Programmable intelligent control system for batch production of cosmetic bottles

Abstract

The application relates to the technical field of intelligent control, and discloses a programmable intelligent control system for batch production of cosmetic bottles, which comprises a data feedforward processing module, a space state modeling module and an execution cooperative control module. The data feedforward processing module is used for receiving product formula, material parameters and multi-layer co-extrusion layer thickness parameters, and generating bottle body heat treatment risk parameters. The space state modeling module is used for establishing a two-dimensional shift register matrix according to a bottle body entrance detection signal, calculating bottle body peripheral arrangement state parameters, and generating comprehensive heat treatment risk parameters in combination with heat treatment temperature data and conveying state data. The execution cooperative control module is used for generating execution mechanism control instructions according to the above parameters. The quality feedback correction module is used for associating and generating formula correction parameters according to quality detection data. The application generates linkage instructions through the combination of risk assessment and space modeling of multiple modules, realizes the cooperative regulation of multiple execution elements to avoid process parameter mismatch, perceives arrangement shielding and equipment aging to improve heat transfer fitting degree, and completes correction based on data binding to open the traceability link.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically a programmable intelligent control system for the mass production of cosmetic bottles. Background Technology

[0002] After mass production, cosmetic bottles typically undergo a continuous heat treatment production line, including cooling and shaping, hot air stabilization, and infrared-assisted heat treatment, to ensure the stability of bottle dimensions and physical quality. During this continuous heat treatment process, the control system needs to output control commands to multiple actuators, such as cooling fans, heating devices, and conveyor belts. Current production lines mostly employ conventional programmable control systems, which maintain the temperature and conveying status of each heat treatment zone by reading preset basic formula parameters.

[0003] Existing control systems typically treat cooling, heating, and conveying as independent units for isolated control, lacking a coordinated adjustment mechanism for multiple actuators. This makes them prone to mismatches in control parameters during process fluctuations. Furthermore, existing control logic relies heavily on the setpoint of the heat treatment zone temperature, failing to consider the obstruction effect caused by the arrangement of adjacent bottles on the conveyor belt and neglecting the impact of heater aging on heat treatment intensity. This results in insufficient fit of parameter compensation calculations. In addition, current production lines lack mechanisms for validating and tracking backend quality inspection data after processing, failing to establish a data traceability link from finished product inspection to front-end formula parameter updates. This prevents the system from achieving closed-loop parameter correction of the basic formula based on defect types.

[0004] Therefore, providing a programmable intelligent control system for the mass production of cosmetic bottles that enables multi-actuator collaboration, senses actual heat transfer intervention, and has closed-loop parameter correction capabilities is a problem that needs to be solved in this field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a programmable intelligent control system for the mass production of cosmetic bottles. This system solves the problems of parameter mismatch caused by isolated control of actuators, insufficient compensation fitting due to lack of awareness of bottle arrangement obstruction and equipment aging, and inability to perform closed-loop correction of the basic formula due to the lack of data traceability links in existing systems.

[0006] To address the above problems, the present invention provides the following technical solution: This invention provides a programmable intelligent control system for mass production of cosmetic bottles, employing the following technical solution: A programmable intelligent control system for mass production of cosmetic bottles includes: The data feedforward processing module is used to receive product formulas, material parameters, and multilayer co-extrusion layer thickness parameters, and generate bottle heat treatment risk parameters. The spatial state modeling module is used to establish a two-dimensional shift register matrix based on the bottle inlet detection signal and the conveying encoder signal, calculate the arrangement state parameters around the bottle, and combine the bottle heat treatment risk parameters, molding process parameters, heat treatment temperature data, heating power feedback data, conveying state data and actuator state data to generate bottle thermal state parameters and comprehensive heat treatment risk parameters. The execution coordination control module is used to generate control commands for the actuators based on the bottle heat treatment risk parameters, the bottle surrounding layout parameters, the bottle thermal state parameters, and the comprehensive heat treatment risk parameters. The quality feedback correction module is used to associate backend quality inspection data with product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters to generate formula correction parameters.

[0007] By adopting the above technical solution, the data feedforward processing module generates basic risk indicators based on the physical and material properties of the bottle. Combined with the spatial state modeling module's dynamic two-dimensional position tracking and thermal state estimation during the transport process, the execution and coordination control module outputs control commands in a coordinated manner. Finally, the quality feedback correction module completes the correlation and correction of the front-end and back-end data. Therefore, a closed-loop control architecture is obtained that spans the entire heat treatment process after cosmetic bottle molding.

[0008] This system overcomes the shortcomings of continuous heat treatment production lines, such as lag and isolation in parameter adjustment and inability to adapt to local thickness differences. It enables the control actions of cooling and shaping, hot air stabilization, and infrared-assisted heat treatment to match the real-time thermodynamic needs of individual bottles, significantly reducing the defect rate in batch production and improving process stability.

[0009] Furthermore, the logic for the data feedforward processing module to generate the bottle heat treatment risk parameters includes: The bottle bottom thickness, bottle body wall thickness, bottle neck wall thickness, and bottle shoulder wall thickness are extracted from the product formula. The maximum effective wall thickness and minimum effective wall thickness of the bottle body are determined based on the bottle bottom thickness, the bottle body wall thickness, the bottle neck wall thickness, and the bottle shoulder wall thickness. Then, the bottle body wall thickness difference parameter is calculated based on the maximum effective wall thickness and the minimum effective wall thickness of the bottle body. The thickness ratio of each layer of material is obtained according to the thickness parameters of the multi-layer co-extrusion layer, and the thickness deviation parameter of the multi-layer structure is calculated according to the thickness ratio of each layer of material and the set thickness ratio. Call the material shrinkage correction factor corresponding to the material parameters; The bottle wall thickness difference parameter, the material shrinkage correction coefficient, and the multi-layer structure layer thickness deviation parameter are fused based on the set wall thickness risk weight coefficient and layer thickness deviation weight coefficient to generate the bottle heat treatment risk parameter.

[0010] By adopting the above technical solution, the system can quantify the potential deformation risks caused by local wall thickness differences and material shrinkage characteristics in advance before the bottle actually enters the heat treatment process, and take into account the material distribution deviation in the multi-layer co-extrusion process, thereby providing a feedforward control basis for subsequent temperature compensation and airflow distribution.

[0011] Furthermore, the logic for determining the thickness ratio of each material layer includes: When online layer thickness detection data exists, the actual layer thickness ratio of each layer of material is used as the corresponding layer thickness ratio of each layer of material; When there is no online layer thickness detection data, the melt flow rate of each layer of material is collected. Based on the ratio of the melt flow rate of each layer of material to the total melt flow rate corresponding to the number of material layers involved in the calculation, the estimated layer thickness ratio of each layer of material is determined, and the estimated layer thickness ratio of each layer of material is used as the corresponding layer thickness ratio of each layer of material. The melt flow rate of each layer of material is collected by a flow meter; Without setting a flow meter, the melt flow rate of each layer of material is calculated by multiplying the screw speed of the extruder for the corresponding layer by the amount of material discharged per unit speed.

[0012] By adopting the above technical solution, the system's compatibility with production lines with different hardware configurations is increased. Regardless of whether the production line is equipped with an online layer thickness detection device or flow meter, the system can calculate the layer thickness ratio through the extruder screw operating parameters, ensuring that the feedforward risk assessment logic will not be interrupted due to the absence of a single sensor.

[0013] Furthermore, the logic by which the spatial state modeling module calculates the arrangement state parameters around the bottle includes: The horizontal grid coordinates of the bottle are determined based on the bottle inlet detection signal, and the vertical grid coordinates of the bottle are determined based on the conveying encoder signal. The horizontal grid coordinates and vertical grid coordinates of the bottle are written into a two-dimensional shift register matrix, and the matrix cell placeholder value corresponding to the written matrix cell is set to a preset placeholder value. At the same time, the two-dimensional shift register matrix is ​​synchronously shifted along the conveying direction according to the conveying encoder signal. Using the matrix cell corresponding to the target bottle in the two-dimensional shift register matrix as the center, the surrounding grid area is selected as the layout calculation window. Based on the different grid weights set for different grid areas in the layout calculation window and the matrix cell occupancy value in the layout calculation window, the layout state parameters around the bottle are calculated and generated.

[0014] By adopting the above technical solution, the discrete bottles on the physical conveyor belt are transformed into two-dimensional logical coordinates within the controller. By introducing a weighted arrangement calculation window, the system can quantify the cooling airflow obstruction or heat radiation resistance effects caused by adjacent bottles around the target bottle, providing spatial environmental data for correcting local fan frequency or heating power.

[0015] Furthermore, the bottle's thermal state parameters include the bottle's equivalent temperature and equivalent heat treatment intensity; the actuator status data includes real-time cooling fan frequency, hot air circulation fan frequency, and heater aging correction parameters; and the logic for the spatial state modeling module to generate comprehensive heat treatment risk parameters includes: Extract the target bottle temperature and target heat treatment intensity from the product formulation; Extract mold cooling water temperature and bottle demolding time from molding process parameters, extract zone dwell time from conveying status data, obtain heat treatment zone temperature from heat treatment temperature data, and obtain real-time cooling fan frequency from actuator status data; Based on the temperature estimation calibration coefficient, the equivalent temperature of the bottle is estimated and generated according to the mold cooling water temperature, bottle demolding time, zone dwell time, heat treatment zone temperature, real-time cooling fan frequency, and bottle periphery arrangement parameters. Based on the calibration coefficient for estimating heat treatment intensity, the active power of heating and the power adjustment ratio of heating are extracted from the heating power feedback data, and the frequency of hot air circulating fan and the aging correction parameters of heater are extracted from the actuator status data. The equivalent heat treatment intensity is estimated and generated based on the active power of heating, the power adjustment ratio of heating, the temperature of heat treatment zone, the frequency of hot air circulating fan and the aging correction parameters of heater. Based on the comprehensive risk calibration coefficient, the risk parameters of bottle heat treatment, the arrangement parameters around the bottle, the deviation between the equivalent temperature of the bottle and the target temperature of the bottle, and the deviation between the equivalent heat treatment intensity and the target heat treatment intensity are integrated to generate comprehensive heat treatment risk parameters.

[0016] By adopting the above technical solution, the system establishes a data link from the initial molding state to intervention in the heat treatment process. The estimation of the equivalent heat treatment intensity incorporates the heater aging correction parameter and the zone residence time, ensuring that the thermal state assessment accurately reflects the effective heat transfer effect experienced by the target bottle at the current workstation, thus improving the physical fit of the control calculation.

