A smart control system and method for flour production process

Abstract

This invention relates to the field of intelligent control, and more particularly to an intelligent control system and method for flour production. It includes: collecting key parameters during the flour production process; standardizing these key parameters to obtain standardized key parameters, including standardized flour attribute parameters and standardized mechanical control parameters; generating standardized adjustment values ​​for the standardized mechanical control parameters using a dynamic interactive weighted fuzzy control algorithm; updating the standardized mechanical control parameters based on the standardized adjustment values ​​and physical constraint ranges to obtain updated values; and sending these updated values ​​to the actuator for regulation. This invention solves the technical problem that existing control methods do not fully consider the dynamic interaction between flour attribute parameters and mechanical control parameters, leading to deviations in the regulation results.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and in particular to an intelligent control system and method for flour production process. Background Technology

[0002] With the rapid development of industrial automation and intelligent manufacturing technologies, the flour production industry is gradually transforming from traditional manual or semi-automated production methods to intelligent and digitalized processes. Traditional flour production relies primarily on manual operation or simple automated equipment, with control methods mostly based on experience-driven manual adjustment or semi-automatic control. The setting and adjustment of process parameters depend heavily on the operator's experience. Due to significant differences in the experience levels of different operators, the stability of the production process is difficult to guarantee, and there is a lack of real-time data acquisition and analysis capabilities, making it impossible to comprehensively monitor raw material characteristics, equipment status, and process parameters. To overcome the limitations of traditional production methods, the flour production industry has an increasingly urgent need for intelligent control systems. Intelligent control systems, by integrating sensor technology, data analysis, automatic control, and artificial intelligence technologies, can achieve precise monitoring, optimized control, and efficient management of the production process. With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent control systems for flour production processes will further promote the intelligent, green, and efficient development of the industry, providing strong technical support for the sustainable development of the food industry.

[0003] However, the existing intelligent control system and method for flour production process still have technical problems such as the lack of intelligence and adaptability of traditional control methods, insufficient control precision of production process, and insufficient consideration of the interaction of parameters. Summary of the Invention

[0004] This invention provides an intelligent control system and method for flour production process to solve the problems of existing control methods not fully considering the dynamic interaction between flour attribute parameters and mechanical control parameters, which leads to deviations in the control results; traditional fuzzy control algorithms are mostly based on fixed rules and static weights, which cannot adapt to the dynamic changes in the flour production process; the generation of mechanical control parameter adjustment quantities lacks systematicity, and the control is prone to instability due to data fluctuations or rule conflicts.

[0005] The present invention provides an intelligent control system and method for flour production, specifically comprising the following technical solutions: A method for intelligent control of a flour production process includes the following steps: S1. Collect key parameters in the flour production process, standardize the key parameters to obtain standardized key parameters, including: standardized flour attribute parameters and standardized mechanical control parameters, and generate standardized adjustment amounts for the standardized mechanical control parameters through a dynamic interactive weighted fuzzy control algorithm; S2. Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters to obtain the updated values ​​of the standardized mechanical control parameters, and send the updated values ​​of the standardized mechanical control parameters to the actuator for regulation.

[0006] Preferably, S1 specifically includes: The dynamic interactive weighted fuzzy control algorithm generates standardized adjustment amounts for standardized mechanical control parameters based on standardized key parameters through five parts: membership degree calculation, dynamic interaction factor calculation, rule evaluation, rule output definition, and defuzzification.

[0007] Preferably, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, for each standardized key parameter, namely the standardized flour attribute parameter and the standardized mechanical control parameter, a membership function is used to map it to three fuzzy sets: "low", "medium", and "high", generating a membership value.

[0008] Preferably, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, the covariance of each pair of standardized flour attribute parameters and standardized mechanical control parameters is calculated within the time window, and the absolute value is taken. The absolute value of the covariance is normalized by dividing it by the product of the historical standard deviations of the two standardized key parameters to generate a dynamic interaction factor. At the same time, a denominator protection mechanism is introduced, and the maximum value between the product of the historical standard deviations of the two standardized key parameters and the smallest positive number is selected as the denominator.

