Servo control method based on distributor filling line

Abstract

The invention discloses a servo control method based on a distributor filling line, and relates to the technical field of filling intelligent control, and the method comprises the steps: determining a plurality of assembly control targets based on process nodes based on a target filling production line; traversing a plurality of assembly control targets to perform hierarchical target control decomposition, and determining a basic control law; based on the basic control law, configuring an intelligent control unit of the servo controller in a cascade control mode; interacting production line control tasks, and extracting effective task labels; identifying effective task labels, transmitting the effective task labels to an intelligent control unit for control decision making and control response, and executing automatic control of a target filling production line; and synchronously performing control monitoring, and performing feedback regulation management on the target filling production line. The technical problems that in the prior art, the filling precision is not stable, and the filling machining requirement cannot be met are solved, and the technical effects that the filling precision is improved, and the filling machining requirement is met are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent filling control, and in particular to a servo control method based on a dispenser filling line. Background Art

[0002] Filling lines are a critical component of modern manufacturing, particularly in industries such as food, beverages, cosmetics, and pharmaceuticals. With growing market demand, production line efficiency and product quality are crucial for business competitiveness. However, traditional servo control methods for dispenser filling lines suffer from unstable filling accuracy and an inability to adapt to rapidly changing production demands and high quality standards. Summary of the Invention

[0003] The present application provides a servo control method based on a dispenser filling line, which is used to solve the technical problems of the prior art in that the filling accuracy is unstable and cannot meet the filling processing requirements.

[0004] In view of the above problems, the present application provides a servo control method based on a dispenser filling line.

[0005] The present application provides a servo control method based on a dispenser filling line, the method comprising: Based on the target filling production line, multiple assembly control objectives based on process nodes are determined, wherein the process nodes include at least automatic bottle unscrambling, filling, and capping. The multiple assembly control objectives are traversed to perform hierarchical target control decomposition and determine a basic control law, wherein the basic control law uses the assembly control objective - multiple virtual control layers - minimum control unit as the control logic layer, and the basic control law includes direct control dimensions and indirect control dimensions, wherein the indirect control dimension includes a regulation constraint relationship. Based on the basic control law, an intelligent control unit of the servo controller is configured in a cascade control manner, wherein the intelligent control unit includes an inner loop control area and an outer loop control area. Interactive production line control tasks are performed to extract valid task tags, wherein the tag dimension includes at least bottle characteristics, filling requirements, and limit requirements. The valid task tags are identified and transmitted to the intelligent control unit for control decision-making and control response, thereby executing automated control of the target filling production line. Simultaneous control monitoring is performed to provide feedback regulation management for the target filling production line, wherein the feedback regulation methods include cascade regulation and online feedback regulation.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application is based on a target filling production line, and determines multiple assembly control targets based on process nodes, wherein the process nodes include at least automatic bottle unscrambling, filling and capping; traverses multiple assembly control targets to perform hierarchical target control decomposition, and determines the basic control law, wherein the basic control law uses the assembly control target-multiple virtual control layers-minimum control unit as the control logic layer, and the basic control law includes direct control dimensions and indirect control dimensions, and the indirect control dimension includes adjustment constraint relationships; based on the basic control law, an intelligent control unit of the servo controller is configured in a cascade control manner, and the intelligent control unit includes an inner loop control area and an outer loop control area; interactive production line control tasks, extracting valid task labels, wherein the label dimension includes at least bottle body characteristics, filling requirements, and limit requirements; identifying valid task labels, transmitting them to the intelligent control unit for control decision-making and control response, and executing automated control of the target filling production line; synchronously performing control monitoring, and performing feedback adjustment management on the target filling production line, wherein the feedback adjustment method includes cascade adjustment and online feedback adjustment. The present invention solves the technical problems in the prior art that the filling accuracy is unstable and cannot adapt to the filling processing requirements. By determining the assembly control target, hierarchical target control decomposition, configuring the intelligent control unit, interactive production line control tasks to identify effective task tags, and synchronously performing control monitoring and feedback adjustment management, the technical effect of improving the filling accuracy and meeting the filling processing requirements is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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.

