A control method for a food grinding device

Abstract

The present invention discloses a control method for a food grinding device, which relates to the field of grinding equipment. The method includes: determining the food to be ground, and determining the structural characteristics of the compound grinding device; training the grinding decision module, and determining the control constraints, making dynamic control decisions for a single grinding cycle, and determining the pre-grinding strategy; setting the pre-grinding strategy with a PLC program; synchronously performing grinding monitoring, transmitting back for off-axis control analysis, and locating the pre-adjustment control point; combining the grinding decision module to make feedback control decisions, and determining the feedback control scheme; identifying the strategy response nodes of the feedback control scheme, generating adjustment response instructions, and performing grinding feedback. It solves the technical problems of low demand compliance and low degree of automation under complex grinding conditions, ensures the precise control of demand compliance under complex grinding conditions, and improves the degree of automation and the technical effects of dynamic control adaptability.

Description

Technical Field

[0001] The present application relates to the technical field of grinding equipment, and in particular to a control method for a food grinding device. Background Art

[0002] With the rapid development of the food processing industry, the role of food grinding devices in the food processing process is becoming increasingly important. Traditional food grinding devices often adopt simple mechanical structures and fixed operating modes, which are difficult to meet the complex and changing grinding conditions. In addition, as consumers' requirements for food quality and safety continue to increase, higher requirements are also placed on the control accuracy and stability of food grinding devices. Traditional food grinding devices often lack flexibility and adaptability, and it is difficult to make precise adjustments according to different ingredients and grinding requirements. This leads to the difficulty in achieving the expected grinding effect and quality under complex grinding conditions.

[0003] In summary, the prior art has technical problems of low compliance with requirements and low degree of automation under complex grinding conditions. Summary of the invention

[0004] The present application provides a control method for a food grinding device, aiming to solve the technical problems of low demand compliance and low degree of automation under complex grinding conditions.

[0005] In view of the above problems, the present application provides a control method for a food grinding device.

[0006] The present application discloses a control method for a food grinding device, which is applied to a compound grinding device, and the method includes: determining the material properties and grinding quality standards of the food to be ground, and determining the structural characteristics of the compound grinding device, wherein the structural characteristics include hardware characteristics and operation characteristics; training a grinding decision module with the structural characteristics as a baseline, and determining control constraints, making dynamic control decisions for a single grinding cycle based on the material properties and the grinding quality standards, and determining a pre-grinding strategy; introducing a precision grinding system, performing PLC program setting for the pre-grinding strategy, and responding to the precision grinding system; synchronously performing grinding monitoring, and transmitting the information back to a temporary data center for off-axis control analysis, and locating pre-adjustment control points, wherein the off-axis dimension includes single-point deviation and trend deviation, and the operation dimension includes independent operation and collaborative operation, and the collaborative operation includes same-room collaboration and cross-room collaboration; based on the pre-adjustment control point, performing feedback control decisions in combination with the grinding decision module, and determining a feedback control scheme; identifying the strategy response node of the feedback control scheme, generating an adjustment response instruction, and performing grinding feedback.

[0007] The control method for a food grinding device solves the technical problems of low demand compliance and low automation under complex grinding conditions, achieves precise control to ensure demand compliance under complex grinding conditions, and improves the automation and dynamic control adaptability technical effects, specifically including:

[0008] 1. As traditional food grinding devices often lack flexibility and adaptability, it is difficult to make precise adjustments according to different ingredients and grinding requirements, and monitor various parameters in the grinding process in real time, such as temperature, humidity, material particle size, etc. According to the changes in these parameters, the grinding parameters, such as grinding speed, pressure, etc., are automatically adjusted to ensure that the expected grinding effect and quality can be achieved under different grinding conditions.

[0009] 2. Use advanced automatic control technology to realize the automatic operation of the grinding process. Through the preset grinding program and parameter settings, one-click start and automatic completion of the grinding process can be achieved. This not only reduces labor costs, but also improves the consistency and stability of the grinding process.

