A dual-circuit egg-laying hen feed pelleting and crushing device

Abstract

This invention relates to the field of feed processing technology and discloses a dual-line layer hen feed pelleting and crushing device, including a hammer mill and two buffer bins at its inlet. The buffer bins have a feeding assembly at their output end, and the device also includes an adaptive feed flow rate control system. This system includes a load sensing module that calculates the load coefficient based on current, voltage, and power factor; a raw material property analysis module that calculates the raw material characteristic coefficient based on moisture content and bulk density; a process suitability assessment module that calculates the process matching degree based on the average output particle size, the proportion of excessively coarse particles, the load coefficient, and the raw material characteristic coefficient; an operating status monitoring module that calculates the equipment health coefficient based on vibration intensity and material level; and a multi-source fusion decision module that calculates the target feed flow rate based on the baseline feed flow rate, process matching degree, and equipment health coefficient. This invention achieves multi-dimensional intelligent control of the feed flow rate, improving crushing quality and operational stability.

Description

Technical Field

[0001] This invention belongs to the field of feed processing technology, and in particular relates to a dual-line egg-laying hen feed pelleting and crushing device. Background Technology

[0002] In the production of layer hen feed, the pelleting and crushing processes have a decisive impact on the uniformity of feed particle size distribution and the feed intake efficiency of livestock and poultry. Traditional dual-line layer hen feed pelleting and crushing devices generally use a fixed-speed feeding auger for material supply. This static feeding mechanism is difficult to cope with the dynamic changes in raw material characteristics and equipment status during the production process. When the moisture content of the raw material fluctuates, for example, the adhesion of the raw material is significantly enhanced in a high-humidity environment, which easily leads to material agglomeration in the crushing chamber, causing a sharp increase in the crusher load. This may not only cause motor overload shutdown but also cause internal blockage of the equipment. Under low-humidity conditions, the excessive fluidity of the raw material can easily lead to uneven feeding and dust emission problems. Batch-to-batch differences in the bulk density of the raw material also directly affect the load characteristics of the crusher. When the bulk density is higher, the risk of overload increases, while when the bulk density is lower, the crushing effect may not meet the standards. In terms of controlling the stability of the output particle size, when the proportion of excessively coarse particles exceeds the standard, the feed cannot meet the feed intake requirements of layer hens, and the production line must be interrupted for manual intervention, which seriously restricts the continuity and efficiency of production. In terms of equipment operation safety, the vibration intensity of the crushing device and the material level in the buffer silo lack a linkage monitoring mechanism. Abnormal vibration may indicate wear of mechanical parts or rotor imbalance. Too low a material level can easily lead to material interruption, while too high a material level may cause overflow risks. These hidden dangers are often difficult to identify and handle in a timely manner in traditional systems. Existing control technologies mainly rely on threshold triggering control of single parameters such as the main motor current. Such methods have slow response times and cannot comprehensively analyze the inherent relationship between motor load, raw material physical properties, output quality indicators, and equipment health status, thus making it difficult to achieve precise and intelligent dynamic adjustment of the feed flow rate. The industry urgently needs a feeding control solution that can integrate multi-dimensional operating condition information in real time and make autonomous decisions to comprehensively improve the quality consistency of the feed crushing process and the level of equipment operation safety.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a dual-line egg-laying hen feed pelleting and crushing device to solve the above-mentioned problems.

[0005] This invention is implemented as follows: a dual-line layer hen feed pelleting and crushing device includes a hammer mill and two buffer hoppers installed on the feed inlet of the hammer mill. Each of the two buffer hoppers has a feeding assembly installed at its output end. The feeding assembly is used to feed feed into the hammer mill. The device also includes a feed flow adaptive control system for real-time adjustment of the feed flow of the feeding assembly. The feed flow adaptive control system includes: a load sensing module, which calculates a load coefficient characterizing the load level of the main motor of the crusher based on the acquired current, voltage, and power factor; and a raw material property analysis module, which analyzes the acquired raw material properties. The system calculates and obtains the raw material characteristic coefficient, which characterizes the ease of crushing the raw material, based on the moisture content and raw material bulk density. The process suitability assessment module calculates and obtains the process matching degree, which characterizes the matching degree between the current output quality and the expected quality, based on the obtained average output particle size and the proportion of excessively coarse particles, as well as the load coefficient and raw material characteristic coefficient. The operation status monitoring module calculates and obtains the equipment health coefficient, which characterizes the safety of the equipment operation status, based on the obtained vibration intensity of the crushing device and the material level in the buffer bin. The multi-source fusion decision module calculates and obtains the target feed flow rate based on the benchmark feed flow rate, process matching degree, and equipment health coefficient, and adjusts the current flow rate to the target feed flow rate.

[0006] A further technical solution involves calculating the load factor as follows: obtaining the current, voltage, and power factor of the crusher's main motor; and substituting the current, voltage, and power factor of the crusher's main motor into the formula. Calculate and obtain the actual power of the crusher's main motor. (kW), of which, This refers to the voltage of the main motor of the crusher. This represents the current of the main motor of the crusher. The power factor of the main motor of the crusher; the maximum value of the actual power of the main motor of the crusher. After minimum normalization and limiting the normalized value to 0-1, the load factor is obtained. , , The smaller the value, the lighter the motor load.

[0007] A further technical solution involves the following steps for calculating the raw material characteristic coefficient: obtaining the raw material moisture content and raw material bulk density; and maximizing both the raw material moisture content and raw material bulk density. After minimum normalization and limiting the normalized value to 0-1, the raw material moisture content index and raw material bulk density index are obtained; the raw material characteristic coefficient is obtained by multiplying the raw material moisture content index and the raw material bulk density index. , , A smaller value indicates that the raw material is more easily broken, allowing for a higher feed rate; conversely, a larger value indicates a more easily broken raw material. The smaller the value, the less feed should be given.

[0008] A further technical solution involves the following steps for calculating the process matching degree: obtaining the average particle size and proportion of oversized particles in the discharge, as well as the loading coefficient and raw material characteristic coefficient; determining the desired average particle size and desired proportion of oversized particles based on the loading coefficient and raw material characteristic coefficient; and substituting the average particle size in the discharge and the desired average particle size into the formula. Obtain the average particle size matching degree ,in, The average particle size of the discharged material. For the desired average particle size, To determine the allowable positive deviation in average particle size, substitute the proportion of excessively coarse particles in the discharge and the desired proportion of excessively coarse particles into the formula. Obtain the matching degree of coarse particle ratio ,in, To reduce the proportion of excessively coarse particles in the output, To achieve the desired coarse ratio, To allow for a positive deviation in the proportion of excessively coarse particles; the smaller value between the average particle size matching degree and the proportion of excessively coarse particles matching degree is taken as the process matching degree. , , It is used to comprehensively reflect the degree of matching between the current working condition and the ideal state.

