Feed production quality management method and system

Abstract

The invention is suitable for the field of feed quality management, and provides a feed production quality management method and system, and the method comprises the steps: collecting a physical shape index of a to-be-detected feed; identifying physical shape indexes of the to-be-detected feed, and sorting primarily-selected qualified feed and primary abnormal feed; the safety limit requirement information of the primarily-selected qualified feed is recognized, and non-threat feed and secondary abnormal feed in the primarily-selected qualified feed are sorted; identifying nutrient component targeting information of the non-threat feed, and sorting high-quality feed and third-level abnormal feed in the non-threat feed; according to the method and the system, the follow-up detection cost is greatly reduced, the safety of the feed is monitored, harmful feed is effectively prevented from flowing into a breeding process, and meanwhile, the quality of the feed is improved. The nutrient targeted recognition assistance precise formula ensures the growth performance of animals, real-time proportion calculation and feedback are achieved, and feed waste is reduced.

Description

Technical Field

[0001] The invention belongs to the field of feed quality management, and in particular relates to a feed production quality management method and system. Background Art

[0002] As the modern animal husbandry industry is booming, feed quality is directly related to animal growth efficiency, health status, and the quality and safety of animal products such as meat, eggs, and milk, which in turn affects the economic benefits and market competitiveness of the entire animal husbandry industry chain. However, the current feed production process has exposed many difficult problems, which seriously restrict the steady progress of the industry.

[0003] The process of feed from production to breeding and use goes through multiple stages of production and storage. During production and processing, the quality is easily affected by production equipment or production conditions. During storage, it is easily affected by environmental factors, resulting in inconsistent quality of feed used. Once unqualified feed flows into the breeding link, it is very easy to induce animal mass epidemics, causing heavy losses to farmers. In order to solve the above technical problems and solve the problem of feed quality management, it is urgent to develop a more integrated feed production quality management method and system. Summary of the invention

[0004] The purpose of the present invention is to provide a feed production quality management method and system, aiming to solve the problems raised in the above background technology.

[0005] The present invention is achieved in that, on the one hand, a method for managing feed production quality comprises: Collect and identify the physical shape indicators of the feed to be inspected, and sort the preliminarily qualified feed and the first-level abnormal feed; Collect and identify the safety limit requirements of preliminarily qualified feed, and sort the non-threatening feed and secondary abnormal feed from the preliminarily qualified feed; Collect and identify targeted information on the nutritional composition of non-threatening feeds, and sort high-quality feeds and third-level abnormal feeds from non-threatening feeds; The proportion of first-level abnormal feed, second-level abnormal feed and third-level abnormal feed in the feed to be inspected is calculated, and feedback intervention information is generated based on the proportion information.

[0006] As a further solution of the present invention, the collecting and identifying the physical shape index of the feed to be inspected, and sorting the preliminarily qualified feed and the first-level abnormal feed specifically includes: Collect images of uniform distribution of feed particles to be inspected; Import the image of uniform distribution of feed particles to be inspected and generate a particle size histogram; Analyze particle size histogram concentration; If the particle size histogram concentration is less than the concentration threshold, a first-level sorting instruction is generated; Based on the first-level sorting instructions, the preliminarily qualified feed and the first-level abnormal feed are sorted.

[0007] As a further solution of the present invention, the collecting and identifying the safety limit requirement information of the preliminarily selected qualified feed, and sorting the non-threatening feed and the secondary abnormal feed in the preliminarily selected qualified feed specifically include: Selecting preliminary qualified feed samples; Obtaining near infrared spectroscopy data and microbial physicochemical data of the preliminarily selected qualified feed samples, wherein the microbial physicochemical data include but are not limited to the total number of bacteria, total number of molds, number of yeasts and specific pathogens, wherein the specific pathogens include but are not limited to Escherichia coli, Salmonella, Shigella or Staphylococcus aureus; A safety limit prediction model was established based on the near-infrared spectral data and microbial physicochemical data of the preliminarily selected qualified feed samples; Near-infrared spectroscopy is used to detect the initially qualified feed through the safety limit prediction model to sort out the non-threatening feed and the secondary abnormal feed.

