A method and device for controlling online hole punching of ointment

Abstract

The present application provides an online ointment punching control method and device, which relates to the field of pharmaceutical machinery processing technology, including: building an online ointment punching control device; real-time monitoring of the ointment production line, and performing multi-channel diversion processing to obtain ointment production feature information; collecting a punching control record data set, and performing retrieval matching and optimization; performing punching control monitoring on the production line, and performing feature loss analysis; optimizing and analyzing the ointment punching control parameters, determining the punching optimization control parameters, and performing online punching control on the ointment production line. This application can solve the technical problem in the prior art that the existing ointment production process usually requires manual operation or the use of fixed mechanical devices, resulting in low production efficiency. Through real-time monitoring and precise control, automatic analysis of feedback data, and optimization of control parameters, precise control of the ointment punching process is achieved, thereby improving production efficiency.

Description

Technical Field

[0001] The present application relates to the field of pharmaceutical mechanical processing technology, and in particular to an ointment online punching control method and device. Background Art

[0002] In the existing Yunnan Baiyao ointment production line, the punching process of the ointment usually adopts an intermittent punching method. This method has several significant disadvantages, which limit production efficiency and quality. The intermittent punching method has a slow punching speed, resulting in low overall production efficiency. This limits the production capacity of the production line and increases production costs. The large aperture used causes a large amount of ointment to be wasted during the punching process and cannot be recycled, which not only increases the consumption of raw materials but also increases the scrap rate. The existing punching equipment lacks automated (PLC) control and cannot be effectively connected to the back-end process of the production line, which limits the degree of automation of the production line and increases the complexity and error rate of manual operation.

[0003] In summary, the prior art has a technical problem of low production efficiency due to the fact that the existing ointment production process usually requires manual operation or the use of fixed mechanical devices. Summary of the Invention

[0004] The purpose of this application is to provide an online ointment punching control method and device to solve the technical problem in the prior art that the existing ointment production process usually requires manual operation or the use of fixed mechanical devices, resulting in low production efficiency.

[0005] In view of the above problems, the present application provides a method and device for controlling online ointment punching.

[0006] In the first aspect, the present application provides an ointment online punching control method, which is implemented by an ointment online punching control device, wherein the ointment online punching control method comprises: building an ointment online punching control device, wherein the ointment online punching control device comprises a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor and a position sensor; using the sensor module to monitor the ointment production line in real time, obtaining the ointment production line operation data flow, and uploading the ointment production line operation data flow to the control center for multi-channel diversion processing, and obtaining the ointment production line operation data flow. characteristic information; acquiring the punching control record data set of the laser punching equipment through the control center, performing retrieval, matching and optimization based on the ointment production characteristic information and the punching control record data set, and determining the ointment punching control parameters; performing punching control monitoring on the ointment production line based on the ointment punching control parameters, obtaining the ointment punching production feedback data stream, performing characteristic loss analysis on the ointment punching production feedback data stream, and obtaining ointment punching loss characteristic information; performing optimization analysis on the ointment punching control parameters based on the ointment punching loss characteristic information, determining the punching optimization control parameters, and using the punching optimization control parameters to perform online punching control on the ointment production line.

[0007] In the second aspect, the present application also provides an online ointment punching control device for executing an online ointment punching control method as described in the first aspect, wherein the online ointment punching control device comprises: a data acquisition module, the data acquisition module is used to build an online ointment punching control device, the online ointment punching control device comprises a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor and a position sensor; a real-time monitoring module, the real-time monitoring module is used to use the sensor module to perform real-time monitoring of the ointment production line, obtain the ointment production line operation data flow, and upload the ointment production line operation data flow to the control center for multi-channel diversion processing to obtain ointment production characteristic information; a control parameter determination module, the control The control parameter determination module is used to collect and obtain the punching control record data set of the laser punching equipment through the control center, perform retrieval matching and optimization based on the ointment production characteristic information and the punching control record data set, and determine the ointment punching control parameters; the punching control monitoring module is used to perform punching control monitoring on the ointment production line based on the ointment punching control parameters, obtain the ointment punching production feedback data stream, perform feature loss analysis on the ointment punching production feedback data stream, and obtain ointment punching loss feature information; the control parameter updating module is used to optimize and analyze the ointment punching control parameters based on the ointment punching loss feature information, determine the punching optimization control parameters, and use the punching optimization control parameters to perform online punching control on the ointment production line.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] An online ointment punching control device is constructed, which includes a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor; the sensor module is used to monitor the ointment production line in real time, obtain the ointment production line operation data stream, and upload the ointment production line operation data stream to the control center for multi-channel diversion processing to obtain ointment production characteristic information; the control center collects and obtains the punching control record data set of the laser punching device, and performs retrieval, matching and optimization based on the ointment production characteristic information and the punching control record data set to determine the ointment punching control parameters; based on the ointment punching control parameters, the ointment production line is subjected to punching control monitoring to obtain the ointment punching production feedback data stream, and the ointment punching production feedback data stream is subjected to feature loss analysis to obtain ointment punching loss feature information; based on the ointment punching loss feature information, the ointment punching control parameters are optimized and analyzed to determine the punching optimization control parameters, and the ointment production line is subjected to online punching control using the punching optimization control parameters. In other words, through real-time monitoring and precise control, automatic analysis of feedback data, and optimization of control parameters, precise control of the ointment punching process is achieved, thereby improving production efficiency.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 A schematic flow chart of an online ointment punching control method for this application;

