Large yellow croaker feed processing and mixing method and device

Abstract

The present application provides a method and device for processing and mixing large yellow croaker feed. The method provided in the present application includes: determining the growth stage of large yellow croaker; determining the formula data of large yellow croaker based on the growth stage; an intelligent control system determining the processing parameters of a processing and mixing system based on the formula data; monitoring the changing state of the raw materials in the processing and mixing system in real time based on a sensor; the intelligent control system optimizing the formula data of large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state; processing and mixing are performed according to the formula data and the processing parameters of the processing and mixing system to obtain large yellow croaker feed. The method and device provided in the present application are used to timely adjust the formula data and corresponding processing parameters according to the growth stage and actual needs of large yellow croaker, thereby achieving accurate feeding of large yellow croaker feed and ensuring the growth and health of large yellow croaker.

Description

Technical Field

[0001] The present application relates to the field of aquaculture technology, and in particular to a large yellow croaker feed processing and mixing method and device. Background Art

[0002] Because yellow croaker have varying nutritional requirements at different stages of growth, specialized yellow croaker feeds can provide the appropriate amount of protein, fat, vitamins, and minerals to ensure healthy growth. Furthermore, with the advancement of feed technology, feed manufacturing processes, formulations, and raw material selection are constantly innovating. Providing higher-quality yellow croaker feed for yellow croaker farming has become a crucial issue.

[0003] The traditional yellow croaker feed processing and mixing method is based on fixed formula data to process yellow croaker feed. It is unable to adjust the formula data and corresponding processing parameters in time according to the growth stage and actual needs of yellow croaker. In addition, the fixed formula data relies on experience, which may lead to unbalanced nutritional composition of yellow croaker feed, affect the growth and health of yellow croaker, and cause feed waste.

[0004] Therefore, there is an urgent need for a method to timely adjust the formula data and corresponding processing parameters according to the growth stage and actual needs of large yellow croaker, so as to achieve accurate feeding of large yellow croaker feed and ensure the growth and health of large yellow croaker. Summary of the Invention

[0005] In view of this, the present application provides a large yellow croaker feed processing and mixing method and device, which is used to timely adjust the formula data and corresponding processing parameters according to the growth stage and actual needs of the large yellow croaker, achieve accurate feeding of the large yellow croaker feed, and ensure the growth and health of the large yellow croaker.

[0006] Specifically, this application is implemented through the following technical solutions:

[0007] The first aspect of the present application provides a large yellow croaker feed processing and mixing method, the method comprising:

[0008] Determine the growth stage of large yellow croaker;

[0009] Determining recipe data for the large yellow croaker based on the growth stage; the recipe data including the ratio of different raw materials and the sizes of different raw materials, and different growth stages correspond to different nutritional requirements;

[0010] The intelligent control system determines processing parameters of a processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, determines the order of raw materials entering the grinding system according to the ratio in the recipe data, determines the grinding processing parameters of the grinding system according to the size in the recipe data; and determines the mixing processing parameters of the mixing system according to the ratio;

[0011] Real-time monitoring of the changing state of the raw materials in the processing and mixing system based on sensors;

[0012] The intelligent control system optimizes the recipe data of large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state;

[0013] Processing and mixing are performed according to the formula data and the processing parameters of the processing and mixing system to obtain large yellow croaker feed.

[0014] The second aspect of the present application provides a large yellow croaker feed processing and mixing device, the device comprising an intelligent control system and a processing and mixing system connected to each other; wherein,

[0015] The intelligent control system is used to determine the formula data of the large yellow croaker based on the growth stage of the large yellow croaker; the formula data includes the ratio of different raw materials and the size of different raw materials, and different growth stages correspond to different nutritional requirements;

[0016] The intelligent control system is further configured to determine processing parameters of a processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, wherein the order of raw materials entering the grinding system is determined according to the ratio in the recipe data, and the grinding processing parameters of the grinding system are determined according to the size in the recipe data; and the mixing processing parameters of the mixing system are determined according to the ratio;

[0017] The processing and mixing system is used to process and mix based on the formula data and the processing parameters to obtain large yellow croaker feed;

[0018] The intelligent control system is further used to monitor the changing state of the raw materials in the processing and mixing system in real time based on sensors, and optimize the recipe data and the processing parameters in real time based on the changing state.

[0019] The large yellow croaker feed processing and mixing method and device provided by the present application, firstly, by being equipped with an intelligent control system, the intelligent control system can determine the large yellow croaker formula data and the processing parameters of the processing and mixing system according to the determined growth stage of the large yellow croaker, and perform processing and mixing based on the formula data and processing parameters. This ensures that the large yellow croaker feed obtained by processing and mixing is nutritionally balanced and can meet the needs of large yellow croaker at different growth stages. Compared with the traditional large yellow croaker feed processing and mixing method that processes large yellow croaker feed based on fixed formula data, the present application does not cause feed waste and ensures the growth and health of large yellow croaker. Secondly, by integrating multiple sensors in the processing and mixing system, the multiple sensors monitor the changing state of the raw materials in the processing and mixing system in real time, and the intelligent control system optimizes the large yellow croaker formula data and the processing parameters of the processing and mixing system by analyzing and predicting the changing state monitored by the sensors. This allows the formula data and processing parameters to be adjusted in real time and more accurately, so that the large yellow croaker feed obtained by processing and mixing can better meet the needs of large yellow croaker at different growth stages, reducing feed waste, improving production efficiency, and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flowchart of the large yellow croaker feed processing and mixing method provided in Example 1 of the present application;

[0021] Figure 2 This is a schematic diagram of the structure of the large yellow croaker feed processing and mixing device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0022] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0023] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0024] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0025] Specific embodiments are given below to introduce the technical solutions of the present application in detail.