[0017] Furthermore, the actuator control commands include cooling fan frequency control commands, hot air heating intensity control commands, infrared heating power control commands, conveyor belt speed control commands, and lane-specific cycle control commands. The logic by which the execution coordination control module generates the cooling fan frequency control commands, the hot air heating intensity control commands, the infrared heating power control commands, the conveyor belt speed control commands, and the lane-specific cycle control commands includes: Extract the basic cooling fan frequency, basic hot air heating intensity, basic infrared heating power, basic conveyor belt speed, and basic lane cycle time from the product formula; Using the basic cooling fan frequency as a reference, the cooling fan frequency control command is generated by correcting the parameters of the bottle heat treatment risk, the parameters of the arrangement around the bottle, and the deviation between the equivalent temperature of the bottle and the target bottle temperature. Using the basic hot air heating intensity as a benchmark, the hot air heating intensity control command is generated by correcting the bottle heat treatment risk parameters and the deviation between the bottle equivalent temperature and the target bottle temperature. Using the base infrared heating power as a reference, the infrared heating power control command is generated based on the deviation between the equivalent heat treatment intensity and the target heat treatment intensity. Using the basic conveyor belt speed as a benchmark, and correcting it according to the comprehensive heat treatment risk parameters, the conveyor belt speed control command is generated. Using the basic lane division rhythm as a benchmark, the lane division rhythm control command is generated by correcting the parameters of the bottle's surrounding layout.

[0018] By adopting the above technical solution, the system abandons independent adjustment in a single dimension and achieves multi-variable coordinated control of the fan, heater, and conveyor drive components. In response to changes in the overall heat treatment risk, the system can automatically coordinate heating output and conveyor speed, ensuring a balance between product processing effectiveness and production line operating efficiency.

[0019] Furthermore, the logic of the collaborative control module before outputting control commands to the actuators also includes: Obtain the control variables and smoothing coefficients from the previous control cycle; Combining the control quantities and smoothing coefficients of the previous control cycle, the cooling fan frequency control command, hot air heating intensity control command, infrared heating power control command, conveyor belt speed control command and lane cycle control command are smoothed to generate smoothed control quantities. The set allowable variation of each control quantity is obtained, the variation rate of each smoothed control quantity is limited, and each control quantity after variation rate limitation is output as the corresponding actuator control command.

[0020] By adopting the above technical solution, control quantity jumps in the actuator caused by fluctuations in feedforward parameters or changes in sensor data are avoided. Smoothing and limiting processes reduce transient load impacts on electrical components, enabling the long-term stable operation of the production line.

[0021] Furthermore, the execution coordination control module also includes exception handling logic, which includes: When at least one of the following abnormal states is detected: heat treatment temperature data exceeding the safety threshold, invalid sensor data, heater failure, fan failure, conveyor blockage, and communication anomaly, the generation of actuator control commands using comprehensive heat treatment risk parameters is stopped, and safety backoff control is triggered. Safety retraction control includes at least one of the following actions: reducing heating output, turning off heating output, maintaining cooling fan frequency, increasing cooling fan frequency, reducing conveyor belt speed, stopping upstream bottle feeding, and maintaining a set lane spacing.

[0022] By adopting the above technical solution, the control system is endowed with underlying protection logic. In the event of unforeseen hardware failures or data interruptions, the system can switch to a safe shutdown or degraded operation mode to prevent production safety accidents caused by overheating.

[0023] Furthermore, before associating with backend quality inspection data, the quality feedback correction module also includes the following logic: Perform validity verification on the backend quality inspection data. Validity verification includes checking whether the backend quality inspection data is empty, whether it exceeds the equipment range, whether the inspection time matches the production time, and whether the inspection object number matches the product number in the product formula. Once the backend quality inspection data passes the validity verification and a single bottle tracking number exists, the backend quality inspection data is bound to the product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters based on the single bottle tracking number. When there is no individual bottle tracking number, the backend quality inspection data is bound to the product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters based on the production timestamp, bottle entry order, bottle inspection order, and conveying delay time.

[0024] By adopting the above technical solutions, the authenticity and correspondence of the sample data used for closed-loop correction are ensured. By being compatible with both single-bottle tracking and time-series-based batch-level binding modes, the system can trace the source of finished product quality defects to front-end control parameters on production lines with different configurations.

[0025] Furthermore, the logic for the quality feedback correction module to generate the formula correction parameters includes: Extract the target value and allowable deviation value corresponding to the backend quality inspection data; The quality deviation parameters of the continuous testing items are calculated by combining the target values ​​and allowable deviation values ​​corresponding to the continuous testing items in the backend quality inspection data; The quality deviation parameter of the leakage detection result and the appearance defect result is calculated based on the ratio between the number of non-conforming items and the number of inspections in the back-end quality inspection data. By combining the deviation direction and magnitude of the continuous detection items, the deviation direction and magnitude of the leakage detection results and the appearance defect results, the defect type is identified; The formula correction coefficient is determined based on the defect type. The formula correction parameter is then calculated by combining the formula correction coefficient, the quality deviation parameter of the continuous testing item, the quality deviation parameter of the leakage test result and the appearance defect result, and the product formula. The calculation result is then checked for upper and lower limits before generating the formula correction parameter.

[0026] By adopting the above technical solution, the system can visualize the quality inspection results as formula compensation values ​​that the system can directly access. Targeted adjustments based on defect types and upper / lower limit safety controls replace the parameter adjustment process that relies on manual experience, enabling the entire production control system to adapt to parameters between batches.

[0027] This invention provides a programmable intelligent control system for the mass production of cosmetic bottles. It has the following beneficial effects: 1. This invention receives product formula and material parameters through a data feedforward processing module to generate bottle heat treatment risk parameters, and combines them with a spatial state modeling module to generate comprehensive heat treatment risk parameters based on a two-dimensional shift register matrix. Then, the execution coordination control module uniformly generates control instructions for various actuators, realizing the coordinated linkage adjustment of multiple actuators in the continuous heat treatment process, and avoiding process parameter mismatch caused by isolated control actions.

[0028] 2. This invention generates the surrounding arrangement state parameters of the bottle through the spatial state modeling module based on the arrangement calculation window, and estimates the bottle thermal state parameters including the equivalent temperature and equivalent heat treatment intensity by combining the regional residence time, the temperature of the heat treatment area and the heater aging correction parameters. This enables the system to perceive the interference of the arrangement obstruction and equipment aging on the heat transfer process of the target bottle, and improves the calculation fitting degree of the control parameter compensation action.

[0029] 3. This invention, through a quality feedback correction module, verifies the validity of backend quality inspection data, binds it to the product formula and actuator control instructions based on the tracking number, and identifies the defect type by comprehensively considering quality deviation parameters, thereby generating formula correction parameters. This establishes a data traceability link from finished product inspection to parameter updates, enabling the production system to have the ability to calibrate the basic formula parameters in a closed loop based on defect type. Attached Figure Description

[0030] Figure 1 This is a diagram illustrating the architecture of a programmable intelligent control system for mass production of cosmetic bottles according to an embodiment of the present invention. Figure 2 This is a flowchart of a programmable intelligent control method for mass production of cosmetic bottles according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the generation of risk parameters for bottle heat treatment according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the calculation of the bottle's perimeter arrangement in an embodiment of the present invention. Figure 5 This is a flowchart illustrating the generation process of comprehensive heat treatment risk parameters according to an embodiment of the present invention. Figure 6 This is a flowchart illustrating the collaborative control process of the actuators according to an embodiment of the present invention. Figure 7 This is a flowchart illustrating the quality feedback correction process according to an embodiment of the present invention. Figure 8 This is the equivalent temperature fluctuation power spectrum of the bottle body according to an embodiment of the present invention; Figure 9 This is a logarithmic amplitude spectrum of the quality deviation in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] See attached document Figure 1 This invention provides a programmable intelligent control system for mass production of cosmetic bottles, which may include a data feedforward processing module, a spatial state modeling module, an execution collaborative control module, and a quality feedback correction module.

[0033] This invention is applied to a continuous intelligent heat treatment production line after cosmetic bottles are formed. After being output from blow molding equipment or multi-layer co-extrusion blow molding equipment, cosmetic bottles enter the continuous intelligent heat treatment production line and successively undergo processing steps such as cooling and shaping, hot air stabilization, infrared-assisted heat treatment, slow cooling, secondary cooling, and post-processing inspection.

[0034] This invention uses a PLC or PAC controller as the programmable control core. The PLC or PAC controller is connected to a human-machine interface (HMI), a molding equipment communication interface, a bottle position detection interface, a heat treatment status detection interface, an actuator control interface, and a quality inspection interface. The HMI is used to input product formulas and display operating status; the molding equipment communication interface is used to receive molding process parameters; the bottle position detection interface is used to receive bottle entry signals and conveyor displacement signals; the heat treatment status detection interface is used to receive temperature and power feedback data; the actuator control interface is used to output cooling, heating, conveying, and channel control commands; and the quality inspection interface is used to receive backend inspection data.

[0035] The data feedforward processing module receives product formulations, material parameters, bottle type parameters, molding process parameters, and multilayer co-extrusion layer thickness parameters. Based on differences in bottle wall thickness, material shrinkage characteristics, and multilayer structural parameters, it generates bottle heat treatment risk parameters. The product formulation includes product number, bottle type, material type, bottle weight, bottom thickness, body wall thickness, neck wall thickness, shoulder wall thickness, mold number, basic cooling parameters, basic heat treatment parameters, and basic conveying parameters. For multilayer co-extruded cosmetic bottles, the product formulation also includes the specified proportions of structural layers, adhesive layers, barrier layers, or functional layers. The bottle heat treatment risk parameters output by the data feedforward processing module are used to form the basis for feedforward control related to the product structure and material properties.

[0036] The spatial state modeling module is used to establish a two-dimensional shift register matrix corresponding to the conveying plane within the PLC or PAC controller based on the bottle inlet detection signal and the conveyor encoder signal. This two-dimensional shift register matrix records the positional changes of the bottle in the width and direction of the conveyor belt. Based on this matrix, the module calculates the surrounding arrangement of the bottle and, combined with heat treatment temperature data, heating power feedback data, conveying status data, and actuator status data, generates bottle spatial arrangement parameters, bottle thermal state parameters, and comprehensive heat treatment risk parameters.