[0013] Figure 2 This is a structural diagram of an online ointment punching control device for this application.

[0014] Description of the accompanying drawings: data acquisition module 11, real-time monitoring module 12, control parameter determination module 13, drilling control monitoring module 14, control parameter updating module 15. DETAILED DESCRIPTION

[0015] This application provides a method and device for controlling online ointment perforation, addressing the existing technical problem of low production efficiency, which often results from manual operation or the use of fixed mechanical devices during ointment production. Through real-time monitoring and precise control, automatic analysis of feedback data, and optimization of control parameters, precise control of the ointment perforation process is achieved, improving production efficiency.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example 1, please refer to the attached Figure 1 The present application provides an ointment online punching control method, wherein the ointment online punching control method is applied to an ointment online punching control device, and the ointment online punching control method specifically includes the following steps:

[0018] Step 1: Build an online ointment punching control device, which includes a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor.

[0019] Specifically, a control system for online ointment drilling was built, consisting of laser drilling equipment, a sensor module, and a control center. Laser drilling equipment utilizes the high energy density of a laser beam to precisely drill holes in materials, offering advantages such as high speed, high precision, and contactless processing. The sensor module is a system integrating multiple sensors, including vision sensors, speed sensors, temperature and humidity sensors, and position sensors, to collect information about the environment or equipment status. By integrating multiple sensors, the ointment production status is comprehensively monitored, enabling precise control of the ointment drilling process.

[0020] Step 2: Use the sensor module to monitor the ointment production line in real time, obtain the ointment production line operation data flow, and upload the ointment production line operation data flow to the control center for multi-channel diversion processing to obtain ointment production characteristic information.

[0021] Specifically, sensor modules monitor the status of the ointment production line in real time, collecting key parameters such as the ointment's flow rate, temperature, humidity, and position. The data collected by the sensor modules is integrated into a continuous data stream containing real-time status information about the ointment production line. This collected data is uploaded to the control center via wired or wireless channels. The control center then utilizes a pre-configured data processing pipeline to perform real-time processing on the ointment production line's operational data stream. Data is then distributed to the appropriate processing channel based on its type and characteristics, where it is processed according to pre-defined preprocessing and feature extraction procedures. For example, image data collected by a vision sensor, after being uploaded to the control center, is distributed to the vision data channel. Image preprocessing and feature extraction are performed to extract characteristic information such as the ointment's shape and size. Speed ​​data collected by a speed sensor, after being uploaded to the control center, is distributed to the speed data channel. Filtering and smoothing are performed to extract the ointment's flow rate characteristics. Multi-channel processing allows for simultaneous processing of multiple data types. Each channel is configured with a preprocessing pipeline and feature extraction algorithm tailored to its data type, enhancing data processing capabilities and accuracy.

[0022] Step 3: The control center collects and obtains the drilling control record data set of the laser drilling equipment, performs retrieval, matching and optimization based on the ointment production feature information and the drilling control record data set, and determines the ointment drilling control parameters.