[0008] As a further solution of the present invention, the collecting and identifying the targeted information of the nutritional components of the non-threatening feed and sorting the high-quality feed and the third-level abnormal feed in the non-threatening feed specifically include: Collect protein content in non-threatening feed; Marking feed pellets with protein content above a threshold level; Sorting high-quality feed and level 3 abnormal feed.

[0009] As a further solution of the present invention, the calculating of the proportion information of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected and generating the feedback intervention information based on the proportion information specifically includes: When the proportion of first-level abnormal feed exceeds the proportion threshold, supplier qualification review information and equipment maintenance information are generated; When the proportion of secondary abnormal feed exceeds the proportion threshold, information on increasing the disinfection frequency is generated; When the proportion of level 3 abnormal feed is greater than the proportion threshold, feed formula adjustment information is generated.

[0010] As a further embodiment of the present invention, on the other hand, a feed production quality management system comprises: The first collection and identification module is used to collect and identify the physical shape index of the feed to be inspected; The first sorting module is used to sort the preliminarily qualified feed and the first-level abnormal feed; The second collection and identification module is used to collect and identify the safety limit requirement information of the preliminarily selected qualified feed; The second sorting module is used to sort the non-threatening feed and the secondary abnormal feed from the initially qualified feed; The third collection and identification module is used to collect and identify the targeted information of the nutritional components of non-threatening feed; The third sorting module is used to sort high-quality feed and level 3 abnormal feed from non-threatening feed; A calculation module, used to calculate the proportion of first-level abnormal feed, second-level abnormal feed and third-level abnormal feed in the feed to be inspected; The generation module is used to generate feedback intervention information based on the proportion information.

[0011] As a further solution of the present invention, the first acquisition and identification module specifically includes: The first acquisition unit is used to acquire an image of uniform distribution of feed particles to be inspected; An import generation unit is used to import the uniform distribution image of feed particles to be inspected and generate a particle size histogram; an analysis unit for analyzing the concentration of a particle size histogram; A first generating unit, configured to generate a first-level sorting instruction if the concentration of the particle size histogram is less than a concentration threshold; The first sorting unit is used to sort the preliminarily qualified feed and the first-level abnormal feed based on the first-level sorting instruction.

[0012] As a further solution of the present invention, the second acquisition and identification module specifically includes: A selection unit is used to select preliminary qualified feed samples; An acquisition unit, used to acquire near infrared spectrum data and microbial physicochemical data of preliminarily selected qualified feed samples; The prediction model unit is used to establish a safety limit prediction model based on the near infrared spectroscopy data and microbiological physicochemical data of the preliminarily selected qualified feed samples; The second sorting unit is used to perform near-infrared spectroscopy detection on the preliminarily qualified feed through a safety limit prediction model, and to sort out non-threatening feed and secondary abnormal feed.

[0013] As a further solution of the present invention, the third acquisition and identification module specifically includes: The second collection unit is used to collect the protein content in the non-threatening feed; A marking unit, which is used to mark feed pellets with protein content above a content threshold; The third sorting unit is used to sort high-quality feed and third-level abnormal feed.

[0014] The present invention provides a feed production quality management method and system. The method and system realize accurate initial screening of physical shape, greatly reduce the subsequent detection cost, monitor the safety of feed, and effectively prevent harmful feed from entering the breeding process. At the same time, targeted nutritional identification helps accurate formulation, ensures animal growth performance, and calculates and feedbacks the proportion in real time, so that production adjustments are targeted, optimizes the production process, reduces feed waste, and improves the overall stability of feed quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a main flow chart of a feed production quality management method.

[0016] Figure 2 The invention discloses a flow chart for collecting and identifying the physical shape indexes of feed to be inspected and sorting out the preliminarily qualified feed and the first-level abnormal feed in the feed production quality management method.