[0010] 3. By introducing real-time feedback and dynamic control mechanisms, real-time monitoring and dynamic adjustment of the grinding process are achieved. When a parameter in the grinding process changes, the control system can respond quickly and automatically adjust the grinding parameters to maintain the stability of the grinding effect. This dynamic control and adaptive performance enables the grinding device to better adapt to complex and changeable grinding conditions.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a control method for a food grinding device in one embodiment;

[0014] Figure 2 A schematic flow chart of determining a pre-adjustment control point of a control method for a food grinding device in one embodiment. DETAILED DESCRIPTION

[0015] The embodiment of the present application provides a control method for a food grinding device to solve the technical problems of low demand compliance and low degree of automation under complex grinding conditions, thereby ensuring precise control of demand compliance under complex grinding conditions and improving the technical effects of automation and dynamic control adaptability.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] like Figure 1 As shown, the present application provides a control method for a food grinding device, which is applied to a compound grinding device, and the method includes:

[0020] Determine the material characteristics and grinding quality standards of the food to be ground, and determine the structural characteristics of the compound grinding device, the structural characteristics including hardware characteristics and operation characteristics;

[0021] The food grinding device is a special equipment used to grind various food raw materials into the required particle size or texture. It usually includes a drive system, a grinding system, a control system, etc. Among them, the drive system is the power source of the food grinding device, which is usually powered by a motor or a pneumatic system. The drive system controls the rotation speed and direction of the grinding parts to meet the grinding requirements of different food raw materials; the grinding system includes grinding parts and a grinding chamber. The grinding parts can be blades, grinding discs, grinding rollers, etc., which cut, squeeze or rub the food raw materials in different ways and speeds to achieve the desired grinding effect. The grinding chamber is a closed space for accommodating food raw materials and grinding parts, and ensuring hygiene and safety during the grinding process; control system: The control system is the core of the food grinding device and is responsible for monitoring and controlling the entire grinding process. The control system can set grinding parameters such as grinding speed, grinding time, grinding particle size, etc., and adjust these parameters according to the real-time monitored data to obtain the best grinding effect. Applied to compound grinding devices, the compound grinding device refers to a mechanical equipment designed for food, chemical, pharmaceutical and other industries that can achieve efficient and multifunctional grinding. Compared with traditional single grinding equipment, compound grinding devices usually have more complex structures and more functions to meet the needs of different materials and processes. The present application provides a control method for a food grinding device to achieve automated management and optimization of the food grinding process to ensure the accuracy, safety and efficiency of the grinding operation.

[0022] Determine the material properties and grinding quality standards of the food to be ground, where the food to be ground refers to the food that needs to be ground, and the material properties refer to a series of physical, chemical and mechanical properties of the food raw materials to be ground. These properties directly affect the selection of the grinding process, the design of the grinding equipment and the parameter settings during the grinding process, thereby determining the quality of the ground food, for example, the hardness, humidity, particle size requirements, viscosity and chemical composition of the food to be ground. The hardness of the food raw material determines the difficulty of grinding. Raw materials with higher hardness require stronger grinding force and longer grinding time; humidity affects the fluidity and grinding effect of the raw materials. Overly wet or overly dry raw materials may cause uneven grinding or equipment blockage; different foods have different requirements for the particle size after grinding, for example, sauces require a delicate texture, while fillings may require larger particles; raw materials with strong viscosity are easy to adhere to the equipment during the grinding process, affecting the grinding efficiency and cleanliness; the chemical composition of the food raw materials determines whether it is corrosive to the grinding equipment, thereby affecting the material selection and service life of the equipment. The grinding quality standard refers to the specific requirements and standards set for the quality of the ground material during the grinding process. These standards usually include requirements for the particle size, uniformity, color, odor, impurity content, etc. of the material to ensure that the ground material meets the predetermined usage requirements and quality requirements. The structural characteristics of the compound grinding device refer to the unique properties and characteristics of the device in terms of design, construction and composition, which determine the function, performance, applicability and durability of the device. In this application, the structural characteristics include hardware characteristics and operating characteristics. The hardware characteristics mainly refer to the characteristics of the grinding device in terms of physical structure, components and materials. These characteristics determine the basic functions and performance of the device, such as physical structure, components, etc. The operating characteristics are the performance and performance of the grinding device during operation, which determine the stability and reliability of the device in actual operation, such as grinding performance, stability and adjustability. Through a comprehensive analysis of the material characteristics of the food to be ground, the grinding quality standards and the structural characteristics of the compound grinding device, a foundation is laid for the subsequent improvement of grinding efficiency and product quality.