[0023] Specifically, a control center collects a data set of punching control records from laser punching equipment. This data includes relevant attribute data for ointment production (such as ointment type, dosage form specifications, and production process), production feature data (such as ointment flow rate, temperature, humidity, and location), punching control parameters (such as punching location, speed, and depth), and punching effect data (such as aperture size, distribution uniformity, and edge quality). The ointment production attribute information, including ointment type, dosage form specifications, and production process, is obtained, and the punching control record data set is classified into different categories. The ointment production line is classified based on the ointment production attribute information, and the target ointment production attribute parameters are determined. Punching control data related to the target attribute parameters is extracted from the data set, and a target punching control database is constructed, containing punching control data applicable to the current ointment attributes. Using the ointment production feature information, the target punching control database is searched, matched, and optimized to find the punching control parameters that best suit the current production characteristics. Through search, matching, and optimization, the optimal punching control parameters for the current ointment can be found, improving the accuracy of punching control.

[0024] Step 4: Based on the ointment punching control parameters, the ointment production line is monitored for punching control, and an ointment punching production feedback data stream is obtained. A feature loss analysis is performed on the ointment punching production feedback data stream to obtain ointment punching loss feature information.

[0025] Specifically, the control center applies the ointment punching control parameters on the production line based on the ointment punching control parameters to perform punching operations on the ointment patches. The ointment production line is monitored in real time through the sensor module and feedback data on the punching operations on the production line is collected. This data reflects the actual effect of the punching process, such as the actual aperture size, aperture distribution, and hole wall smoothness. Feature loss analysis is performed on the ointment punching production feedback data stream to evaluate the actual effect of the punching parameters and identify loss characteristics during the punching process, including aperture deviation, hole edge roughness, and ointment loss. By monitoring the punching operations on the production line in real time, problems in the punching process can be discovered in a timely manner. Feature loss analysis of the feedback data stream can be performed to understand the loss situation during the punching process, continuously optimize the punching control parameters, and improve production efficiency and product quality.

[0026] Step 5: Optimize and analyze the ointment punching control parameters based on the ointment punching loss characteristic information, determine the punching optimization control parameters, and use the punching optimization control parameters to perform online punching control on the ointment production line.

[0027] Specifically, the perforation control parameters are optimized and analyzed using the characteristic information of the ointment's perforation loss to identify which control parameters are most correlated with perforation loss, thereby determining the parameters that require optimization. Through associative optimization analysis, a set of possible perforation control parameter values ​​is determined, forming a solution space. The positions and velocities of a set of particles are randomly initialized. Particle positions represent possible perforation control parameter combinations, while particle velocities control particle movement. Using the ointment's perforation effect fitness function, an iterative search and evaluation of the particle swarm parameters within the perforation control parameter selection solution space is performed. By evaluating each particle position, a set of perforation control parameter fitness values ​​is obtained. Based on the fitness set, the particle swarm parameters are iteratively updated, including updating particle positions and adjusting particle velocities. This iterative update process continues until pre-defined convergence criteria are met, including reaching the maximum number of iterations and ensuring that the fitness change is less than a certain threshold. After the iterative update is complete, the particle with the highest fitness is found, representing the optimized perforation control parameters. Based on the optimized perforation control parameters, online perforation control is performed on the ointment production line to ensure that the perforation process adheres to the optimized control parameters and achieves the desired perforation effect. The control center monitors and adjusts the drilling process in real time based on the optimized drilling control parameters, ensuring that the equipment operates within the required parameters. Based on real-time feedback data, the drilling control parameters are fine-tuned to maintain optimal drilling results. Through optimization analysis, the optimal drilling control parameters are found, optimizing the drilling process and improving drilling results.

[0028] Furthermore, step 2 of this application includes:

[0029] Determine a data acquisition type set according to the sensor module, and perform a preprocessing step analysis on the data acquisition type set to obtain a multi-type data preprocessing process set; obtain an ointment production feature extraction algorithm set, perform feature matching based on the data acquisition type set and the ointment production feature extraction algorithm set, and obtain a multi-type data feature extraction algorithm set; perform multi-channel configuration based on the multi-type data preprocessing process set and the multi-type data feature extraction algorithm set, and construct an ointment production data processing multi-channel; call the ointment production data processing multi-channel through the control center, perform multi-channel diversion processing on the ointment production line operation data flow based on the ointment production data processing multi-channel, and obtain the ointment production feature information.

[0030] Specifically, based on the capabilities and requirements of the sensor modules, determine the data types to be collected. For example, vision sensors collect image data, speed sensors collect speed data, temperature and humidity sensors collect temperature and humidity data, and position sensors collect position data. Preprocessing steps are analyzed for each data type to remove noise, correct errors, and standardize the data format to ensure data quality. Data preprocessing aims to improve data quality and facilitate subsequent feature extraction and analysis. Because different sensors collect different data types, their preprocessing steps will vary. For example, image data collected by vision sensors requires preprocessing such as denoising, contrast enhancement, and segmentation, while speed data collected by speed sensors requires preprocessing such as filtering and smoothing. Develop a preprocessing process tailored to the data type, forming a set of processes.