[0026] Figure 1 This is a flow chart of the large yellow croaker feed processing and mixing method provided in Example 1 of this application. Figure 1 The method provided in this embodiment may include:

[0027] S101. Determine the growth stage of large yellow croaker.

[0028] Specifically, the growth stages of large yellow croaker characterize the changes in their physiological characteristics and requirements during their development and growth. Different growth stages correspond to different nutritional requirements. Large yellow croaker's growth stages typically include juvenile, seedling, and adult stages. Juveniles have a weaker ability to adapt to their environment and rely on high-energy, easily digestible feeds, requiring high levels of protein (approximately 40-50%) and fat. During the seedling stage, large yellow croaker gradually adapts to the aquatic environment and begins to consume a more diverse diet, with a slightly lower protein requirement (approximately 35-45%). During the adult stage, requirements for feed digestibility and palatability increase, growth slows, and protein requirements decrease to 30-40%, while the need for fat and vitamins remains important.

[0029] For example, the protein requirement for juveniles (corresponding to an initial weight of 12.8 grams) is 48.3%; for fry (corresponding to an initial weight of 137.9 grams), it is 44.8%; and for adult fish (corresponding to an initial weight of 194.1 grams), it is 42.7%. The protein requirement for juveniles gradually decreases as the fish grows. As the fish matures, the requirement for protein, a primary nutrient in the feed, also decreases.

[0030] In practice, large yellow croaker may exhibit different body colors and spots at different growth stages (especially in juvenile and adult stages), and their length and weight change at different rates (faster in juveniles and slower in adults). The growth stage of large yellow croaker can be determined by regularly measuring their length and weight and observing their body color and spots.

[0031] Alternatively, a camera can be installed in the intelligent control system to capture images of large yellow croaker, automatically identifying the large yellow croaker's growth stage through image recognition. Large yellow croaker identification results can be directly obtained using a large model. Alternatively, body color and spot features can be extracted to obtain a first identification result, resulting in a preliminary growth stage result. The length and weight change rates of adjacent images are calculated, and the first identification result is corrected based on these length and weight change rates to accurately determine the growth stage. Alternatively, the large yellow croaker's boundary is located in the captured image, a neighborhood region at a first distance from the large yellow croaker's boundary is determined, and a first color within the large yellow croaker's boundary and a second color within the neighborhood region are identified. A corrected color for the large yellow croaker is obtained based on the difference between the first and second colors, and the color of the large yellow croaker in the corrected color recognition image is identified. The area within the boundary is used as a second image, and the base pixel color in the second image is identified. The color of each pixel in the second image is determined, and the color corresponding to the pixels with the most identical colors is defined as the base pixel color. Pixels in the second image corresponding to colors different from the base pixel color are connected. Adjacent pixels are connected. If the connected shape forms a closed shape, it is considered an identification target, and the identification target is a spot. Extract all the recognition targets in the second image, identify the spot features of the recognition targets, such as color, size, and shape, and obtain the preliminary result of the growth stage based on the color and spot features of the large yellow croaker in the image.

[0032] Furthermore, the body length change rate and weight change rate of adjacent images are calculated, the boundary of the large yellow croaker is located in the captured image, the longest line connecting any two points of the large yellow croaker boundary is taken as the body length, the body length value is calculated, the area of ​​the large yellow croaker is calculated based on the large yellow croaker boundary, and the weight of the large yellow croaker is estimated; the body length values ​​of adjacent images are calculated, if the change in the body length values ​​of adjacent images is greater than or equal to a threshold, the two images are determined to be target images, the weight of the large yellow croaker in the target image is estimated respectively, the weight change rate and body length change rate are calculated, the preliminary result is corrected according to the weight change rate and body length change rate, and the final correction result of the growth stage is obtained.

[0033] The method provided by this application, first of all, can directly obtain preliminary results of the growth stage of large yellow croaker by combining large models and image recognition, greatly reducing manual participation and significantly improving the automation level of growth stage recognition. In addition, the preliminary results are corrected in combination with body length and weight changes, making the recognition results more accurate and reducing recognition errors. In addition to direct model recognition, surface characteristics such as body color and spots and dynamic characteristics of body length and weight changes are also introduced. The comprehensive utilization of these multi-dimensional features can help the detection system analyze the growth status of large yellow croaker more carefully, and is particularly suitable for identifying relatively small stage changes. Secondly, the correction color is calculated based on the difference in color inside and outside the boundary to adapt to color deviation under different shooting conditions. At the same time, the change rate of physical characteristics such as body length and weight is used as a correction method to alleviate the interference of factors such as ambient light and angle on recognition accuracy, and adapt to a wider range of application scenarios. Using the body length and weight change rate of adjacent images as a correction basis, dynamic monitoring of the growth stage can be achieved, reflecting the growth status of large yellow croaker in real time, and enabling timely adjustments, especially in feeding management and feed formulation adjustment. It can play a guiding role.

[0034] S102. Determine recipe data for large yellow croaker based on the growth stage; the recipe data includes ratios of different raw materials and sizes of different raw materials, and different growth stages correspond to different nutritional requirements.

[0035] Specifically, the recipe data of large yellow croaker records the raw materials used to process and manufacture large yellow croaker feed, including the ratio of different raw materials and the sizes of different raw materials.

[0036] In a specific implementation, the formula data of the large yellow croaker is determined based on the growth stage, including: determining the nutritional requirements of the large yellow croaker based on the growth stage of the large yellow croaker; the nutritional requirements include nutrients with different components; determining the raw materials for providing the nutrients based on the nutrients; and determining the ratio and size of different raw materials based on the nutrients provided by the raw materials and the nutritional requirements.