[0017] Figure 3 The invention is a flow chart for collecting and identifying the safety limit requirement information of the preliminarily qualified feed and sorting the non-threatening feed and the secondary abnormal feed in the preliminarily qualified feed in the feed production quality management method.

[0018] Figure 4 The present invention is a flow chart for collecting and identifying the targeted information of the nutritional components of non-threatening feeds and sorting high-quality feeds and third-level abnormal feeds in non-threatening feeds in a feed production quality management method.

[0019] Figure 5 The invention relates to a flow chart for calculating the proportion information of first-level abnormal feed, second-level abnormal feed and third-level abnormal feed in the feed to be inspected in a feed production quality management method and generating feedback intervention information based on the proportion information.

[0020] Figure 6 It is a main structure diagram of a feed production quality management system.

[0021] Figure 7 The present invention is a structural block diagram of a first collection and identification module in a feed production quality management system.

[0022] Figure 8 The present invention is a structural block diagram of a second collection and identification module in a feed production quality management system.

[0023] Fig. 9 The present invention is a structural block diagram of the third collection and identification module in a feed production quality management system. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0025] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0026] The present invention provides a feed production quality management method and system, which solves the technical problems in the background technology.

[0027] like Figure 1 FIG. 1 is a main flow chart of a feed production quality management method provided by an embodiment of the present invention, wherein the feed production quality management method comprises: Step S100: collecting and identifying the physical shape indexes of the feed to be inspected, and sorting the preliminarily qualified feed and the first-level abnormal feed; Step S200: collecting and identifying the safety limit requirement information of the preliminarily qualified feed, and sorting the non-threatening feed and the secondary abnormal feed among the preliminarily qualified feed; Step S300: collecting and identifying the targeted information of the nutritional components of the non-threatening feed, and sorting the high-quality feed and the third-level abnormal feed in the non-threatening feed; Step S400: calculating the proportion of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected, and generating feedback intervention information based on the proportion information; This embodiment is used in applications to achieve accurate initial screening of physical shapes, significantly reduce subsequent testing costs, monitor feed safety, and effectively prevent harmful feed from entering the breeding process. At the same time, targeted nutritional identification helps with accurate formulation, ensures animal growth performance, and provides real-time proportion calculation and feedback, allowing production adjustments to be targeted, optimize production processes, reduce feed waste, and overall improve feed quality stability.

[0028] like Figure 2 As shown, as a preferred embodiment of the present invention, the collecting and identifying the physical shape index of the feed to be inspected, and sorting the preliminarily qualified feed and the first-level abnormal feed specifically include: Step S101: collecting an image of uniform distribution of feed particles to be inspected; Step S102: importing a uniform distribution image of feed particles to be inspected and generating a particle size histogram; Step S103: analyzing the particle size histogram concentration; Step S104: if the particle size histogram concentration is less than the concentration threshold, a first-level sorting instruction is generated; Step S105: sorting the preliminarily qualified feed and the first-level abnormal feed based on the first-level sorting instruction; When this embodiment is used, a screening device and image analysis software are provided. First, the uniformity of the feed particles to be tested is identified. The uniformity of the feed particles is identified by image analysis. A high-resolution digital microscope or industrial camera is equipped. Its resolution should be able to clearly distinguish the outline and size of the feed particles. The feed particles are evenly dispersed on a slide or a transparent sample table to ensure that the particles do not overlap each other. Use a digital microscope or industrial camera to take clear particle images. When taking pictures, it is necessary to ensure uniform lighting to avoid the influence of shadows on the particle size measurement. The taken images are imported into the image analysis software. The diameter of each feed particle is measured through the image segmentation and size measurement functions of the software. After the measurement is completed, the software can count the distribution of the particle size and generate a particle size histogram. According to the histogram, the concentration of the particle size can be intuitively seen. If the concentration of the particle size histogram is less than the concentration threshold, a first-level sorting instruction is generated. Based on the first-level sorting instruction, the primary qualified feed and the first-level abnormal feed are sorted.