[0031] Based on different aspects of ointment production, a series of feature extraction algorithms are collected and organized to form an algorithm set, including but not limited to image processing algorithms and statistical analysis algorithms. Feature matching is performed based on the data acquisition type set and the ointment production feature extraction algorithm set. For example, image data uses image processing algorithms to extract features, while velocity data uses time series analysis algorithms. Through feature matching, a set of feature extraction algorithms for multiple data types is obtained, with each algorithm corresponding to one or more data types. Based on the preprocessing process and feature extraction algorithms, a multi-channel data processing system is constructed. Each channel is responsible for processing a specific type of data, forming a complete data processing chain from data acquisition to preprocessing to feature extraction. Each channel requires the corresponding sensor, preprocessing process, and feature extraction algorithm. For example, the visual data channel requires an image sensor, image preprocessing process, and image feature extraction algorithm. The purpose of a multi-channel configuration is to effectively combine the preprocessing process and feature extraction algorithm to form an efficient system capable of processing multiple data types simultaneously. This multi-channel configuration can optimize the data processing process and improve data processing efficiency and accuracy.

[0032] The control center utilizes multiple channels for ointment production data processing to perform real-time processing of the data streams flowing through the production line. Data streams are assigned to corresponding processing channels based on their data type. Each channel is configured with a preprocessing process and feature extraction algorithm tailored to its data type. Upon entering each channel, the data stream undergoes the corresponding preprocessing and feature extraction steps. Through multi-channel diversion processing, key feature information, such as flow rate, temperature, humidity, and location, is extracted from the ointment production data. This multi-channel configuration allows for parallel processing of different types of data, improving overall data processing speed.

[0033] Furthermore, step three of this application includes:

[0034] Obtain ointment production attribute information, classify and identify the punching control record data set according to the ointment production attribute information, and obtain a punching attribute calibration data set; classify the ointment production line according to the ointment production attribute information to obtain target ointment production attribute parameters; perform association extraction on the punching attribute calibration data set based on the target ointment production attribute parameters to obtain a target punching control database; search and match the target punching control database based on the ointment production feature information to determine the ointment punching control parameters.

[0035] Specifically, the production attribute information of the ointment is collected through the production management system, database or directly from the production line, including external ointment, internal ointment), dosage form specifications (such as gel, cream, powder) and production process (such as hot pressing, cold pressing). According to the ointment production attribute information, the punching control record data set is classified and identified in order to find the best punching parameters for each ointment type. The data set is divided into different categories, and each category corresponds to a specific combination of ointment attributes. Through classification and identification, a punching attribute calibration data set is obtained, which contains punching control records related to various ointment attributes.

[0036] Based on ointment production attribute information, ointments on the production line are classified to identify the most appropriate production parameters for each ointment type. Based on information such as ointment type, dosage form, and production process, ointment production lines are categorized into different categories. Target ointment production attribute parameters, including production speed, temperature control, humidity control, and pressure settings, are then determined to optimize the parameter settings for each ointment production line category. Punching control parameters associated with the target ointment production attribute parameters are extracted from a punching attribute calibration dataset. Association extraction aims to match the target ointment production attribute parameters with the optimal punching parameters in the punching attribute calibration dataset. This association extraction generates a database containing punching control data matching the target ointment production attribute parameters, ensuring that the punching parameters match the ointment production requirements. For example, for an ointment with a specific production process, the database contains the corresponding temperature and humidity control parameters. The control center compares and matches the current ointment production attribute information with the parameters in the database to identify the punching control parameters that best suit the current production characteristics. Through attribute classification and matching, it ensures that each punching operation is consistent with the best practices for specific ointment types and specifications. The optimal punching parameters are automatically selected based on real-time production feature information, improving the intelligence level of the system.

[0037] Furthermore, the present application further comprises the following steps:

[0038] Based on the ointment production characteristic information, the target punching control database is searched and matched for analysis to obtain a punching data matching set; according to the punching data matching set, the data within a preset similarity threshold is screened, mapped and divided to obtain an available punching control data set; a set of ointment punching effect evaluation indicators is obtained, and an ointment punching effect fitness function is constructed based on the ointment punching effect evaluation indicator set; based on the ointment punching effect fitness function, the available punching control data set is evaluated, optimized and adjusted to determine the ointment punching control parameters.