[0009] A further technical solution involves the following steps for calculating the equipment health coefficient: obtaining the vibration intensity of the crushing device and the material level in the buffer silo; comparing the vibration intensity of the crushing device with the maximum allowable vibration intensity, setting the upper limit of the ratio to 1, and then taking the complement of the ratio as the vibration limitation index; comparing the material level in the buffer silo with the lower limit of the material level safety, setting the upper limit of the ratio to 1, and then obtaining the buffer silo material level index; and taking the smaller value between the vibration limitation index and the buffer silo material level index as the equipment health coefficient. , , A higher value indicates that the equipment is in good condition and can maintain a higher feeding rate; The smaller the value, the less feeding is needed to protect the equipment or avoid feed interruption.

[0010] A further technical solution involves the following steps for calculating and obtaining the target feed flow rate: obtaining the baseline feed flow rate, process matching degree, and equipment health coefficient; multiplying the baseline feed flow rate by the process matching degree and the equipment health coefficient to obtain the target feed flow rate.

[0011] In a further technical solution, the feeding assembly includes an arc-shaped cavity at the output end of the buffer bin, a feeding impeller is rotatably connected inside the arc-shaped cavity, and a servo motor for driving the feeding impeller to rotate is installed on the side wall of the buffer bin.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0013] 1. This invention, through an adaptive control system that integrates multi-source information, can comprehensively consider motor load, raw material characteristics, output quality, and equipment health status to precisely adjust the feed flow rate in real time. This avoids the lag and one-sidedness of traditional single-parameter control, and significantly improves the intelligence level and control accuracy of the crushing process.

[0014] 2. Based on the dynamic adjustment of process matching degree and equipment health coefficient, this invention can effectively prevent motor overload, severe equipment vibration and material interruption risks while ensuring that the output particle size is qualified, thus extending the service life of the equipment and ensuring production continuity and safety.

[0015] 3. This invention adopts a dual-line design with two buffer silos and adaptive control, which can realize the alternating or parallel feeding of raw materials, further improve production efficiency, reduce the risk of production interruption caused by the failure of a single feeding line, and improve the overall reliability of the system. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a dual-line egg-laying hen feed pelleting and crushing device provided by the present invention;

[0017] Figure 2 The flowchart of the adaptive feed flow rate control system provided by the present invention is shown.

[0018] In the attached diagram: 1. Hammer mill; 2. Buffer hopper; 3. Arc-shaped cavity; 4. Feed impeller; 5. Servo motor. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In traditional dual-line layer hen feed pelleting and crushing devices, the feeding assembly is configured to operate at a fixed speed, making it impossible to dynamically adjust the feed rate according to actual working conditions. Specifically, when the moisture content or bulk density of the raw materials changes, the load on the main motor of the crusher is detected to fluctuate drastically, easily leading to overload or blockage. Simultaneously, the output particle size distribution is difficult to maintain stably; when the proportion of excessively coarse particles exceeds the standard, manual intervention and shutdown are required, affecting production continuity. Furthermore, the lack of a coordinated monitoring mechanism between equipment vibration intensity and buffer bin material level introduces potential operational risks. Existing control strategies rely solely on single parameter threshold judgments, resulting in observed lag in response. Moreover, the coupling relationship between multiple sources of information, such as motor load, raw material characteristics, product quality, and equipment status, is not effectively integrated, thus hindering the refined and intelligent adjustment of the feeding process.

[0021] For example, during a certain feed production process, the moisture content of the raw materials increased due to changes in ambient humidity. The fixed-speed feeding component fed the material at a preset flow rate, causing the current of the main motor of the crusher to exceed the safety limit. The proportion of overly coarse particles in the output was recorded as increased. The operator interrupted production to adjust the feeding speed. During this period, the vibration intensity of the equipment was measured as increased, the fluctuation range of the buffer bin material level was expanded, and the stability of the system was further threatened.

[0022] If the above-mentioned technical problems are not solved, unplanned downtime will occur frequently, reducing production efficiency; the uniformity of feed particle size distribution will be affected, reducing livestock and poultry feeding efficiency; wear of key components will be accelerated, shortening their service life; equipment failures and even safety accidents will occur, posing a major challenge to the reliability and safety of the production system.

[0023] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0024] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a dual-line layer hen feed pelleting and crushing device, including a hammer mill 1 and two buffer hoppers 2 installed on the feed inlet of the hammer mill 1. The hammer mill 1 is a crushing device whose working principle is to crush large particles into fine particles of the required particle size by impacting, shearing, and grinding the incoming material through high-speed rotating hammers. This device is used in the feed processing industry to prepare feed products of different particle sizes. The buffer hoppers 2 are material storage containers located above the feed inlet of the hammer mill 1. Their function is to temporarily store the raw materials to be crushed and ensure a continuous and stable material flow to the crusher, avoiding idling or overloading of the crusher due to uneven feeding. Feeding components are installed at the output ends of both buffer hoppers 2, and the feeding components are mechanisms installed between the output ends of the buffer hoppers 2 and the feed inlet of the hammer mill 1. This component is responsible for conveying the material in the buffer bin 2 to the inside of the hammer mill 1 at a controlled flow rate. The accurate control of its feed flow rate has an impact on the stable operation of the mill and the product quality.

[0025] It also includes: a feed flow rate adaptive control system, used to adjust the feed flow rate of the feeding component in real time. The feed flow rate adaptive control system includes:

[0026] The load sensing module calculates a load factor characterizing the load level of the crusher's main motor based on the acquired current, voltage, and power factor. As a component of the adaptive control system, its responsibility is to acquire electrical parameters such as current, voltage, and power factor of the crusher's main motor in real time. Through calculation and analysis of these parameters, the module can derive the load factor characterizing the load level of the crusher's main motor, thus reflecting the working load status of the crusher.

[0027] The raw material property analysis module calculates a raw material characteristic coefficient that characterizes the ease of crushing the raw material based on its moisture content and bulk density. This module, another component of the adaptive control system, is used to obtain the moisture content and bulk density of the raw material to be crushed. By analyzing these physical properties, this module can calculate the raw material characteristic coefficient that characterizes the ease of crushing, providing a basis for adjusting the feed flow rate.

[0028] The process suitability assessment module, based on the acquired average discharge particle size and proportion of overly coarse particles, as well as the load coefficient and raw material characteristic coefficient, calculates the process suitability degree, which characterizes the degree of matching between the current discharge quality and the desired quality. This module is a key component of the adaptive control system. Its function is to calculate the process suitability degree, based on the acquired average discharge particle size and proportion of overly coarse particles, combined with the load coefficient output from the load sensing module and the raw material characteristic coefficient output from the raw material property analysis module. This degree of matching reflects the extent to which the current crushing process meets the target product quality requirements.