[0023] Training a grinding decision module with the structural characteristics as a baseline, determining control constraints, making dynamic control decisions for a single grinding cycle based on the material characteristics and the grinding quality standards, and determining a pre-grinding strategy;

[0024] In the training of the grinding decision module, the baseline usually refers to a starting point or reference point for measuring and comparing the performance of subsequent models or algorithms. In this application, it refers to training the grinding decision module based on the structural characteristics as a reference. The grinding decision module is trained and optimized based on the structural characteristics of the compound grinding device to achieve intelligent control of the grinding process. The grinding decision module can receive input grinding conditions, such as material characteristics, grinding quality standards, etc., and dynamically formulate and adjust the grinding strategy based on these conditions. By collecting and analyzing data in the grinding process in real time, such as grinding speed, energy consumption, particle size distribution, etc., the decision module can predict the grinding effect and make corresponding parameter adjustments based on the prediction results to achieve the optimal grinding performance. Key features are extracted from structural characteristics, material characteristics and grinding quality standards as inputs to the decision module. According to the complexity of the problem and the characteristics of the data, a suitable deep learning algorithm is selected, and the decision module is trained using historical data and expert knowledge so that it can predict the grinding effect based on the input features. Determine the control constraints, which are formulated based on the structural characteristics, material characteristics, grinding quality standards and safe operating specifications of the grinding device, such as grinding speed, grinding pressure, temperature control, etc. A single grinding cycle refers to the time period from the start of grinding to reaching the predetermined grinding target or completing a complete grinding operation during the grinding process, which usually includes the preparation before grinding, the execution of the grinding process, and the cleaning and evaluation after grinding. During the grinding process, dynamic control decisions mean dynamically adjusting and controlling the grinding parameters according to the real-time grinding conditions, material properties and quality standards to achieve the best grinding effect, and determining the pre-grinding strategy, which refers to the initial grinding strategy determined according to the dynamic control decision. Through the above steps, the grinding decision module can be trained based on the structural characteristics of the grinding equipment, and the control constraints and pre-grinding strategies can be determined. It is helpful to realize the dynamic control decision of the single grinding cycle and improve the grinding efficiency and quality.

[0025] Identifying structural characteristics of the compound grinding device and determining effective modeling features, including feature simplification and feature conversion;

[0026] In combination with the effective modeling features, a visual simulation platform is connected to perform low-feature modeling based on the compound grinding device to determine a low-feature model, wherein the low-feature model meets the requirements of logical simulation reasoning;

[0027] Based on the low feature model, supervised training of the grinding decision module is performed.

[0028] A detailed physical analysis of the compound grinding device is conducted to understand its main components, working principles and interactions, and to extract structural characteristics that are closely related to grinding performance, such as the number of grinding heads, layout, rotation speed, stroke, pressure control system, etc. From the extracted structural characteristics, the features that have a significant impact on the grinding process are selected as modeling features, and the complex features are simplified to reduce the complexity of the model while retaining key information. For example, some components, such as supports, can be directly replaced by line drawings, and some complex features are simplified and replaced to retain the process simulation capability. The original features are converted as necessary to better fit the model. A visualization platform suitable for grinding process simulation is selected, such as MATLAB / Simulink, ANSYS, etc., and the modeling features are input into the simulation platform. Using the selected modeling features and simulation platform, a low-feature model of the compound grinding device is established, which can simulate the main dynamic and static characteristics of the grinding process, and ensure that the low-feature model meets the needs of logical simulation reasoning, that is, the model can accurately reflect the interaction and influence between various factors in the grinding process. Collect historical data related to the compound grinding device, including performance data and fault data under different grinding conditions, preprocess the collected data, input the preprocessed data into the grinding decision module, use the supervised learning algorithm for training, use the validation set to evaluate the trained model to ensure that the model has a high accuracy, and make necessary adjustments and optimizations to the model based on the evaluation results. The above method can significantly improve the modeling efficiency, model accuracy and reliability, enhance the intelligence level of the decision module, reduce costs and risks, and improve production efficiency and product quality by identifying the structural characteristics of the compound grinding device, determining effective modeling features, connecting the visual simulation platform for low-feature modeling, and supervising the training of the grinding decision module.

[0029] Build a spatial matrix with risk types as matrix rows, risk characteristics as matrix columns, and parameter control characteristics as matrix verticals;

[0030] Performing industrial big data retrieval to mine a first risk matrix based on the compound grinding device and a second risk matrix based on the material grinding process;

[0031] Based on homology, the first risk matrix and the second risk matrix are mapped to determine control constraints.