[0037] The execution coordination control module receives bottle heat treatment risk parameters from the data feedforward processing module, as well as bottle spatial arrangement parameters, bottle thermal state parameters, and comprehensive heat treatment risk parameters from the spatial state modeling module. It then generates control commands for cooling fan frequency, hot air heating intensity, infrared heating power, conveyor belt speed, and lane switching cycle time. Based on differences in product structure, materials, arrangement, and heat treatment status, the execution coordination control module coordinates the cooling, heating, conveying, and lane switching actions, and performs amplitude limiting, filtering, slope limiting, hysteresis handling, and anomaly backoff handling on the control commands.

[0038] The quality feedback correction module receives backend quality inspection data and correlates it with the current batch's product formula, molding process parameters, bottle space layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters. Backend quality inspection data includes bottle weight, bottle mouth roundness, bottle body ellipticity, bottle bottom concavity, bottle shoulder deformation, bottle height, bottle outer diameter, leakage detection results, appearance defect results, and layer thickness detection results. The quality feedback correction module generates formula correction parameters based on the type of detection deviation. These parameters, after confirmation via the human-machine interface, are written into the corresponding product formula for subsequent batch production control.

[0039] The data feedforward processing module, spatial state modeling module, execution collaborative control module, and quality feedback correction module form a data processing link and a control execution link within the PLC or PAC controller. The data feedforward processing module generates the feedforward control basis, the spatial state modeling module generates the dynamic control basis, the execution collaborative control module outputs execution control commands based on the feedforward control basis and the dynamic control basis, and the quality feedback correction module corrects the product formula and control parameters based on the backend quality inspection results.

[0040] Through this system architecture, the present invention integrates product formula data, molding process data, bottle position data, heat treatment status data, actuator status data, and quality inspection data into the same programmable control link, enabling the continuous intelligent heat treatment production line to coordinate and control the cooling and shaping, hot air stabilization, infrared-assisted heat treatment, slow cooling, secondary cooling, and back-end quality feedback processes according to the differences in the structure, materials, spatial arrangement, and heat treatment status of cosmetic bottles.

[0041] See attached document Figure 2 This invention provides a programmable intelligent control method for mass production of cosmetic bottles, comprising the following steps: The S100, PLC, or PAC controller reads the product formula, material parameters, bottle type parameters, molding process parameters, and multi-layer co-extrusion layer thickness parameters for the current batch. The data feedforward processing module generates bottle heat treatment risk parameters based on bottle wall thickness differences, material shrinkage characteristics, and multi-layer structure parameters. After the S200 cosmetic bottles enter the continuous intelligent heat treatment production line, the spatial state modeling module establishes a two-dimensional shift register matrix based on the bottle inlet detection signal and the conveyor encoder signal, and calculates the arrangement state around the bottle based on the two-dimensional shift register matrix. The S300 spatial state modeling module generates bottle thermal state parameters and comprehensive heat treatment risk parameters based on heat treatment temperature data, heating power feedback data, conveying status data, actuator status data, and the surrounding arrangement of the bottle. S400, the execution coordination control module generates control commands for cooling fan frequency, hot air heating intensity, infrared heating power, conveyor belt speed and lane cycle time based on bottle heat treatment risk parameters, bottle space layout parameters, bottle thermal state parameters and comprehensive heat treatment risk parameters, and outputs the control commands to the corresponding actuators; The S500 quality feedback correction module receives backend quality inspection data and associates it with the current batch's product formula, molding process parameters, bottle space layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters to generate formula correction parameters for subsequent batch production control.

[0042] The steps of the programmable intelligent control method of the present invention will be described in detail below.

[0043] See attached document Figure 3 Before cosmetic bottles enter the continuous intelligent heat treatment production line, the PLC or PAC controller completes the reading, verification and feedforward calculation of the current batch production data, and converts the product formula, material characteristics, molding process status and multi-layer structure status into bottle heat treatment risk parameters that can be called upon by subsequent control.

[0044] The S101, PLC, or PAC controller receives the current batch product number input from the human-machine interface and uses this product number as an index to read the current batch product formula from the formula database. The product formula includes bottle type, material type, bottle weight, bottom thickness, body wall thickness, neck wall thickness, shoulder wall thickness, mold number, basic cooling parameters, basic heat treatment parameters, and basic conveying parameters. Basic cooling parameters include basic cooling fan frequency, basic cooling time, and basic cooling zone number. Basic heat treatment parameters include basic hot air temperature, basic infrared heating power, basic slow cooling time, and basic secondary cooling time. Basic conveying parameters include basic conveyor belt speed, basic lane interval rate, and basic bottle spacing.

[0045] The PLC or PAC controller performs data verification on the read product formula. Data verification includes verifying the existence of the product number, the matching of material type and mold number, the bottle weight range, the bottle bottom thickness range, the bottle body wall thickness range, the bottle neck wall thickness range, the bottle shoulder wall thickness range, the range of basic cooling parameters, the range of basic heat treatment parameters, and the range of basic conveying parameters. If any parameter exceeds the corresponding allowable range, the PLC or PAC controller outputs a formula error message on the human-machine interface and stops feedforward calculation.

[0046] The S102, PLC, or PAC controller collects the molding process parameters for the current batch through the molding equipment's communication interface. These parameters include extruder screw speed, barrel temperature zone, die temperature zone, blowing pressure, holding time, mold cooling water temperature, mold cooling water flow rate, and bottle demolding time. These molding process parameters characterize the basic thermal state of the cosmetic bottle when it leaves the molding die and serve as a basis for correcting bottle heat treatment risk parameters.

[0047] When the current batch of products consists of multi-layer co-extruded cosmetic bottles, the data feedforward processing module reads the set layer thickness ratios of the structural layer, adhesive layer, barrier layer, or functional layer, and acquires the extruder speed, extruder pressure, or historical layer thickness detection data for each layer. If online layer thickness detection data is available, the first... Layer thickness ratio Determined by online layer thickness detection data; if online layer thickness detection data is unavailable, the first... Layer thickness ratio It is determined by the melt flow rate of each layer of material.

[0048] When online layer thickness detection data is unavailable, the total melt flow rate of the material layer involved in the calculation Calculate using the following formula: ; No. Layer thickness ratio Calculate using the following formula: ; in, The total melt flow rate of the material layer involved in the calculation. For the first Layer thickness ratio of the material For the first Melt flow rate of layer material The number of material layers involved in the calculation. This is the material layer number. The melt flow rate of the layer material is collected by a flow meter, or by the first... The output is obtained by multiplying the screw speed of the layered extruder by the output per unit speed. The output per unit speed is determined during the equipment commissioning phase and stored in the formula database.

[0049] S103. The data feedforward processing module determines the maximum and minimum effective wall thickness of the bottle body based on the bottle bottom thickness, bottle body wall thickness, bottle neck wall thickness, and bottle shoulder wall thickness in the current batch product formula. The maximum effective wall thickness is the maximum value among the bottle bottom thickness, bottle body wall thickness, bottle neck wall thickness, and bottle shoulder wall thickness, while the minimum effective wall thickness is the minimum value among these three values. Bottle neck chamfers, flash, gate residue, and local decorative protrusions are not included in the calculation of the maximum and minimum effective wall thickness of the bottle body.

[0050] Bottle wall thickness variation parameters Calculate using the following formula: ; in, This is a parameter representing the difference in bottle wall thickness. This is the maximum effective wall thickness of the bottle. This represents the minimum effective wall thickness of the bottle. Bottle wall thickness variation parameters. This serves as a basis for characterizing the shrinkage differences in the current batch of cosmetic bottles caused by variations in local thickness during the cooling, shaping, and heat stabilization processes.

[0051] The data feedforward processing module retrieves the material shrinkage correction factor based on the material type in the current batch product formulation. The material shrinkage correction factor corresponds to the dimensional shrinkage characteristics of the material during the cooling process after molding. HDPE, PP, PET, PETG, EVOH composites, or other plastic materials each correspond to different material shrinkage correction factors. The material shrinkage correction factor is determined from material supply data, sample trial production data, or historical production data and stored in the formulation database.

[0052] S104. When the current batch of products consists of multi-layer co-extruded cosmetic bottles, the data feedforward processing module calculates the multi-layer structure thickness deviation parameter based on the layer thickness ratio of each material and the set layer thickness ratio of each material. When the current batch of products consists of a single-layer structure, the multi-layer structure thickness deviation parameter is set to zero.

[0053] Multilayer structure layer thickness deviation parameters Calculate using the following formula: ; in, This refers to the layer thickness deviation parameter for multi-layer structures. For the first Layer thickness ratio of the material For the first The layer material sets the layer thickness ratio. The number of material layers involved in the calculation. This is the material layer number.

[0054] The data feedforward processing module will process the bottle wall thickness difference parameter. Material shrinkage correction factor and multi-layer structure thickness deviation parameter The parameters are then integrated to generate bottle heat treatment risk parameters. These parameters indicate the degree of parameter compensation required for the current batch of cosmetic bottles during cooling and shaping, hot air stabilization, infrared-assisted heat treatment, slow cooling, and secondary cooling.

[0055] Bottle heat treatment risk parameters Calculate using the following formula: ; in, For the risk parameters of heat treatment of the bottle body, This is a parameter representing the difference in bottle wall thickness. This is the material shrinkage correction factor. This refers to the layer thickness deviation parameter for multi-layer structures. This is the wall thickness risk weighting coefficient. This refers to the layer thickness deviation weighting coefficient. The wall thickness risk weighting coefficient and the layer thickness deviation weighting coefficient are calibrated from sample trial data or historical production data and then incorporated into the product formulation.

[0056] S105, PLC, or PAC controllers are used based on bottle heat treatment risk parameters. Determine the heat treatment risk level for the current batch of products. The PLC or PAC controller will input the bottle heat treatment risk parameters. Compare with the first, second, and third risk thresholds. Bottle heat treatment risk parameters. When the risk level falls below the first risk threshold, the current batch of products is classified as Level 1 risk; bottle heat treatment risk parameters. When the risk level is not lower than the first risk threshold but lower than the second risk threshold, the current batch of products is classified as Level 2 risk; bottle heat treatment risk parameters When the risk level is not lower than the second risk threshold but lower than the third risk threshold, the current batch of products is classified as Level 3 risk; bottle heat treatment risk parameters If the risk level is not lower than the third risk threshold, the current batch of products is classified as level four risk.

[0057] The PLC or PAC controller will input the risk parameters of the bottle's heat treatment. Heat treatment risk level, bottle wall thickness variation parameters Material shrinkage correction factor and multi-layer structure thickness deviation parameters The data is written to the data register of the PLC or PAC controller and a corresponding relationship is established with the current batch product number, mold number, and material batch number. The data in the data register serves as feedforward input for bottle space tracking, comprehensive heat treatment risk modeling, and multi-actuator collaborative control.