[0029] The operation status monitoring module calculates an equipment health coefficient, which characterizes the safety of the equipment's operation, based on the acquired vibration intensity of the crushing unit and the material level in the buffer silo. This module is part of the adaptive control system and is used to monitor the vibration intensity of the crushing unit and the material level in the buffer silo in real time. By analyzing these parameters, the module can calculate the equipment health coefficient, thereby assessing the operational risks of the equipment.

[0030] The multi-source fusion decision module calculates the target feed flow rate based on the baseline feed flow rate, process compatibility, and equipment health coefficient, and adjusts the current flow rate to the target feed flow rate. This module is the main decision-making unit of the adaptive control system. It comprehensively considers the baseline feed flow rate, the process compatibility assessment module's output process compatibility, and the equipment health coefficient's output operating status monitoring module. Based on this multi-source information, the module calculates an optimized target feed flow rate and instructs the system to adjust the current feed flow rate to this target value.

[0031] The multi-source fusion decision module in this embodiment can integrate multi-source information such as baseline feed flow rate, process matching degree, and equipment health coefficient to calculate the optimized target feed flow rate. This multi-parameter, multi-dimensional fusion decision mechanism overcomes the limitations of existing control methods, such as slow response and inability to comprehensively evaluate the coupling relationship of multiple factors. By adjusting the feed flow rate of the feeding components in real time, the device in this embodiment can effectively avoid motor overload and blockage, stably control the discharge particle size, reduce the proportion of excessively coarse particles, and ensure equipment operation safety, thereby significantly improving feed crushing quality and equipment operation reliability.

[0032] This application further proposes the following steps for calculating and obtaining the load factor:

[0033] The system acquires the current, voltage, power factor, minimum actual power, and maximum actual power of the crusher's main motor. Current, voltage, and power factor are key electrical parameters characterizing the motor's real-time operating status, directly reflecting its energy consumption under current operating conditions. The minimum and maximum actual power values ​​provide a reference benchmark for normalizing motor power, ensuring the comparability and practical significance of the calculated load factor. These parameters can be collected in real-time by installing appropriate sensors (such as current transformers, voltage sensors, and power factor sensors) in the crusher's main motor's power supply line and transmitted to the load sensing module via a data acquisition unit. Alternatively, these electrical parameters can be directly read and output using a smart meter or power monitoring module integrated into the motor control system.

[0034] Substitute the current, voltage, and power factor of the crusher's main motor into the formula. Calculate and obtain the actual power of the crusher's main motor. (kW), the purpose of this step is to convert the collected electrical parameters into actual power values ​​with clear physical meaning. This formula is the standard formula for calculating the actual power of a three-phase AC motor, where... This refers to the voltage of the main motor of the crusher. This represents the current of the main motor of the crusher. This is the power factor of the crusher's main motor. This calculation accurately quantifies the energy output of the crusher's main motor at a given moment, providing a direct basis for assessing its load level. This calculation can be performed within the processor of the load sensing module, for example, by executing a preset calculation program via an embedded microcontroller or industrial PC. Alternatively, it can be directly output using a dedicated power parameter measurement chip or module.

[0035] Maximize the actual power of the crusher's main motor After minimum normalization and limiting the normalized value to 0-1, the load factor is obtained. , , A smaller value indicates a lighter motor load (and greater safety). This step aims to convert the motor's actual power value into a dimensionless load factor ranging from 0 to 1. Normalization eliminates differences between motor specifications or operating conditions, making the load factor universal and facilitating unified processing by subsequent decision-making modules. Through maximum-minimum normalization, the actual power can be mapped to a 0-1 range, where 0 represents motor power not exceeding the rated value (i.e., light load or no load), and 1 represents motor power reaching the maximum allowable value (i.e., full load or overload threshold). Limiting ensures the calculation results remain within an effective range. This processing can be implemented in the software algorithm of the load sensing module, by programming to compare and calculate the actual power value with preset minimum and maximum actual power values. Another implementation method is to use a dedicated signal processing unit to perform analog or digital normalization on the power signal and then limit the output.

[0036] In this application, to accurately assess the load level of the crusher's main motor, the load sensing module calculates the load factor through a series of rigorous steps. First, the system acquires the current, voltage, and power factor of the crusher's main motor in real time; these are fundamental electrical data reflecting the motor's operating status. Simultaneously, to establish a reference benchmark for load assessment, the system also acquires the motor's minimum and maximum actual power values. Subsequently, the real-time acquired current, voltage, and power factor are substituted into the standard three-phase AC motor power calculation formula to accurately calculate the actual power of the crusher's main motor. This actual power value directly reflects the motor's current energy consumption level. To ensure this power value can be uniformly and standardized for subsequent decision-making, the system performs maximum-minimum normalization processing, ensuring that the normalized value is limited to the range of 0 to 1, ultimately yielding the load factor. This load factor This is a dimensionless indicator; a smaller value indicates a lighter motor load, closer to a safe operating state; conversely, a larger value indicates a heavier motor load. A value of 1 signifies that the motor has reached its maximum allowable power, requiring immediate feed reduction measures to protect the equipment. Through this precise calculation and standardized processing, the load sensing module provides an accurate and reliable motor load assessment for the adaptive control system. This allows the multi-source fusion decision module to fully consider the actual load-bearing capacity of the main motor when adjusting the feed flow, avoiding damage to the equipment due to excessive load or impact on production efficiency due to insufficient load.

[0037] For example, the load sensing module can be configured with an industrial-grade programmable logic controller (PLC) or embedded controller as the core processing unit. This controller connects to current sensors, voltage sensors, and power factor sensors installed on the main motor power supply circuit of the crusher via analog input modules. These sensors collect the motor's current, voltage, and power factor signals in real time and convert them into standard electrical signals (such as 4-20mA or 0-10V) input to the PLC. The PLC internally presets the minimum and maximum actual power values ​​of the crusher's main motor. When the PLC receives real-time current, voltage, and power factor data, it immediately executes the pre-programmed calculation logic. First, it substitutes these data into the formula... Calculate the actual power of the crusher's main motor. Next, the PLC will calculate the actual power based on the preset minimum and maximum actual power values. Max-min normalization is performed to ensure the load factor. The value remains between 0 and 1. The PLC also performs a limiting operation; for example, if the calculated result is less than 0, it is set to 0; if it is greater than 1, it is set to 1. The final load factor is... It will be stored and transmitted to the multi-source fusion decision module as an important basis for adjusting the feed flow rate.

[0038] Through the above technical solution, this application provides an accurate and reliable method for calculating the load factor of a crusher's main motor. This method comprehensively considers the motor's current, voltage, and power factor, and combines this with normalization of actual power, making the load factor assessment more accurate and standardized. This effectively solves the problems of insufficient accuracy and inability to uniformly quantify motor load levels that may exist in traditional load assessment methods. Because the load factor... The adaptive control system can accurately reflect the degree of motor load, providing a more precise basis for decision-making when adjusting the feed flow rate. This not only helps avoid equipment damage or shortened lifespan due to overload of the main crusher motor, but also prevents energy waste and low production efficiency caused by underload, thus ensuring that the crushing device always operates under safe and efficient conditions, significantly improving the operational stability and economic benefits of the entire dual-line layer hen feed pelleting and crushing unit.