[0029] like Figure 3 As shown, as a preferred embodiment of the present invention, the collecting and identifying the safety limit requirement information of the preliminarily selected qualified feed, and sorting the non-threatening feed and the secondary abnormal feed in the preliminarily selected qualified feed specifically include: Step S201: Selecting preliminarily qualified feed samples; Step S202: Acquire near infrared spectrum data and microbial physicochemical data of preliminarily selected qualified feed samples; The microbial physicochemical data include but are not limited to the total number of bacteria, total number of molds, number of yeasts and specific pathogens, wherein the specific pathogens include but are not limited to Escherichia coli, Salmonella, Shigella or Staphylococcus aureus; Step S203: establishing a safety limit prediction model based on the near infrared spectroscopy data and microbial physicochemical data of the preliminarily selected qualified feed samples; Step S204: performing near infrared spectroscopy detection on the preliminarily qualified feeds through the safety limit prediction model, and sorting out the non-threatening feeds and the secondary abnormal feeds; When this embodiment is used, firstly, a pre-selected qualified feed sample is selected, and the pre-treated pre-selected qualified feed sample is placed in the sample pool of the spectrometer to ensure that the sample is evenly distributed and fully in contact with the light path, and then the spectrum is collected to obtain the near-infrared spectrum data of the sample, and the microbial physicochemical data are obtained, wherein the microbial physicochemical data include but are not limited to the total number of bacteria, the total number of molds, the number of yeasts and specific pathogens, wherein the specific pathogens include but are not limited to Escherichia coli, Salmonella, Shigella or Staphylococcus aureus. The total number of bacteria can reflect the overall number of bacteria in the feed, and a high number may indicate that the feed is contaminated or the storage conditions are poor. The total number of molds can be used to measure the content of molds in the feed, and a large number of molds may produce mycotoxins, which affect the quality and Animal health, yeast count may also affect the quality of feed under certain conditions, specific pathogens: such as Escherichia coli, Salmonella, Shigella, Staphylococcus aureus, etc., the presence of these pathogens will pose a serious threat to animal health. Based on the near-infrared spectral data and microbial physicochemical data of the preliminary qualified feed samples, a safety limit prediction model is established, and the collected near-infrared spectral data and microbial physicochemical data of the preliminary qualified feed samples are divided into training sets and test sets, generally in a ratio of 8:2, so that the training set can be used to train the model, and finally the preliminary qualified feed is tested by near-infrared spectroscopy through the safety limit prediction model, and the predicted value of the microbial content in the preliminary qualified feed samples can be obtained, so that the threat-free feed and the secondary abnormal feed can be sorted according to the predicted value.

[0030] like Figure 4 As shown, as a preferred embodiment of the present invention, the collecting and identifying the targeted information of the nutritional components of the non-threatening feed and sorting the high-quality feed and the third-level abnormal feed in the non-threatening feed specifically include: Step S301: collecting the protein content in non-threatening feed; Step S302: marking feed particles whose protein content is higher than a content threshold; Step S303: sorting high-quality feed and third-level abnormal feed; It should be understood that when collecting the protein content of non-threatening feed, ultrasonic testing can be used, and the ultrasonic testing equipment can be started to emit ultrasonic waves of selected parameters to pass through the non-threatening feed. During the ultrasonic wave propagation process, the protein and other components in the non-threatening feed will absorb, scatter and reflect the ultrasonic wave, thereby causing the propagation characteristics of the ultrasonic wave to change. The propagation time of the ultrasonic wave in the feed and the intensity of the reflected wave are recorded to complete the collection of the protein content in the non-threatening feed, and then the magnetic labeling and separation methods can be used to sort high-quality feed and third-level abnormal feed.