[0008] Figure 1 A schematic flow chart of a servo control method based on a dispenser filling line provided in an embodiment of the present application; Figure 2 This is a flow chart of determining the basic control law in the servo control method for the dispenser filling line provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] This application provides a servo control method based on a dispenser filling line to solve the technical problems of the existing technology such as unstable filling accuracy and inability to adapt to filling processing requirements. By determining the assembly control target, hierarchical target control decomposition, configuring an intelligent control unit, interactive production line control tasks to identify effective task tags, and synchronously performing control monitoring and feedback adjustment management, the technical effect of improving filling accuracy and meeting filling processing requirements is achieved.

[0010] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0011] It should be noted that any variations of the terms "include" and "have" 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 clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0012] like Figure 1 As shown, the present application provides a servo control method based on a dispenser filling line, the method comprising: Step S100: Based on the target filling production line, multiple assembly control targets based on process nodes are determined, wherein the process nodes at least include automatic bottle unscrambling, filling and capping.

[0013] In the embodiment of the present application, a plurality of assembly control targets based on process nodes are first determined. The assembly control target refers to the specific control requirements and target settings for each major process node of the production line, which are pre-set by technical experts. In the target filling production line, at least automatic bottle sorting, filling and capping are included. Among them, automatic bottle sorting is the initial link of the production line, which refers to the use of automated equipment to sort and arrange the bottles neatly to ensure that they can enter the next filling process in the correct way; filling is the core link of the entire production line, which refers to the precise pouring of liquid into each bottle; capping is the last process node of the production line, which means that after filling is completed, the bottle cap is automatically screwed on each bottle to ensure the sealing and safety of the product.

[0014] Step S200: Traverse the multiple assembly control objectives to perform hierarchical target control decomposition and determine the basic control law, wherein the basic control law uses the assembly control objective-multiple virtual control layers-minimum control unit as the control logic layer, and the basic control law includes a direct control dimension and an indirect control dimension, and the indirect control dimension includes a regulation constraint relationship.

[0015] In an embodiment of the present application, multiple assembly control targets are traversed to perform hierarchical target control decomposition, and these assembly control targets are decomposed into multiple virtual control layers using a hierarchical control method. The virtual control layer is an intermediate control layer between the assembly control target and the minimum control unit. For example, the filling node is decomposed into a liquid flow control layer, a filling accuracy control layer, and a filling speed control layer. After the virtual control layer is determined, it is further refined into the minimum control unit. The minimum control unit is a specific, operational control task, such as adjusting the valve opening of the filling machine, adjusting the arrangement of bottles, etc. The basic control law is the principle and rule that guides the operation of these minimum control units. They are organized in a logical hierarchy of assembly control target-virtual control layer-minimum control unit to ensure the systematic and orderly nature of the control process.

[0016] Basic control laws encompass direct control and indirect regulation. Direct control involves directly adjusting specific parameters and variables within the production process. For example, sensors monitor the filling volume in real time, and a servo controller adjusts the valve opening of the filling machine to ensure the correct amount of liquid in each bottle. These direct control operations are implemented through actuators, ensuring precise and real-time control. Indirect regulation involves indirectly influencing the control effect by adjusting the production environment or related conditions. For example, adjusting the spacing of bottles on a conveyor belt to optimize filling speed and accuracy, or adjusting the temperature and humidity in the production workshop to ensure liquid stability and fluidity. Indirect regulation influences the behavior of the entire control system by altering the constraints.

[0017] The indirect control dimension includes regulatory constraints, which are restrictions and rules that must be followed during the control process. For example, to avoid spills and contamination, it is necessary to ensure that each bottle is accurately positioned and filled.

[0018] Step S300: Based on the basic control law, an intelligent control unit of a servo controller is configured in a cascade control manner, wherein the intelligent control unit includes an inner loop control area and an outer loop control area.

[0019] In the embodiment of the present application, the basic control law is first used to configure the servo controller, and the basic control law is used to ensure that each control step can be executed according to the predetermined standard.

[0020] Next, we'll configure the system using cascade control. Cascade control is a multi-level control strategy in which the output of one control loop serves as the input of another. This approach achieves more precise and stable control. Cascade control consists of an inner control loop and an outer control loop.