[0037] Specifically, based on the above description, the nutritional requirements of large yellow croaker vary depending on its growth stage. Nutrients of different components mainly include protein, carbohydrates, and functional substances. Different nutrients require different energy sources. Proteins require fish meal, soybean meal, and chlorella powder; carbohydrates require sweet potato flour, gluten, and barley flour; and functional substances require astaxanthin and phospholipids.

[0038] In specific implementation, the nutritional requirements of the large yellow croaker are determined based on the determined growth stage of the large yellow croaker (for example, when the large yellow croaker is in the juvenile stage, it requires high protein and fat; when the large yellow croaker is in the fry stage, it requires high protein and an appropriate amount of fat and vitamins; when the large yellow croaker is in the adult stage, it requires high protein, fat and other nutrients). Furthermore, the corresponding raw materials are determined based on the nutrients included in the nutritional requirements of the large yellow croaker. The ratio and size of each raw material are determined based on the nutrients that each raw material can provide (protein provides amino acids, carbohydrates and fat provide energy, and functional substances provide trace elements and vitamins) and the total nutrients required by the large yellow croaker at each growth stage.

[0039] The method provided in this application improves the color of large yellow croaker, reduces its fishy odor, and controls its muscle fat content by adding natural pigments such as astaxanthin and functional ingredients such as phospholipids to large yellow croaker feed. The addition of these ingredients makes the large yellow croaker feed more suitable for the growth needs and market demands of large yellow croaker.

[0040] S103. The intelligent control system determines the processing parameters of the processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, determines the order of raw materials entering the grinding system according to the ratio in the recipe data, and determines the grinding processing parameters of the grinding system according to the size in the recipe data; and determines the mixing processing parameters of the mixing system according to the ratio.

[0041] Specifically, the processing and mixing system is used to process and mix the raw materials in the recipe data to produce large yellow croaker feed. The processing and mixing system includes a grinding system and a mixing system. The grinding system grinds the raw materials based on the raw material sequence and grinding parameters. The raw material sequence is determined by the ratio of the different raw materials in the recipe data, and the grinding parameters are determined by the size of the different raw materials in the recipe data. The mixing system mixes the raw materials ground by the grinding system based on the mixing parameters. The mixing parameters are determined by the ratio of the different raw materials in the recipe data.

[0042] In a specific implementation, the intelligent control system determines the raw material sequence of the grinding system based on the ratios of the different raw materials in the recipe data, and determines the grinding process parameters of the grinding system and the mixing process parameters of the mixing system based on the sizes of the different raw materials in the recipe data. Furthermore, the raw material sequence of the grinding system and the grinding process parameters of the grinding system are determined as the processing parameters of the grinding system, the mixing process parameters of the mixing system are determined as the processing parameters of the mixing system, and the processing parameters of the grinding system and the processing parameters of the mixing system are determined as the processing parameters of the mixing system.

[0043] It should be noted that the grinding system comprises multiple subsystems, and the mixing system comprises multiple subsystems. Each subsystem in the grinding system has different grinding parameters, and each subsystem in the mixing system also has different mixing parameters. Each subsystem will be described below and will not be further elaborated here.

[0044] Optionally, the recipe data can be specified to meet the nutritional requirements for a predicted growth stage, and the raw material names, raw material sizes that can meet the requirements for large yellow croaker at that growth stage, and the proportion of each raw material can be configured. The gap between the initial performance of each raw material and the performance required in the recipe data is determined, and the processing steps required for each raw material are determined based on the gap. The processing parameters for each processing step, such as mixing time, processing temperature, etc., are determined based on the characteristics of each raw material. A final processing plan is generated based on the processing steps and processing parameters. The processing plan can be determined by the connection method and processing time of the processing mixing system.

[0045] S104: Monitor the changing state of the raw materials in the processing and mixing system in real time based on sensors.

[0046] Specifically, sensors are integrated into the processing and mixing system and connected to the intelligent control system. Sensors include various types, such as temperature, humidity, and weight sensors. It should be noted that since the processing and mixing system includes both a grinding system and a mixing system, raw materials entering the processing and mixing system must first be ground in the grinding system and then mixed in the mixing system. The grinding and mixing systems process the raw materials differently, requiring different sensors for monitoring and correspondingly different states of change in the raw materials.

[0047] In specific implementation, based on the target system (grinding system or mixing system) where the raw materials are located in the processing and mixing system, corresponding sensors are selected to monitor the changing state of the raw materials in the target system in real time.

[0048] The processing plan specifies the desired processing state for each raw material at each processing step. Furthermore, based on the relationship between the real-time monitored state, the target processing state in the processing plan, and the remaining processing time for that step, processing parameters are adjusted to ultimately achieve the target processing state within the remaining processing time.

[0049] S105. The intelligent control system optimizes the recipe data of the large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state.

[0050] In specific implementation, the intelligent control system optimizes the recipe data of large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state, including: the intelligent control system compares the changing state with the quality standards corresponding to different raw materials in the recipe data; adjusts the recipe data and corresponding processing parameters based on the comparison result and the changing trend of the changing state along with the recipe data; when the changing state always exceeds the quality standard after adjusting a preset number of cycles, an alarm sounds an alarm, prompting to adjust the intelligent control system.

[0051] Specifically, during the entire processing and mixing process, sensors are used to monitor in real time the changing states of the raw materials in the processing and mixing system (since the raw materials need to be processed by the grinding system and the mixing system respectively after entering the processing and mixing system, the changing states of the raw materials are different when different systems process the raw materials, and the corresponding quality parameters are also different, including particle size, moisture content, nutritional components, etc.). These changing states are transmitted to the intelligent control system through sensors for analysis and processing. When quality problems are found or the changing states deviate from the set values ​​corresponding to the quality standards, the alarm in the intelligent control system will immediately sound an alarm, prompting the operator to make adjustments. The operator can adjust the formula data of the large yellow croaker, the processing parameters of the processing and mixing system, or the equipment status according to the prompts of the intelligent control system to ensure that the large yellow croaker feed finally produced meets the quality standards.