[0009] Preferably, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, the rule evaluation is based on the fuzzy rule base. Each rule determines the standardized key parameters involved. According to the initial weight and dynamic interaction factor, the dynamic weight is calculated. The product of the membership values ​​of the standardized key parameters involved and the dynamic weight is summed and then divided by the total number of standardized key parameters involved for normalization. The excitation intensity of each rule is then output.

[0010] Preferably, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, defuzzification transforms the rule excitation intensity and rule output into control adjustment quantities. The weighted average method is used to multiply the output value of each rule by the rule excitation intensity and sum them up, and then divide by the sum of all rule excitation intensities to obtain the standardized adjustment quantity of each standardized mechanical control parameter.

[0011] Preferably, S2 specifically includes: For each standardized mechanical control parameter, if the standardized adjustment of the standardized mechanical control parameter is not zero, the standardized mechanical control parameter is updated; if the standardized adjustment of the standardized mechanical control parameter is zero, the current updated baseline value is kept unchanged.

[0012] Preferably, S2 specifically includes: In the process of updating the standardized mechanical control parameters, the standardized adjustment amount of the standardized mechanical control parameters is multiplied by the historical standard deviation to convert it into the adjustment amount in actual units. This is then added to the current updated baseline value to obtain the preliminary update value. Through comparison operations, the preliminary update value is restricted to the minimum and maximum physical constraints of the equipment to obtain the updated value of the standardized mechanical control parameters.

[0013] An intelligent control system for flour production process includes the following components: Data acquisition module, standardization processing module, dynamic interactive weighted fuzzy control module, mechanical control parameter update module, actuator control module; Data acquisition module: Collects key parameters during the flour production process and outputs the collected key parameters to the standardization processing module; Standardization processing module: Standardizes each key parameter from the data acquisition module to obtain standardized key parameters, and outputs the standardized key parameters to the dynamic interactive weighted fuzzy control module; Dynamic interactive weighted fuzzy control module: Based on the standardized key parameters of the standardized processing module, the standardized adjustment amount of the standardized mechanical control parameters is generated through the dynamic interactive weighted fuzzy control algorithm, and the standardized adjustment amount of the standardized mechanical control parameters is output to the mechanical control parameter update module. Mechanical control parameter update module: Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters and output the updated value of the standardized mechanical control parameters to the actuator control module; Actuator control module: Based on the updated values ​​of the standardized mechanical control parameters from the mechanical control parameter update module, precise control is achieved.

[0014] The beneficial effects of the technical solution of the present invention are: 1. By introducing a dynamic interactive weighted fuzzy control algorithm, the algorithm comprehensively considers the real-time interactive influence between standardized flour attribute parameters and standardized mechanical control parameters, generating precise standardized adjustment values ​​for standardized mechanical control parameters. Compared with traditional control methods, this algorithm achieves dynamic response and intelligent optimization of the flour production process through membership calculation, dynamic interaction factors, rule evaluation, rule output definition, and defuzzification, ensuring the pertinence and adaptability of control actions, thereby significantly improving the stability of flour quality and equipment operating efficiency.

[0015] 2. By calculating the covariance between standardized flour attribute parameters and standardized mechanical control parameters, a dynamic interaction factor is generated, which can dynamically capture the coordinated change trend between parameters. This enables the intelligent control system of the flour production process to accurately reflect the actual production status, thereby optimizing adjustment strategies, reducing ineffective or excessive adjustments, and improving the stability and energy efficiency of equipment operation.

[0016] 3. Based on a fuzzy rule base and dynamic weights, combined with expert experience and real-time data, the importance of each parameter in control decisions is dynamically adjusted. This enables the intelligent control system for flour production to flexibly adjust the applicability of rules according to the production status, avoiding rule conflicts and achieving smooth control actions that meet actual needs. This further improves the stability of flour quality and the reliability of equipment operation.

[0017] 4. When updating the standardized mechanical control parameters, the physical constraints of the equipment are fully considered to ensure that the adjusted parameter values ​​are within the safe range, preventing operation outside the range and ensuring the safety of the equipment and the stability of the process. At the same time, by converting the standardized adjustment amount of the standardized mechanical control parameters into actual physical units, the executability of the control commands is guaranteed, thereby achieving precise control of the actuator. Attached Figure Description

[0018] Figure 1 This is a structural diagram of an intelligent control system for flour production process according to the present invention; Figure 2 This is a flowchart of an intelligent control method for flour production process according to the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. 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.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific solution of the intelligent control system and method for flour production provided by this invention.