[0032] Define a spatial matrix structure, where the matrix rows include risk types, such as equipment failure risk, operational risk, material quality risk, etc., the matrix columns include risk characteristics, such as the frequency of risk occurrence, degree of impact, detectability, etc., and the matrix vertical includes parameter control characteristics, such as the effectiveness of control measures, implementation cost, implementation difficulty, etc. Create a multidimensional space, where the rows represent different risk types, the columns represent risk characteristics, and the vertical represents parameter control characteristics. In this spatial matrix, specific risk assessment data and parameter control measures information can be filled in. The industrial big data platform is used to retrieve historical failure data, maintenance records, operation logs, etc. related to the compound grinding device, clean and organize these data, extract information related to risk types and risk characteristics, and fill the extracted information into the spatial matrix to form the first risk matrix based on the compound grinding device; retrieve data related to the material grinding process, such as material quality data, grinding parameter records, production abnormality data, etc. through the industrial big data platform, extract information related to risk. The information related to the risk type and risk characteristics is filled into the spatial matrix to form a second risk matrix based on the material grinding process. Homology analysis refers to analyzing the risk types and risk characteristics in the first risk matrix and the second risk matrix to find the common points and correlations between the two. Based on the results of the homology analysis, the risk types and risk characteristics in the two risk matrices are mapped to identify the common risk points in the compound grinding device and the material grinding process. For the common risk points identified after mapping, the position coordinates in the spatial matrix are found, and the corresponding information of the risk points is obtained in combination with the spatial matrix, and the control constraints are determined according to the corresponding information. Through the above method, a spatial matrix based on risk type, risk characteristics and parameter control characteristics is constructed, and the risk matrix of the compound grinding device and the material grinding process is determined through industrial big data retrieval and analysis, and then based on homology, mapping is performed and control constraints are determined, providing effective risk management support for industrial production.

[0033] Introducing a precision grinding system, performing PLC program setting on the pre-grinding strategy, and responding to the precision grinding system;

[0034] The purpose of precision grinding is to grind food raw materials into the required particle size or powder state so that they can be used in the subsequent production process. The precision grinding system is a system that integrates a variety of technologies and equipment, mainly used to grind food raw materials into the required particle size or powder state. PLC refers to programmable logic controller. By introducing the precision grinding system and combining it with the programmable controller for periodic dynamic control, the automation capability is improved. Ensure that all key components of the precision grinding system are properly connected to the PLC and the communication lines are stable and reliable. Define all inputs and outputs related to the precision grinding system in the PLC program. Inputs can include sensor data such as temperature, pressure, flow, etc., and outputs are used to control actuators such as motor speed, valve opening, etc.; write logic control code according to the pre-grinding strategy. This includes setting the initial values ​​of grinding parameters such as grinding time, speed, pressure, etc., and dynamically adjusting these parameters based on the data fed back by the system. Write a data processing program to receive real-time data from the precision grinding system and analyze and judge it. These data can be used to judge the stability of the grinding process and the quality of the product so that necessary adjustments can be made. Map the parameters in the pre-grinding strategy with the variables in the PLC program. In this way, when the grinding parameters need to be adjusted, only the corresponding variables in the PLC program need to be modified, ensuring that the control logic in the PLC program is associated with the actual operating status of the precision grinding system. For example, when the system detects that the grinding temperature is too high, the PLC should be able to automatically reduce the motor speed or increase the cooling water flow to reduce the temperature. Combining the precision grinding system with the PLC program can achieve precise control of the pre-grinding strategy. It can improve grinding efficiency and product quality and reduce production costs and risks.

[0035] Based on the structural characteristics, control elements are determined and underlying control logic is built, wherein the control elements are identified with distribution weights and collaborative relevance;

[0036] Based on the underlying control logic, the precision grinding system is initialized and configured.

[0037] Conduct an in-depth analysis of the structure of the precision grinding system to understand its main components, such as grinders, sensors, actuators, cooling systems, etc., as well as their interactions and dependencies. Based on the system structure characteristics, determine the key control factors that affect the grinding effect, including grinding speed, grinding pressure, cooling water flow, material flow, etc., to maintain the continuity and stability of the grinding process. According to the degree of influence of the control factor on the grinding effect, assign a distribution weight to each factor. This weight reflects the importance of the factor in the control system. For example, grinding speed and grinding pressure have a direct and significant impact on the grinding effect, so their weights may be high; analyze the synergistic correlation between the control factors, that is, the interactions and dependencies between them. For example, increasing the grinding speed may cause the temperature to rise. At this time, it is necessary to increase the cooling water flow to keep the temperature stable. This synergistic correlation needs to be reflected in the control logic. Based on the control factors, distribution weights and synergistic correlations, build the underlying control logic. This logic should be able to monitor and control the status of each factor in real time, and adjust their values ​​as needed to achieve the best grinding effect. The underlying control logic should be applicable to a variety of materials and universal underlying logic for various needs. After the system is configured, subsequent strategy execution and response are based on it. Use the built-in underlying control logic to initialize the configuration of the precision grinding system. This includes setting the initial values ​​of the control parameters, configuring the parameters of the sensors and actuators, and testing the response and stability of the system. During the initialization process, it is necessary to ensure that all control elements can work as expected and that the system can achieve the desired grinding effect. Through the above method, the control elements are determined based on the structural characteristics of the precision grinding system and the underlying control logic is built, and then the system is initialized and configured. This will provide a stable and reliable control foundation for the subsequent grinding process.