[0052] In a specific implementation, in one possible approach, an intelligent control system comprehensively analyzes the changing states of raw materials in a processing and mixing system, as monitored by multiple sensors. If quality issues are detected or the overall state of change deviates from the set value corresponding to the quality standard, the intelligent control system identifies the processing and mixing system corresponding to the state of change that deviates from the set value by more than a preset threshold as the processing and mixing system to be optimized. Based on the analysis results, the intelligent control system optimizes the processing parameters of the processing and mixing system to be optimized, as well as the recipe data for large yellow croaker. This holistic analysis considers the synergistic relationships between multiple states of change, making it possible to handle complex processing and mixing processes. For example, if rising temperature has a compounding effect on humidity and viscosity, the holistic analysis can simultaneously focus on the correlations between these states, more accurately identifying the root cause of the deviation and optimizing the entire processing and mixing process. While some states of change may not directly affect product quality when deviating from the set value individually, simultaneous deviations of multiple states can lead to quality issues. Holistic analysis can identify the impact of these combined states, reducing false alarms caused by a single abnormal state. When multiple states of change influence each other, holistic analysis can identify which states require simultaneous adjustment to achieve optimal results. This method is particularly effective when multiple states constrain each other. It can avoid multiple repeated adjustments caused by individual adjustments and improve system operation efficiency.

[0053] In another possible implementation, an intelligent control system receives the changing states of raw materials in the processing and mixing system as monitored by each sensor and analyzes each state. If a quality issue is detected or a state deviates from the set value corresponding to the quality standard, the processing and mixing system corresponding to that state is identified as the one to be optimized. Based on the intelligent control system's analysis results, the processing parameters of the processing and mixing system to be optimized, along with the recipe data for large yellow croaker, are then determined. This single analysis approach allows for a faster response to single-state anomalies, making it suitable for timely adjustments to particularly sensitive states (such as sudden temperature changes or humidity surges) to prevent the spread of these anomalies. Furthermore, it allows for the independent monitoring and adjustment of key parameters. For example, during humidity control of a recipe, if humidity is high but other parameters, such as temperature, are normal, the system only needs to adjust the humidity parameter without affecting other states, making it suitable for systems requiring precise control. When a specific state fluctuates, single analysis facilitates fine-tuning of processing parameters for that state without requiring comprehensive adjustments to the entire system. This makes it suitable for optimizing local states that deviate from quality standards, avoiding excessive disruption to the overall system.

[0054] S106: Processing and mixing are performed according to the formula data and the processing parameters of the processing and mixing system to obtain large yellow croaker feed.

[0055] Specifically, the formula data of the large yellow croaker is input into the processing and mixing system, and the processing and mixing system grinds and mixes the raw materials in sequence to obtain the processed and mixed large yellow croaker feed.

[0056] Based on the above description, the processing and mixing system includes a grinding system and a mixing system. The grinding system includes a crusher, a screening machine, and a batching scale, while the mixing system includes a mixer, a granulator, a cooler, an automatic packaging machine, and a sealing machine. The crusher is connected to the screening machine, which is connected to the batching scale, which is connected to the mixer, which is connected to the granulator, which is connected to the cooler, which is connected to the automatic packaging machine, and which is connected to the sealing machine. The crusher is used to crush the raw materials specified in the recipe data to produce small particles suitable for subsequent processing. The screening machine is used to screen the raw materials crushed by the crusher to remove impurities. The batching scale is used to accurately weigh the raw materials screened by the screening machine according to the ratio specified in the recipe data. The mixer is used to evenly mix the various raw materials weighed by the batching scale according to the ratio. The granulator is used to press the feed raw materials mixed by the mixer into regularly shaped granules for easy ingestion by the large yellow croaker. The cooler is used to quickly cool the feed granules produced by the granulator to prevent high temperatures from affecting the quality of the large yellow croaker feed. The automatic packaging machine uses a precision metering device to automatically package the cooled feed pellets, and the sealing machine is used to seal the packaged feed pellets to ensure the freshness and safety of the large yellow croaker feed.

[0057] Optionally, the intelligent control system determines the grinding processing parameters of the crusher based on the size in the recipe data; the crusher crushes the raw material based on the grinding processing parameters to obtain crushed raw material; the intelligent control system determines the average particle size as the screen aperture of the screening machine based on the particle size of the crushed raw material; the screening machine screens the crushed raw material based on the screen aperture, removes the raw material with a particle size exceeding the average particle size, and obtains the screened raw material.

[0058] Optionally, the intelligent control system determines the raw materials required for processing and mixing and the weight of the raw materials based on the ratio in the recipe data; the ingredient scale uses a precision metering device to weigh the raw materials based on the weight of the raw materials; during the weighing process, the weight and flow of the raw materials are monitored in real time based on sensors, and the weighed raw materials are adjusted based on the monitored weight and flow.

[0059] Optionally, the intelligent control system determines processing batches of different raw materials based on the ratios and sizes of different raw materials in the formula data; wherein, processing batches of different raw materials correspond to different mixing times and mixing speeds; corresponding raw materials are added to the mixer in sequence based on the processing batches, and each processing batch is mixed according to the corresponding mixing time and mixing speed to obtain mixed feed raw materials for each processing batch; during the mixing process, the physical properties of the mixed feed raw materials for each processing batch are monitored in real time based on sensors, and the mixing time and mixing speed are adjusted based on the monitoring results.