[0022] See attached document Figure 1 The diagram illustrates a structural diagram of an intelligent control system for a flour production process according to an embodiment of the present invention. The system includes the following components: Data acquisition module, standardization processing module, dynamic interactive weighted fuzzy control module, mechanical control parameter update module, actuator control module; Data acquisition module: Collects key parameters in the flour production process in real time through sensors and outputs the collected key parameters to the standardization processing module; Standardization processing module: Standardizes each key parameter from the data acquisition module to obtain standardized key parameters, and outputs the standardized key parameters to the dynamic interactive weighted fuzzy control module; Dynamic interactive weighted fuzzy control module: Based on the standardized key parameters of the standardized processing module, the dynamic interactive weighted fuzzy control algorithm generates accurate standardized adjustment values ​​for the standardized mechanical control parameters, and outputs the standardized adjustment values ​​of the standardized mechanical control parameters to the mechanical control parameter update module. Mechanical control parameter update module: Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters and output the updated value of the standardized mechanical control parameters to the actuator control module; Actuator control module: Based on the updated values ​​of the standardized mechanical control parameters from the mechanical control parameter update module, precise control is achieved.

[0023] See attached document Figure 2 The diagram illustrates a flowchart of an intelligent control method for a flour production process according to an embodiment of the present invention. The method includes the following steps: S1. Collect key parameters in the flour production process, standardize the key parameters to obtain standardized key parameters, including: standardized flour attribute parameters and standardized mechanical control parameters, and generate standardized adjustment amounts for the standardized mechanical control parameters through a dynamic interactive weighted fuzzy control algorithm. The key parameters of the flour production process are collected in real time by sensors. These key parameters are divided into two categories: flour property parameters (such as moisture content, whiteness, and particle size distribution) and mechanical control parameters (such as grinding pressure, mill speed, and ambient temperature). The flour property parameters reflect the quality status of the flour. For example, moisture content affects dough characteristics, whiteness affects product appearance, and particle size distribution affects taste. The mechanical control parameters reflect the operating status of the equipment and are directly adjustable. For example, grinding pressure affects grinding effect, mill speed affects production efficiency, and ambient temperature affects process stability. The key parameters are standardized one by one. The standardization process is to subtract the historical mean of the key parameter and divide it by the historical standard deviation to obtain the standardized key parameters, namely the standardized flour attribute parameters and the standardized mechanical control parameters. The historical mean and historical standard deviation of the key parameters are calculated based on long-term production data and reflect the statistical characteristics of the key parameters. In order to achieve intelligent control of standardized mechanical control parameters in flour production and solve the problem that traditional control methods ignore parameter classification and dynamic interaction, a dynamic interaction weighted fuzzy control algorithm is used to generate precise standardized adjustment amounts for standardized mechanical control parameters. The dynamic interactive weighted fuzzy control algorithm, based on standardized key parameters, generates precise standardized adjustment amounts for standardized mechanical control parameters through five parts: membership degree calculation, dynamic interaction factor calculation, rule evaluation, rule output definition, and defuzzification, ensuring stable flour quality and optimizing equipment operating efficiency. The membership degree calculation is the foundation of the dynamic interactive weighted fuzzy control algorithm. For each standardized key parameter, namely the standardized flour attribute parameter and the standardized mechanical control parameter, a predefined membership degree function (such as a triangular membership function) is used to map it to three fuzzy sets: "low," "medium," and "high," generating a membership degree value. The membership degree function is defined by three boundary values ​​(low, medium, and high), which are determined based on the statistical distribution of historical production data obtained from a historical database or by expert experience. The standardized key parameters are linearly interpolated within the boundary range to generate membership degree values, ranging from 0 to 1, reflecting the degree to which the standardized key parameters belong to the three fuzzy sets: "low," "medium," and "high." The formula is expressed as follows:

[0024] in, Indicates the standardized key parameters in time For fuzzy sets Membership values ​​for ("low", "medium", "high"); The independent variable representing the membership function represents the standardized key parameters; , , Representing fuzzy sets respectively Boundary values ​​of the membership function for triangles of "low", "medium", and "high"; To quantify the real-time interaction between standardized flour attribute parameters and standardized mechanical control parameters, a dynamic interaction factor is introduced. Specifically, the covariance of each pair of standardized flour attribute parameters and standardized mechanical control parameters is calculated within a time window, and the absolute value is taken. The covariance is used to measure the coordinated change trend of the two standardized key parameters within the time window. The absolute value processing ensures that the dynamic interaction factor is positive, reflecting the absolute magnitude of the interaction strength. The absolute value of the covariance is normalized by dividing it by the product of the historical standard deviations of the two standardized key parameters to generate the dynamic interaction factor, with a value between 0 and 1. To avoid the product of the historical standard deviations of the two standardized key parameters being too small, leading to numerical overflow or instability, a denominator protection mechanism is introduced. The larger value between the product of the historical standard deviations of the two standardized key parameters and the smallest positive number is selected as the denominator to ensure that the dynamic interaction factor remains stable. The formula for calculating the dynamic interaction factor is:

[0025] in, Represents the dynamic interaction factor, i.e., the first... The standardized flour property parameters and the first A standardized mechanical control parameter in time The intensity of interaction; Indicates time window The covariance of the standardized flour property parameters and the standardized mechanical control parameters reflects the dynamic correlation between the standardized key parameters. The calculation of the covariance is a well-known technique in the art and will not be elaborated here. The time window is set according to the equipment response time. Indicates time The A standardized set of flour property parameters; Indicates time The A standardized set of mechanical control parameters; Indicates taking the first The historical standard deviation of the standardized flour property parameters and the first The product of the historical standard deviations of the standardized mechanical control parameters and the maximum value of the smallest positive number are used to prevent the denominator from being too small. Indicates the first The historical standard deviation of the standardized flour property parameters; Indicates the first The historical standard deviation of a standardized mechanical control parameter; Represents a very small positive number, used for denominator protection, with a range of values. ; The rule evaluation is based on a fuzzy rule base, combining membership values ​​and dynamic interaction factors to calculate the activation intensity of each rule. The fuzzy rule base contains a series of rules, such as "If the moisture content is high and the grinding pressure is low, then increase the grinding pressure." Each rule involves a combination of standardized flour attribute parameters and standardized mechanical control parameters. The rule activation intensity is obtained through a weighted sum calculation. Specifically, for each rule, the standardized key parameters involved are determined. Based on the initial weights and dynamic interaction factors, dynamic weights are calculated. These dynamic weights are obtained by multiplying the initial weights by the weighted sum of the dynamic interaction factors and normalizing, ensuring that the total sum of dynamic weights is 1. The rule activation intensity is obtained by summing the product of the membership values ​​of the standardized key parameters involved and the dynamic weights. To ensure the result is between 0 and 1, it is then normalized by dividing by the total number of standardized key parameters involved. The activation intensity of each rule is output, reflecting the applicability of the rule under the current production state. The formula is expressed as follows:

[0026] in, Representation rules In time The intensity of the rule-based excitation; Representation rules The number of standardized flour property parameters involved and the number of standardized mechanical control parameters sum; Representation rules The sum of the weighted membership degrees of all standardized flour attribute parameters involved; Representation rules The sum of the weighted membership degrees of all standardized mechanical control parameters involved; Indicates time The first Membership values ​​of standardized flour attribute parameters; Indicates time The first Membership values ​​of a standardized mechanical control parameter; Indicates the first A standardized mechanical control parameter in time The dynamic weights are used to adjust the importance of the standardized mechanical control parameters in the rule-based excitation, and the... A standardized flour property parameter over time The dynamic weights are the same, and are dynamically updated based on the initial weights and dynamic interaction factors. Indicates the first A standardized flour property parameter over time The dynamic weights are used to adjust the importance of standardized flour attribute parameters in rule activation. They are dynamically updated based on the initial weights and dynamic interaction factors, and the formula is expressed as follows:

[0027] in, Indicates the first The initial weights of the standardized flour attribute parameters reflect their initial importance to flour quality. These weights are set based on expert experience and their values ​​range from [specific value range missing]. ; Indicates the first The initial weights of the standardized mechanical control parameters reflect their initial importance to the mechanical control. These weights are set based on expert experience and their range is [not specified]. ; Representation rules The normalization factor; The rule output is defined as a suggested adjustment amount for each rule based on the standardized mechanical control parameters, quantifying the amplitude and direction of the control action; the rule output is a standardized numerical value. ,in, This indicates that the range of control action has "increased". This indicates that the amplitude of the controlled action has "decreased". This indicates that the amplitude of the control action remains "unchanged," based on the physical characteristics of the standardized mechanical control parameters (such as the adjustable range of pressure and the step value of the rotational speed) and expert experience. Defuzzification transforms the rule excitation intensity and rule output into specific control adjustment quantities, generating standardized adjustment quantities that can be directly used for machine control. Specifically, a weighted average method is used: the output value of each rule is multiplied by the rule excitation intensity and summed, then divided by the sum of all rule excitation intensities to obtain the standardized adjustment quantity for each standardized machine control parameter. The formula is expressed as follows:

[0028] in, Indicates the first A standardized mechanical control parameter in time Standardized adjustment amount; Representation rules For the The standardized mechanical control parameters are adjusted as a set of rules, which define the control actions, including the magnitude of the control action: "increase", "decrease", or "remain unchanged". These rules are defined by a fuzzy rule base, based on the characteristics of the mechanical control parameters and expert experience. ; This indicates the standardized adjustment range of the rule output, used to quantify the magnitude of the control action. This represents the sum of the excitation intensities of all rules; Defuzzification integrates the contributions of all rules, generating precise standardized adjustment values ​​for the standardized mechanical control parameters. This avoids rule conflicts, ensures smooth control actions that meet actual needs, directly guides the updating of mechanical control parameters, and indirectly optimizes flour quality. S2. Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters to obtain the updated values ​​of the standardized mechanical control parameters, and send the updated values ​​of the standardized mechanical control parameters to the actuator for adjustment; For each standardized mechanical control parameter, if the standardized adjustment is not zero, the parameter is updated. Specifically, the standardized adjustment is multiplied by the historical standard deviation to convert it to an adjustment in actual units. This is then added to the current updated baseline value to obtain a preliminary updated value. A comparison operation is used to limit the preliminary updated value to the minimum and maximum physical constraints of the equipment. If the standardized adjustment is zero, the current updated baseline value remains unchanged. The formula is as follows:

[0029] in, Indicates the first A standardized mechanical control parameter in time The updated value has actual physical units; Indicates the first A standardized mechanical control parameter in time The actual value, which has actual physical units, serves as the current updated baseline value and reflects the current state of the device; Indicates the first The actual unit adjustment of a standardized mechanical control parameter is converted into a physical quantity that the equipment can execute. , They represent the first The minimum and maximum values ​​of the actual units of the standardized mechanical control parameters are used as the physical constraint range to apply physical constraints to the equipment and prevent it from operating outside the range. These are set by the equipment specifications or process requirements. , These represent the minimum and maximum value operations, respectively, ensuring that the updated value is within the device's allowed range. This indicates the initial update value; Furthermore, the updated values ​​of the mechanical control parameters are sent one by one to the actuators, such as water pumps and grinding machines, to achieve precise control.

[0030] In summary, an intelligent control system and method for flour production process have been developed.

[0031] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0032] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0033] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of a flour production process, characterized in that, Includes the following steps: S1. Collect key parameters in the flour production process, standardize the key parameters to obtain standardized key parameters, including: standardized flour attribute parameters and standardized mechanical control parameters. The standardization process involves subtracting the historical mean of the key parameters and dividing by the historical standard deviation to obtain the standardized key parameters. The historical mean and historical standard deviation of the key parameters are calculated based on long-term production data, reflecting the statistical characteristics of the key parameters. A dynamic interactive weighted fuzzy control algorithm is then used to generate standardized adjustment values ​​for the standardized mechanical control parameters. In the implementation of the dynamic interactive weighted fuzzy control algorithm, the covariance of each pair of standardized flour attribute parameters and standardized mechanical control parameters is calculated within the time window, and the absolute value is taken. The absolute value of the covariance is normalized by dividing it by the product of the historical standard deviations of the two standardized key parameters to generate a dynamic interaction factor. At the same time, a denominator protection mechanism is introduced, and the maximum value between the product of the historical standard deviations of the two standardized key parameters and the smallest positive number is selected as the denominator. S2. Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters to obtain the updated values ​​of the standardized mechanical control parameters, and send the updated values ​​of the standardized mechanical control parameters to the actuator for regulation.