[0021] The intelligent control unit is the core of the servo controller and consists of an inner and outer control loop. The inner control loop handles direct control tasks. It performs high-frequency, rapid control operations to ensure that key parameters in the production process, such as valve opening and liquid flow rate in the filling machine, remain within predetermined ranges. For example, by monitoring liquid flow rate and filling accuracy in real time, the inner control loop can quickly respond to changes and make adjustments to ensure the correct amount of liquid is filled in each bottle. The outer control loop handles indirect control tasks. It performs low-frequency, slow control operations to optimize the stability and coordination of the entire production process. For example, it adjusts to changes in the production environment, such as temperature and humidity, to ensure a smooth filling process. It also adjusts the spacing of bottles to optimize filling speed and efficiency.

[0022] Step S400: Interact with the production line control task and extract valid task labels, wherein the label dimensions at least include bottle characteristics, filling requirements, and limit requirements.

[0023] In this embodiment of the present application, production line control tasks refer to specific operations and objectives that need to be completed during the production process, such as filling, bottle unscrambling, and capping. These tasks predefine various parameters and requirements required for production. Next, valid task tags are extracted through interactive production line control tasks. These tags contain key information related to the production process, including at least bottle characteristics, filling requirements, and limit requirements.

[0024] Bottle characteristics include size, shape, and material. For example, different product batches require specific bottle specifications. Filling requirements include fill volume, filling speed, and liquid type. For example, different product formulas require specific filling parameters. Limiting requirements include bottle arrangement, movement paths, and limiting conditions. For example, the production line layout and bottle movement paths are already planned during the task definition phase.

[0025] Step S500: Identify the valid task tag, transmit it to the intelligent control unit for control decision and control response, and execute automated control of the target filling production line.

[0026] In this embodiment of the present application, a tag parsing algorithm is used to analyze and identify valid task tags, parse the information in the tags, and convert it into specific control instructions. By reading the bottle characteristic tags, the bottle's size, shape, and material are identified. By reading the filling requirement tags, the filling volume, filling speed, and liquid type of each bottle are identified. By reading the limit requirement tags, the bottle's arrangement position, movement path, and limit conditions are identified.

[0027] The identified task tag information is then transmitted to the intelligent control unit via the data bus. Specifically, the bottle characteristics, filling requirements, and limit requirement tag information are encoded into a standard data format, transmitted via the data bus, and received and decoded by the intelligent control unit.

[0028] The intelligent control unit makes control decisions and responds based on the task tag information it receives. The inner control loop adjusts the filling equipment's operating parameters, such as valve opening and filling speed, based on the bottle characteristic tags. It also adjusts the filling pressure and flow rate in real time based on the filling requirement tags. The outer control loop adjusts the conveyor speed and bottle arrangement based on the limit requirement tags. Finally, the intelligent control unit translates control decisions into specific operational instructions, implementing automated control through actuators such as servo motors, sensors, and pumps.

[0029] Step S600: synchronously perform control monitoring and conduct feedback regulation management on the target filling production line, wherein the feedback regulation mode includes cascade regulation and online feedback regulation.

[0030] In this embodiment, a sensor network and monitoring system are first used to synchronously control and monitor each link of the target filling production line. The sensor network includes position sensors, flow sensors, and pressure sensors, which are used to collect key parameter data of the production process in real time.

[0031] Specifically, simultaneous control and monitoring involves the use of these sensors for real-time data collection and analysis. Position sensors monitor the position of bottles on the conveyor belt to ensure correct bottle alignment; flow sensors monitor the flow of the filling liquid to ensure that each bottle's fill volume meets the preset requirements; and pressure sensors monitor pressure during the filling process to ensure a smooth filling process. Based on real-time monitoring data, feedback control management is implemented to adjust parameters and operations during the production process. Feedback control methods include cascade control and online feedback control.

[0032] In cascade control, a multi-level control strategy is employed. The inner control loop rapidly adjusts key parameters based on real-time monitoring data. For example, if the flow sensor detects that the filling flow rate is below a preset value, the inner control loop immediately adjusts the valve opening to increase flow. The outer control loop performs global optimization and adjustments. For example, if the position sensor detects that the bottles are misaligned, the outer control loop adjusts the conveyor speed and bottle alignment to ensure that the bottles reach the filling position accurately.

