverization according to a certain sampling time interval, and also includes a spectral analysis system for obtaining the corresponding nutrient content according to the spectral data.

[0036] The formula optimization module mainly includes a data processing module and a formula correction module, the data processing module is used to calculate the mean value of each nutrient content of each raw material obtained by near-infrared spectrum analysis, and take it as the actual value of the nutrient content of the raw material, and also used to calculate the actual proportion of each raw material according to the difference between the actual value of each nutrient content of each raw material and the theoretical value of each nutrient content of each raw material referred to in the original formula, in order to correct the original formula.

[0037] The ingredient execution control module (central control system) is configured to control the feed preparation process according to the modified formula, and the central control system is configured with a database for storing the original formula of the feed, the theoretical values of the contents of each nutrient of each raw material referred to by the original formula, and the modified formula and the contents of each nutrient of each raw material referred to by the modified formula.

[0038] The above system can perform the nutrient control method described in embodiment one, has the corresponding functional modules and beneficial effects of the method, and the technical details not described in detail in this embodiment can be referred to the nutrient control method provided in embodiment one of the present application.

[0039] For the central control system that cannot directly access the online near-infrared spectrometer, the spectrum analysis system and the formula optimization module are deployed on the local computer (such as Figure 3 ); for the central control system that can directly access the online near-infrared spectrometer, the spectrum analysis system and the formula optimization module are directly deployed on the central control system (such as Figure 4 ).

[0040] Regarding the data acquisition, processing and storage in the system, the following forms can be used: Suppose the time difference between the spectrum collection time point of the online near-infrared sensor and the raw material entering the warehouse time point is: (6) Wherein, represents the time point of collecting the spectrum of the near-infrared probe numbered i; is the time point of the collected raw material entering the ingredient bin numbered i.

[0041] Table 2: Transport time from near-infrared probe point to ingredient bin

[0042] The online near-infrared spectrometer can be installed at any position between the crusher and the ingredient bin (as long as it meets the detection requirements of the near-infrared spectrometer), for example, the online near-infrared spectrometer is installed at the root of the elevator, and the time difference is determined by the running speed of the elevator and the scraper. One elevator can supply material for multiple bins, and the system can obtain the time difference data from Table 2 according to the number of the elevator and the grain bin (the data in the table is obtained by actual measurement or calculation, and the table corresponds to n ingredient bins transported by 4 elevators).

[0043] As shown in Table 3, the moisture content and crude protein content data obtained after spectrum analysis are stored with the spectrum collection time as the index.

[0044] Table 3: Spectrum data collection time and corresponding spectrum analysis data

[0045] The ingredient bin needs to record the time when the raw material enters the ingredient bin and the weight of the raw material in the bin, for example, the raw material passes through the near-infrared probe point numbered i into the bin j, and in the process of the raw material entering the ingredient bin, the corresponding time T j The weight of the raw material in the ingredient bin at the moment G j .

[0046] Table 4: Weight of material in the bin detected when the material enters the ingredient bin and detection time

[0047] According to Table 3 and Table 4, the correlation is established by formula (6) and Table 2, so that the weight of the raw material in the ingredient bin and the moisture content and crude protein content detected by the near-infrared sensor establish a corresponding relationship as shown in Table 5.

[0048] Table 5: Weight of material in the bin and corresponding near-infrared spectrum detection data

[0049] Take the consumption of 0.4T material in a certain time as an example: the weight of the material discharged from the bin j is 0.4T, according to the discharge weight, Pi1 and Pi2 are extracted from Table 5, at this time, the extracted Pi1 and Pi2 are both 5 data, and the mean value of this group of data represents the measurement value of this batch of ingredients (moisture content ≈ 14.00%, crude protein content ≈ 7.67%). After taking the material, the data in Table 5 is updated, and the updated data is shown in Table 6.

[0050] Table 6: Weight of material in the bin and corresponding near-infrared spectrum detection data

[0051] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software plus a general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions essentially or say the part that contributes to the related art can be embodied in the form of a software product, which can be stored in a computer readable storage medium such as ROM / RAM, magnetic disk, optical disc, etc., including a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some part of the embodiment.

