A high-efficiency multi-stage crushing system for grains

Abstract

The application relates to the technical field of grain processing, and discloses a high-efficiency multi-stage crushing system for five cereals and coarse grains, which comprises a pretreatment module, a primary crushing module, a secondary crushing module, a grading screening module and an intelligent control module. The pretreatment module first removes impurities and adjusts the humidity of input materials, and outputs the pretreated materials through an adjusting execution unit; when the impurity content exceeds a preset threshold, a cleaning program is automatically triggered; the primary crushing module performs coarse crushing and monitors the particle size distribution in real time; the secondary crushing module realizes fine crushing; the grading screening module separates qualified particle size products through multi-stage screens and circulates the oversized materials back to the secondary crushing module; and the intelligent control module integrates a central processing unit, generates an optimized crushing strategy, and activates a parameter adjustment program when the particle size deviates from the target. The application improves the overall processing quality, improves the product quality, reduces the comprehensive production cost and improves the economic benefits.

Description

Technical Field

[0001] This invention relates to the field of grain processing technology, specifically to a high-efficiency multi-stage pulverizing system for grains. Background Technology

[0002] Grain processing refers to business activities that transform raw grains into semi-finished grains and finished grains, as well as transform semi-finished grains into finished grains.

[0003] Currently, due to the differences in material characteristics during the processing of grains, the mechanical parameters used in multi-stage crushing are often fixed and cannot be detected in real time to see if changes in material hardness and moisture cause abnormal crushing results. If fluctuations in material state are not adjusted in time, it will result in uneven particle size and make it impossible to guarantee the consistency of product quality.

[0004] Therefore, a high-efficiency multi-stage pulverizing system for grains is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-efficiency multi-stage pulverizing system for grains and cereals, which solves the problems mentioned in the background art, such as the inability to detect in real time whether changes in material hardness and moisture lead to abnormal pulverizing effects, and the inability to guarantee the consistency of product quality.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency multi-stage grinding system for grains, comprising:

[0007] The pretreatment module identifies and separates foreign impurities in the raw materials through the impurity removal unit, obtains the moisture content data of the materials through the humidity detection unit, and outputs the pretreated materials through the adjustment execution unit.

[0008] The primary crushing module receives the pre-treated material, performs coarse crushing through a rotating blade assembly unit, collects particle size distribution data in real time using a particle size monitoring unit, and outputs the coarsely crushed material.

[0009] The secondary crushing module receives the coarsely crushed material, performs fine crushing through a high-speed impact grinding unit, dynamically adjusts the impact intensity based on particle size feedback using an impact intensity control unit, and outputs finely crushed material.

[0010] The grading and screening module receives the finely crushed material, performs grading processing according to a preset particle size standard through a multi-stage screening unit, outputs qualified particle size material as product using a material diversion unit, and returns oversized particle size material to the secondary crushing module.

[0011] The intelligent control module receives real-time operating data from each module, generates an optimized crushing strategy by combining material characteristics and environmental parameters through the strategy generation unit, executes control commands when the particle size deviates from the target using the parameter adjustment unit, and feeds back control signals to each execution module through the data communication unit.

[0012] Preferably, the pretreatment module identifies and separates foreign impurities in the raw materials through an impurity removal unit, obtains material moisture content data through a humidity detection unit, and outputs the pretreated material through an adjustment execution unit.

[0013] The process by which the impurity removal unit identifies and separates foreign impurities from the raw material includes: calculating the degree of deviation of the current material impurity content from a baseline value;

[0014] If the deviation is greater than zero, it means that the impurity content exceeds the warning level, and the high-pressure airflow cleaning device will be activated.

[0015] The process of obtaining material moisture content data using a humidity detection unit includes: detecting the initial humidity of the material; if it is lower than the target humidity range, injecting atomized water vapor; if it is higher than the target humidity range, starting the hot air drying unit.

[0016] Preferably, activating the high-pressure airflow cleaning device includes:

[0017] The difference between the degree of deviation of impurity content and the cleaning threshold is calculated to obtain the cleaning urgency value;

[0018] If the cleaning urgency value is less than the preset urgency threshold, then a fast cleaning cycle is executed;

[0019] If the cleaning urgency value is greater than or equal to the preset urgency threshold, then a deep cleaning cycle is executed.