[0058] Through this process, the product formula, molding process parameters, and multi-layer structure parameters of the current batch of cosmetic bottles are converted into bottle heat treatment risk parameters that can be accessed by a PLC or PAC controller. This establishes a correspondence between subsequent cooling, heating, conveying, and channel control and the bottle wall thickness structure, material shrinkage characteristics, and multi-layer structure deviations. Communication with the molding equipment, reading from the formula database, writing to the data register area, and human-machine interface alarms are standard implementation methods in the field of industrial control. Those skilled in the art can configure these according to the PLC or PAC controller model and the production line communication protocol, and will not be elaborated upon here.

[0059] See attached document Figure 4 Risk parameters of heat treatment of the bottle body for the current batch of cosmetic bottles Once formed, the PLC or PAC controller needs to determine the conveying position and surrounding arrangement of each bottle in the continuous intelligent heat treatment production line, so that subsequent heat treatment state estimation and actuator coordinated control have a corresponding spatial basis. The position of the bottle on the conveyor belt is determined by the inlet detection signal and the conveyor encoder signal, and the arrangement relationship between the bottles is recorded and updated by the two-dimensional shift register matrix inside the PLC or PAC controller.

[0060] S201. When cosmetic bottles are output from the forming equipment and enter the continuous intelligent heat treatment production line, the spatial state modeling module receives the bottle entry detection signal. The bottle entry detection signal is output by a photoelectric detection unit, a light curtain detection unit, a vision detection unit, or a column position detection unit. When using a multi-column guide conveyor structure, each conveyor column is equipped with a corresponding photoelectric detection unit or column position detection unit, and the detection channel number corresponds to the column position in the conveyor belt width direction. When using a free-arrangement conveyor structure, the light curtain detection unit or vision detection unit outputs the bottle's position data in the conveyor belt width direction.

[0061] After receiving the bottle inlet detection signal, the PLC or PAC controller records the trigger state of the bottle entering the continuous intelligent heat treatment production line and binds the trigger state to the current encoder pulse count value. The bottle inlet detection signal is used to determine whether the bottle has entered the conveyor line, and the encoder pulse count value is used to determine the displacement of the bottle after entering the conveyor line.

[0062] S202. The spatial state modeling module determines the horizontal grid coordinates of the bottle based on the inlet detection signal and the vertical grid coordinates of the bottle based on the conveyor encoder signal. When a multi-column guide conveyor structure is used, the horizontal grid coordinates of the bottle are determined by the detection channel number; when light curtain detection or visual detection is used, the horizontal grid coordinates of the bottle are obtained by dividing the detected center position of the bottle by the horizontal grid width and then rounding down.

[0063] Bottle body vertical grid coordinates Calculate using the following formula: ; in, The vertical grid coordinates of the bottle body The number of encoder pulses accumulated after the bottle enters. This represents the number of encoder pulses corresponding to a single vertical grid. The parameters are determined based on the conveyor belt encoder resolution, conveyor roller diameter, and preset longitudinal grid length, and written into the parameter area of ​​the PLC or PAC controller.

[0064] The horizontal grid coordinates of the bottle are as follows When using a multi-column guide conveyor structure, This equals the detection channel number that triggered the entrance detection signal. When using light curtain detection or visual detection, It is calculated based on the detection position of the bottle center in the width direction of the conveyor belt and the width of the transverse grid.

[0065] S203, the spatial state modeling module establishes a two-dimensional shift register matrix inside the PLC or PAC controller, and sets the horizontal grid coordinates of the bottle. and bottle body vertical grid coordinates Write to the two-dimensional shift register matrix. Each matrix cell in the two-dimensional shift register matrix corresponds to a grid area in the conveyor belt plane. The lateral dimension of the grid area is set according to the conveyor column spacing, the maximum outer diameter of the bottle, or the visual recognition accuracy, and the longitudinal dimension of the grid area is set according to the minimum allowable spacing of the bottles, the conveyor speed, and the encoder resolution.

[0066] The spatial state modeling module will use the horizontal grid coordinates of the bottle. and bottle body vertical grid coordinates Write to the two-dimensional shift register matrix and set the corresponding matrix cell placeholder value. Set to 1. Matrix cells not written to the bottle have a placeholder value of 0. The horizontal grid coordinates of the bottle in the two-dimensional shift register matrix and bottle body vertical grid coordinates The corresponding matrix cell placeholder value, The coordinates of the bottle's horizontal grid are... The coordinates are the vertical grid coordinates of the bottle.

[0067] The PLC or PAC controller synchronously shifts the two-dimensional shift register matrix based on encoder pulses. Whenever the accumulated encoder pulse value reaches the number of encoder pulses corresponding to a single vertical grid, the two-dimensional shift register matrix moves one vertical grid along the conveyor direction. After the bottle leaves the continuous intelligent heat treatment production line or enters the back-end inspection area, the corresponding matrix cell's position value is cleared to zero. In this way, the logical position in the two-dimensional shift register matrix corresponds to its physical position on the conveyor belt.

[0068] S204. When the target bottle moves to the control position of the cooling and shaping area, hot air stabilization area, infrared-assisted heat treatment area, slow cooling area, or secondary cooling area, the spatial state modeling module selects the surrounding grid area as the layout calculation window, centered on the matrix unit corresponding to the target bottle. The layout calculation window includes the area in front of the target bottle, the area behind it, the area to its left, the area to its right, and the upwind area related to the current wind direction. The horizontal width of the layout calculation window is set according to the number of conveyor columns and the maximum outer diameter of the bottle, while the vertical length of the layout calculation window is set according to the bottle spacing, the effective range of the air duct, and the length of the heat treatment area.

[0069] Different grid regions within the layout calculation window correspond to different weights. Grid regions closer to the target bottle have a greater weight than those farther away; grid regions on the upwind side have a greater weight than those on the downwind side; and grid regions coinciding with the main airflow direction have a greater weight than those deviating from it. These weight values ​​are set during equipment commissioning based on the airflow layout, bottle outer diameter, conveyor column spacing, and cooling airflow range, and are then entered into the parameter area of ​​the PLC or PAC controller.

[0070] S205. The spatial state modeling module calculates the arrangement state parameters around the bottle body based on the matrix cell occupancy values ​​and corresponding weights within the arrangement calculation window. These arrangement state parameters represent the degree to which adjacent bottles within the target bottle's surrounding area occupy space for cooling airflow, hot air circulation, or infrared-assisted heat treatment.

[0071] Bottle perimeter arrangement parameters Calculate using the following formula: ; in, These are the parameters representing the arrangement around the bottle. For the first Each grid weight, The first in the two-dimensional shift register matrix The matrix cell place value corresponding to each grid. For the first The horizontal grid coordinates of each grid. For the first The vertical grid coordinates of each grid. To determine the number of grid cells participating in the calculation within the calculation window, This is used to arrange the grid numbers that participate in the calculation within the calculation window.

[0072] When the parameters of the surrounding arrangement of the bottle body As the target bottle increases, the number of grid cells occupied by adjacent bottles around the target bottle increases, or adjacent bottles are located in positions with higher weights. The spatial state modeling module arranges state parameters around the bottle. The data is written to the PLC or PAC controller's data register and correlated with the heat treatment area where the target bottle is located, the current batch product number, and the bottle's entry order. This establishes a correspondence between the bottle's surrounding layout parameters and the data. It serves as an input for heat treatment condition estimation and multi-actuator coordinated control.

[0073] Through this process, the bottle inlet detection signal and the conveyor encoder signal are converted into bottle position data in a two-dimensional shift register matrix. The bottle position data in the two-dimensional shift register matrix is ​​further converted into bottle perimeter arrangement state parameters. This enables the PLC or PAC controller to identify the local arrangement of bottles during continuous conveying. Photoelectric detection, light curtain detection, vision detection, encoder counting, and matrix register updates are common implementation methods in the field of industrial control. Those skilled in the art can configure them according to the production line structure and the PLC or PAC controller model, and will not be elaborated further here.

[0074] See attached document Figure 5 The spatial state modeling module obtains the arrangement state parameters around the bottle. Subsequently, temperature, power, and conveying status data in the continuous intelligent heat treatment production line are collected, and the arrangement status around the bottle is associated with the heat treatment process parameters, so that the thermal status of the target bottle in the corresponding heat treatment area can be identified and called by the PLC or PAC controller.

[0075] The S301, PLC, or PAC controller collects temperature data from the cooling and shaping zone, hot air stabilization zone, infrared-assisted heat treatment zone, slow cooling zone, and secondary cooling zone via a heat treatment status detection interface. Temperature data includes ambient temperature, hot air temperature, or channel temperature. Each heat treatment zone is assigned a corresponding zone number, and temperature data is written to the PLC or PAC controller's data register according to the zone number, establishing a correspondence between this data and the current position of the target bottle in the two-dimensional shift register matrix.

[0076] When a target bottle enters a heat treatment zone, the spatial state modeling module reads the zone number corresponding to the current position of the target bottle and retrieves the temperature data under that zone number. For cases where multiple temperature detection points are arranged within the same heat treatment zone, the spatial state modeling module uses the average value, median value, or the detection value corresponding to the bottle's location in the channel as the temperature of the heat treatment zone. The specific value selection method is determined by the product formula or equipment debugging parameters.

[0077] The S302, PLC, or PAC controller collects the voltage, current, heating active power, heating power adjustment ratio, and cumulative energy consumption of the infrared heater, hot air heater, or ceramic heater through a heat treatment power feedback device. Heating active power characterizes the electrical power currently input by the heater to the heat treatment area; the heating power adjustment ratio characterizes the output ratio of the power regulator or power controller to the heater; and cumulative energy consumption records the energy input status during the current batch of heat treatment.

[0078] The PLC or PAC controller synchronously collects conveyor status data. This data includes real-time conveyor belt speed, the time the bottle enters each heat treatment zone, the time the bottle leaves each heat treatment zone, zone dwell time, and lane status. Zone dwell time can be obtained from the difference between the entry and exit times, or it can be calculated from the effective length of the heat treatment zone and the real-time conveyor belt speed.

[0079] Time spent in the area Calculate using the following formula: ; in, For the time spent in the area, The effective length of the heat treatment zone. Real-time conveyor belt speed. Area dwell time. Used to characterize the duration for which a target bottle is subjected to cooling, heating, or slow cooling in the corresponding heat treatment area.