[0033] Online feedback regulation relies on real-time data feedback and dynamic adjustments. First, the system feeds real-time data collected by sensors to the control center for dynamic analysis. Then, based on the results of this real-time data analysis, control parameters are dynamically adjusted. For example, if significant filling pressure fluctuations are detected, the pump's pressure output is immediately adjusted to ensure the stability of the filling process. Finally, the adjusted control parameters are converted into specific operational instructions, and the actuators implement these adjustments. For example, real-time adjustments to filling speed, valve opening, and conveyor speed are made to ensure smooth production.

[0034] Through simultaneous control monitoring and feedback regulation management, various parameters in the production process are adjusted in real time to ensure efficient production line operation and consistent product quality. The combination of cascade regulation and online feedback regulation can not only quickly respond to immediate changes in the production process, but also perform global optimization and adjustment, improving the system's response speed and control accuracy.

[0035] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the determining of the basic control law further includes: Identify the first assembly control target, perform target control decomposition and assign underlying control logic, and determine the control logic layer, wherein the minimum control unit is directly regulated; based on the control target trend of any control target of the assembly control target-multiple virtual control layers, respond to the unit regulation relationship of the minimum control unit and determine the regulation constraint relationship; based on the control logic layer and the regulation constraint relationship, determine the first basic control law.

[0036] In this embodiment of the present application, a single assembly control target is randomly selected from multiple assembly control targets as the first assembly control target. After identifying the first assembly control target, target control decomposition and underlying control logic assignment are performed to determine the control logic layer and ensure that the minimum control unit performs direct control. A hierarchical control approach is used to decompose the assembly control target into multiple virtual control layers, each representing an intermediate control target. This process ensures the gradual refinement and effective management of control targets. For example, the filling process is decomposed into a liquid flow control layer, a filling precision control layer, and a filling speed control layer. Each control layer is assigned specific operational logic. For example, the liquid flow control layer is responsible for adjusting flow sensor data, the filling precision control layer is responsible for monitoring and adjusting filling volume, and the filling speed control layer is responsible for adjusting filling speed. After decomposing the assembly control target and assigning underlying control logic, the control logic layer is determined. The control logic layer includes all virtual control layers and minimum control units. The minimum control units perform specific, actionable control tasks and directly control specific parameters in the production process. For example, the minimum control unit in the flow control layer adjusts valve opening, while the minimum control unit in the precision control layer monitors and adjusts filling volume.

[0037] After determining the control logic layer, the control constraints are determined based on the overall control objective—the trend of any control objective across multiple virtual control layers—and the unit regulation relationships that respond to the smallest control unit. Regulation constraints are the limiting conditions and rules that must be followed during the control process to ensure the stability and accuracy of each control step. For example, the target trend of the liquid flow control layer is to ensure stable flow. Valve opening is adjusted to maintain flow stability, and upper and lower valve opening limits are set to ensure flow remains within a safe range.

[0038] Based on the control logic layer and regulatory constraints, the first basic control law is ultimately determined. Basic control laws are the principles and rules that guide specific operations, ensuring the effective execution of each control step. For example, the logical layers and constraints of flow control, precision control, and speed control are integrated into an overall control strategy to determine the operating procedures for each step in the filling process, ensuring the accurate filling volume for each bottle.

[0039] Furthermore, the method provided in the application embodiment also includes: The limiting requirements include filling line limiting and bottle position limiting, and there is a degree of freedom in limiting.

[0040] In the embodiment of the present application, the limitation requirements include filling line limitation and bottle position limitation, and there is limitation freedom. Filling line limitation refers to the position and operation limitation of the entire filling production line to ensure the stability and safety of the production line operation. Filling line limitation includes limiting the position and operation range of key equipment such as filling equipment, conveyor belts, and capping machines. Bottle position limitation refers to limiting the arrangement and position of bottles on the production line to ensure that each bottle is in the correct position for filling, capping and other operations. Limitation freedom means that while meeting the limitation requirements, a certain amount of adjustment and optimization space is still retained to adapt to changes and needs in the production process. The limitation freedom sets an allowable range for the filling line limitation and bottle position limitation, such as the operating range of the equipment and the arrangement spacing of the bottles.

[0041] Furthermore, in the method provided in the embodiment of the application, the intelligent control unit includes a control decision module and a control response module, and the intelligent control unit configured with the servo controller further includes: Based on the basic control law, data-driven construction is carried out in combination with sample data to generate a control decision module; the control response module includes an inner loop control area and an outer loop control area, and the control response module is post-connected to the control decision module to generate the intelligent control unit.