[0020] Preferably, the process of real-time acquisition of particle size distribution data in the primary crushing module is as follows:

[0021] Construct a real-time curve with grinding time as the X-axis and particle size distribution as the Y-axis;

[0022] A standard particle size evolution curve was constructed based on historical pulverization data as a reference;

[0023] Multiple time-synchronized sampling points are selected on the real-time curve and the reference curve;

[0024] The granular values ​​of each sampling point of the real-time curve are obtained and integrated into the first granularity data group. ;

[0025] The granularity values ​​of the same sampling points as the reference curve are obtained and integrated into a second granularity data group. ;

[0026] Calculate the data similarity between the first granularity data group and the second granularity data group. ;

[0027] The data similarity Let be a dimensionless coefficient, whose range is normalized to the interval [-1, 1], and calculated using the following formula:

[0028] ;

[0029] in, The similarity coefficient. This represents the dimensionless granularity value of the i-th sampling point in the first granularity data set after normalization. This represents the dimensionless granularity value of the i-th sampling point in the second granularity data set after normalization. The arithmetic mean of the first granularity data set. This is the arithmetic mean of the second granularity data set. Indicates the total number of sampling points;

[0030] Set granularity similarity threshold It is 0.85, if This indicates that the particle size distribution is uniform. This indicates that the particle size distribution is non-uniform.

[0031] Preferably, the primary grinding module further includes setting a particle size similarity threshold:

[0032] If the data similarity is less than the granularity similarity threshold, it indicates that the granularity distribution is uniform.

[0033] If the data similarity is greater than or equal to the granularity similarity threshold, it indicates that the granularity distribution is non-uniform.

[0034] Preferably, the process of dynamically adjusting the impact intensity in the secondary crushing module includes:

[0035] The predicted impact strength value is a dimensionless value obtained by normalizing the optimal impact strength adjustment value. It is used to compare with the actual equipment safety limit to determine whether the impact strength can be safely increased.

[0036] If the particle size distribution is uniform, linear regression analysis is used to predict the current particle size trend and output the optimal impact strength adjustment value.

[0037] The optimal impact strength adjustment value is normalized to obtain the predicted impact strength value;

[0038] The predicted impact intensity value is compared with the safety upper limit; if it exceeds the upper limit, the protection mechanism is activated.

[0039] Preferably, the process of comparing the predicted impact strength value with the safety upper limit is as follows:

[0040] If the predicted impact strength value is less than the safe upper limit, it means that the impact strength can be safely increased.

[0041] If the predicted impact strength value is greater than or equal to the safety limit, it means that the impact strength needs to be reduced to avoid equipment overload.

[0042] Preferably, the process of dynamically adjusting the impact intensity in the secondary crushing module further includes:

[0043] If the particle size distribution is non-uniform, perform a rate of change analysis on the first particle size data set to determine the maximum particle size change rate.

[0044] Calculate the difference between the current particle size and the target particle size to obtain the particle size approximation value;

[0045] Divide the approximate particle size value by the maximum rate of change to obtain the time required for the particle size to reach the standard.

[0046] The impact strength parameters are adjusted based on the duration.

[0047] Preferably, the process of generating the optimized crushing strategy in the intelligent control module is as follows:

[0048] Obtain current material batch information and ambient humidity data;

[0049] By combining material hardness data with environmental humidity data, a system can be constructed. Hardness data set of data points and environmental data group ;

[0050] Correlation analysis was used to calculate the matching degree between the hardness data set and the environmental data set. ;

[0051] The matching degree R is a dimensionless coefficient with a normalized range of [-1, 1], calculated using the following formula:

[0052] ;

[0053] in, The matching degree coefficient, This represents the j-th dimensionless hardness value in the hardness data set after dimension normalization. This represents the j-th dimensionless humidity value in the environmental data set after dimensional normalization. This represents the arithmetic mean of the hardness data set. This represents the arithmetic mean of the environmental data set. Set a matching threshold for the total number of data points. It is 0.7, when At that time, it was determined that the hardness of the material was correlated with the ambient humidity, and an optimized crushing strategy was generated accordingly.

[0054] Preferably, the process of returning ultrafine materials to the secondary crushing module in the grading and screening module is as follows:

[0055] Collect information on screen vibration frequency, blockage status and instantaneous material flow rate, match it with the screening strategy library pre-stored in the database, and select a circulating crushing scheme that is suitable for the current working conditions based on a multi-dimensional matching algorithm.