[0038] Grinding monitoring is carried out simultaneously and transmitted back to the temporary data center for off-axis control analysis and positioning of pre-adjustment control points. The off-axis dimension includes single-point deviation and trend deviation, and the operation dimension includes independent operation and collaborative operation. The collaborative operation includes same-room collaboration and cross-room collaboration.

[0039] Grinding monitoring is carried out simultaneously. High-precision sensors are installed to monitor key parameters in the grinding process in real time, such as grinding speed, pressure, temperature, material flow, etc. Data from different sensors are collected simultaneously to ensure the accuracy and real-time nature of the data. A temporary data center is established to receive, store and process real-time data from the grinding system. The monitoring data is transmitted back to the temporary data center in real time using wired or wireless transmission methods to ensure the stability and security of data transmission. In the temporary data center, advanced control algorithms and data analysis tools are used to perform off-axis control analysis on the returned data. The off-axis dimensions include single-point deviation and trend deviation. Single-point deviation refers to the abnormal fluctuation of a certain parameter at a certain moment, while trend deviation refers to the overall change trend of the parameter over a period of time. Analyzing these deviations can help identify problems in the grinding process, such as equipment failure and unstable material quality. Based on the results of the off-axis control analysis, the control points that need to be adjusted in advance are located. These control points include grinding speed, pressure, cooling water flow, etc. By adjusting the parameter values ​​of these control points, the grinding process can be optimized and product quality and production efficiency can be improved. Consider two operation dimensions: independent operation and collaborative operation. Independent operation refers to the situation where a single grinding system operates independently, while collaborative operation involves the coordination and cooperation between multiple grinding systems. Collaborative operation includes same-room collaboration and cross-room collaboration. Same-room collaboration refers to the collaboration between multiple grinding systems located in the same studio, while cross-room collaboration involves the coordination between grinding systems in different studios or different locations. In collaborative operation, it is necessary to ensure data synchronization and smooth communication between various systems to achieve collaborative control and optimization. By implementing the above solutions, comprehensive monitoring and intelligent control of precision grinding systems can be achieved, product quality and production efficiency can be improved, and production costs and risks can be reduced.

[0040] like Figure 2 As shown, the self-regulation characteristics of the precision grinding system are determined, including the self-regulation type-risk response capability, and the risk response capability includes the safety management dimension and the deviation adjustment dimension;

[0041] Measuring off-axis deviation information of grinding monitoring data to determine whether it belongs to the self-regulating feature;

[0042] If not, off-axis deviation information not attributable to the self-regulating feature is identified and integrated to determine the pre-regulating control point.

[0043] Determine the self-regulation characteristics of the precision grinding system, including the self-regulation type - risk response capability, active self-regulation, the system can actively monitor the key parameters in the grinding process and automatically adjust according to the preset threshold or algorithm; passive self-regulation, the system requires external intervention or trigger conditions to regulate, such as manual adjustment or based on specific events, such as fault alarms. The risk response capability includes the safety management dimension and the deviation adjustment dimension; the safety management dimension is used to evaluate the system's response capabilities when facing safety risks, such as equipment overheating, material blockage, etc., including safety shutdown, emergency braking, etc. The deviation adjustment dimension is used to analyze the system's automatic adjustment capabilities for deviations in grinding parameters such as speed, pressure, temperature, etc., to ensure that the grinding process is carried out within the preset range. Measuring the off-axis deviation information of grinding monitoring data means using sensors to collect key parameter data of the grinding process in real time, comparing the real-time monitoring data with a preset benchmark value or range, calculating the off-axis deviation value, analyzing the duration, change trend and other information of the off-axis deviation to evaluate its impact on the grinding process and product quality, obtaining the off-axis deviation information, determining whether the off-axis deviation information belongs to the self-regulation feature, and judging whether the system has automatically adjusted the off-axis deviation information according to the self-regulation type. Checking the system records to understand whether the system has triggered the corresponding self-regulation mechanism when the off-axis deviation occurs. If the system has effectively processed the off-axis deviation, it is determined that the off-axis deviation information belongs to the self-regulation feature. If the system fails to effectively process the off-axis deviation or the processing effect is not ideal, it is determined that the off-axis deviation information does not belong to the self-regulation feature. Analyze these non-attributed off-axis deviation information to determine the causes and influencing factors of their generation. According to the non-attributed off-axis deviation information, identify the control points that need to be pre-adjusted, analyze the correlation and mutual influence between these control points, determine the adjustment strategy and method, formulate a detailed pre-adjustment control plan, and clarify the adjustment parameters, adjustment time and adjustment goals. Through the above method, it is ensured that the precision grinding system has effective self-regulation capabilities and can respond quickly when faced with risks, thereby ensuring the stability of the grinding process and product quality.