[0060] For example, the formula data for large yellow croaker includes fish meal, soybean meal, sweet potato flour, and lecithin. Based on this data, the ratios and physical properties of each ingredient in the current feed formula are determined. Because fish meal, soybean meal, and sweet potato flour have fine particles, they are suitable for medium mixing speeds. Lecithin, as a functional additive, requires small additions and is therefore suited to low-speed, short-time mixing to ensure uniform distribution. Furthermore, in the first batch, the fish meal, soybean meal, and sweet potato flour are added. The intelligent control system sets a medium mixing speed (e.g., 60 RPM) and a moderate mixing time (e.g., 5 minutes) to ensure thorough mixing of the three main ingredients. In the second batch, lecithin is added to the first batch, using a low mixing speed (e.g., 30 RPM) and a shorter mixing time (e.g., 2 minutes) to prevent uneven distribution of the lecithin with other ingredients. During the mixing process, sensors monitor the mixture's physical properties, such as viscosity, temperature, and particle distribution, in real time. If uneven particle distribution is detected during the first batch, the intelligent control system automatically increases the mixing time or fine-tunes the mixing speed (e.g., from 60 RPM to 70 RPM) to improve mixing. If uneven phospholipid distribution is detected in the second batch, the intelligent control system may extend the low-speed mixing time (for example, from 2 minutes to 3 minutes) to ensure uniform distribution. After completing the two batches of automatic mixing, the intelligent control system ensures that all ingredients are fully and evenly mixed, ultimately producing a feed that meets the needs of the large yellow croaker at the current growth stage.

[0061] Furthermore, the sensor monitors the mixed product of each processing batch, verifies the gap between the product and the finished product requirements in the formula data, and modifies the mixing processing parameters of the subsequent processing batch based on the gap. The mixing processing parameters include mixing time and mixing speed.

[0062] Optionally, the intelligent control system determines the mixing processing parameters of the pellet mill based on the moisture content, particle size and shape, ratio and fluidity of different raw materials in the recipe data; the mixing processing parameters include pressure, temperature and speed; the pellet mill pelletizes the mixed feed raw materials based on the set pressure, temperature and speed to obtain pelletized feed pellets; during the pelletizing process, the size, shape and density of the feed pellets are monitored in real time based on sensors, and the mixing processing parameters are adjusted based on the monitoring results.

[0063] In specific implementation, the pressure of the granulator is determined according to the moisture content of different raw materials in the recipe data and the change trend of the granulator pressure with the moisture content.

[0064] Specifically, the moisture content of different raw materials is directly proportional to the pelletizer pressure. Within a certain range, higher moisture content increases the pelletizer pressure to promote gelatinization and cohesion of the raw materials. In a typical pelletizing process, the moisture content of the raw materials is generally controlled between 8% and 10%, and the total moisture content is controlled between 16% and 17%. Accordingly, the pelletizer pressure is generally controlled between 394 and 490 kPa.

[0065] Furthermore, the preliminary temperature of the granulator is determined according to the moisture content of different raw materials in the recipe data and the changing trend of the granulator temperature with the moisture content, and the preliminary temperature of the granulator is adjusted based on the particle size and shape of different raw materials in the recipe data and the changing trend of the granulator temperature with the particle size and shape to obtain the temperature of the granulator.

[0066] Specifically, the moisture content, particle size, and shape of different raw materials are closely related to the pelletizer temperature. Pellets from different raw materials include hard pellets, soft pellets, and expanded pellets, each with a different shape and density. Within a certain range, when the moisture content is high, the particle size is large, and the shape is difficult to pelletize, the pelletizer temperature needs to be increased accordingly to promote pelletization. During the general pelletizing process, the pelletizer temperature does not exceed 88°C and is generally controlled between 82-88°C.

[0067] No single parameter is independent. Again, we comprehensively control temperature information based on moisture content, particle size, and shape. Temperature is directly proportional to moisture content, directly proportional to particle size, and inversely proportional to granulation ease. Specifically, we calculate the difference between each parameter in the current state based on the target moisture content and particle size for that process step. We then determine the weight of each parameter based on the relative size of the difference, and adjust the initial temperature accordingly. For example, if there is a significant difference in moisture content but the particle size is essentially similar, we will primarily adjust the parameters to meet the moisture content requirement.

[0068] Furthermore, according to the ratio of different raw materials in the recipe data and the influence of different ratios on the fluidity of the raw materials, the fluidity of different raw materials in the recipe data is adjusted, and the initial rotational speed of the granulator is determined based on the adjusted fluidity and the correspondence between the fluidity and the flow rate; wherein, the fluidity of the raw material is positively correlated with the flow rate of the raw material in the granulator; the rotational speed of the granulator is positively correlated with the flow rate of the raw material; based on the particle size and shape of different raw materials in the recipe data and the correspondence between the particle size and shape and the flow rate, the initial rotational speed of the granulator is adjusted to obtain the rotational speed of the granulator.

[0069] Specifically, the particle size and shape, proportion and fluidity of different raw materials are related to the rotational speed of the granulator. The proportion of different raw materials will lead to changes in fluidity, density and volume. The fluidity will directly affect the flow rate of the raw material in the granulator. Raw materials with good fluidity are easy to be evenly distributed in the granulator and have a faster flow rate in the granulator. In addition, the particle size and shape of different raw materials will also affect the flow rate of the raw material in the granulator. The particles with smaller and more regular shapes will have a faster flow rate in the granulator. In summary, when the granulator is granulating raw materials with good fluidity, smaller particles and more regular shapes, the rotational speed of the granulator can be appropriately reduced. In specific implementation, the calculation formula for the rotational speed of the granulator is:

[0070] ;

[0071] Among them, the is the rotation speed of the granulator; is the flow rate of different raw materials; is the diameter of the granulator. The rotational speed is proportional to the flow rate and inversely proportional to the size of the granules.