[0056] The ultra-fine particles refer to coarse particles with a particle size value higher than the upper limit of the preset qualified range and fine powder agglomerates with a particle size value lower than the lower limit of the preset qualified range after being processed by the grading and screening module.

[0057] Based on the selected scheme, and combining real-time collected equipment load data with system production demand indicators, a cyclical execution plan including return path, crushing times, and energy configuration parameters is generated through dynamic programming algorithms.

[0058] Compared with the prior art, the present invention provides a high-efficiency multi-stage grinding system for grains and cereals, which has the following beneficial effects:

[0059] 1. In this invention, by setting a pretreatment module, when processing grains and cereals, different pretreatment parameters are set according to different material characteristics by establishing threshold values ​​for impurity content and humidity benchmark values. This ensures the targeted processing of different types of grain raw materials. At the same time, the material status is monitored in real time and cleaning and adjustment programs are automatically triggered. This can solve the problem of low grinding efficiency caused by excessive impurities and improper humidity in raw materials, ensure the stable operation of subsequent grinding processes, and further improve the overall processing quality.

[0060] 2. In this invention, by setting up multi-stage crushing and dynamic adjustment, when controlling the particle size of grains, the primary and secondary crushing modules work together, and the impact intensity and crushing parameters are dynamically adjusted based on real-time particle size feedback. This enables the system to reduce the uneven particle size distribution during the crushing process, and when the particle size deviates from the target, the intelligent control module can adjust the equipment operating status in real time to ensure that the finished product has a uniform particle size and improve product quality.

[0061] 3. In this invention, by setting up a grading and screening module, after the crushing process is completed, the material is accurately graded by a multi-stage screen, and the oversized material is automatically returned to the crushing module for secondary processing, realizing the closed-loop recycling of materials. This enables the system to reduce material waste and excessive energy consumption, while improving the continuity and automation level of the production line, further reducing overall production costs and improving economic benefits. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the structure of a high-efficiency multi-stage pulverizing system for grains according to the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Please see Figure 1 The specific implementation of a high-efficiency multi-stage grinding system for grains is as follows:

[0065] The pretreatment module identifies and separates foreign impurities in the raw materials through the impurity removal unit, obtains the moisture content data of the materials through the humidity detection unit, and outputs the pretreated materials through the adjustment execution unit.

[0066] The primary crushing module receives pre-treated materials, performs coarse crushing through a rotating blade assembly unit, collects particle size distribution data in real time using a particle size monitoring unit, and outputs the coarsely crushed material.

[0067] The secondary crushing module receives coarsely crushed materials, performs fine crushing through a high-speed impact grinding unit, dynamically adjusts the impact intensity based on particle size feedback using an impact intensity control unit, and outputs finely crushed materials.

[0068] The grading and screening module receives finely ground materials, processes them according to preset particle size standards through a multi-stage screen unit, outputs qualified particle size materials as products using a material diversion unit, and returns oversized materials to the secondary grinding module.

[0069] The intelligent control module receives real-time operating data from each module, generates an optimized crushing strategy by combining material characteristics and environmental parameters through the strategy generation unit, executes control commands when the particle size deviates from the target using the parameter adjustment unit, and feeds back control signals to each execution module through the data communication unit.

[0070] The pretreatment module identifies and separates foreign impurities from the raw materials through an impurity removal unit, acquires material moisture content data through a humidity detection unit, and outputs the pretreated material through an adjustment execution unit.

[0071] The impurity removal unit identifies and separates foreign impurities from the raw materials, including:

[0072] Calculate the deviation of the current material impurity content from the baseline value;

[0073] The degree of deviation is the normalized dimensionless ratio Calculated using the formula:

[0074] ;

[0075] in, This represents the percentage of impurities detected in real time by a sensor. The deviation threshold is set to the preset impurity content percentage. ;

[0076] If the deviation is greater than zero, it means that the impurity content exceeds the warning level, and the high-pressure airflow cleaning device will be activated.