[0042] In the embodiments of the present application, a data-driven construction is first performed based on the basic control law and combined with sample data. Specifically, sample data is collected from the historical operating data of the filling production line. This data includes key parameters such as bottle characteristics, filling requirements, and limit requirements. Through data preprocessing technology, the collected sample data is cleaned and standardized to remove noise and outliers to ensure data quality. Then, machine learning algorithms such as decision trees and support vector machines are used to extract key features from the preprocessed data and perform model training. This model can output corresponding control decisions based on the input feature data, thereby generating a control decision module.

[0043] The control decision module is the core of the intelligent control unit, responsible for making control decisions based on input data. For example, when receiving data on bottle characteristics and filling requirements, the control decision module calculates the optimal filling speed and valve opening to ensure the correct filling volume for each bottle.

[0044] Next, we built a control response module, which consists of an inner control loop and an outer control loop. The inner control loop is responsible for rapidly responding to immediate changes in the production process, performing high-frequency, real-time control operations. For example, the inner control loop adjusts the valve opening and filling speed of the filling machine in real time to account for changes in liquid flow during the filling process. The outer control loop is responsible for global optimization and adjustment, performing low-frequency, global control operations. For example, the outer control loop adjusts the conveyor speed and bottle arrangement based on overall production data to ensure production line efficiency and stability.

[0045] Finally, the control response module is post-connected to the control decision module to create an intelligent control unit. Through system integration and signal transmission, the output signal of the control decision module is transmitted to the control response module, which then performs corresponding operations based on the received signal. By combining the control decision module and the control response module, the intelligent control unit achieves real-time monitoring and dynamic adjustment of the filling production line, thereby improving production efficiency and product quality.

[0046] Furthermore, in the method provided in the embodiment of the application, after the data is transmitted to the intelligent control unit for control decision making, the method further includes: Acquire a cascade control strategy, configure the inner-loop control area based on a direct control strategy, and configure the outer-loop control area based on an indirect control strategy; establish a connection between the inner-loop control area and the outer-loop control area, wherein the outer-loop control area is used to perform inner-loop control adjustment.

[0047] In this embodiment, a cascade control strategy is first derived, determined through data analysis and modeling techniques. By analyzing historical production data and system behavior, a multi-level control strategy suitable for the filling production line is identified. This strategy divides the production process into multiple control levels, with the control output of each level serving as the input for the next level, thereby achieving more refined control.

[0048] Next, the inner control area is configured based on a direct control strategy. This area is responsible for rapidly responding to immediate changes in the production process and performing high-frequency, real-time control operations. A direct control strategy is a method of achieving precise control by directly adjusting key parameters in the production process. This is achieved using sensor technology and real-time control algorithms. High-precision sensors, such as flow sensors and pressure sensors, are installed at key locations such as filling machines and conveyor belts to collect data on key parameters in the production process in real time. Real-time control algorithms, such as PID control algorithms, are used to quickly process the collected data. For example, when the flow sensor detects a fluctuation in the filling flow, the real-time control algorithm immediately calculates an instruction to adjust the valve opening. It then uses actuators such as servo motors to rapidly adjust the filling machine's valve opening and filling speed to ensure the correct filling volume for each bottle.

[0049] Next, the outer control zone is configured based on the indirect control strategy. This outer control zone is responsible for global optimization and adjustment, performing low-frequency, global control operations. The indirect control strategy is a control method that indirectly influences the production process by adjusting the production environment and related conditions. This is achieved using big data analytics and machine learning algorithms. Using big data analytics, overall production data is analyzed to identify key factors affecting production efficiency and stability. Machine learning algorithms, such as decision trees and neural networks, are applied to predict potential future production process issues and implement proactive adjustments. For example, by analyzing historical data, misaligned bottles can be predicted and the conveyor belt speed can be adjusted to re-align the bottles. The outer control zone can also use environmental control systems, such as air conditioners and humidifiers, to adjust based on ambient conditions such as temperature and humidity in the production workshop. For example, if the workshop temperature fluctuates, the environmental control system adjusts the operating parameters of the filling machine to ensure the stability of the filling process.