[0072] Optionally, the particle sizes that yellow croaker can tolerate vary at different growth stages, and the particle sizes of different raw materials are also different, and their corresponding nutritional values ​​are also different. Therefore, it is necessary to determine the particle size that yellow croaker can tolerate based on the growth stage of yellow croaker. The pelletizer performs pelleting according to preset mixing processing parameters, and continuously compares the feed pellets with the particle size that yellow croaker can tolerate during the pelleting process. The nutritional value corresponding to the particle size is then compared with the nutritional requirements corresponding to the growth stage of yellow croaker. This ensures that the feed pellets after pelleting are of a particle size that yellow croaker can tolerate while meeting the nutritional requirements corresponding to the growth stage of yellow croaker.

[0073] Optionally, the intelligent control system uses fuzzy rules to determine the processing parameters of the cooler based on the temperature, humidity, particle size and shape of the feed particles; the processing parameters include cooling time and cooling rate; the cooler cools the feed particles at the cooling time and cooling rate to obtain cooled feed particles; during the cooling process, the temperature and physical property changes of the feed particles are monitored in real time based on sensors, and the cooling time and cooling rate are adjusted based on the monitoring results.

[0074] Optionally, the intelligent control system uses fuzzy rules to determine the mixing processing parameters of the cooler based on the temperature, humidity, particle size and shape of the feed particles, including: fuzzy processing the temperature and humidity of the feed particles, mapping to obtain fuzzy sets corresponding to the temperature and humidity; determining fuzzy rules based on the changing trend of temperature and humidity with mixing processing parameters in the historical processing and mixing process; based on the fuzzy sets corresponding to the temperature and humidity, reasoning according to the fuzzy rules to calculate the fuzzy values ​​of the mixing processing parameters; converting the fuzzy values ​​into specific values ​​through a defuzzification method to determine specific mixing processing parameters.

[0075] For example, the fuzzy sets corresponding to humidity include "low humidity" (LH), "medium humidity" (MH), and "high humidity" (HH), while the fuzzy sets corresponding to temperature include "low temperature" (LT), "moderate temperature" (MT), and "high temperature" (HT). Each fuzzy set corresponds to a fuzzy membership function that describes the degree to which the input variable belongs to that set. Similarly, the mixing process parameters include cooling time and cooling rate. After fuzzification, the fuzzy sets corresponding to cooling time include "short time" (ST), "medium time" (MT), and "long time" (LT). The fuzzy sets corresponding to mixing time include "low speed" (LS), "medium speed" (MS), and "high speed" (HS).

[0076] Furthermore, fuzzy rules are determined based on the changing trends of temperature and humidity along with the mixing process parameters during the historical processing and mixing process. The designed fuzzy rules are as follows: If the humidity is LH and the temperature is MT, the cooling time is ST and the cooling rate is MS (because raw materials with low humidity and moderate temperature are easier to mix evenly, so too long cooling time and too high cooling rate are not required). If the humidity is MH and the temperature is HT, the cooling time is MT and the cooling rate is HS (because raw materials with moderate humidity and high temperature may require a higher cooling rate to ensure uniform mixing, but the cooling time does not need to be too long). If the humidity is HH and the temperature is LT, the cooling time is LT and the cooling rate is LS (because raw materials with high humidity and low temperature may be more difficult to mix evenly, so a longer cooling time and a lower cooling rate are required).

[0077] When the raw material's humidity is moderate and the temperature is low, the fuzzy rules calculate that the cooling time should be medium and the cooling rate should be fast. These fuzzy values ​​are then converted into specific control parameters (e.g., cooling time T = 300 seconds, cooling rate V = 60 rpm) through defuzzification and output to the cooler for real-time control.

[0078] It should be noted that after obtaining the temperature and physical property changes of the feed particles based on real-time monitoring by sensors, the intelligent control system also uses fuzzy rules to fuzzy the temperature and physical property changes of the feed particles, and determines the fuzzy rules based on the trend of changes in the temperature and physical properties of the feed particles along with the mixing processing parameters during the historical processing and mixing process. The optimized mixing processing parameters are determined based on the fuzzy processing results and the fuzzy rules.

[0079] The method provided in this embodiment, firstly, is equipped with an intelligent control system, which can determine the formula data of the large yellow croaker and the processing parameters of the processing and mixing system according to the growth stage of the large yellow croaker determined, and process and mix based on the formula data and the processing parameters. This ensures that the large yellow croaker feed obtained by processing and mixing is nutritionally balanced and can meet the needs of large yellow croaker at different growth stages. Compared with the traditional large yellow croaker feed processing and mixing method that processes large yellow croaker feed based on fixed formula data, this application does not cause feed waste and ensures the growth and health of large yellow croaker. Secondly, by integrating multiple sensors in the processing and mixing system, the multiple sensors monitor the changing state of the raw materials in the processing and mixing system in real time, and the intelligent control system optimizes the formula data of the large yellow croaker and the processing parameters of the processing and mixing system by analyzing and predicting the changing state monitored by the sensors. This allows the formula data and processing parameters to be adjusted in real time and more accurately, so that the large yellow croaker feed obtained by processing and mixing can better meet the needs of large yellow croaker at different growth stages, reducing feed waste, improving production efficiency, and reducing costs. Thirdly, this embodiment provides a large yellow croaker feed processing and mixing method that integrates multiple functions, including crushing, screening, batching, mixing, granulation, cooling, packaging, and sealing. The processing parameters (grinding or mixing parameters) for each process are determined based on the large yellow croaker's recipe data, and sensors monitor and adjust each process in real time. This integrated design not only improves the efficiency of large yellow croaker feed processing but also ensures that the processed and mixed large yellow croaker feed better meets the needs of large yellow croaker at different growth stages, reducing the footprint and labor costs of the large yellow croaker feed processing and mixing equipment. Fourthly, by adding natural pigments such as astaxanthin and functional ingredients such as phospholipids to the large yellow croaker feed, the color of the large yellow croaker can be improved, the fishy smell can be reduced, and the muscle fat content can be controlled. The addition of these ingredients makes the large yellow croaker feed more suitable for the growth needs and market requirements of large yellow croaker. Fifthly, by using precision metering devices in the batching scale and automatic packaging machine, the accuracy and stability of the raw materials used in the processing and manufacturing of the large yellow croaker feed can be ensured, thereby improving the quality of the large yellow croaker feed and reducing production costs.