[0039] Specifically, ointment production feature information is used to search the target punching control database, and the current ointment production feature information is compared with the parameters in the database. The matching degree is calculated using similarity algorithms such as cosine similarity and Euclidean distance. Through matching analysis, a punching data matching set is obtained, which contains the matching degree score of each punching control parameter and the current ointment production feature. A similarity threshold is pre-set, and only records with a matching degree above this threshold are considered. Punching parameters that highly match the ointment production features are selected from the punching data matching set. Through mapping, the selected punching parameters are associated with their matching degree scores to form an ordered data set, which is the usable punching control data set. Obtained through experiments or historical data, it is usually determined based on the physical properties and production requirements of the ointment. A set of evaluation indicators for the ointment punching effect is obtained, including pore size, pore size distribution uniformity, pore wall smoothness, ointment integrity, etc., which reflect the quality of the punching effect.

[0040] Based on the set of evaluation indicators, a fitness function is constructed to convert each evaluation indicator into a quantifiable value, and these indicators are comprehensively considered to evaluate the drilling effect. For example, F(x) = w1*pore diameter consistency + w2*pore edge smoothness - w3*drilling time - w4*ointment loss rate, where x represents a set of control parameters, such as drilling speed, depth, etc., and w i is the weight of each indicator, reflecting its importance to the overall perforation effect. Each record in the available perforation control dataset is evaluated using the fitness function, the fitness function value is calculated, and the optimal perforation control parameters are selected. Based on the evaluation and tuning results, the optimal ointment perforation control parameters are determined.

[0041] For example, it is assumed that the matching set obtained through retrieval matching analysis is: the matching degree of record 1 is 0.85, the matching degree of record 2 is 0.75, and the matching degree of record 3 is 0.90. The pre-set similarity threshold is 0.80, and record 1 and record 3 are filtered into the available punching data set. Use the fitness function F(x) = 0.4*aperture consistency + 0.3*hole edge smoothness - 0.2*punching time - 0.1*ointment loss rate. The fitness score of record 1 is 85, and the fitness score of record 3 is 90, then the punching control parameters corresponding to record 3 are used as the optimal parameters. Through matching analysis and fitness function evaluation, the optimal punching parameters are selected, and the punching parameters are automatically selected and optimized, thereby improving the intelligence and automation level of the system.

[0042] Furthermore, the present application further comprises the following steps:

[0043] Based on the ointment punching effect fitness function, the available punching control data set is evaluated for fitness and optimized to obtain the punching optimization control parameters; based on the punching optimization control parameters, the production feature matching deviation parameters are determined; the ointment production feature deviation data and the corresponding punching control parameter variables in the punching control record data set are mapped and associated with each other to construct an ointment punching parameter regression model; based on the ointment punching parameter regression model, the production feature matching deviation parameters are tuned and updated to determine the ointment punching control parameters.

[0044] Specifically, the ointment punching effect fitness function is used to evaluate the available punching control data set to determine which control parameters can produce the best punching effect and select the optimal control parameters, namely the punching optimal control parameters. According to the punching optimal control parameters, the matching degree with the current ointment production characteristic information is analyzed to determine whether there is a deviation. Deviation analysis is performed based on the matching degree of the parameter and the ointment production characteristic information. According to the deviation parameters, there may be problems in actual application, such as whether it will cause ointment loss, pore size unevenness, etc. If there is a significant deviation, the parameter needs to be fine-tuned to ensure the actual applicability of the parameter. The purpose of fine-tuning is to optimize the punching parameters so that they are more in line with the ointment production characteristics.

[0045] Ointment production characteristic deviation data refers to deviations that occur during the production process, such as the difference between the actual and expected positions of ointment patches. The ointment production characteristic deviation data in the punching control record dataset is mapped and fitted with the corresponding punching control parameter variables. Punching control parameter variables include control parameters that affect punching results, such as punching speed, depth, and position. The ointment production characteristic deviation data is associated with the corresponding punching control parameter variables, and a mapping relationship is established using statistical methods or machine learning algorithms. Through this mapping and fitting, a regression model is constructed to predict or fine-tune the punching control parameters based on the deviations in the ointment production characteristic data. The constructed regression model is used to fine-tune the production characteristic matching deviation parameters, ensuring that the control parameters more accurately adapt to actual production conditions. Based on the predictions of the ointment punching parameter regression model, the production characteristic matching deviation parameters are adjusted to reduce deviations from the ointment production characteristics. Through this fine-tuning and updating process, precise ointment punching control parameters suitable for current production conditions are ultimately determined.