[0050] To achieve coordinated control, a connection is established between the inner and outer control zones. This connection is achieved through data transmission and signal feedback, ensuring coordinated operation between the two. Specifically, IoT technology and real-time communication protocols are used to achieve this goal. Real-time data from the inner control zone is transmitted to the outer control zone via a data bus, enabling the outer control zone to monitor its operations in real time. Real-time communication protocols such as MQTT are used to enable rapid signal feedback between the inner and outer control zones. The outer control zone makes global adjustments based on the data received from the inner control zone and feeds back the adjustment instructions to the inner control zone.

[0051] Furthermore, in the method provided in the embodiment of the application, after obtaining the cascade control strategy, the method further includes: Identify the production line control task and determine the filling risk point, wherein the filling risk point is determined based on at least charge variability and production line safety; based on the filling risk point, determine a risk control threshold as a rigid constraint condition; based on the rigid constraint condition, perform constraint compensation on the cascade control strategy.

[0052] In an embodiment of the present application, the production line control task is first identified. The production line control task includes all operational steps and control objectives that need to be completed in the production process, such as filling, bottle unscrambling, capping, etc. Then the filling risk points are determined. Filling risk points refer to problems or hidden dangers that may arise in the production process, which may affect production efficiency, product quality or production safety. In the process of determining the filling risk points, judgments are made based on charge variability and production line safety. By performing data analysis on historical production data, common faults and abnormal conditions in the production process are identified, such as unstable filling flow and abnormal pressure. At the same time, the fault tree analysis method is used to analyze the possible faults in the production process, and each risk point and its possible causes are determined, such as filling machine failure, valve blockage and other risk points in the operating process of the filling equipment.

[0053] After identifying filling risk points, the corresponding risk control threshold is determined. The risk control threshold refers to the safety range or critical value set for key parameters during the production process. Exceeding this range may cause problems in the production process. Through statistical analysis of historical production data, the normal and abnormal ranges of each key parameter are determined. For example, the normal range of filling flow is 100-120 ml / min. Exceeding this range may pose a risk. In addition, technical experts combine production experience and industry standards to determine the control thresholds for each key parameter. Based on the determined risk control thresholds, rigid constraints are set. Rigid constraints are restrictions that must be strictly adhered to during the production process.

[0054] Finally, constraint compensation is applied to the cascade control strategy based on rigid constraints. Constraint compensation refers to real-time adjustment of the control strategy during the production process to ensure that the production process always operates within a safe range. Using feedback control and dynamic adjustment algorithm technology, key parameters in the production process are monitored in real time to promptly detect situations where rigid constraints are exceeded, and adjustments are made through the feedback control mechanism. For example, by real-time monitoring of the filling flow rate, when the flow rate approaches 120 ml / min, the valve opening is automatically adjusted to reduce the flow rate to ensure that the flow rate is within a safe range. In addition, using a dynamic adjustment algorithm, the control strategy is adjusted in real time according to actual production conditions to ensure the stability and safety of the production process. For example, by dynamically adjusting the filling speed and pressure, the production process always operates within a safe range.

[0055] Furthermore, in the method provided in the embodiment of the application, the feedback regulation method includes cascade regulation and online feedback regulation, and further includes: Control monitoring is carried out synchronously, production line monitoring data is fed back and bias control data is determined. The bias judgment satisfies the bias degree of freedom, including control vector bias and control trend bias; the outer loop control area is assisted to perform cascade feedback control and adjustment on the bias control data; if the bias control data meets the preset frequency, online feedback regulation is performed.

[0056] In the embodiments of the present application, the production process is first synchronously controlled and monitored using a sensor network and a real-time monitoring system. The sensor network includes flow sensors, pressure sensors, and temperature sensors installed at key locations such as filling machines and conveyor belts. These sensors collect data on key parameters of the production process in real time and transmit this data to a central monitoring system via a data acquisition system. The real-time monitoring data is then transmitted back to the central control system via a data transmission network. In the central control system, data analysis algorithms, such as statistical analysis and time series analysis, are used to process the returned data and determine the deviation between the actual parameters of the production process and the preset targets. This deviation data is referred to as bias control data.