[0031] like Figure 5As shown, as a preferred embodiment of the present invention, the calculation of the proportion information of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected, and the generation of feedback intervention information based on the proportion information specifically includes: Step S401: When the proportion of first-level abnormal feed is greater than the proportion threshold, supplier qualification review information and equipment maintenance information are generated; Step S402: When the proportion of secondary abnormal feed is greater than the proportion threshold, information on increasing the disinfection frequency is generated; Step S403: when the proportion of the third-level abnormal feed is greater than the proportion threshold, feed formula adjustment information is generated; When this embodiment is used, a percentage threshold is preset, and the percentage information of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected is calculated respectively. When the percentage of the first-level abnormal feed is greater than the percentage threshold, the supplier qualification review information and equipment maintenance information are generated to check whether the raw material supplier needs to be replaced, because the physical properties of the raw materials from different suppliers may be different. At the same time, the crushing, mixing and other equipment parameters in the raw material processing process are checked to see if there is any equipment failure that causes the abnormal physical shape. When the percentage of the second-level abnormal feed is greater than the percentage threshold, the disinfection frequency increase information is generated, and the production workshop is fully disinfected, and the disinfection frequency is increased to prevent the growth of microorganisms. The temperature and humidity of the production environment are strictly controlled to avoid the reproduction of harmful microorganisms such as mold. When the percentage of the third-level abnormal feed is greater than the percentage threshold, the feed formula adjustment information is generated. There may be problems with the feed formula, such as inaccurate addition of nutrients, or the actual content of the nutrients in the raw materials does not match the nominal value. The feed formula should be reviewed and adjusted to ensure the accurate addition of nutrients.

[0032] like Figure 6 As shown, as another preferred embodiment of the present invention, on the other hand, a feed production quality management system, the system comprises: The first collection and identification module 100 is used to collect and identify the physical shape index of the feed to be inspected; The first sorting module 200 is used to sort the preliminarily qualified feed and the first-level abnormal feed; The second collection and identification module 300 is used to collect and identify the safety limit requirement information of the preliminarily selected qualified feed; The second sorting module 400 is used to sort the non-threatening feed and the secondary abnormal feed from the initially selected qualified feed; The third collection and identification module 500 is used to collect and identify the targeted information of the nutritional components of the non-threatening feed; The third sorting module 600 is used to sort high-quality feed and third-level abnormal feed from non-threatening feed; A calculation module 700 is used to calculate the proportion of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected; A generating module 800, for generating feedback intervention information based on the proportion information; When this embodiment is applied, the first collection and identification module 100 collects and identifies the physical shape indicators of the feed to be inspected, the first sorting module 200 sorts the preliminarily qualified feed and the first-level abnormal feed, the second collection and identification module 300 collects and identifies the safety limit requirement information of the preliminarily qualified feed, the second sorting module 400 sorts the non-threatening feed and the second-level abnormal feed in the preliminarily qualified feed, the third collection and identification module 500 collects and identifies the targeted information of the nutritional components of the non-threatening feed, the third sorting module 600 sorts the high-quality feed and the third-level abnormal feed in the non-threatening feed, the calculation module 700 calculates the proportion information of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected, and based on the proportion information, the generation module 800 generates feedback intervention information.

[0033] like Figure 7 As shown, as another preferred embodiment of the present invention, the first acquisition and identification module 100 specifically includes: The first acquisition unit 101 is used to acquire an image of uniform distribution of feed particles to be inspected; The import generation unit 102 is used to import the uniform distribution image of feed particles to be inspected and generate a particle size histogram; An analysis unit 103, used for analyzing the concentration of the particle size histogram; The first generating unit 104 is used to generate a first-level sorting instruction if the concentration of the particle size histogram is less than the concentration threshold; The first sorting unit 105 is used to sort the primary qualified feed and the primary abnormal feed based on the primary sorting instruction; When this embodiment is applied, the first acquisition unit 101 acquires the uniform distribution image of the particles of the feed to be inspected, and the import generation unit 102 imports the uniform distribution image of the particles of the feed to be inspected to generate a particle size histogram. The analysis unit 103 analyzes the concentration of the particle size histogram. If the concentration of the particle size histogram is less than the concentration threshold, the first generation unit 104 generates a first-level sorting instruction. Based on the first-level sorting instruction, the first sorting unit 105 sorts the preliminarily qualified feed and the first-level abnormal feed.