[0080] S303, the spatial state modeling module is based on the mold cooling water temperature, heat treatment zone temperature, real-time cooling fan frequency, bottle demolding time, and zone dwell time. Parameters of the arrangement around the bottle body Estimate the equivalent temperature of the bottle. The equivalent temperature of the bottle is used to represent the temperature state of the target bottle in the corresponding heat treatment region for control calculations.

[0081] Bottle equivalent temperature Calculate using the following formula: ; in, The equivalent temperature of the bottle body. The temperature of the mold cooling water. Temperature of the heat treatment zone. For real-time cooling fan frequency, This refers to the bottle demolding time. For the time spent in the area, These are the parameters representing the arrangement around the bottle. , , , , , and The calibration factor is used for temperature estimation.

[0082] Temperature estimation calibration coefficients are determined from sample trial data or historical production data and stored in the product formula or controller parameter area. For the cooling shaping area and secondary cooling area, the real-time cooling fan frequency... and time spent in the area Equivalent temperature of the bottle The direction of influence is determined by the temperature estimation calibration coefficient; for the hot air stable region and the infrared-assisted heat treatment region, the temperature of the heat treatment region... Heating status and zone dwell time Equivalent temperature of the bottle The direction of influence is determined by the temperature estimation calibration coefficient. In this way, the same formula can be adapted to temperature estimation for different heat treatment zones.

[0083] S304, the spatial state modeling module estimates the equivalent heat treatment intensity based on the heating active power, heating power adjustment ratio, heat treatment zone temperature, hot air circulation fan frequency, and heater aging correction parameters. The equivalent heat treatment intensity is used to represent the actual degree of thermal effect formed by the hot air heater, infrared heater, or ceramic heater when the target bottle passes through the corresponding heat treatment zone.

[0084] Equivalent heat treatment strength Calculate using the following formula: ; in, For equivalent heat treatment strength, For heating active power, For heating power adjustment ratio, Temperature of the heat treatment zone For the frequency of the hot air circulation fan, Correction parameters for heater aging. , , , , and To estimate the calibration factor for heat treatment intensity.

[0085] Heater aging correction parameters The calibration coefficient for estimating heat treatment intensity is determined based on the heater's cumulative operating time, the change in heating active power under the same power adjustment ratio, or equipment maintenance records. It is determined from equipment commissioning data, sample trial production data, or historical production data, and stored in the product formula or controller parameter area.

[0086] S305, the spatial state modeling module will incorporate bottle heat treatment risk parameters. Bottle perimeter arrangement parameters Bottle equivalent temperature and equivalent heat treatment strength The parameters are then integrated to generate comprehensive heat treatment risk parameters. These parameters indicate the degree to which the target bottle requires cooling, heating, conveying, or channel compensation within the current heat treatment area.

[0087] Comprehensive heat treatment risk parameters Calculate using the following formula: ; in, To comprehensively assess the risk parameters of heat treatment, For the risk parameters of heat treatment of the bottle body, These are the parameters representing the arrangement around the bottle. The target bottle temperature, The equivalent temperature of the bottle body. To achieve the target heat treatment strength, For equivalent heat treatment strength, , , , and This is the comprehensive risk calibration coefficient.

[0088] Target bottle temperature and target heat treatment strength The risk assessment coefficient is determined by the current batch product formula and stored separately in the cooling and shaping area, hot air stabilization area, infrared-assisted heat treatment area, slow cooling area, and secondary cooling area. The comprehensive risk assessment coefficient is determined by sample trial data or historical production data and stored in the product formula or controller parameter area.

[0089] The spatial state modeling module will integrate heat treatment risk parameters. Write the data to the PLC or PAC controller's data register area, and include the target bottle entry sequence, current batch product number, current heat treatment zone number, and bottle perimeter arrangement parameters. Establish corresponding relationships. Integrate heat treatment risk parameters. This serves as the input for the collaborative control module to generate control commands for cooling fan frequency, hot air heating intensity, infrared heating power, conveyor belt speed, and lane timing.

[0090] Through this process, heat treatment temperature data, heating power feedback data, conveying status data, and the arrangement status around the bottle are converted into the equivalent temperature of the bottle. Equivalent heat treatment strength and comprehensive heat treatment risk parameters This enables the PLC or PAC controller to perform control calculations based on the actual conveying position, local arrangement, and heat treatment process status of the target bottle. Temperature acquisition, power acquisition, encoder timing, conveying speed reading, and data register writing are standard implementation methods in the field of industrial control. Those skilled in the art can configure them according to the PLC or PAC controller model and the production line communication protocol, and will not be elaborated further here.

[0091] See attached document Figure 6 The equivalent temperature of the bottle is generated in the spatial state modeling module. Equivalent heat treatment strength and comprehensive heat treatment risk parameters Subsequently, the collaborative control module converts the feedforward parameters and dynamic state parameters into actuator control commands. This process is based on the fundamental parameters in the product formulation, combined with the target bottle's heat treatment risk parameters within the current heat treatment zone. Bottle perimeter arrangement parameters Bottle equivalent temperature and comprehensive heat treatment risk parameters It performs unified calculations for cooling, heating, conveying, and channeling operations.

[0092] S401, The collaborative control module executes the risk parameters of the bottle's heat treatment based on the bottle body's risk parameters. Bottle perimeter arrangement parameters Bottle equivalent temperature and target bottle temperature Generate the cooling fan frequency. The cooling fan frequency is adjusted based on the base cooling fan frequency. Bottle heat treatment risk parameters. At higher speeds, the cooling fan frequency is increased or decreased according to the calibration coefficient; the arrangement parameters around the bottle body... At higher temperatures, the cooling fan frequency is increased according to the calibration factor to compensate for the obstruction of cooling airflow by adjacent bottles; the equivalent temperature of the bottles... Deviation from target bottle temperature At that time, the frequency of the cooling fan is corrected according to the temperature deviation.

[0093] Cooling fan frequency Calculate using the following formula: ; in, For the frequency of the cooling fan, Based on the frequency of the cooling fan, For the risk parameters of heat treatment of the bottle body, These are the parameters representing the arrangement around the bottle. The equivalent temperature of the bottle body. The target bottle temperature, This is the lower limit of the cooling fan frequency. This is the upper limit of the cooling fan frequency. This refers to the cooling calibration coefficient corresponding to the risk parameters of the bottle's heat treatment. This refers to the cooling calibration coefficient corresponding to the arrangement parameters around the bottle body. This represents the cooling calibration coefficient corresponding to the temperature deviation. Basic cooling fan frequency. Lower limit of cooling fan frequency upper limit of cooling fan frequency The cooling calibration coefficients are stored in the product formula or controller parameter area.

[0094] S402, The collaborative control module executes the risk parameters of the bottle's heat treatment based on the bottle body's risk parameters. Multi-layer structure thickness deviation parameters Bottle equivalent temperature and target bottle temperature Generate hot air heating intensity. The hot air heating intensity is adjusted based on the baseline hot air heating intensity. Bottle heat treatment risk parameters. The thickness deviation parameter of a multilayer structure is used to reflect the difference in cooling shrinkage between thick-walled and thin-walled regions. Used to reflect the impact of material ratio deviations in multilayer co-extrusion structures on thermal stabilization treatment, equivalent temperature of the bottle body. Temperature of the target bottle The deviation between them is used to reflect the temperature compensation requirements within the current heat treatment area.

[0095] Hot air heating intensity Calculate using the following formula: ; in, For hot air heating intensity, Based on the basic hot air heating intensity, For the risk parameters of heat treatment of the bottle body, This refers to the layer thickness deviation parameter for multi-layer structures. The target bottle temperature, The equivalent temperature of the bottle body. This is the lower limit of hot air heating intensity. This represents the upper limit of hot air heating intensity. This refers to the hot air calibration coefficient corresponding to the risk parameters of the bottle's heat treatment. This refers to the hot air calibration coefficient corresponding to the layer thickness deviation parameter of the multi-layer structure. This represents the hot air calibration coefficient corresponding to the temperature deviation. Hot air heating intensity. It can correspond to the output power of the hot air heater, the set value of the hot air temperature, or the power adjustment ratio of the hot air heater. The specific correspondence is determined by the equipment parameter table.

[0096] S403, The execution coordination control module is based on the target heat treatment intensity. and equivalent heat treatment strength The deviation between these parameters generates the infrared heating power. The infrared heating power is corrected based on the base infrared heating power. Equivalent heat treatment intensity. Below the target heat treatment strength At that time, the infrared heating power increases according to the calibration factor; equivalent heat treatment intensity Exceeding the target heat treatment strength At this time, the infrared heating power is reduced according to the calibration factor. This control method is used to ensure that the degree of heat treatment within the infrared-assisted heat treatment area is consistent with the target heat treatment intensity in the current batch product formulation. Correspondingly.

[0097] Infrared heating power Calculate using the following formula: ; in, Infrared heating power, Based on the basic infrared heating power, To achieve the target heat treatment strength, For equivalent heat treatment strength, This is the lower limit of infrared heating power. This is the upper limit of infrared heating power. This represents the infrared calibration coefficient corresponding to the heat treatment intensity deviation. Lower limit of infrared heating power. and upper limit of infrared heating power The parameters are determined based on the rated power of the infrared heater, the heat resistance range of the bottle material, and the upper limit of the temperature of the heat treatment area, and are written into the controller parameter area.

[0098] S404, The collaborative control module executes based on the comprehensive heat treatment risk parameters. Parameters of the arrangement around the bottle body Generate conveyor belt speed and track cycle time. The conveyor belt speed is adjusted based on the base conveyor belt speed. Integrate heat treatment risk parameters. At higher speeds, the conveyor belt speed is reduced according to the calibration factor, increasing the residence time of the bottle within the corresponding heat treatment area. Bottle perimeter arrangement parameters. At higher speeds, the cycle time is adjusted according to the calibration coefficient to match the spacing between bottles entering the heat treatment area with the requirements of cooling, hot air, or infrared-assisted heat treatment.

[0099] Conveyor belt speed Calculate using the following formula: ; in, For conveyor belt speed, Based on the basic conveyor belt speed, To comprehensively assess the risk parameters of heat treatment, This is the lower limit of the conveyor belt speed. This is the upper limit of the conveyor belt speed. This refers to the transport calibration coefficient corresponding to the comprehensive heat treatment risk parameters.