[0057] In order to determine whether these bias control data are within the allowable range, statistical analysis techniques are used to set the threshold of the bias freedom. The bias freedom includes the control vector bias and the control trend bias, which represent the instantaneous deviation and trend deviation of the parameters respectively. If the actual deviation exceeds the set threshold, the bias control data is considered to not meet the bias freedom. When the bias control data exceeds the bias freedom, the auxiliary outer loop control area performs cascade feedback control adjustment. The outer loop control area responds to deviations through global optimization and adjustment, such as adjusting the conveyor belt speed or the operating parameters of the filling machine. The cascade control strategy and feedback control algorithm ensure that these adjustments can be transmitted to the inner loop control area to further refine the adjustment operations, such as adjusting the valve opening of the filling machine to ensure that the production process returns to normal.

[0058] If the bias control data exceeds the bias freedom threshold multiple times within a certain period, i.e., the preset frequency is met, the online feedback control mechanism is activated. Online feedback control uses a real-time monitoring system and online control algorithms to adjust production parameters in real time to ensure continuous and stable production processes. For example, this can be achieved by adjusting the operating parameters of the filling machine to maintain the stability of the filling process.

[0059] Furthermore, in the method provided in the embodiment of the application, the online feedback control further includes: Identify the bias control data, match it with the basic control law, and determine the pre-adjustment control target; wherein, the matching method includes: determining the matching level based on the control target magnitude of the bias control data, and performing intra-layer matching to determine the pre-adjustment control target; based on the pre-adjustment control target, determine the online feedback adjustment strategy; based on the online feedback adjustment strategy, perform strategy point positioning replacement of the cascade control strategy.

[0060] In the embodiments of the present application, after identifying the bias control data, matching is performed in conjunction with the basic control law. During this matching process, the matching level is first determined based on the control target magnitude of the bias control data. The control target magnitude refers to the magnitude and significance of parameter changes during the production process. Specifically, if the filling flow rate exhibits a large deviation, it is classified as a flow control level. Using a classification algorithm or hierarchical analysis technique, the bias control data is classified into the corresponding level.

[0061] After determining the matching level, intra-level matching is performed within that level to find the corresponding pre-setting control target. During this intra-level matching process, pattern recognition and similarity calculation techniques are used to compare current deviation data with similar patterns in historical data to determine the most appropriate pre-setting control target. For example, if historical data indicates that a specific flow deviation can be effectively resolved by adjusting valve opening, this adjustment strategy is used as the current pre-setting control target.

[0062] Based on the pre-set control objectives, an online feedback adjustment strategy is developed. This refers to a specific operational plan for real-time monitoring and adjustment of production parameters during the production process to ensure process stability and optimization. This involves using control algorithms such as PID control or adaptive control to make real-time parameter adjustments. For example, if the pre-set control objective is to adjust the filling flow rate, a real-time monitoring system continuously tracks flow data, and the control algorithm adjusts valve opening and filling speed in real time to ensure that the flow rate remains stable within the preset range. Based on the development and implementation of the online feedback adjustment strategy, the strategic points of the cascade control strategy are positioned and replaced. The cascade control strategy is a control method that uses a multi-level control mechanism to globally optimize and adjust the production process. This method involves identifying strategic points that require adjustment within the cascade control strategy and updating these points based on the online feedback adjustment strategy. For example, using model predictive control technology, the strategic points for the filling flow rate are determined within the model, and the existing control strategy is replaced with the new adjustment plan to ensure more accurate and efficient control.

[0063] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application is based on a target filling production line, and determines multiple assembly control targets based on process nodes, wherein the process nodes include at least automatic bottle unscrambling, filling and capping; traverses multiple assembly control targets to perform hierarchical target control decomposition, and determines the basic control law, wherein the basic control law uses the assembly control target-multiple virtual control layers-minimum control unit as the control logic layer, and the basic control law includes direct control dimensions and indirect control dimensions, and the indirect control dimension includes adjustment constraint relationships; based on the basic control law, an intelligent control unit of the servo controller is configured in a cascade control manner, and the intelligent control unit includes an inner loop control area and an outer loop control area; interactive production line control tasks, extracting valid task labels, wherein the label dimension includes at least bottle body characteristics, filling requirements, and limit requirements; identifying valid task labels, transmitting them to the intelligent control unit for control decision-making and control response, and executing automated control of the target filling production line; synchronously performing control monitoring, and performing feedback adjustment management on the target filling production line, wherein the feedback adjustment method includes cascade adjustment and online feedback adjustment. The present invention solves the technical problems in the prior art that the filling accuracy is unstable and cannot adapt to the filling processing requirements. By determining the assembly control target, hierarchical target control decomposition, configuring the intelligent control unit, interactive production line control tasks to identify effective task tags, and synchronously performing control monitoring and feedback adjustment management, the technical effect of improving the filling accuracy and meeting the filling processing requirements is achieved.