2. The intelligent control method for flour production process according to claim 1, characterized in that, S1 specifically includes: The dynamic interactive weighted fuzzy control algorithm generates standardized adjustment amounts for standardized mechanical control parameters based on standardized key parameters through five parts: membership degree calculation, dynamic interaction factor calculation, rule evaluation, rule output definition, and defuzzification.

3. The intelligent control method for flour production process according to claim 2, characterized in that, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, for each standardized key parameter, namely the standardized flour attribute parameter and the standardized mechanical control parameter, a membership function is used to map it to three fuzzy sets: "low", "medium", and "high", generating a membership value.

4. The intelligent control method for flour production process according to claim 2, characterized in that, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, the rule evaluation is based on the fuzzy rule base. Each rule determines the standardized key parameters involved. According to the initial weight and dynamic interaction factor, the dynamic weight is calculated. The product of the membership values ​​of the standardized key parameters involved and the dynamic weight is summed and then divided by the total number of standardized key parameters involved for normalization. The excitation intensity of each rule is then output.

5. The intelligent control method for flour production process according to claim 4, characterized in that, S1 specifically includes: In the implementation of the dynamic interactive weighted fuzzy control algorithm, defuzzification transforms the rule excitation intensity and rule output into control adjustment quantities. The weighted average method is used to multiply the output value of each rule by the rule excitation intensity and sum them up, and then divide by the sum of all rule excitation intensities to obtain the standardized adjustment quantity of each standardized mechanical control parameter.

6. The intelligent control method for flour production process according to claim 1, characterized in that, S2 specifically includes: For each standardized mechanical control parameter, if the standardized adjustment of the standardized mechanical control parameter is not zero, the standardized mechanical control parameter is updated; if the standardized adjustment of the standardized mechanical control parameter is zero, the current updated baseline value is kept unchanged.

7. The intelligent control method for flour production process according to claim 6, characterized in that, S2 specifically includes: In the process of updating the standardized mechanical control parameters, the standardized adjustment amount of the standardized mechanical control parameters is multiplied by the historical standard deviation to convert it into the adjustment amount in actual units. This is then added to the current updated baseline value to obtain the preliminary update value. Through comparison operations, the preliminary update value is restricted to the minimum and maximum physical constraints of the equipment to obtain the updated value of the standardized mechanical control parameters.

8. An intelligent control system for a flour production process, applied to the intelligent control method for a flour production process as described in claim 1, characterized in that, Includes the following parts: Data acquisition module, standardization processing module, dynamic interactive weighted fuzzy control module, mechanical control parameter update module, actuator control module; Data acquisition module: Collects key parameters during the flour production process and outputs the collected key parameters to the standardization processing module; Standardization processing module: Standardizes each key parameter from the data acquisition module. The standardization process involves subtracting the historical mean of the key parameter and dividing by the historical standard deviation to obtain the standardized key parameter. The historical mean and historical standard deviation of the key parameter are calculated based on long-term production data and reflect the statistical characteristics of the key parameter. The standardized key parameter is then output to the dynamic interactive weighted fuzzy control module. Dynamic Interactive Weighted Fuzzy Control Module: Based on the standardized key parameters from the standardized processing module, the standardized adjustment amount of the standardized mechanical control parameters is generated through the dynamic interactive weighted fuzzy control algorithm. During the implementation of the dynamic interactive weighted fuzzy control algorithm, the covariance of each pair of standardized flour attribute parameters and standardized mechanical control parameters is calculated within the time window, and the absolute value is taken. The absolute value of the covariance is divided by the product of the historical standard deviations of the two standardized key parameters for normalization, generating a dynamic interactive factor. At the same time, a denominator protection mechanism is introduced, selecting the maximum value between the product of the historical standard deviations of the two standardized key parameters and the smallest positive number as the denominator. The standardized adjustment amount of the standardized mechanical control parameters is output to the mechanical control parameter update module. Mechanical control parameter update module: Based on the standardized adjustment amount and physical constraint range of the standardized mechanical control parameters, update the standardized mechanical control parameters and output the updated value of the standardized mechanical control parameters to the actuator control module; Actuator control module: Based on the updated values ​​of the standardized mechanical control parameters from the mechanical control parameter update module, precise control is achieved.

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