[0052] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not limited to them; under the idea of the present application, the technical features of the above examples or different examples can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above, which are not provided in details for simplicity; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for nutritional control in feed processing, characterized in that, Including the following steps: Obtain the original feed formula and the theoretical values ​​of the content of each nutrient component of each raw material referenced therein; The actual values ​​of the content of each nutrient component in each raw material are obtained in real time through near-infrared spectroscopy analysis. Based on the difference between the actual and theoretical values ​​of the content of each nutrient in each raw material, the raw material ratio of the original formula was adjusted. The final feed was prepared according to the revised formula ratio, and the total content of each nutrient was consistent with the original formula.

2. The nutrient control method as described in claim 1, characterized in that: Near-infrared spectral sampling data of each raw material after crushing are obtained at certain time intervals, and the corresponding values ​​of each nutrient content are obtained by spectral analysis. The average value of the same nutrient content obtained from multiple sampling data of the same raw material within a certain period of time is taken as the actual value of the nutrient content in the raw material.

3. The nutritional control method as described in claim 1, characterized in that: Assume the original formula contains n ingredients, and the proportions of each ingredient are as follows: ； The theoretical values ​​for the content of m kinds of nutrients contained in each raw material referenced in the original formula are: ； The actual values ​​of the content of each nutrient in each raw material obtained in real time are: ； Therefore, the corrected proportions of each raw material should be: ； in, This represents the ratio of n ingredients in the original formula. The original proportion vector formed, This represents a corrected proportion vector containing the adjusted proportions of each raw material. This represents a theoretical content matrix composed of the theoretical values ​​of the content of m nutrients contained in the n raw materials. This represents the theoretical value of the content of the m-th nutrient in the n-th raw material; This represents the actual content matrix, which consists of the actual values ​​of the content of m nutrients contained in the n raw materials. This represents the actual value of the content of the m-th nutrient component contained in the n-th raw material; if ,but for The inverse matrix, otherwise When the feed ingredient cost is minimized or the formula change is minimal The generalized inverse matrix.

4. The nutritional control method as described in claim 1, characterized in that, By adjusting the ratio of two certain ingredients in the original formula, the total content of a certain nutrient in the finished feed can be made consistent with the original formula: Assume the proportions of the n ingredients in the original formula are as follows: … , The theoretical values ​​of the content of a certain nutrient component of these n raw materials are as follows: … , The actual values ​​of the content of this nutrient component for these n raw materials are as follows: … , If the ratio of the first two ingredients in the formula is adjusted, then calculate the replacement amount: , The adjusted ratio of the n raw materials is as follows: , , … .

5. A nutrition control system based on the method of any one of claims 1 to 4, characterized in that, It includes an online detection module, which comprises multiple near-infrared spectrometers installed between each raw material crusher and the batching bin, for acquiring spectral sampling data of each raw material after crushing at certain time intervals, and also includes a spectral analysis system for obtaining the content of each nutrient component based on the spectral sampling data.

6. The nutrition control system as described in claim 5, characterized in that, The system includes a formula optimization module, which comprises a data processing module and a formula correction module. The data processing module is used to calculate the average content of each nutrient of each raw material obtained through near-infrared spectroscopy analysis and use it as the actual value of the content of that nutrient of the raw material. It is also used to calculate the actual ratio of each raw material based on the difference between the actual value of the content of each nutrient of each raw material and the theoretical value of the content of each nutrient of each raw material referenced in the original formula, so as to correct the original formula.

7. The nutrition control system as described in claim 6, characterized in that, The system includes a central control system, which is used to control the execution of the feed formulation process according to the revised formula ratio. The central control system is equipped with a database for storing the original feed formula, the theoretical values ​​of the content of each nutrient of each raw material referenced in the original formula, and the revised formula and the content values ​​of each nutrient of each raw material referenced in it.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 4.

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