[0039] This application further proposes the following steps for calculating and obtaining the raw material characteristic coefficients:

[0040] Obtain the moisture content and bulk density of the raw materials. Moisture content refers to the percentage of water contained in the raw material, which directly affects its toughness and brittleness. Generally, the lower the moisture content, the more brittle the raw material and the easier it is to break. Bulk density refers to the mass of a unit volume of raw material, reflecting its density and compactness. The lower the bulk density, the more porous the raw material structure, and the less energy is required to break it. These parameters can be obtained in various ways. For example, moisture content can be measured in real time using an online moisture sensor, or determined in the laboratory using methods such as drying or Karl Fischer titration after sampling; bulk density can be measured in real time using an online bulk density sensor, or calculated by weighing a known volume of raw material.

[0041] Both the moisture content and bulk density of the raw materials were maximized. Minimum normalization is performed, and the normalized value is limited to 0-1 to obtain the raw material moisture content index and raw material bulk density index. This step aims to convert physical quantities with different dimensions and numerical ranges into unified, dimensionless indices. This processing method can eliminate the influence of the original data dimensions, making different parameters comparable and mapping them to a fixed interval of 0 to 1, which facilitates subsequent mathematical operations and logical judgments. For example, for moisture content, a minimum moisture content (e.g., 5%) and a maximum moisture content (e.g., 20%) can be set, and the actual measured moisture content can be mapped to the 0-1 exponent interval through linear interpolation or more complex functions. A similar method can be used for bulk density, setting a minimum bulk density and a maximum bulk density, and normalizing them to an exponent of 0-1.

[0042] The raw material characteristic coefficient is obtained by multiplying the raw material moisture content index and the raw material bulk density index. This coefficient comprehensively reflects the ease or difficulty of crushing the raw material. By multiplying the two normalized exponents, a comprehensive coefficient between 0 and 1 can be obtained. When the raw material has low moisture content and low bulk density, the corresponding exponent values ​​are all small, and the resulting coefficient is... A smaller value indicates that the raw material is easily crushed; conversely, when the moisture content is high or the bulk density is high, the corresponding index value is larger. Multiplying these values ​​yields the result. A relatively large value indicates that the raw material is difficult to crush. This coefficient provides a crucial input for subsequent feed flow rate control, enabling the system to make more accurate judgments based on the actual physical characteristics of the raw material.

[0043] The solution in this application obtains the moisture content and bulk density of the raw materials and converts them into a unified raw material characteristic coefficient. This allows the adaptive control system to more comprehensively assess the current crushing conditions. In the aforementioned dual-line layer hen feed pelleting and crushing device, while the load level of the crusher's main motor, the output quality, and the equipment's health are important when the feeding component feeds the hammer mill 1, the physical properties of the raw material itself also significantly affect the crushing process. By introducing a raw material characteristic coefficient, the system can take into account the inherent properties of the raw material. For example, when the raw material has a low moisture content and low bulk density, its crushing difficulty is relatively low. Even when the main motor load of the crusher is not high, the system can identify the characteristics of the current raw material that are easy to process, and thus appropriately increase the feeding flow rate to improve production efficiency while ensuring output quality and equipment safety. Conversely, when the raw material has a high moisture content and high bulk density, its crushing difficulty increases. The system will calculate a more conservative target feeding flow rate in the multi-source fusion decision module based on a higher raw material characteristic coefficient, avoiding problems such as crusher blockage, overload, or decreased output quality caused by the difficulty in crushing the raw material. This quantitative assessment of raw material characteristics enables the entire adaptive control system to consider not only the external performance of the equipment operation when adjusting the feed flow rate, but also to deeply analyze the intrinsic properties of the processed material, thereby achieving more refined and intelligent feed control.

[0044] The following is a specific example illustrating this. In practical applications, online moisture content sensors and online bulk density sensors can be configured to acquire real-time data on the moisture content and bulk density of the raw materials entering buffer silo 2. For example, a batch of raw materials was measured to have a moisture content of 12% and a bulk density of 0.65 g / cm³. 3 Assume the normalized range for moisture content is 5% (index 0) to 20% (index 1), and the normalized range for bulk density is 0.5 g / cm³. 3 (Index 0) to 0.8 g / cm³ 3 (Index 1). Therefore, a moisture content of 12%, after normalization, might yield a moisture content index of 0.47, for example, (12-5) / (20-5)≈0.47; bulk density 0.65 g / cm³. 3 After normalization, a bulk density index of 0.5 may be obtained, for example, (0.65-0.5) / (0.8-0.5)≈0.5. Multiplying these two indices yields the raw material characteristic coefficient. It is 0.235. This is relatively low. The value is passed to the multi-source fusion decision module, indicating that the current raw material is relatively easy to crush, thus allowing the feed rate to be appropriately increased when other conditions permit when calculating the target feed flow rate.

[0045] Through the aforementioned technical solution, the adaptive control system can more accurately assess the crushing difficulty of raw materials, thereby calculating a more reasonable target feed flow rate in the multi-source fusion decision module. This helps avoid feed rate mismatch problems caused by changes in raw material characteristics. For example, when the raw material is difficult to crush, the system can promptly reduce the feed rate to prevent the crusher from overloading or clogging; when the raw material is easy to crush, the system can appropriately increase the feed rate, thereby maximizing production efficiency and capacity while ensuring output quality and equipment safety. This refined control based on raw material characteristics significantly improves the intelligence level and operational stability of the entire crushing process.

[0046] This application further proposes the following steps for calculating and obtaining the process matching degree:

[0047] The average particle size and proportion of oversized particles, load factor, and raw material characteristic coefficient of the output are obtained. The average particle size and proportion of oversized particles are key indicators for evaluating the quality of the actual output material from the crushing unit. These can be obtained in real-time through an online particle size analyzer, such as using laser diffraction or image recognition technology, or by periodically sampling the output and performing standard sieve analysis. The load factor, provided by the load sensing module, reflects the real-time working load of the crusher's main motor 1. The raw material characteristic coefficient, provided by the raw material property analysis module, characterizes the ease or difficulty of crushing the raw material.