[0100] Separation rhythm Calculate using the following formula: ; in, For the lane separation rhythm, Based on the basic lane division rhythm, These are the parameters representing the arrangement around the bottle. This is the lower limit of the lane splitting time. This represents the upper limit of the lane division rhythm. Lane calibration coefficients corresponding to the bottle's perimeter layout parameters. Lane cycle time. Used to control the operating cycle of lane dividers, bottle-dispensing mechanisms, or beat distribution mechanisms.

[0101] S405, The collaborative control module will adjust the cooling fan frequency. Hot air heating intensity Infrared heating power Conveyor belt speed and the rhythm of the lanes The output is a set of linked control quantities. The PLC or PAC controller reads the bottle heat treatment risk parameters within the same control cycle. Bottle perimeter arrangement parameters Bottle equivalent temperature Equivalent heat treatment strength and comprehensive heat treatment risk parameters After completing the control quantity calculation, it outputs corresponding control commands to the cooling fan frequency converter, hot air heating actuator, infrared heating actuator, conveying drive mechanism and channel actuator.

[0102] To avoid abrupt changes in control values, the coordinated control module adjusts the cooling fan frequency. Hot air heating intensity Infrared heating power , conveyor belt speed and lane dividing beat are smoothed.

[0103] The smoothed control variable is calculated according to the following formula: ; wherein, is the smoothed control variable, is the control variable of the previous control cycle, is the calculated control variable of the current control cycle, is a smoothing coefficient. The smoothing coefficient has a value greater than 0 and no greater than 1, and is stored in the controller parameter area.

[0104] The execution cooperative control module also performs rate-of-change limitation on the smoothed control variable .

[0105] The rate-of-change limitation is performed according to the following formula: ; wherein, is the smoothed control variable, is the control variable of the previous control cycle, is the allowable variation of the control variable. The allowable variation of the control variable is set according to the response capability of the corresponding actuator and the stability requirement of the equipment process.

[0106] When the temperature in the heat treatment area exceeds the safety threshold, sensor data is invalid, a heater fault occurs, a fan fault occurs, conveyor bottle jamming or communication abnormality occurs, the execution cooperative control module stops using the comprehensive heat treatment risk parameter for linkage control, and enters safety fallback control. The safety fallback control includes reducing or turning off the heating output, maintaining or increasing the frequency of the cooling fan, reducing the conveyor belt speed, stopping upstream bottle feeding, maintaining necessary lane dividing spacing, and outputting abnormality information on the human-machine interface. Temperature over-limit judgment, sensor data validity judgment, equipment fault signal reading, frequency converter control, heater power control and actuator alarm are conventional implementation manners in the field of industrial control, and those skilled in the art can perform configuration according to the model of the PLC or PAC controller and the equipment communication protocol, which will not be repeated herein.

[0107] Through this processing procedure, the bottle heat treatment risk parameter , the bottle peripheral arrangement state parameter , the bottle equivalent temperature , the equivalent heat treatment intensity and the comprehensive heat treatment risk parameter The commands are converted into control instructions for cooling, heating, conveying, and channeling actuators, enabling the continuous intelligent heat treatment production line to coordinate the control of multiple actuators within the same control cycle.

[0108] See attached document Figure 7 After the cosmetic bottles undergo cooling and shaping, hot air stabilization, infrared-assisted heat treatment, slow cooling, and secondary cooling, the quality feedback correction module correlates the backend quality inspection results with the corresponding batch's process data. This process determines the correspondence between the current batch's control parameters and the finished product quality, and generates correction parameters that can be written into the product formula, enabling subsequent batches to utilize the quality feedback-corrected control parameters.

[0109] S501, the quality feedback correction module receives backend quality inspection data through the quality inspection interface. The backend quality inspection data includes bottle weight, bottle mouth roundness, bottle body ovality, bottle bottom concavity, bottle shoulder deformation, bottle height, bottle outer diameter, leakage detection results, appearance defect results, and layer thickness detection results. Bottle weight is output by the weighing detection device; bottle mouth roundness, bottle body ovality, bottle bottom concavity, bottle shoulder deformation, bottle height, and bottle outer diameter are output by the dimensional detection device or visual inspection device; leakage detection results are output by the leakage detection device; appearance defect results are output by the visual inspection device; and layer thickness detection results are output by the layer thickness detection device or the sampling data input interface.

[0110] The quality feedback correction module performs validity checks on the backend quality inspection data. Validity checks include verifying whether the inspection data is empty, whether the inspection data exceeds the equipment's range, whether the inspection time matches the current batch's production time, and whether the inspection object number matches the current batch's product number. When inspection data fails the validity check, the quality feedback correction module records an anomaly and stops generating formula correction parameters for that inspection object.

[0111] S502, the quality feedback correction module binds the backend quality inspection data, which has undergone validity verification, to the corresponding process data. The binding criteria include the current batch product number, mold number, material batch number, bottle entry order, bottle inspection order, and production timestamp. When the production line is configured with a single-bottle tracking number, the quality feedback correction module binds according to the single-bottle tracking number; when no single-bottle tracking number is configured, the quality feedback correction module binds at the batch level according to the bottle entry order, bottle inspection order, and conveying delay time.

[0112] The process data linked to the backend quality inspection data includes product formula, molding process parameters, and bottle perimeter layout parameters. Bottle equivalent temperature Equivalent heat treatment strength Comprehensive heat treatment risk parameters Cooling fan frequency Hot air heating intensity Infrared heating power Conveyor belt speed and the rhythm of the lanes .

[0113] S503, the quality feedback correction module calculates quality deviation parameters based on the backend quality inspection data and the permissible quality range in the product formula. For continuous testing items, the quality feedback correction module calculates the normalized deviation based on the detected value, target value, and permissible deviation value; for leakage detection results and appearance defect results, the quality feedback correction module calculates the deviation according to the ratio between the number of non-conforming items and the number of tests.

[0114] Quality deviation parameters Calculate using the following formula: ; in, For quality deviation parameters, For the first The test values ​​of a continuous testing item For the first Target values ​​for continuous testing items For the first Permissible deviation values ​​for continuous testing items For the first Quality weights of continuous testing items This represents the number of continuous testing items included in the calculation. This refers to the serial number of continuous testing items. The test results for bottle weight, bottle mouth roundness, bottle body ovality, bottle bottom concavity, bottle shoulder deformation, bottle height, bottle outer diameter, and layer thickness can all be included in the calculation as continuous testing items. Quality weights are set by the product formula and stored in the formula database according to product quality control requirements.

[0115] The quality feedback correction module identifies defect types based on the direction and magnitude of deviations in each inspection item. A bottle bottom concavity exceeding the allowable range is identified as a bottle bottom concavity defect; a bottle body ellipticity exceeding the allowable range is identified as a bottle body elliptic defect; a bottle mouth roundness exceeding the allowable range is identified as a bottle mouth deformation defect; a bottle shoulder deformation exceeding the allowable range is identified as a bottle shoulder deformation defect; a leakage inspection result exceeding the allowable number of non-conforming items is identified as a leakage defect; a visual defect result exceeding the allowable number of non-conforming items is identified as a visual defect; and a layer thickness inspection result deviating from the set layer thickness ratio is identified as a layer thickness deviation defect.

[0116] S504, the quality feedback correction module generates formula correction parameters based on the correlation between defect types and process data. A correlation is established between concave bottle bottom defects and insufficient cooling in the thick-walled area of ​​the bottle body, insufficient slow cooling time, insufficient hot air heating intensity, or excessively high conveyor belt speed; elliptical bottle body defects are correlated with mismatched cooling fan frequency distribution, excessively high conveyor belt speed, or bottle perimeter arrangement parameters. Correspondences are established between excessively high values ​​and excessively high values; bottle mouth deformation defects are correlated with excessively high cooling fan frequency, excessively high hot air heating intensity, or excessively high infrared heating power; bottle shoulder deformation defects are correlated with hot air heating intensity, infrared heating power, and residence time in the area; leakage defects are correlated with the heat treatment status of the bottle mouth area, bottle mouth roundness deviation, and molding process parameters; layer thickness deviation defects are correlated with the layer thickness deviation parameters of multi-layer structures. Establish a correspondence between the extruder speed and the die temperature zone of each layer.

[0117] The formula correction parameters include correction values ​​for the basic cooling fan frequency, basic hot air heating intensity, basic infrared heating power, basic conveyor belt speed, basic lane interval rate, and risk weight. The basic cooling fan frequency correction value is used to correct the basic cooling fan frequency of subsequent batches; the basic hot air heating intensity correction value is used to correct the basic hot air heating intensity of subsequent batches; the basic infrared heating power correction value is used to correct the basic infrared heating power of subsequent batches; the basic conveyor belt speed correction value is used to correct the basic conveyor belt speed of subsequent batches; the basic lane interval rate correction value is used to correct the basic lane interval rate of subsequent batches; and the risk weight correction value is used to correct the control weights corresponding to the bottle heat treatment risk parameters.

[0118] Revised formula parameters Calculate using the following formula: ; in, For the revised formula parameters, To correct the formula parameters before modification, This is the formula correction factor. For quality deviation parameters, These are the lower limits of the formulation parameters. This represents the upper limit of the formula parameters. (Revised formula parameters) It is one of the following: basic cooling fan frequency, basic hot air heating intensity, basic infrared heating power, basic conveyor belt speed, basic lane cycle time, or risk weight. Formula correction factor. Determined by the defect type and stored in the formula database. For the same quality deviation parameter Different defect types correspond to different formula correction factors. .

[0119] When the control direction corresponding to the defect type is to increase the control quantity, the formula correction coefficient Take a positive value; when the control direction corresponding to the defect type is to reduce the control quantity, the formula correction coefficient is... Take negative values. The formula correction factor for a concave bottle bottom defect corresponding to increased hot air heating intensity or decreased conveyor belt speed. Values ​​are taken according to the corresponding control direction. When bottle neck deformation defects correspond to a reduction in hot air heating intensity or infrared heating power, the formula correction factor is used. Values ​​are taken according to the corresponding control direction. The quality feedback correction module corrects the formula parameters. Perform upper and lower limit verification and correct the formula parameters. When the value exceeds the allowed range, the boundary value of the corresponding allowed range is used.

[0120] S505, the quality feedback correction module will correct the formula parameters. Defect type, quality deviation parameters The corresponding process data and correction time are written into the recipe correction record, and the correction content is displayed on the human-machine interface. After operator confirmation, the PLC or PAC controller will update the corrected recipe parameters. Write the formula to the formula database corresponding to the current product number. If the operator does not confirm, the quality feedback correction module only saves the formula correction record and does not change the product formula.