[0064] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0066] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A servo control method based on a dispenser filling line, characterized in that: The method comprises: Based on the target filling production line, determine multiple assembly control targets based on process nodes, where the process nodes at least include automatic bottle unscrambling, filling and capping; Traversing the multiple assembly control objectives to perform hierarchical target control decomposition and determine a basic control law, wherein the basic control law uses the assembly control objective - multiple virtual control layers - minimum control unit as a control logic layer, and the basic control law includes a direct control dimension and an indirect control dimension, and the indirect control dimension includes a regulation constraint relationship; Based on the basic control law, an intelligent control unit of the servo controller is configured in a cascade control manner, wherein the intelligent control unit includes an inner loop control area and an outer loop control area; Interactive production line control tasks, extracting valid task labels, where the label dimensions at least include bottle characteristics, filling requirements, and limit requirements; Identify the valid task tag, transmit it to the intelligent control unit for control decision and control response, and execute automated control of the target filling production line; Control monitoring is carried out simultaneously, and feedback adjustment management is performed on the target filling production line, wherein the feedback adjustment method includes cascade adjustment and online feedback adjustment.

2. The servo control method based on the dispenser filling line according to claim 1, characterized in that: The determining of the basic control law includes: Identify the control target of the first assembly, perform target control decomposition and assign underlying control logic, and determine the control logic layer, wherein the minimum control unit is directly controlled; Determine the regulation constraint relationship based on the change of the control target of the assembly control target and any control target of the plurality of virtual control layers, in response to the unit regulation relationship of the minimum control unit; Based on the control logic layer and the regulation constraint relationship, a first basic control law is determined.

3. The servo control method based on the dispenser filling line according to claim 1, characterized in that: The limiting requirements include filling line limiting and bottle position limiting, and there is a degree of freedom in limiting.

4. The servo control method based on the dispenser filling line according to claim 1, characterized in that: The intelligent control unit includes a control decision module and a control response module. The intelligent control unit configured with the servo controller includes: Based on the basic control law, combined with sample data, data-driven construction is performed to generate a control decision module; The control response module includes an inner loop control area and an outer loop control area. The control response module is connected to the control decision module to generate the intelligent control unit.

5. The servo control method based on the dispenser filling line according to claim 1, characterized in that: After being transmitted to the intelligent control unit for control decision making, it includes: Acquire a cascade control strategy, configure the inner loop control area based on a direct control strategy, and configure the outer loop control area based on an indirect control strategy; A connection is established between the inner-loop control area and the outer-loop control area, wherein the outer-loop control area is used for performing inner-loop control adjustment.

6. The servo control method based on the dispenser filling line according to claim 5, characterized in that: After obtaining the cascade control strategy, the following steps are included: Identifying the production line control tasks and determining filling risk points, wherein the filling risk points are determined based on at least charge variability and production line safety; Based on the filling risk points, a risk control threshold is determined as a rigid constraint condition; Based on the rigid constraint condition, constraint compensation is performed on the cascade control strategy.

7. The servo control method based on the dispenser filling line according to claim 5, characterized in that: Feedback regulation methods include cascade regulation and online feedback regulation, including: Simultaneously conduct control monitoring, transmit production line monitoring data back and determine bias control data. Bias determination satisfies bias freedom, including control vector bias and control trend bias. Assisting the outer loop control zone to perform cascade feedback control adjustment on the deflection control data; If the bias control data meets the preset frequency, online feedback control is performed.

8. The servo control method based on the dispenser filling line according to claim 7, characterized in that: The online feedback control comprises: Identifying the bias control data, matching it with the basic control law, and determining a pre-adjustment control target; The matching methods include: Determining a matching level based on the control target magnitude of the bias control data, and performing intra-level matching to determine a pre-adjusted control target; Based on the pre-adjustment control target, determining an online feedback adjustment strategy; Based on the online feedback regulation strategy, the strategy point positioning of the cascade control strategy is replaced.

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