[0048] The desired average particle size and desired over-coarseness ratio are determined based on the loading coefficient and raw material characteristic coefficient. The specific calculation method is as follows:

[0049] Substitute the load factor and raw material characteristic factor into the formula Calculate and obtain the desired average particle size ,in, For the target average particle size, To allow the maximum average particle size, For load factor, For raw material characteristic coefficients;

[0050] Substitute the load factor and raw material characteristic factor into the formula Calculate and obtain the expected overcoarsening ratio ,in, For the target too coarse ratio, To allow for the maximum allowable overcoarsing ratio, For load factor, For raw material characteristic coefficients;

[0051] Calculate the expected average particle size and the proportion of overly coarse expectations The steps involved aim to dynamically adjust the expected output quality based on the current equipment load and raw material characteristics. This means the system does not rigidly pursue a fixed target, but can intelligently adjust its quality objectives according to the complexity of actual working conditions. For example, when the crusher's main motor 1 is under heavy load or the raw material is difficult to crush, the system will appropriately relax its expectations for output particle size and the proportion of coarse particles to avoid over-pursuing quality and causing equipment overload or decreased production efficiency. This dynamic adjustment mechanism makes process matching assessment more flexible and in line with actual production needs.

[0052] Substitute the average discharge particle size and the desired average particle size into the formula. Obtain the average particle size matching degree ,in, The average particle size of the discharged material. For the desired average particle size, The allowable positive deviation in average particle size;

[0053] Substitute the proportion of excessively coarse particles in the output and the desired proportion of excessively coarse particles into the formula. Obtain the matching degree of coarse particle ratio ,in, To reduce the proportion of excessively coarse particles in the output, To achieve the desired coarse ratio, For the allowable positive deviation of the overly coarse ratio;

[0054] Obtain average particle size matching degree Matching degree with the proportion of excessively coarse particles The process involves quantitatively comparing the actual output quality with the dynamically adjusted expected value. Using a specific mathematical formula, the deviation between the actual and expected values ​​is converted into a matching score between 0 and 1. When the actual output quality perfectly matches or exceeds the expected value, the matching score is 1; when the actual value exceeds the allowable deviation range, the matching score decreases accordingly, down to 0. This quantitative method provides a unified and intuitive evaluation standard for subsequent decision-making.

[0055] The maximum permissible average particle size, target average particle size, target overcoarse ratio, and maximum permissible overcoarse ratio are preset quality parameters based on product standards and process requirements, and are usually configured by process engineers during system initialization. The permissible positive deviation of average particle size and the permissible positive deviation of overcoarse ratio define the acceptable tolerance range when the actual output quality deviates from the expected value. These deviation values ​​are used to quantify the degree of quality deviation, and their settings are usually based on production experience and quality control standards.

[0056] The smaller value between the average particle size matching degree and the excessively coarse particle ratio matching degree is taken as the process matching degree. , , This value comprehensively reflects the degree of matching between the current operating conditions and the ideal state. The ideal state is characterized by a light load, fragile raw materials, and output quality that meets expectations; in this state, the value is 1. Any deterioration in any aspect (such as excessive load, difficult-to-crush raw materials, or deviation in quality) will decrease the value, thereby triggering material reduction. The smaller value between the average particle size matching degree and the excessively coarse particle proportion matching degree is taken as the process matching degree. The steps are to ensure process compatibility. This approach comprehensively reflects the "weakest link" in material quality. Specifically, if either the average particle size or the proportion of excessively coarse particles performs poorly (low matching degree), the overall process matching degree will be lowered. This "barrel effect" evaluation method ensures that when adjusting the feed flow rate, the system prioritizes and improves the worst-performing quality indicator, thereby comprehensively enhancing the stability and reliability of the output quality.

[0057] The proposed solution achieves its intended function by constructing a dynamic, multi-dimensional process matching evaluation mechanism. First, the system no longer simply measures the output quality using fixed target values. Instead, it dynamically calculates the "desired" average output particle size and the proportion of oversized particles based on the current load level of the crusher's main motor 1 (load coefficient) and the difficulty of crushing the raw material (raw material characteristic coefficient). This means that when the equipment load is high or the raw material is difficult to crush, the system intelligently adjusts its expectations for output quality, avoiding unrealistic quality requirements that could lead to system instability. Next, the system compares the actually measured average output particle size and the proportion of oversized particles with these dynamically adjusted desired values, and, in conjunction with preset allowable deviations, calculates the average particle size matching degree. Matching degree with the proportion of excessively coarse particles These two matching scores quantify the degree of conformity between the actual output quality and the reasonable expectation under the current operating conditions. Ultimately, the smaller of these two matching scores is selected as the final process matching score. This strategy of taking the smallest value ensures that any deviation in any key indicator of output quality (average particle size or proportion of excessively coarse particles) will immediately be reflected in a lower process matching degree, prompting the system to adjust in a timely manner. This evaluation mechanism is closely integrated with the load sensing module and the raw material property analysis module, enabling the adaptive control system to understand the current production status more comprehensively and intelligently. It not only considers the safety of equipment operation and the inherent properties of raw materials but also provides a deeper evaluation of the final product's quality performance. This provides crucial quality feedback for the subsequent multi-source fusion decision module to accurately calculate the target feed flow rate, thereby optimizing output quality while ensuring equipment safety and efficiency.

[0058] As a specific implementation method, suppose that in a certain production process, the target average particle size is... Set to 1.0 mm, maximum allowable average particle size The allowable average particle size positive deviation is 1.5 mm. The thickness is 0.2mm. Meanwhile, the target is too coarse. The maximum allowable overthrow ratio is 5%. The allowable positive deviation for excessively coarse proportions is 10%. The load factor is 2%. During the production process, the system monitors and calculates the load factor in real time. The coefficient of performance is 0.8, representing the raw material characteristic coefficient. The value is 0.7. Based on these data, the system first calculates the expected average particle size. The thickness is 1.28mm, and the desired over-coarseness ratio is... It is 7.8%. Subsequently, if the actual measured average particle size of the output... The proportion of excessively coarse particles is 1.35mm. If the value is 8.5%, the system will calculate the average particle size matching degree. The value is 0.65, and the matching degree of excessively coarse particles is also considered. The value is 0.65. Ultimately, the process matching degree... It was determined to be 0.65. This calculated process matching degree It will serve as an important input to the multi-source fusion decision module, guiding the feeding component to adjust the feeding flow rate in order to optimize the output quality.

[0059] Through the above technical solution, this application enables a refined assessment of the output quality of the crushing device. Traditional methods often focus only on equipment load or raw material characteristics, neglecting the quality feedback of the final product. This solution dynamically adjusts the desired output quality indicators and calculates a comprehensive process matching degree based on the actual output conditions. This allows the system to more accurately determine the degree of matching between the current production state and the ideal state under different operating conditions, avoiding misjudgments or over-adjustments caused by fixed target values. When deviations occur in the output quality, this matching degree can be reflected promptly and sensitively, thus providing a reliable decision-making basis for the adaptive control system. This ensures that while guaranteeing equipment safety and efficiency, the average particle size and the proportion of excessively coarse particles in the output are continuously optimized, significantly improving the stability and consistency of product quality.