[0044] combining the grinding monitoring data with the pre-grinding strategy to map and measure single-point bias;

[0045] Based on the single point deflection degree, the deviation trend analysis is performed in combination with the upper single point deviation degree to determine the trend deflection degree, the deviation reduction trend or the deviation expansion trend;

[0046] If the single point deviation does not meet the first deviation threshold, and the trend deviation does not meet the second deviation threshold, a control positioning decision is made.

[0047] Key parameter data, such as grinding speed, pressure, temperature, etc., are obtained from the grinding monitoring system in real time. The collected data are compared with the target value or range set in the pre-grinding strategy. According to the comparison results, the deflection of each monitoring point, such as the speed monitoring point, the pressure monitoring point, etc., is calculated. The deflection can be expressed by the difference or percentage difference between the actual value and the target value. Consider the single-point deviation in the historical data as a reference for trend analysis. Based on the current single-point deviation and the upper single-point deviation, the change trend of the deviation is analyzed. According to the results of the trend analysis, it is determined whether the deviation presents a reduction trend, that is, the deflection gradually decreases, or an expansion trend, that is, the deflection gradually increases. According to the characteristics and quality requirements of the grinding system, a first deviation threshold is set for the judgment of the single-point deflection, and a second deviation threshold is set for the judgment of the trend deflection. If the single-point deflection does not meet the first deviation threshold, that is, the deflection is within the acceptable range, and the trend deflection does not meet the second deviation threshold, that is, the deviation trend is stable or improving, then a control positioning decision needs to be made, including determining the control points, adjustment parameters, and adjustment targets that need to be adjusted. Through the above method, the grinding process can be accurately controlled and optimized to ensure the steady improvement of product quality and production efficiency.

[0048] Based on the pre-adjustment control point, the feedback control decision is made in combination with the grinding decision module to determine the feedback control scheme;

[0049] Identify pre-adjustment control points, obtain key parameters such as grinding speed, pressure, temperature, and conduct in-depth analysis, including checking real-time data, historical data, and correlation with other control points. Input the status information of the pre-adjustment control points into the grinding decision module, and make feedback control decisions based on the input data and built-in algorithms. This includes determining which control points need to be adjusted, the direction of adjustment, the magnitude of adjustment, and the priority of adjustment. According to the feedback control decision of the grinding decision module, formulate a specific feedback control plan. This plan should include detailed adjustment steps, expected adjustment effects, and monitoring and evaluation methods. According to the above method, feedback control decisions based on pre-adjustment control points are implemented to ensure that the grinding system can maintain stable and efficient operation in a real-time changing environment.

[0050] Identify grinding monitoring data to determine the uniformity of material particle size;

[0051] Determining whether the particle size uniformity of the material satisfies a preset diameter difference, the preset diameter difference includes a first preset difference and a second preset difference, the first preset difference being smaller than the second preset difference;

[0052] If the conditions are met, a balanced grinding analysis is performed in combination with the grinding decision module to determine a first adjustment strategy.

[0053] Collect key parameter data from the grinding process, including material flow, grinding speed, grinding time, temperature, etc., obtain grinding monitoring data, perform statistical analysis based on the grinding monitoring data, such as calculating standard deviation, coefficient of variation and other indicators to measure the uniformity of particle size distribution, determine the uniformity of material particle size, set the preset diameter difference of material particle size, which can be determined based on product quality requirements, process standards or customer needs. The preset diameter difference includes a first preset difference and a second preset difference, wherein the first preset difference is more stringent and represents higher quality requirements. Compare the statistically obtained material particle size uniformity index with the preset diameter difference. If the material particle size uniformity meets the first preset difference, that is, the more stringent standard, it means that the material particle size is already very uniform and may not need to be adjusted significantly. If the material particle size uniformity meets the second preset difference but does not reach the first preset difference, certain adjustments are required to optimize product quality. If the material particle size uniformity does not meet the second preset difference, a larger adjustment is required immediately to ensure product quality. When the uniformity of the material particle size needs to be adjusted, the relevant data is input into the grinding decision module. The balanced grinding analysis includes evaluating the impact of different grinding parameters on the uniformity of the particle size, predicting the particle size distribution after adjustment, etc. Based on the balanced grinding analysis results of the grinding decision module, the first adjustment strategy is determined, including adjusting the grinding speed, changing the material flow rate, adjusting the grinding time, etc. The goal is to make the material particle size more uniform, while considering factors such as production efficiency and cost. Through the above method, the best first adjustment strategy can be determined, thereby optimizing the grinding process and improving production efficiency.