[0077] Starting the high-pressure airflow cleaning device includes:

[0078] The difference between the degree of deviation of impurity content and the cleaning threshold is calculated to obtain the cleaning urgency value;

[0079] The cleaning urgency value is a normalized dimensionless value. Calculated using the formula:

[0080] ;

[0081] in, The preset urgency threshold is a ratio with one dimension, used to set the cleaning action threshold. ;

[0082] If the cleaning urgency value is less than the preset urgency threshold, then a fast cleaning cycle is executed;

[0083] If the cleaning urgency value is greater than or equal to the preset urgency threshold, then a deep cleaning cycle is executed;

[0084] The process of obtaining material moisture content data using a humidity detection unit includes:

[0085] Detect the initial humidity of the material. If it is lower than the target humidity range, inject atomized water vapor. If it is higher than the target humidity range, start the hot air drying unit.

[0086] The process of adjusting the output of the execution unit after preprocessing includes:

[0087] The system receives impurity content and humidity detection data. When the impurity content is below the threshold and the humidity value is within the target range, the material is judged as qualified and output to the primary crushing module. When any indicator exceeds the allowable range, the corresponding processing program is triggered until the indicator meets the standard and the material is output.

[0088] The process of real-time acquisition of particle size distribution data in the primary grinding module is as follows:

[0089] Construct a real-time curve with grinding time as the X-axis and particle size distribution as the Y-axis;

[0090] A standard particle size evolution curve was constructed based on historical pulverization data as a reference;

[0091] Multiple time-synchronized sampling points are selected on the real-time curve and the reference curve;

[0092] The granular values ​​of each sampling point of the real-time curve are obtained and integrated into the first granularity data group. ;

[0093] The granularity values ​​of the same sampling points as the reference curve are obtained and integrated into a second granularity data group. ;

[0094] Calculate the data similarity between the first granularity data group and the second granularity data group. ;

[0095] Data similarity Let be a dimensionless coefficient, whose range is normalized to the interval [-1, 1], and calculated using the following formula:

[0096] ;

[0097] in, The similarity coefficient. This represents the dimensionless granularity value of the i-th sampling point in the first granularity data set after normalization. This represents the dimensionless granularity value of the i-th sampling point in the second granularity data set after normalization. The arithmetic mean of the first granularity data set. This is the arithmetic mean of the second granularity data set. Indicates the total number of sampling points;

[0098] Set granularity similarity threshold It is 0.85, if This indicates that the particle size distribution is uniform. This indicates that the particle size distribution is non-uniform;

[0099] The process of outputting coarsely ground material in the primary grinding module is as follows:

[0100] Based on the calculated data similarity Similar threshold to preset granularity If a comparison is made, If the coarse grinding is deemed satisfactory, the material is output to the secondary grinding module. If necessary, adjust the parameters of the rotating blade assembly and extend the crushing time until the particle size meets the standard before outputting the material.

[0101] The primary grinding module also includes setting a particle size similarity threshold:

[0102] Set granularity similarity threshold This threshold is a coefficient with a dimensionless value. Let be the similarity coefficient, and define the granularity distribution judgment function as follows: :

[0103] when hour, Uniform output;

[0104] when hour, The output is non-uniform.

[0105] The process of dynamically adjusting the impact intensity in the secondary crushing module includes:

[0106] The predicted impact strength value is a dimensionless value obtained by normalizing the optimal impact strength adjustment value. It is used to compare with the actual equipment safety limit to determine whether the impact strength can be safely increased.

[0107] If the particle size distribution is uniform, linear regression analysis is used to predict the current particle size trend and output the optimal impact strength adjustment value.

[0108] The impact strength adjustment value is a normalized dimensionless value. Calculated using a linear regression model:

[0109] ;

[0110] in, For the normalized time variable, The regression slope, To determine the regression intercept, a safe upper limit for impact strength is set. ;

[0111] The predicted impact intensity value is compared with the safety upper limit; if it exceeds the upper limit, the protection mechanism is activated.

[0112] The process of comparing the predicted impact strength value with the safety upper limit is as follows:

[0113] If the predicted impact strength value is less than the safe upper limit, it means that the impact strength can be safely increased.

[0114] If the predicted impact strength value is greater than or equal to the safety limit, it means that the impact strength needs to be reduced to avoid equipment overload;

[0115] The process of outputting finely ground material in the secondary grinding module is as follows:

[0116] Based on the real-time data fed back by the particle size monitoring unit, when the particle size value of the material is detected to be continuously within the preset qualified range and reaches the stable time threshold, the fine grinding process is determined to be completed, and the material is output to the grading and screening module. If the particle size value fluctuates beyond the allowable range or does not meet the standard, the dynamic adjustment process of impact strength is returned until the particle size of the material meets the standard and is output.