[0060] This application further proposes the following steps for calculating and obtaining the equipment health coefficient:

[0061] The vibration intensity of the crushing unit and the material level in the buffer hopper are measured. Vibration intensity refers to the strength of the mechanical vibration generated during operation. Excessive vibration intensity usually indicates equipment malfunction, wear, or imbalance, potentially leading to equipment damage. Vibration intensity can be obtained using vibration sensors installed at key locations on the crushing unit, such as accelerometers or displacement sensors. These sensors can monitor and output vibration signals in real time. Another method is to use acoustic sensors to monitor equipment operating noise and indirectly assess the vibration level through noise spectrum analysis. The material level in the buffer hopper (material 2) refers to the filling height or capacity of the material. Too low a level may cause feeding interruptions, affecting production continuity; too high a level may cause overflow or blockage. Material level information can be obtained in various ways, such as using non-contact sensors like ultrasonic level gauges, radar level gauges, or capacitive level gauges for real-time measurement. Alternatively, multiple limit switches or pressure sensors installed on the side wall of the buffer hopper (material 2) can be used to detect the material level in segments.

[0062] The vibration intensity of the crushing device is compared with the maximum permissible vibration intensity, and the upper limit of the amplitude limit ratio is set to 1. The complement of this ratio (1 minus the amplitude limit ratio) is then taken as the vibration limitation index. This step aims to quantify and compare the actual vibration intensity with the safety threshold, transforming it into an index reflecting the equipment's vibration risk. Ratio processing standardizes vibration data of different dimensions. Setting the upper limit of the amplitude limit ratio to 1 ensures a reasonable range for the index, preventing its unlimited increase when the vibration intensity far exceeds the permissible value. Taking the complement (1 minus the ratio) aims to ensure a positive correlation between the index and the equipment's health condition; that is, the lower the vibration (the healthier), the larger the index. The maximum permissible vibration intensity refers to the upper limit of vibration intensity that the crushing device can withstand under normal and safe operating conditions. This value is usually determined based on the equipment manufacturer's specifications, industry standards, or historical operating data analysis. It can be a fixed threshold or a parameter dynamically adjusted based on equipment operating time, maintenance cycles, etc.

[0063] The buffer bin level is compared to the lower safety limit, with the ratio capped at 1, to obtain the buffer bin level index. This step quantifies the sufficiency of material in buffer bin 2 and converts it into an index reflecting feeding stability. By comparing the actual level with the lower safety limit, level data for different buffer bin 2 sizes and material types can be standardized. A capped ratio of 1 ensures a reasonable range for the index, preventing its unlimited increase when the level far exceeds the lower safety limit. The lower safety limit refers to the minimum safe filling height of material in buffer bin 2. Below this limit, the feeding assembly may idle or draw in air, affecting feeding stability or causing equipment wear. This value is typically set based on the working principle of the feeding assembly, material characteristics, and production process requirements.

[0064] The smaller value between the vibration limitation index and the buffer silo level index is taken as the equipment health coefficient. This step comprehensively assesses the equipment's vibration and feeding status by "taking the smaller value." This approach reflects the "weakest link effect," meaning the overall health of the equipment depends on its weakest link. If vibration is excessive, even with sufficient feeding, the equipment's health coefficient will be lowered; conversely, if feeding is insufficient, even with normal vibration, the equipment's health coefficient will decrease. This ensures that the equipment health coefficient comprehensively and conservatively reflects the equipment's operational safety, thus enabling timely responses to any risks that may affect equipment safety or production continuity during subsequent feed flow control. , A higher value indicates that the equipment is in good condition (low vibration, sufficient material supply) and can maintain a higher feeding level; The smaller the value, the less feeding is needed to protect the equipment or avoid feed interruption.

[0065] In this application, in order to accurately assess the operational safety of the dual-line layer hen feed pelleting and crushing device, the operational status monitoring module calculates the equipment health coefficient through a series of quantitative processing steps. First, the system acquires the vibration intensity of the crushing device and the material level in buffer silo 2 in real time, while pre-setting the maximum allowable vibration intensity and the lower limit of the material level as safety benchmarks. Then, the measured vibration intensity of the crushing device is compared with the maximum allowable vibration intensity, and the result is limited by an upper limit of 1. A vibration limitation index is then generated by subtracting the limitation ratio from the measured intensity. This index directly reflects the deviation of the equipment's vibration condition from safety standards; the lower the vibration, the higher the index. Simultaneously, the material level in buffer silo 2 is also compared with the lower limit of the material level, similarly limited by an upper limit of 1, to obtain the buffer silo material level index. This index characterizes the sufficiency of material supply; the higher the material level, the higher the index. Finally, the operation status monitoring module selects the smaller of these two indices as the equipment health coefficient. This "smaller-case scenario" strategy ensures that the equipment health coefficient conservatively reflects the overall operational safety of the equipment; that is, any problem in vibration or material feeding will immediately be reflected in a decrease in the equipment health coefficient. Subsequently, the multi-source fusion decision module is used to calculate the target feed flow rate, so that when the equipment is not operating well, the feed flow rate can be adjusted in time to protect the equipment from damage or avoid production interruption due to insufficient material supply. This ensures that the crushing device operates under safe and stable conditions and effectively solves the problem of how to accurately quantify the health status of the equipment to guide feed control.

[0066] As a specific implementation method, the equipment health coefficient in a dual-line layer hen feed pelleting and crushing device can be calculated using the following method. First, a piezoelectric accelerometer is installed on the bearing housing or frame of the hammer mill 1 to monitor and output the vibration intensity data of the crushing device in real time. Simultaneously, a non-contact ultrasonic level sensor is installed on the inner wall of the buffer hopper 2 to detect the material height within the hopper in real time. The preset maximum allowable vibration intensity is 10 mm / s (root mean square value), and the lower limit of the material level is 20% of the total height of the buffer hopper 2. When the sensor detects a current vibration intensity of 2 mm / s, its ratio to the maximum allowable vibration intensity is 0.2, and after rounding, the vibration limitation index is 0.8. When the material level in the buffer hopper 2 is 50% of the total height, its ratio to the lower limit of the material level is 2.5, and after limiting it to 1, the buffer hopper level index is 1. At this point, the operating status monitoring module takes the smaller value between the vibration limitation index 0.8 and the buffer hopper level index 1, i.e., 0.8, as the current equipment health coefficient. If the vibration intensity increases to 8 mm / s, the vibration limitation index becomes 0.2; while the material level drops to 15% of the total height, the ratio of the material level to the safety lower limit is 0.75, and the material level index is 0.75. At this time, the equipment health coefficient... The smaller value between 0.2 and 0.75, i.e., 0.2, will be used. This will give the calculated equipment health coefficient. It is then sent to the multi-source fusion decision module to guide the adjustment of the feeding flow rate of the feeding component.