[0054] If the material particle size uniformity is greater than the first preset difference and less than the second preset difference, in combination with the grinding decision module, a vibration control decision based on a vibration motor is executed as the first adjustment strategy;

[0055] If the material particle size uniformity is greater than the second preset difference, a grinding control decision is made by using screen material stratification and cross-chamber synchronous grinding as the first adjustment strategy;

[0056] Among them, the vibration control decision is based on the relative movement characteristics of the material, including at least the relative movement mode and movement trajectory of the particle size.

[0057] If the material particle size uniformity is greater than the first preset difference and less than the second preset difference, in combination with the grinding decision module, a vibration control decision based on a vibration motor is executed. The beneficial effects of the vibration control decision include improving the particle size uniformity. The vibration generated by the vibration motor through the vibration control decision increases the relative movement between materials, thereby improving the uniformity of the material particle size distribution; reducing energy consumption. By optimizing the movement mode and trajectory of the material, vibration control can accelerate the grinding process and improve production efficiency. The vibration control decision is based on the relative movement characteristics of the material, such as the relative movement mode and movement trajectory of the particle size, so that the adjustment is more accurate and effective. When the material particle size uniformity is greater than the second preset difference, the grinding control decision is made in the form of screen material stratification and cross-chamber synchronous grinding. The screen material stratification can quickly screen out materials with larger particle sizes, and provide materials with more uniform particle sizes for subsequent grinding processes, thereby improving grinding efficiency and product quality. Cross-chamber synchronous grinding can ensure that materials of different particle size levels are synchronously ground in their respective grinding chambers, reduce the difference in the residence time of materials during the grinding process, and further improve the uniformity of particle size distribution. The combination of these two adjustment strategies ensures that the grinding process can be effectively controlled regardless of the material size uniformity. Vibration control decisions are suitable for situations where the material size uniformity is within an acceptable range but needs further optimization, while screen material stratification and cross-chamber synchronous grinding are suitable for situations where the material size uniformity is poor and requires a larger adjustment. This diversified control strategy helps to improve the flexibility and controllability of the grinding process, ensuring a steady improvement in product quality and grinding efficiency.

[0058] Identify the strategy response node of the feedback control scheme, generate adjustment response instructions, and perform grinding feedback.

[0059] During the grinding process, after the feedback control scheme is determined, the key parameters that affect the grinding effect, such as grinding speed, pressure, temperature, etc., are identified. According to the key parameters, the corresponding control points or sensors are located in the grinding equipment or system. These points are the strategy response nodes. According to the actual situation of the strategy response node and the requirements of the feedback control scheme, set specific adjustment parameter values, such as increasing the grinding speed, reducing the pressure, etc., and convert the adjustment parameter values ​​into instructions that can be recognized by the device or system, that is, generate adjustment response instructions, and send the adjustment response instructions to the grinding equipment or system. The equipment or system performs corresponding adjustment operations according to the received instructions. In summary, by identifying the strategy response nodes of the feedback control scheme, generating adjustment response instructions and performing grinding feedback, it helps to improve grinding efficiency, reduce costs and ensure product quality.

[0060] In summary, the beneficial effects of this application include:

[0061] 1. As traditional food grinding devices often lack flexibility and adaptability, it is difficult to make precise adjustments according to different ingredients and grinding requirements, and monitor various parameters in the grinding process in real time, such as temperature, humidity, material particle size, etc. According to the changes in these parameters, the grinding parameters, such as grinding speed, pressure, etc., are automatically adjusted to ensure that the expected grinding effect and quality can be achieved under different grinding conditions.

[0062] 2. Use advanced automatic control technology to realize the automatic operation of the grinding process. Through the preset grinding program and parameter settings, one-click start and automatic completion of the grinding process can be achieved. This not only reduces labor costs, but also improves the consistency and stability of the grinding process;

[0063] 3. By introducing real-time feedback and dynamic control mechanisms, real-time monitoring and dynamic adjustment of the grinding process are achieved. When a parameter in the grinding process changes, the control system can respond quickly and automatically adjust the grinding parameters to maintain the stability of the grinding effect. This dynamic control and adaptive performance enables the grinding device to better adapt to complex and changeable grinding conditions.