[0117] The process of dynamically adjusting the impact intensity in the secondary crushing module also includes:

[0118] If the particle size distribution is non-uniform, perform a rate of change analysis on the first particle size data set to determine the maximum particle size change rate.

[0119] Calculate the difference between the current particle size and the target particle size to obtain the particle size approximation value;

[0120] Divide the approximate particle size value by the maximum rate of change to obtain the time required for the particle size to reach the standard.

[0121] The duration is a normalized dimensionless value, calculated using the formula:

[0122] ;

[0123] in, To normalize the granularity to a value close to the normalized value, To determine the maximum rate of change for normalization, a threshold time for achieving the target is set. ;

[0124] The impact strength parameters are adjusted based on the duration.

[0125] The process of generating an optimized crushing strategy in the intelligent control module is as follows:

[0126] Obtain current material batch information and ambient humidity data;

[0127] By combining material hardness data with environmental humidity data, a system can be constructed. Hardness data set of data points and environmental data group ;

[0128] Correlation analysis was used to calculate the matching degree between the hardness data set and the environmental data set. ;

[0129] The matching degree R is a dimensionless coefficient with a normalized range of [-1, 1], calculated using the following formula:

[0130] ;

[0131] in, The matching degree coefficient, This represents the j-th dimensionless hardness value in the hardness data set after dimension normalization. This represents the j-th dimensionless humidity value in the environmental data set after dimensional normalization. This represents the arithmetic mean of the hardness data set. This represents the arithmetic mean of the environmental data set. Set a matching threshold for the total number of data points. It is 0.7, when At that time, it was determined that the hardness of the material was correlated with the ambient humidity, and an optimized crushing strategy was generated accordingly.

[0132] The process of using the parameter adjustment unit to execute control commands when the granularity deviates from the target is as follows:

[0133] When the deviation between the real-time detected particle size data and the target particle size range exceeds the preset tolerance, the parameter adjustment unit generates specific adjustment parameters based on the optimized crushing strategy, including adjusting the rotation speed of the rotating blade group of the primary crushing module, the impact intensity of the secondary crushing module, and the screen vibration frequency of the grading and screening module.

[0134] The process of feeding control signals back to each execution module through the data communication unit is as follows:

[0135] The data communication unit uses an industrial bus protocol to encapsulate adjustment parameters into control commands, which are then sent to the local controllers of the preprocessing module, primary crushing module, secondary crushing module, and grading and screening module to drive the synchronous operation of each actuator.

[0136] The process of returning ultrafine materials to the secondary crushing module in the grading and screening module is as follows:

[0137] Collect information on screen vibration frequency, blockage status and instantaneous material flow rate, match it with the screening strategy library pre-stored in the database, and select a circulating crushing scheme that is suitable for the current working conditions based on a multi-dimensional matching algorithm.

[0138] The multidimensional matching algorithm specifically includes: collecting the vibration frequency of the screen. Blockage state coefficient k, instantaneous material flow rate Construct feature vectors ;

[0139] Among them, ultra-fine particles refer to coarse particles with a particle size value higher than the upper limit of the preset qualified range and fine powder agglomerates with a particle size value lower than the lower limit of the preset qualified range after being processed by the grading and screening module.

[0140] Perform Euclidean distance matching with the pre-stored filtering strategy library in the database, and select the strategy with the smallest distance as the current adaptation solution;

[0141] The dynamic programming algorithm is specifically used to solve for the optimal return path, number of crushing operations, and energy configuration parameters based on a selected strategy, with the objective functions of minimum energy consumption and shortest time.

[0142] Based on the selected scheme, and combining real-time collected equipment load data with system production demand indicators, a cyclical execution plan including return path, crushing times, and energy configuration parameters is generated through dynamic programming algorithms.

[0143] The operating steps of a high-efficiency multi-stage grinding system for grains are as follows:

[0144] Step 1: Raw material pretreatment and status monitoring

[0145] The system first uses a pre-processing module to detect impurities and analyze the humidity of the input grains. The impurity removal unit uses high-pressure airflow technology to automatically separate foreign objects. At the same time, the humidity control unit monitors the moisture content of the material in real time. When the impurity content exceeds the preset threshold or the humidity deviates from the target range, the system immediately triggers the cleaning program and temperature and humidity control mechanism to provide a stable raw material guarantee for the subsequent crushing process.