[0067] Through the above technical solution, this application can evaluate the operational safety of the dual-line layer hen feed pelleting and crushing device in real time and accurately. By comprehensively considering the vibration intensity of the crushing device and the material level in the buffer hopper 2, and adopting the "smaller" principle, a health coefficient for the equipment is generated. This ensures sensitive detection of potential equipment risks. When equipment vibration is abnormal or material supply is insufficient, the equipment health coefficient... The feed flow rate will decrease rapidly, prompting the adaptive control system to adjust the feed rate of the feeding components in a timely manner. This effectively avoids equipment damage and production stoppages caused by equipment overload, accelerated wear, or material interruption, significantly improving the operational reliability and production continuity of the equipment. This quantitative evaluation method provides a reliable safety basis for the intelligent control of the feed flow rate, making the entire crushing process more stable and efficient.

[0068] This application further proposes the following steps for calculating and obtaining the target feed flow rate:

[0069] The system obtains the baseline feed rate, process matching degree, and equipment health coefficient. The baseline feed rate refers to the initial feed rate set under ideal or standard operating conditions based on production experience or process requirements. It typically represents an average or rated feed level that the system expects to achieve without special limitations or optimization needs. This value can be manually entered by the operator according to the production plan, or automatically loaded through historical data analysis or preset process parameters. The process matching degree reflects the degree of conformity between the current output quality (such as average particle size and the proportion of oversized particles) and the desired quality, comprehensively considering the influence of the load coefficient and raw material characteristic coefficient on the desired quality. Its value ranges from 0 to 1; a larger value indicates that the current process state is closer to the ideal target. In the calculation of the target feed rate, the process matching degree serves as a key adjustment factor to ensure that adjustments to the feed rate guide the output quality towards the desired target. The equipment health coefficient characterizes the operational safety of the crushing device, comprehensively considering factors such as vibration intensity and buffer bin material level. Its value also ranges from 0 to 1; a larger value indicates a better equipment operating condition and lower risk. In the calculation of the target feed flow rate, the equipment health coefficient is another important adjustment factor. It is used to reduce the feed flow rate in a timely manner when the equipment malfunctions (such as excessive vibration or insufficient material level) in order to protect the equipment and avoid production interruption.

[0070] The target feed rate is obtained by multiplying the baseline feed rate by the process matching degree and the equipment health coefficient. Specifically, the calculation method is as follows: substitute the baseline feed rate, process matching degree, and equipment health coefficient into the formula. Calculate and obtain the target feed flow rate By using the baseline feed flow rate Compatibility with process and equipment health coefficient A product operation is performed. This multiplicative relationship allows the target feed flow rate to be simultaneously constrained and adjusted by both production quality requirements and equipment safety conditions. A decrease in any coefficient will directly lead to a corresponding decrease in the target feed flow rate, thereby achieving refined, multi-dimensional adaptive control of the feed flow rate. Based on the baseline feed flow rate, For process matching, This refers to the equipment health coefficient.

[0071] This application's solution introduces a multi-factor product model to calculate the target feed flow rate, achieving precise adaptive control of the feed flow rate of the feeding component. During operation, the system first acquires a preset baseline feed flow rate. This baseline flow rate represents the expected feed level under normal operating conditions. Simultaneously, the system continuously monitors and calculates the process matching degree. and equipment health coefficient Process matching degree This comprehensively reflects the degree to which the current output quality meets the expected quality, as well as the impact of the crusher's main motor load and raw material characteristics on the crushing effect. A higher value indicates a more ideal process condition. Equipment Health Coefficient The vibration intensity of the crushing device and the material level in the buffer hopper were comprehensively evaluated; higher values ​​indicate safer and more stable equipment operation. Subsequently, these three key parameters were substituted into the product formula. The final target feed flow rate is calculated. This multiplicative operation mechanism ensures that the target feed rate can simultaneously respond to comprehensive changes in process quality, equipment load, raw material characteristics, and equipment health. For example, when the process matching degree decreases (poor output quality or excessive load) or the equipment health coefficient decreases (abnormal vibration or insufficient material level), the target feed rate will decrease accordingly even if the baseline feed rate remains unchanged, thereby automatically adjusting the feed rate to optimize crushing effect, protect equipment, or avoid material interruption. Conversely, when all indicators are in good condition, the system allows maintaining or increasing the feed rate to improve production efficiency. In this way, the solution of this application can dynamically balance production efficiency, product quality, and equipment safety, solving the problem of difficulty in accurately controlling the feed rate under complex and variable operating conditions.

[0072] The following is a concrete example to illustrate this. Assume that at a certain time period, the baseline feed flow rate... The flow rate is set to 100 kg / h. At this point, the adaptive control system, through real-time monitoring and calculation of various parameters, determines the process matching degree. The equipment health coefficient is 0.85 (indicating a slight deviation in current output quality or a slightly heavy load). A value of 0.90 (indicating that the equipment is operating well but there is slight vibration or the material level is slightly below the ideal value). Substitute these values ​​into the formula. The target feed flow rate is calculated. The flow rate is 76.5 kg / h. At this point, the system will instruct the feeding assembly to adjust the feeding flow rate to 76.5 kg / h. In another example, if the process matching... A health coefficient of 1.0 (indicating perfect output quality and moderate load) is required for the equipment. A value of 1.0 (indicating the equipment is operating normally and the material level is sufficient) indicates the target feed flow rate. This will be equal to the baseline feed rate. That is, 100 kg / h. If the equipment health coefficient... The vibration intensity of the crushing device dropped to 0.5 due to excessive vibration, even with process matching. Still 1.0, target feed rate The speed will also be reduced to 50 kg / h to protect the equipment.

[0073] By integrating the benchmark feed flow rate, process matching degree, and equipment health coefficient in a product-like manner through the above technical solution, refined and multi-dimensional adaptive control of the feed flow rate can be achieved. This calculation method allows the system to comprehensively consider multiple key factors such as production efficiency, product quality, raw material characteristics, equipment load, and equipment operational safety, avoiding the one-sided problems that may be caused by single-parameter control. When any influencing factor changes adversely (e.g., decreased output quality, excessive equipment load, difficult raw material crushing, or abnormal equipment operation), the target feed flow rate will immediately decrease accordingly, effectively preventing problems such as equipment overload, substandard product quality, or equipment damage caused by improper feed amount. At the same time, when all indicators are in good condition, the system can maintain a high feed rate to ensure production efficiency. This dynamic balance adjustment mechanism significantly improves the intelligence level and operational stability of the crushing device, ensuring the continuity, efficiency, and safety of the egg-laying hen feed pelleting and crushing process.

[0074] like Figure 1 As shown, in a preferred embodiment of the present invention, the feeding assembly includes an arc-shaped cavity 3 disposed at the output end of the buffer bin material 2, a feeding impeller 4 rotatably connected inside the arc-shaped cavity 3, and a servo motor 5 for driving the feeding impeller 4 to rotate is installed on the side wall of the buffer bin material 2.