[0034] like Figure 8 As shown, as another preferred embodiment of the present invention, the second acquisition and identification module 300 specifically includes: A selection unit 301 is used to select a preliminary qualified feed sample; An acquisition unit 302 is used to acquire near infrared spectrum data and microbial physicochemical data of the preliminarily selected qualified feed samples; The prediction model unit 303 is used to establish a safety limit prediction model based on the near infrared spectrum data and microbial physicochemical data of the preliminarily selected qualified feed samples; The second sorting unit 304 is used to perform near infrared spectroscopy detection on the preliminarily selected qualified feeds through the safety limit prediction model, and sort the non-threatening feeds and the secondary abnormal feeds; When this embodiment is applied, the selection unit 301 selects preliminarily qualified feed samples, the acquisition unit 302 acquires near-infrared spectral data and microbial physicochemical data of the preliminarily qualified feed samples, and the prediction model unit 303 establishes a safety limit prediction model based on the near-infrared spectral data and microbial physicochemical data of the preliminarily qualified feed samples. The preliminarily qualified feed is subjected to near-infrared spectral detection through the safety limit prediction model, and the second sorting unit 304 sorts non-threatening feed and secondary abnormal feed.

[0035] like Fig. 9 As shown, as another preferred embodiment of the present invention, the third acquisition and identification module 500 specifically includes: The second collection unit 501 is used to collect the protein content in the non-threatening feed; A marking unit 502 is used to mark feed particles with a protein content higher than a content threshold; The third sorting unit 503 is used to sort high-quality feed and third-level abnormal feed; When this embodiment is used, the second collecting unit 501 collects the protein content in the non-threatening feed, the marking unit 502 marks the feed particles with protein content higher than the content threshold, and the third sorting unit 503 sorts the high-quality feed and the third-level abnormal feed.

[0036] In order to load the above-mentioned method and system and enable it to run smoothly, the system, in addition to the various modules mentioned above, may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0037] The processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, and various interfaces and lines are used to connect various parts.

[0038] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0039] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing feed production quality, characterized in that: The method comprises: Collect and identify the physical shape indicators of the feed to be inspected, and sort the preliminarily qualified feed and the first-level abnormal feed; Collect and identify the safety limit requirements of preliminarily qualified feed, and sort the non-threatening feed and secondary abnormal feed from the preliminarily qualified feed; Collect and identify targeted information on the nutritional composition of non-threatening feeds, and sort high-quality feeds and third-level abnormal feeds from non-threatening feeds; Calculate the proportion of first-level abnormal feed, second-level abnormal feed and third-level abnormal feed in the feed to be inspected, and generate feedback intervention information based on the proportion information; The collecting and identifying the physical shape index of the feed to be inspected and sorting the preliminarily qualified feed and the first-level abnormal feed specifically include: Collect images of uniform distribution of feed particles to be inspected; Import the image of uniform distribution of feed particles to be inspected and generate a particle size histogram; Analyze particle size histogram concentration; If the particle size histogram concentration is less than the concentration threshold, a first-level sorting instruction is generated; Based on the first-level sorting instructions, sort the qualified feed and the first-level abnormal feed; The collecting and identifying the safety limit requirement information of the preliminarily qualified feed and sorting the non-threatening feed and the secondary abnormal feed in the preliminarily qualified feed specifically include: Selecting preliminary qualified feed samples; Obtain near infrared spectroscopy data and microbiological physicochemical data of preliminarily qualified feed samples; The microbial physicochemical data include but are not limited to the total number of bacteria, total number of molds, number of yeasts and specific pathogens, wherein the specific pathogens include but are not limited to Escherichia coli, Salmonella, Shigella or Staphylococcus aureus; A safety limit prediction model was established based on the near-infrared spectral data and microbial physicochemical data of the preliminarily selected qualified feed samples; The process of establishing the safety limit prediction model is as follows: the collected near infrared spectroscopy data and microbial physicochemical data of the preliminarily selected qualified feed samples are divided into a training set and a test set, the ratio of the training set to the test set is 8:2, a vector regression model is selected as the basic model framework, the basic model framework is trained using the training set, and a safety limit prediction model is generated; The safe limit prediction model is used to perform near infrared spectroscopy testing on the preliminarily qualified feed, and to sort out the non-threatening feed and the secondary abnormal feed; The collecting and identifying the targeted information of the nutritional components of the non-threatening feed and sorting the high-quality feed and the third-level abnormal feed in the non-threatening feed specifically include: Collect protein content in non-threatening feed; Marking feed pellets with protein content above a threshold level; Sorting high-quality feed and level 3 abnormal feed.