[0080] Corresponding to the aforementioned embodiment of a large yellow croaker feed processing and mixing method, the present application also provides an embodiment of a large yellow croaker feed processing and mixing device.

[0081] Figure 2 This is a schematic diagram of the structure of the large yellow croaker feed processing and mixing device provided in Example 2 of this application. Figure 2 The device provided in this embodiment includes an intelligent control system and a processing mixing system connected to each other; wherein,

[0082] The intelligent control system is used to determine the formula data of the large yellow croaker based on the growth stage of the large yellow croaker; the formula data includes the ratio of different raw materials and the size of different raw materials, and different growth stages correspond to different nutritional requirements;

[0083] The intelligent control system is further configured to determine processing parameters of a processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, wherein the order of raw materials entering the grinding system is determined according to the ratio in the recipe data, and the grinding processing parameters of the grinding system are determined according to the size in the recipe data; and the mixing processing parameters of the mixing system are determined according to the ratio;

[0084] The processing and mixing system is used to process and mix based on the formula data and the processing parameters to obtain large yellow croaker feed;

[0085] The intelligent control system is further used to monitor the changing state of the raw materials in the processing and mixing system in real time based on sensors, and optimize the recipe data and the processing parameters in real time based on the changing state.

[0086] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0087] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0088] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A large yellow croaker feed processing and mixing method, characterized in that: The method comprises: Determine the growth stage of large yellow croaker; Among them, a camera is installed in the intelligent control system to take pictures of large yellow croaker, and the growth stage of large yellow croaker is automatically identified through image recognition and a large model; the body color and spot characteristics of large yellow croaker are extracted to obtain a first recognition result, and a preliminary result of the growth stage is obtained. The body length change rate and weight change rate of adjacent images are calculated, and the first recognition result is corrected based on the body length change rate and weight change rate to obtain the corrected growth stage of large yellow croaker; Among them, the boundary of the large yellow croaker is located in the captured image, the neighborhood area at a first distance from the boundary of the large yellow croaker is determined, the first color within the boundary of the large yellow croaker and the second color within the neighborhood area are respectively identified, the large yellow croaker correction color is obtained based on the difference between the first color and the second color, and the color of the large yellow croaker in the large yellow croaker correction color is identified; the boundary area is used as the second image, the color of each pixel in the second image is determined, the color corresponding to the pixels with the most identical colors is determined as the basic pixel color, the first pixel in the second image that is different from the basic pixel color is identified, the adjacent first pixels are connected, the recognition target is determined based on the connected shape, all recognition targets in the second image are extracted, and the spot features of the recognition targets are identified; the preliminary result of the growth stage is determined based on the color of the large yellow croaker and the spot features; Determining recipe data for the large yellow croaker based on the growth stage; the recipe data including the ratio of different raw materials and the sizes of different raw materials, and different growth stages correspond to different nutritional requirements; The intelligent control system determines processing parameters of a processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, determines the order of raw materials entering the grinding system according to the ratio in the recipe data, determines the grinding processing parameters of the grinding system according to the size in the recipe data; and determines the mixing processing parameters of the mixing system according to the ratio; The formula data is the nutritional requirements for the predicted growth stage. The difference between the initial performance of each raw material and the performance required in the formula data is determined. The processing steps required for each raw material are determined based on the difference. The processing parameters for each processing step are determined based on the characteristics of each raw material. Real-time monitoring of the changing state of the raw materials in the processing and mixing system based on sensors; The intelligent control system optimizes the recipe data of large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state; Processing and mixing are performed according to the formula data and the processing parameters of the processing and mixing system to obtain large yellow croaker feed.

2. The method according to claim 1, characterized in that The grinding system at least includes a batching scale; The intelligent control system determines the raw materials required for processing and mixing and the weight of the raw materials based on the ratio in the recipe data; The batching scale uses a precision measuring device to weigh the raw materials based on their weight; During the weighing process, the weight and flow rate of the raw materials are monitored in real time based on sensors, and the weighed raw materials are adjusted based on the monitored weight and flow rate.

3. The method according to claim 1, characterized in that The mixing system comprises at least a mixer; The intelligent control system determines processing batches of different raw materials based on the ratios and sizes of different raw materials in the recipe data; wherein the processing batches of different raw materials correspond to different mixing times and mixing speeds; Adding corresponding raw materials to the mixer in sequence based on the processing batches, mixing each processing batch according to the corresponding mixing time and mixing speed to obtain mixed feed raw materials for each processing batch; During the mixing process, the physical property changes of the mixed feed raw materials in each processing batch are monitored in real time based on sensors, and the mixing time and mixing speed are adjusted based on the monitoring results.