[0075] In this embodiment of the invention, the arc-shaped cavity 3 is a key structure in the feeding assembly. Its main function is to provide a controlled channel for the material and to house the feeding impeller 4. Its arc-shaped design helps guide the material smoothly from the output end of the buffer bin 2 into the working area of ​​the feeding impeller 4, reducing blockage or accumulation during the conveying process and ensuring that the material is uniformly and continuously pushed out by the feeding impeller 4. This cavity can be manufactured by casting, welding, or integral molding, and its inner wall is usually polished to reduce frictional resistance. The feeding impeller 4 is the core actuator of the feeding assembly. Its function is to quantitatively push the material out of the arc-shaped cavity 3 at a controlled speed and direction of rotation, driven by the servo motor 5, achieving precise control of the feeding flow rate. The impeller blade design can vary, for example, it can be a spiral blade, a multi-compartment blade, or a scraper blade, to adapt to the characteristics of different materials and the required feeding flow rate. Its rotational speed directly determines the material conveying volume and is key to achieving flow rate regulation. The servo motor 5 is the power source driving the feed impeller 4. Its key features include high-precision, high-response speed, and wide-range speed control. By receiving commands from the adaptive control system, the servo motor 5 can precisely adjust the speed of the feed impeller 4, thereby achieving real-time, dynamic adjustment of the feed flow rate. Besides servo motors, stepper motors or DC motors equipped with encoders can also be used; these motors all possess the ability to precisely control speed to meet the requirements of feed flow rate adjustment.

[0076] 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 within the protection scope of the present invention.

Claims

1. A double-line granulated layer feed breaking device, comprising a hammer mill, two buffer hoppers mounted on the feed inlet of the hammer mill, and a feeding assembly mounted on the output end of each of the two buffer hoppers, the feeding assembly being used for feeding into the hammer mill, characterized in that, Also includes: An adaptive feed flow control system is used to adjust the feed flow rate of the feeding component in real time. The adaptive feed flow control system includes: The load sensing module calculates and obtains the load factor, which characterizes the load level of the main motor of the crusher, based on the current, voltage and power factor of the main motor of the crusher. The raw material property analysis module calculates and obtains raw material characteristic coefficients that characterize the ease of crushing the raw materials based on the obtained raw material moisture content and raw material bulk density. The process adaptability assessment module calculates the process matching degree, which characterizes the degree of matching between the current output quality and the expected quality, based on the obtained average particle size and proportion of coarse particles, as well as the loading coefficient and raw material characteristic coefficient. The operation status monitoring module calculates and obtains the equipment health coefficient, which characterizes the safety of the equipment's operation status, based on the obtained vibration intensity of the crushing device and the material level in the buffer bin. The multi-source fusion decision module calculates and obtains the target feed flow rate based on the baseline feed flow rate, process matching degree, and equipment health coefficient, and adjusts the current flow rate to the target feed flow rate.

2. The double line layer feed pelleting and breaking apparatus of claim 1 wherein, The steps for calculating and obtaining the load factor are as follows: Obtain the current, voltage, and power factor of the main motor of the crusher; The current, voltage and power factor of the main motor of the crusher are substituted into the formula to calculate the actual power of the main motor of the crusher (kW), wherein, is the voltage of the main motor of the crusher, is the current of the main motor of the crusher, is the power factor of the main motor of the crusher. Maximize the actual power of the crusher's main motor After minimum normalization and limiting the normalized value to 0-1, the load factor is obtained. , , The smaller the value, the lighter the motor load.

3. The dual-line layer hen feed pelleting and crushing device according to claim 1, characterized in that, The steps for calculating and obtaining the raw material characteristic coefficients are as follows: Obtain the moisture content and bulk density of the raw materials; Both the moisture content and bulk density of the raw materials were maximized. Minimum normalization is performed, and the normalized value is limited to 0-1 to obtain the raw material moisture content index and raw material bulk density index; The raw material characteristic coefficient is obtained by multiplying the raw material moisture content index and the raw material bulk density index. , , A smaller value indicates that the raw material is more easily broken, allowing for a higher feed rate; conversely, a larger value indicates a more easily broken raw material. The smaller the value, the less feed should be given.

4. The dual-line layer hen feed pelleting and crushing device according to claim 3, characterized in that, The steps for calculating and obtaining the process matching degree are as follows: Obtain the average particle size and proportion of coarse particles in the output, as well as the loading coefficient and raw material characteristic coefficient; The desired average particle size and the desired oversized proportion are determined based on the loading factor and the raw material characteristic factor. Substitute the average discharge particle size and the desired average particle size into the formula. Obtain the average particle size matching degree ,in, The average particle size of the discharged material. For the desired average particle size, The allowable positive deviation in average particle size; Substitute the proportion of excessively coarse particles in the output and the desired proportion of excessively coarse particles into the formula. Obtain the matching degree of coarse particle ratio ,in, To reduce the proportion of excessively coarse particles in the output, To achieve the desired coarse ratio, For the allowable positive deviation of the overly coarse ratio; The smaller value between the average particle size matching degree and the excessively coarse particle ratio matching degree is taken as the process matching degree. , , It is used to comprehensively reflect the degree of matching between the current working condition and the ideal state.

5. The dual-line layer hen feed pelleting and crushing device according to claim 1, characterized in that, The steps for calculating and obtaining the equipment health coefficient are as follows: Obtain the vibration intensity of the crushing device and the material level in the buffer silo; The vibration intensity of the crushing device is compared with the maximum allowable vibration intensity. After limiting the upper limit of the amplitude ratio to 1, the complement of the amplitude ratio is taken as the vibration limiting index. The buffer silo level is obtained by comparing the buffer silo level with the lower safety limit of the buffer silo level and limiting the upper limit of the ratio to 1. The smaller value between the vibration limitation index and the buffer silo level index is taken as the equipment health coefficient. , , A higher value indicates that the equipment is in good condition and can maintain a higher feeding rate; The smaller the value, the less feeding is needed to protect the equipment or avoid feed interruption.

6. The dual-line layer hen feed pelleting and crushing device according to claim 1, characterized in that, The steps for calculating and obtaining the target feed flow rate are as follows: Obtain baseline feed flow rate, process compatibility, and equipment health coefficient; The target feed flow rate is obtained by multiplying the baseline feed flow rate by the process matching degree and the equipment health coefficient.

7. The dual-line layer hen feed pelleting and crushing device according to claim 1, characterized in that, The feeding assembly includes an arc-shaped cavity at the output end of the buffer bin, a feeding impeller is rotatably connected inside the arc-shaped cavity, and a servo motor for driving the feeding impeller to rotate is installed on the side wall of the buffer bin.

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