2. The feed production quality management method according to claim 1, characterized in that: The calculating the proportion information of the first-level abnormal feed, the second-level abnormal feed and the third-level abnormal feed in the feed to be inspected and generating the feedback intervention information based on the proportion information specifically includes: When the proportion of first-level abnormal feed exceeds the proportion threshold, supplier qualification review information and equipment maintenance information are generated; When the proportion of secondary abnormal feed exceeds the proportion threshold, information on increasing the disinfection frequency is generated; When the proportion of level 3 abnormal feed is greater than the proportion threshold, feed formula adjustment information is generated.

3. A feed production quality management system, characterized in that: The feed production quality management method according to any one of claims 1 to 2 is applied, wherein the system comprises: The first collection and identification module is used to collect and identify the physical shape index of the feed to be inspected; The first sorting module is used to sort the preliminarily qualified feed and the first-level abnormal feed; The second collection and identification module is used to collect and identify the safety limit requirement information of the preliminarily selected qualified feed; The second sorting module is used to sort the non-threatening feed and the secondary abnormal feed from the initially qualified feed; The third collection and identification module is used to collect and identify the targeted information of the nutritional components of non-threatening feed; The third sorting module is used to sort high-quality feed and level 3 abnormal feed from non-threatening feed; A calculation module, used to calculate the proportion of first-level abnormal feed, second-level abnormal feed and third-level abnormal feed in the feed to be inspected; A generation module, used for generating feedback intervention information based on the proportion information; The first acquisition and identification module specifically includes: The first acquisition unit is used to acquire an image of uniform distribution of feed particles to be inspected; An import generation unit is used to import the uniform distribution image of feed particles to be inspected and generate a particle size histogram; an analysis unit for analyzing the concentration of a particle size histogram; A first generating unit, configured to generate a first-level sorting instruction if the concentration of the particle size histogram is less than a concentration threshold; The first sorting unit is used to sort the preliminarily qualified feed and the first-level abnormal feed based on the first-level sorting instruction; The second acquisition and identification module specifically includes: A selection unit is used to select preliminary qualified feed samples; An acquisition unit, used to acquire near infrared spectrum data and microbial physicochemical data of preliminarily selected qualified feed samples; The prediction model unit is used to establish a safety limit prediction model based on the near infrared spectroscopy data and microbiological physicochemical data of the preliminarily selected qualified feed samples; The second sorting unit is used to perform near infrared spectroscopy detection on the preliminarily qualified feeds through the safety limit prediction model, and sort the non-threatening feeds and the secondary abnormal feeds; The third acquisition and identification module specifically includes: The second collection unit is used to collect the protein content in the non-threatening feed; A marking unit, which is used to mark feed pellets with protein content above a content threshold; The third sorting unit is used to sort high-quality feed and third-level abnormal feed.

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