[0121] When producing cosmetic bottles with the same product number in subsequent batches, the PLC or PAC controller reads the revised formula parameters from the formula database. The product formula. Revised formula parameters. It participates in the calculation of risk parameters for bottle heat treatment, the calculation of comprehensive heat treatment risk parameters, and the generation of control instructions for the actuator, so that the back-end quality inspection results can be incorporated into the subsequent batch production control process.

[0122] Through this process, a correspondence is established between backend quality inspection data, current batch process data, and product formula parameters. The quality feedback correction module can then generate corrected formula parameters that can be written into the formula database based on the type of quality deviation. This allows batch quality results to be used for cooling, heating, conveying, and channel control of subsequent batches. Quality inspection data acquisition, production timestamp recording, recipe database writing, human-machine interface confirmation, and batch data storage are standard implementation methods in the industrial control field. Those skilled in the art can configure these methods according to the PLC or PAC controller model and production line data interface, and will not be elaborated upon here.

[0123] Specific application examples: To further illustrate the application of this invention in a continuous intelligent heat treatment production line, a batch of multi-layer co-extruded PETG cosmetic bottles was selected as the test object. This batch of cosmetic bottles consists of a structural layer, an adhesive layer, and a barrier layer, with a target capacity of 120mL. After being output from the multi-layer co-extrusion blow molding equipment, the bottles enter the continuous intelligent heat treatment production line and sequentially pass through a cooling and shaping zone, a hot air stabilization zone, an infrared-assisted heat treatment zone, a slow cooling zone, and a secondary cooling zone. The PLC or PAC controller operates according to a control link formed by a data feedforward processing module, a spatial state modeling module, an execution collaborative control module, and a quality feedback correction module.

[0124] In this batch of product formulation, the bottle bottom thickness is 2.80mm, the bottle body wall thickness is 1.20mm, the bottle neck wall thickness is 1.50mm, the bottle shoulder wall thickness is 1.90mm, the maximum effective wall thickness of the bottle body is 2.80mm, and the minimum effective wall thickness of the bottle body is 1.20mm. The set layer thickness ratios for the structural layer, adhesive layer, and barrier layer are 0.82, 0.06, and 0.12, respectively, while the actual layer thickness ratios obtained from online detection are 0.80, 0.07, and 0.13, respectively. The material shrinkage correction factor is 1.10, the wall thickness risk weight factor is 0.32, and the layer thickness deviation weight factor is 1.60.

[0125] The bottle wall thickness difference parameter is calculated using the following formula: ; Substituting the maximum effective wall thickness of the bottle (2.80 mm) and the minimum effective wall thickness (1.20 mm) into the above formula, we get: ; The thickness deviation parameter of a multi-layer structure is calculated using the following formula: ; Substituting the actual layer thickness ratios of the structural layer, adhesive layer, and barrier layer (0.80, 0.07, and 0.13), and the set layer thickness ratios (0.82, 0.06, and 0.12) into the above formula, we obtain: ; The risk parameters for heat treatment of the bottle body are calculated using the following formula: ; Substituting the bottle wall thickness difference parameter 2.33, material shrinkage correction factor 1.10, wall thickness risk weight factor 0.32, layer thickness deviation weight factor 1.60, and multi-layer structure layer thickness deviation parameter 0.04 into the above formula, we obtain: ; The calculation results show that the data feedforward processing module can convert bottle wall thickness differences, multi-layer structure thickness deviations, and material shrinkage characteristics into bottle heat treatment risk parameters. These parameters serve as feedforward inputs for subsequent bottle thermal state estimation and actuator coordinated control, establishing a correspondence between the control process and the bottle structure and material properties of the current batch of cosmetic bottles.

[0126] When the target bottle enters the hot air stabilization zone, the spatial state modeling module updates the two-dimensional shift register matrix based on the bottle inlet detection signal and the conveyor encoder signal, and selects a calculation window centered on the matrix cell corresponding to the target bottle. The total weight of the grid cells participating in the calculation within this window is 14.40, and the total weight of the adjacent bottle-occupied grid cells is 6.20.

[0127] The parameters of the bottle's perimeter layout are calculated using the following formula: ; Substituting the sum of the weighted values ​​of the adjacent bottle-occupying grids (6.20) and the sum of the grid weights (14.40) within the layout calculation window into the above formula, we get: ; The effective length of the hot air stabilization zone is 1.80m, the real-time conveyor belt speed is 0.18m / s, and the zone residence time is 10.00s. The mold cooling water temperature is 16.50℃, the heat treatment zone temperature is 64.00℃, the real-time cooling fan frequency is 42.00Hz, and the bottle demolding time is 13.00s. The temperature estimation calibration coefficients are taken as 7.50, 0.20, 0.58, -0.12, -0.15, 0.32, and 3.00, respectively.

[0128] The equivalent temperature of the bottle is calculated using the following formula: ; Substituting the corresponding values ​​into the above formula, we get: ; The heating active power is 5.60kW, the heating power adjustment ratio is 0.72, the hot air circulation fan frequency is 38.00Hz, and the heater aging correction parameter is 0.93. The heat treatment intensity estimation calibration coefficients are taken as 0.40, 0.18, 1.15, 0.035, 0.012, and -0.25 respectively.

[0129] The equivalent heat treatment strength is calculated using the following formula: ; Substituting the corresponding values ​​into the above formula, we get: ; In the current product formulation, the target bottle temperature corresponding to the hot air stabilization zone is 46.50℃, and the target heat treatment intensity is 5.00. The comprehensive risk calibration coefficients are taken as 0.12, 0.68, 0.42, 0.08, and 0.22, respectively.

[0130] The comprehensive heat treatment risk parameters are calculated using the following formula: ; Substituting the corresponding values ​​into the above formula and completing the multiplication, we get: ; The calculation results show that the comprehensive heat treatment risk parameters can simultaneously reflect the bottle structure risk, the surrounding layout, temperature deviation, and heat treatment intensity deviation. The collaborative control module uses a base cooling fan frequency of 38.00Hz as the benchmark, with cooling calibration coefficients of 4.00, 5.20, and 0.80 respectively. The lower limit of the cooling fan frequency is 30.00Hz, and the upper limit is 55.00Hz.

[0131] The frequency of the cooling fan is calculated using the following formula: ; Substituting the corresponding values ​​into the above formula and completing the multiplication, we get: ; The calculated cooling fan frequency is within the allowable range, representing an increase of 4.90 Hz compared to the base cooling fan frequency. This result indicates that when the surrounding arrangement parameters of the bottle increase and the heat treatment risk parameters of the bottle are high, the collaborative control module can automatically increase the cooling compensation to reduce the impact of adjacent bottles on airflow distribution and heat treatment status.

[0132] In the back-end quality inspection stage, the continuous inspection items selected are bottle ovality, bottle bottom concavity, and bottle height. The measured values ​​are 0.38mm, 0.56mm, and 121.10mm, respectively, the target values ​​are 0.30mm, 0.45mm, and 121.00mm, respectively, the allowable deviation values ​​are 0.20mm, 0.25mm, and 0.80mm, respectively, and the quality weights are 0.35, 0.45, and 0.20, respectively.

[0133] The quality deviation parameter is calculated using the following formula: ; Substituting the measured values, target values, allowable deviation values, and mass weights of the bottle body ovality, bottom concavity, and height into the above formula, we obtain the following: ; For the basic conveyor belt speed, the original formula parameter is 0.180 m / s, the formula correction coefficient is -0.030, the lower limit of the formula parameter is 0.120 m / s, and the upper limit of the formula parameter is 0.220 m / s.

[0134] The revised formula parameters are calculated using the following formula: ; Substituting the corresponding values ​​into the above formula, we get: ; The correction results show that the quality feedback correction module can reduce the base conveyor belt speed of subsequent batches based on the quality deviation at the back end, so that the bottle can obtain a longer residence time in the heat treatment area, thereby providing feedback correction for bottle ellipse, bottle bottom concavity and height deviation.

[0135] Experimental verification was conducted using the same bottle type, material batch, and molding equipment for comparison. The control group used a fixed formula control, while the experimental group used the programmable intelligent control system of this invention, with each group continuously producing 5000 cosmetic bottles. The test results showed that the control group had a bottle body elliptical defect rate of 3.6%, a bottle bottom concave defect rate of 2.4%, a leakage defect rate of 1.1%, and an overall pass rate of 94.8%; the experimental group had a bottle body elliptical defect rate of 1.4%, a bottle bottom concave defect rate of 0.9%, a leakage defect rate of 0.4%, and an overall pass rate of 98.1%. These results demonstrate that this invention, through feedforward calculation, spatial state modeling, collaborative execution control, and quality feedback correction, can reduce the impact of heat treatment process fluctuations on finished product quality.

[0136] See attached document Figure 8 In the power spectrum of the equivalent temperature fluctuation of the bottle, the light gray curve represents the fixed formula control, and the black curve represents the control of the present invention. The fixed formula control curve has obvious peaks around 0.045Hz, 0.075Hz, 0.18Hz, and 0.28Hz, indicating that the equivalent temperature of the bottle fluctuates periodically under the fixed formula control. The control curve of the present invention also has corresponding peaks at the same frequency positions, but the peak amplitude is generally lower than that of the fixed formula control curve, especially in the range of 0.03Hz to 0.10Hz where it decreases significantly. This result shows that the present invention, through the coordinated adjustment of cooling fan frequency, hot air heating intensity, infrared heating power, and conveyor belt speed, can reduce the low-frequency fluctuations of the equivalent temperature of the bottle, making the thermal state of the bottle in the heat treatment area more stable.

[0137] See attached document Figure 9In the logarithmic amplitude spectrum of the quality deviation, the light gray curve represents the fixed formula control, and the black curve represents the control of the present invention. The fixed formula control curve has a higher logarithmic amplitude in the low-frequency range, indicating a slow drift in quality deviation within the batch. The control curve of the present invention is generally lower than that of the fixed formula control curve, with reduced amplitude in the low-frequency range and the main fluctuation range, and the curve change is smoother. This result shows that after the quality feedback correction parameter is involved in the subsequent batch production control, the periodic fluctuations of quality deviations such as bottle ellipticity, bottle bottom concavity, and bottle height are suppressed.