[0064] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A control method for a food grinding device, characterized in that: Applied to a compound grinding device, the method comprises: Determine the material characteristics and grinding quality standards of the food to be ground, and determine the structural characteristics of the compound grinding device, the structural characteristics including hardware characteristics and operation characteristics; Training a grinding decision module with the structural characteristics as a baseline, determining control constraints, making dynamic control decisions for a single grinding cycle based on the material characteristics and the grinding quality standards, and determining a pre-grinding strategy; Introducing a precision grinding system, performing PLC program setting on the pre-grinding strategy, and responding to the precision grinding system; Grinding monitoring is carried out simultaneously and transmitted back to the temporary data center for off-axis control analysis and positioning of pre-adjustment control points. The off-axis dimension includes single-point deviation and trend deviation, and the operation dimension includes independent operation and collaborative operation. The collaborative operation includes same-room collaboration and cross-room collaboration. Based on the pre-adjustment control point, the feedback control decision is made in combination with the grinding decision module to determine the feedback control scheme; Identify the strategy response node of the feedback control scheme, generate adjustment response instructions, and perform grinding feedback; The determining of the control constraint condition comprises: Build a spatial matrix with risk types as matrix rows, risk characteristics as matrix columns, and parameter control characteristics as matrix verticals; Performing industrial big data retrieval to mine a first risk matrix based on the compound grinding device and a second risk matrix based on the material grinding process; Based on homology, the first risk matrix and the second risk matrix are mapped to determine control constraints.

2. The method according to claim 1, characterized in that The step of training the grinding decision module based on the structural characteristics as a baseline includes: Identifying structural characteristics of the compound grinding device and determining effective modeling features, including feature simplification and feature conversion; In combination with the effective modeling features, a visual simulation platform is connected to perform low-feature modeling based on the compound grinding device to determine a low-feature model, wherein the low-feature model meets the requirements of logical simulation reasoning; Based on the low feature model, supervised training of the grinding decision module is performed.

3. The method according to claim 1, characterized in that After the introduction of the precision grinding system, it includes: Based on the structural characteristics, control elements are determined and underlying control logic is built, wherein the control elements are identified with distribution weights and collaborative relevance; Based on the underlying control logic, the precision grinding system is initialized and configured.

4. The method according to claim 1, characterized in that Based on the self-regulation capability and external regulation of the precision grinding system, positioning the pre-regulation control point includes: Determining the self-regulation characteristics of the precision grinding system, including the self-regulation type-risk response capability, wherein the risk response capability includes a safety management dimension and a deviation adjustment dimension; Measuring off-axis deviation information of grinding monitoring data to determine whether it belongs to the self-regulating feature; If not, off-axis deviation information not attributable to the self-regulating feature is identified and integrated to determine the pre-regulating control point.

5. The method according to claim 1, characterized in that Before the positioning and pre-adjusting control point, the method includes: combining the grinding monitoring data with the pre-grinding strategy to map and measure single-point bias; Based on the single point deflection degree, the deviation trend analysis is performed in combination with the upper single point deviation degree to determine the trend deflection degree, the deviation reduction trend or the deviation expansion trend; If the single point deviation does not meet the first deviation threshold, and the trend deviation does not meet the second deviation threshold, a control positioning decision is made.

6. The method according to claim 1, characterized in that Combined with the grinding decision module, feedback control decision is made. If the particle size uniformity is biased, it includes: Identify grinding monitoring data to determine the uniformity of material particle size; Determining whether the particle size uniformity of the material satisfies a preset diameter difference, the preset diameter difference includes a first preset difference and a second preset difference, and the first preset difference is smaller than the second preset difference; If the conditions are met, a balanced grinding analysis is performed in combination with the grinding decision module to determine a first adjustment strategy.

7. The method according to claim 6, characterized in that The balanced grinding analysis performed in combination with the grinding decision module includes: If the material particle size uniformity is greater than the first preset difference and less than the second preset difference, in combination with the grinding decision module, a vibration control decision based on a vibration motor is executed as the first adjustment strategy; If the material particle size uniformity is greater than the second preset difference, a grinding control decision is made by using screen material stratification and cross-chamber synchronous grinding as the first adjustment strategy; Among them, the vibration control decision is based on the relative movement characteristics of the material, including at least the relative movement mode and movement trajectory of the particle size.

Images