[0146] Step 2: Multi-stage crushing and coordinating operations

[0147] The pre-treated material enters the primary crushing stage, where a high-speed rotating blade assembly performs coarse crushing. During the crushing process, the particle size monitoring unit continuously collects material particle size distribution data and generates a real-time evolution curve. Subsequently, the material is conveyed to the secondary crushing module, which dynamically adjusts the intensity and frequency of the impact grinding mechanism based on particle size feedback data. Through the synergistic effect of high-speed impact and fine grinding, the material is transformed from coarse particles to the target particle size.

[0148] Step 3: Intelligent Grading and Recycling

[0149] The crushed material enters a multi-stage vibrating screening system, where it is precisely graded according to preset particle size standards. Qualified particle size material is directly conveyed to the finished product area, while substandard oversized material is automatically returned to the secondary crushing module through a closed-loop return channel. This intelligent circulation mechanism ensures that the material is fully processed, reducing raw material waste and equipment idling.

[0150] Step 4: Dynamic Strategy Generation and Optimization

[0151] As the core of the system, the intelligent control module integrates the operational data of each link in real time. By analyzing material characteristics, environmental parameters and equipment status, it generates the optimal crushing strategy: when the particle size distribution deviates from the target, it automatically triggers the parameter adjustment program; when the system load fluctuates, it dynamically optimizes the impact intensity and screening frequency; and it continuously updates the crushing model based on historical data to achieve continuous improvement in processing efficiency.

[0152] Step 5: Closed-loop control and adaptive optimization

[0153] The system continuously collects operational data to build performance profiles and automatically identifies the optimal processing parameters for different materials. During long-term operation, the intelligent module dynamically adjusts the pre-processing threshold, crushing intensity benchmark value, and grading standard, forming a closed-loop control system of "monitoring-decision-execution-feedback". This adaptive mechanism enables the system to proactively adapt to the processing needs of various grains, ultimately achieving the dual goals of improving crushing uniformity and reducing energy consumption.

[0154] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0155] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency multi-stage grinding system for grains and cereals, characterized in that: include: The pretreatment module identifies and separates foreign impurities in the raw materials through the impurity removal unit, obtains the moisture content data of the materials through the humidity detection unit, and outputs the pretreated materials through the adjustment execution unit. The primary crushing module receives the pre-treated material, performs coarse crushing through a rotating blade assembly unit, collects particle size distribution data in real time using a particle size monitoring unit, and outputs the coarsely crushed material. The secondary crushing module receives the coarsely crushed material, performs fine crushing through a high-speed impact grinding unit, dynamically adjusts the impact intensity based on particle size feedback using an impact intensity control unit, and outputs finely crushed material. The grading and screening module receives the finely crushed material, performs grading processing according to a preset particle size standard through a multi-stage screening unit, outputs qualified particle size material as product using a material diversion unit, and returns oversized particle size material to the secondary crushing module. The intelligent control module receives real-time operating data from each module, generates an optimized crushing strategy by combining material characteristics and environmental parameters through the strategy generation unit, executes control commands when the particle size deviates from the target using the parameter adjustment unit, and feeds back control signals to each execution module through the data communication unit. The pretreatment module uses an impurity removal unit to identify and separate foreign impurities from the raw material, a humidity detection unit to obtain the material's moisture content data, and an adjustment execution unit to output the pretreated material. The process by which the impurity removal unit identifies and separates foreign impurities from the raw material includes: calculating the degree of deviation of the current material impurity content from a baseline value; If the deviation is greater than zero, it means that the impurity content exceeds the warning level, and the high-pressure airflow cleaning device will be activated. The process of obtaining material moisture content data using a humidity detection unit includes: detecting the initial humidity of the material; if it is lower than the target humidity range, injecting atomized water vapor; if it is higher than the target humidity range, starting the hot air drying unit. Starting the high-pressure airflow cleaning device includes: The difference between the degree of deviation of impurity content and the cleaning threshold is calculated to obtain the cleaning urgency value; If the cleaning urgency value is less than the preset urgency threshold, then a fast cleaning cycle is executed; If the cleaning urgency value is greater than or equal to the preset urgency threshold, then a deep cleaning cycle is executed.

2. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The process of real-time acquisition of particle size distribution data in the primary crushing module is as follows: Construct a real-time curve with grinding time as the X-axis and particle size distribution as the Y-axis; A standard particle size evolution curve was constructed based on historical pulverization data as a reference; Multiple time-synchronized sampling points are selected on the real-time curve and the reference curve; The granular values ​​of each sampling point of the real-time curve are obtained and integrated into the first granularity data group. ; The granularity values ​​of the same sampling points as the reference curve are obtained and integrated into a second granularity data group. ; Calculate the data similarity between the first granularity data group and the second granularity data group. ; The data similarity Let be a dimensionless coefficient, whose range is normalized to the interval [-1, 1], and calculated using the following formula: ； in, The similarity coefficient. This represents the dimensionless granularity value of the i-th sampling point in the first granularity data set after normalization. This represents the dimensionless granularity value of the i-th sampling point in the second granularity data set after normalization. The arithmetic mean of the first granularity data set. This is the arithmetic mean of the second granularity data set. Indicates the total number of sampling points; Set granularity similarity threshold It is 0.85, if This indicates that the particle size distribution is uniform. This indicates that the particle size distribution is non-uniform.

3. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The primary grinding module also includes setting a particle size similarity threshold: If the data similarity is less than the granularity similarity threshold, it indicates that the granularity distribution is uniform. If the data similarity is greater than or equal to the granularity similarity threshold, it indicates that the granularity distribution is non-uniform.

4. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The process of dynamically adjusting the impact intensity in the secondary crushing module includes: The predicted impact strength value is a dimensionless value obtained by normalizing the optimal impact strength adjustment value. It is used to compare with the actual equipment safety limit to determine whether the impact strength can be safely increased. If the particle size distribution is uniform, linear regression analysis is used to predict the current particle size trend and output the optimal impact strength adjustment value. The optimal impact strength adjustment value is normalized to obtain the predicted impact strength value; The predicted impact intensity value is compared with the safety upper limit; if it exceeds the upper limit, the protection mechanism is activated.

5. The high-efficiency multi-stage grinding system for grains and cereals according to claim 4, characterized in that: The process of comparing the predicted impact strength value with the safety upper limit is as follows: If the predicted impact strength value is less than the safe upper limit, it means that the impact strength can be safely increased. If the predicted impact strength value is greater than or equal to the safety limit, it means that the impact strength needs to be reduced to avoid equipment overload.

6. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The process of dynamically adjusting the impact intensity in the secondary crushing module also includes: If the particle size distribution is non-uniform, perform a rate of change analysis on the first particle size data set to determine the maximum particle size change rate. Calculate the difference between the current particle size and the target particle size to obtain the particle size approximation value; Divide the approximate particle size value by the maximum rate of change to obtain the time required for the particle size to reach the standard. The impact strength parameters are adjusted based on the duration.

7. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The process of generating the optimized crushing strategy in the intelligent control module is as follows: Obtain current material batch information and ambient humidity data; By combining material hardness data with environmental humidity data, a system can be constructed. Hardness data set of data points and environmental data group ; Correlation analysis was used to calculate the matching degree between the hardness data set and the environmental data set. ; The matching degree R is a dimensionless coefficient with a normalized range of [-1, 1], calculated using the following formula: ； in, The matching degree coefficient, This represents the j-th dimensionless hardness value in the hardness data set after dimension normalization. This represents the j-th dimensionless humidity value in the environmental data set after dimensional normalization. This represents the arithmetic mean of the hardness data set. This represents the arithmetic mean of the environmental data set. Set a matching threshold for the total number of data points. It is 0.7, when At that time, it was determined that the hardness of the material was correlated with the ambient humidity, and an optimized crushing strategy was generated accordingly.

8. The high-efficiency multi-stage grinding system for grains and cereals according to claim 1, characterized in that: The process of returning ultrafine materials to the secondary crushing module in the grading and screening module is as follows: Collect information on screen vibration frequency, blockage status and instantaneous material flow rate, match it with the screening strategy library pre-stored in the database, and select a circulating crushing scheme that is suitable for the current working conditions based on a multi-dimensional matching algorithm. The ultra-fine particles refer to coarse particles with a particle size value higher than the upper limit of the preset qualified range and fine powder agglomerates with a particle size value lower than the lower limit of the preset qualified range after being processed by the grading and screening module. Based on the selected scheme, and combining real-time collected equipment load data with system production demand indicators, a cyclical execution plan including return path, crushing times, and energy configuration parameters is generated through dynamic programming algorithms.

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