[0138] In summary, this application example, by substituting actual process parameters into key formulas, verifies that the data link between the bottle heat treatment risk parameters, bottle perimeter arrangement parameters, bottle equivalent temperature, equivalent heat treatment intensity, comprehensive heat treatment risk parameters, actuator control commands, and formula correction parameters is calculable, executable, and capable of feedback. (See attached...) Figure 8 and attached Figure 9 It is understood that the control of the present invention can reduce the fluctuation of the equivalent temperature of the bottle and the fluctuation of the quality deviation, so that the continuous intelligent heat treatment production line has a more stable heat treatment state and higher quality consistency in the mass production process.

Claims

1. A programmable intelligent control system for mass production of cosmetic bottles, characterized in that, include: The data feedforward processing module is used to receive product formulas, material parameters, and multilayer co-extrusion layer thickness parameters, and generate bottle heat treatment risk parameters. The spatial state modeling module is used to establish a two-dimensional shift register matrix based on the bottle inlet detection signal and the conveying encoder signal, calculate the arrangement state parameters around the bottle, and combine the bottle heat treatment risk parameters, molding process parameters, heat treatment temperature data, heating power feedback data, conveying state data and actuator state data to generate bottle thermal state parameters and comprehensive heat treatment risk parameters. The execution coordination control module is used to generate control commands for the actuators based on the bottle heat treatment risk parameters, the bottle surrounding layout parameters, the bottle thermal state parameters, and the comprehensive heat treatment risk parameters. The quality feedback correction module is used to associate backend quality inspection data with product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters to generate formula correction parameters.

2. The programmable intelligent control system for mass production of cosmetic bottles according to claim 1, characterized in that, The logic for the data feedforward processing module to generate bottle heat treatment risk parameters includes: The bottle bottom thickness, bottle body wall thickness, bottle neck wall thickness, and bottle shoulder wall thickness are extracted from the product formula. The maximum effective wall thickness and minimum effective wall thickness of the bottle body are determined based on the bottle bottom thickness, bottle body wall thickness, bottle neck wall thickness, and bottle shoulder wall thickness. Then, the bottle body wall thickness difference parameter is calculated based on the maximum effective wall thickness and minimum effective wall thickness of the bottle body. The thickness ratio of each layer of material is obtained based on the thickness parameters of the multi-layer co-extrusion layer, and the thickness deviation parameter of the multi-layer structure is calculated based on the thickness ratio of each layer of material and the set thickness ratio. Call the material shrinkage correction factor corresponding to the material parameters; The bottle wall thickness difference parameter, material shrinkage correction coefficient, and multi-layer structure layer thickness deviation parameter are fused based on the set wall thickness risk weight coefficient and layer thickness deviation weight coefficient to generate bottle heat treatment risk parameters.

3. The programmable intelligent control system for mass production of cosmetic bottles according to claim 2, characterized in that, The logic for determining the thickness ratio of each material layer includes: When online layer thickness detection data exists, the actual layer thickness ratio of each layer of material is used as the corresponding layer thickness ratio of each layer of material. When there is no online layer thickness detection data, the melt flow rate of each layer of material is collected. Based on the ratio of the melt flow rate of each layer of material to the total melt flow rate corresponding to the number of material layers involved in the calculation, the estimated layer thickness ratio of each layer of material is determined, and the estimated layer thickness ratio of each layer of material is used as the corresponding layer thickness ratio of each layer of material. The melt flow rate of each layer of material is collected by a flow meter; Without setting a flow meter, the melt flow rate of each layer of material is calculated by multiplying the screw speed of the extruder for the corresponding layer by the amount of material discharged per unit speed.

4. The programmable intelligent control system for mass production of cosmetic bottles according to claim 1, characterized in that, The logic for calculating the arrangement state parameters around the bottle using the spatial state modeling module includes: The horizontal grid coordinates of the bottle are determined based on the bottle inlet detection signal, and the vertical grid coordinates of the bottle are determined based on the conveyor encoder signal. Write the horizontal and vertical grid coordinates of the bottle into a two-dimensional shift register matrix, and set the matrix cell place value corresponding to the written matrix cell to the preset place value. At the same time, the two-dimensional shift register matrix is ​​synchronously shifted along the conveying direction according to the conveying encoder signal. The matrix cell corresponding to the target bottle in the two-dimensional shift register matrix is ​​used as the center, and the surrounding grid area is selected as the layout calculation window. Based on the different grid weights set for different grid areas in the layout calculation window and the matrix cell occupancy value in the layout calculation window, the layout state parameters around the bottle are calculated and generated.

5. The programmable intelligent control system for mass production of cosmetic bottles according to claim 1, characterized in that, The bottle's thermal state parameters include the bottle's equivalent temperature and equivalent heat treatment intensity. The actuator status data includes real-time cooling fan frequency, hot air circulation fan frequency, and heater aging correction parameters. The logic for the spatial state modeling module to generate comprehensive heat treatment risk parameters includes: Extract the target bottle temperature and target heat treatment intensity from the product formulation; Extract mold cooling water temperature and bottle demolding time from molding process parameters, extract zone dwell time from conveying status data, obtain heat treatment zone temperature from heat treatment temperature data, and obtain real-time cooling fan frequency from actuator status data; Based on the temperature estimation calibration coefficient, the equivalent temperature of the bottle is estimated and generated according to the mold cooling water temperature, bottle demolding time, zone dwell time, heat treatment zone temperature, real-time cooling fan frequency, and bottle periphery arrangement parameters. Based on the calibration coefficient for estimating heat treatment intensity, the active power of heating and the power adjustment ratio of heating are extracted from the heating power feedback data, and the frequency of hot air circulating fan and the aging correction parameters of heater are extracted from the actuator status data. The equivalent heat treatment intensity is estimated and generated based on the active power of heating, the power adjustment ratio of heating, the temperature of heat treatment zone, the frequency of hot air circulating fan and the aging correction parameters of heater. Based on the comprehensive risk calibration coefficient, the risk parameters of bottle heat treatment, the arrangement parameters around the bottle, the deviation between the equivalent temperature of the bottle and the target temperature of the bottle, and the deviation between the equivalent heat treatment intensity and the target heat treatment intensity are integrated to generate comprehensive heat treatment risk parameters.

6. The programmable intelligent control system for mass production of cosmetic bottles according to claim 5, characterized in that, The actuator control commands include cooling fan frequency control commands, hot air heating intensity control commands, infrared heating power control commands, conveyor belt speed control commands, and lane-specific cycle control commands. The logic for the execution coordination control module to generate cooling fan frequency control commands, hot air heating intensity control commands, infrared heating power control commands, conveyor belt speed control commands, and lane-specific cycle control commands includes: Extract the basic cooling fan frequency, basic hot air heating intensity, basic infrared heating power, basic conveyor belt speed, and basic lane cycle time from the product formula; Using the basic cooling fan frequency as a benchmark, the frequency is adjusted based on the bottle heat treatment risk parameters, the surrounding arrangement parameters of the bottle, and the deviation between the equivalent temperature of the bottle and the target bottle temperature to generate a cooling fan frequency control command. Using the basic hot air heating intensity as a benchmark, the hot air heating intensity control command is generated based on the bottle heat treatment risk parameters and the deviation between the bottle's equivalent temperature and the target bottle temperature. Using the base infrared heating power as a benchmark, corrections are made based on the deviation between the equivalent heat treatment intensity and the target heat treatment intensity to generate infrared heating power control commands. Using the basic conveyor belt speed as a benchmark, and correcting it according to the comprehensive heat treatment risk parameters, a conveyor belt speed control command is generated. Using the basic lane division cycle as a benchmark, the cycle is modified according to the arrangement parameters around the bottle to generate lane division cycle control commands.

7. The programmable intelligent control system for mass production of cosmetic bottles according to claim 6, characterized in that, The logic of the collaborative control module before outputting actuator control commands also includes: Obtain the control variables and smoothing coefficients from the previous control cycle; Combining the control quantities and smoothing coefficients of the previous control cycle, the cooling fan frequency control command, hot air heating intensity control command, infrared heating power control command, conveyor belt speed control command and lane cycle control command are smoothed to generate smoothed control quantities. Obtain the set allowable variation of each control quantity, apply a rate of change limit to each smoothed control quantity, and output each control quantity with the rate of change limit as the corresponding actuator control command.

8. The programmable intelligent control system for mass production of cosmetic bottles according to claim 1, characterized in that, The execution coordination control module also includes exception handling logic, which includes: When at least one of the following abnormal states is detected: heat treatment temperature data exceeding the safety threshold, invalid sensor data, heater failure, fan failure, conveyor blockage, and communication anomaly, the generation of actuator control commands using comprehensive heat treatment risk parameters is stopped, and safety backoff control is triggered. Safety retraction control includes at least one of the following actions: reducing heating output, turning off heating output, maintaining cooling fan frequency, increasing cooling fan frequency, reducing conveyor belt speed, stopping upstream bottle feeding, and maintaining a set lane spacing.

9. A programmable intelligent control system for mass production of cosmetic bottles according to claim 1, characterized in that, Before linking the backend quality inspection data, the quality feedback correction module also includes the following logic: Perform validity verification on the backend quality inspection data. Validity verification includes checking whether the backend quality inspection data is empty, whether it exceeds the equipment range, whether the inspection time matches the production time, and whether the inspection object number matches the product number in the product formula. Once the backend quality inspection data passes the validity verification and a single bottle tracking number exists, the backend quality inspection data is bound to the product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters based on the single bottle tracking number. When there is no individual bottle tracking number, the backend quality inspection data is bound to the product formula, molding process parameters, bottle perimeter layout parameters, bottle thermal state parameters, comprehensive heat treatment risk parameters, and actuator control parameters based on the production timestamp, bottle entry order, bottle inspection order, and conveying delay time.

10. A programmable intelligent control system for mass production of cosmetic bottles according to claim 9, characterized in that, The logic for generating formula correction parameters in the quality feedback correction module includes: Extract the target value and allowable deviation value corresponding to the backend quality inspection data; Calculate the quality deviation parameters of continuous testing items by combining the target values ​​and allowable deviation values ​​corresponding to continuous testing items in the backend quality inspection data; The quality deviation parameter of leakage detection results and appearance defect results is calculated based on the ratio between the number of non-conforming results and the number of inspections in the back-end quality inspection data. By combining the deviation direction and magnitude of continuous inspection items, the deviation direction and magnitude of leakage detection results and appearance defect results, the defect type can be identified. The formula correction factor is determined based on the defect type. The formula correction factor is then calculated by combining the formula correction factor, the quality deviation parameters of continuous testing items, the quality deviation parameters of leakage test results and appearance defect results, and the product formula. The calculation results are then checked against the upper and lower limits to generate the formula correction parameters.

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