4. The method according to claim 1, wherein The mixing system comprises at least a granulator; The intelligent control system determines the mixing processing parameters of the granulator based on the moisture content, particle size and shape, ratio and fluidity of different raw materials in the recipe data; the mixing processing parameters include pressure, temperature and rotation speed; The pelletizer pelletizes the mixed feed raw materials based on the set pressure, temperature and speed to obtain pelletized feed pellets; During the pelleting process, the size, shape and density of the feed particles are monitored in real time based on sensors, and the mixing process parameters are adjusted based on the monitoring results.

5. The method according to claim 4, characterized in that The mixing system includes at least a cooler; The intelligent control system uses fuzzy rules to determine the mixing process parameters of the cooler based on the temperature, humidity, and particle size and shape of the feed particles; the mixing process parameters include cooling time and cooling rate; The cooler cools the feed pellets at the cooling time and cooling rate to obtain cooled feed pellets; During the cooling process, the temperature and physical property changes of the feed pellets are monitored in real time based on sensors, and the cooling time and cooling speed are adjusted based on the monitoring results.

6. The method according to claim 5, characterized in that The intelligent control system uses fuzzy rules to determine the mixing process parameters of the cooler based on the temperature, humidity, and particle size and shape of the feed particles, including: Performing fuzzy processing on the temperature and humidity of the feed particles, and mapping to obtain fuzzy sets corresponding to the temperature and humidity; Determine fuzzy rules based on the changing trends of temperature and humidity along with mixing parameters in the historical mixing process; Based on the fuzzy sets corresponding to the temperature and humidity, reasoning is performed according to the fuzzy rules to calculate the fuzzy values ​​of the mixing processing parameters; The fuzzy value is converted into a specific value through a defuzzification method to determine the specific mixing processing parameters.

7. The method according to claim 2, characterized in that The grinding system comprises at least a crusher and a screening machine; wherein the crusher is connected to the screening machine; The intelligent control system determines the grinding processing parameters of the crusher based on the dimensions in the recipe data; The crusher crushes the raw material based on the grinding process parameters to obtain crushed raw material; The intelligent control system determines the average particle size as the mesh size of the screening machine based on the particle size of the crushed raw material; The screening machine screens the crushed raw material based on the screen mesh aperture, removes the raw material with a particle size exceeding the average particle size, and obtains the screened raw material.

8. The method according to claim 1, characterized in that The intelligent control system optimizes the recipe data of large yellow croaker and the processing parameters of the processing and mixing system in real time according to the changing state, including: The intelligent control system compares the change state with the quality standards corresponding to different raw materials in the recipe data; adjusting the recipe data and corresponding processing parameters based on the comparison result and the change trend of the change state along with the recipe data; When the change state still exceeds the quality standard after a preset number of adjustment cycles, the alarm device sounds an alarm to prompt the intelligent control system to be adjusted.

9. The method according to claim 1, characterized in that The method of determining the recipe data for large yellow croaker based on the growth stage includes: Determining the nutritional requirements of the large yellow croaker based on the growth stage of the large yellow croaker; the nutritional requirements include nutrients of different components; determining raw materials for providing nutrients based on the nutrients; Based on the maximum bearing size of the growth stage and the relationship between the nutritional performance of the raw materials and the size of the raw materials, the ratio and size of different raw materials corresponding to the nutritional requirements are determined to generate recipe data.

10. A large yellow croaker feed processing and mixing device, characterized in that: The device includes an intelligent control system and a processing mixing system connected to each other; wherein, The intelligent control system is used to determine the growth stage of large yellow croaker; Among them, a camera is installed in the intelligent control system to take pictures of large yellow croaker, and the growth stage of large yellow croaker is automatically identified through image recognition and a large model; the body color and spot characteristics of large yellow croaker are extracted to obtain a first recognition result, and a preliminary result of the growth stage is obtained. The body length change rate and weight change rate of adjacent images are calculated, and the first recognition result is corrected based on the body length change rate and weight change rate to obtain the corrected growth stage of large yellow croaker; Among them, the boundary of the large yellow croaker is located in the captured image, the neighborhood area at a first distance from the boundary of the large yellow croaker is determined, the first color within the boundary of the large yellow croaker and the second color within the neighborhood area are respectively identified, the large yellow croaker correction color is obtained based on the difference between the first color and the second color, and the color of the large yellow croaker in the large yellow croaker correction color is identified; the boundary area is used as the second image, the color of each pixel in the second image is determined, the color corresponding to the pixels with the most identical colors is determined as the basic pixel color, the first pixel in the second image that is different from the basic pixel color is identified, the adjacent first pixels are connected, the recognition target is determined based on the connected shape, all recognition targets in the second image are extracted, and the spot features of the recognition targets are identified; the preliminary result of the growth stage is determined based on the color of the large yellow croaker and the spot features; The intelligent control system is further used to determine recipe data for large yellow croaker based on the growth stage of large yellow croaker; the recipe data includes the ratio of different raw materials and the sizes of different raw materials, and different growth stages correspond to different nutritional requirements; The intelligent control system is further configured to determine processing parameters of a processing and mixing system based on the recipe data; wherein the processing and mixing system includes at least a grinding system and a mixing system, wherein the order of raw materials entering the grinding system is determined according to the ratio in the recipe data, and the grinding processing parameters of the grinding system are determined according to the size in the recipe data; and the mixing processing parameters of the mixing system are determined according to the ratio; The formula data is the nutritional requirements for the predicted growth stage. The difference between the initial performance of each raw material and the performance required in the formula data is determined. The processing steps required for each raw material are determined based on the difference. The processing parameters for each processing step are determined based on the characteristics of each raw material. The processing and mixing system is used to process and mix based on the formula data and the processing parameters to obtain large yellow croaker feed; The intelligent control system is further used to monitor the changing state of the raw materials in the processing and mixing system in real time based on sensors, and optimize the recipe data and the processing parameters in real time based on the changing state.

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