[0046] For example, it is assumed that the optimal control parameters for punching are obtained through fitness evaluation: the punching position is 10mm, the punching speed is 50mm / s, and the hole diameter is 5mm. Compared with the actual production feature information, there is a deviation of 0.5mm between the actual position and the expected position, which is used as the production feature matching deviation parameter. The deviation data in the historical punching control record data set and the corresponding control parameter variables are mapped and associated to construct a linear regression model: Y=aX+b, where Y is the punching position deviation, X is the punching speed deviation, and a and b are regression coefficients. Through regression model analysis, when the punching speed deviation increases by 1mm / s, the punching position deviation increases by 0.2mm. Therefore, the punching control parameters are fine-tuned according to this model. Through deviation analysis and fine-tuning, it is ensured that the punching control parameters are more in line with actual production conditions, and the punching parameters are automatically adjusted according to changes in production characteristics, thereby improving the adaptability and flexibility of the system.

[0047] Furthermore, step 4 of this application includes:

[0048] According to the ointment punching control parameters, the preset ointment punching effect information is determined; the preset ointment punching effect information and the ointment punching production feedback data stream are respectively subjected to feature twin extraction processing to obtain a preset ointment punching feature set and an ointment feedback punching feature set; the punching feature analysis target is obtained, the mean square error metric analysis is performed on the punching feature analysis target, and a punching feature loss function is constructed; the punching feature loss function is used to perform loss calculation analysis on the preset ointment punching feature set and the ointment feedback punching feature set to obtain the ointment punching loss feature information.

[0049] Specifically, based on the ointment punching control parameters, the expected punching effect, including aperture size, hole edge quality, punching time, and ointment loss rate, is predicted and set, reflecting the ideal punching result. Features related to punching loss, such as aperture deviation, hole edge roughness, and ointment loss, are extracted from the preset ointment punching effect information to form the preset ointment punching feature set. The same features are extracted from the ointment punching production feedback data stream to reflect the actual punching effect loss, forming the ointment feedback punching feature set. Through feature extraction, two feature sets are obtained: one reflecting the preset punching effect and the other reflecting the actual punching effect. Punching feature analysis targets are obtained, including key features of the ointment punching process, such as aperture deviation, hole edge roughness, and ointment loss. Mean squared error (MSE) metric analysis is performed on the punching feature analysis targets. MSE is a commonly used method for evaluating model prediction accuracy, measuring the difference between predicted and actual values. Based on the MSE metric analysis, a loss function is constructed to quantify the effect loss during the punching process.

[0050] Using the constructed perforation feature loss function, we perform loss calculation analysis on the preset and feedback ointment perforation feature sets. We calculate the loss between the preset and feedback feature sets, and obtain perforation loss feature information for the ointment. This includes loss characteristics during the perforation process, such as aperture deviation, hole edge roughness, and ointment loss. This information reflects the difference between the actual perforation effect and the expected effect, helping to identify and resolve perforation process issues. Using the loss function, we can quantify the perforation loss and understand the specific perforation loss, helping to optimize the production process and reduce losses.

[0051] Furthermore, step five of this application includes:

[0052] Based on the ointment punching loss characteristic information, the ointment punching control parameters are associated optimized and analyzed to obtain a punching control parameter selection solution space; the particle swarm parameters are initialized, and the particle swarm parameters include particle position and particle velocity; the ointment punching effect fitness function is used to iteratively search and evaluate the particle swarm parameters in the punching control parameter selection solution space to obtain a punching control parameter fitness set; based on the punching control parameter fitness set, particles are iteratively updated until a preset convergence condition is met, and the punching optimization control parameters are determined by optimization, and the punching optimization control parameters are the particles with the largest fitness.

[0053] Specifically, the team uses the ointment perforation loss characteristic information to optimize the perforation control parameters. Using statistical analysis or machine learning methods, they analyze the relationship between the loss characteristic information and the control parameters, identifying which control parameters are most correlated with perforation loss. This allows them to determine which parameters require optimization, reduce losses, and identify the optimal parameter combination. Through associative optimization analysis, a set of possible perforation control parameter values ​​is determined, forming a solution space. This solution space encompasses all possible parameter combinations that minimize perforation loss.

[0054] Initialize the particle swarm parameters, which are the basic elements used in the particle swarm optimization algorithm. The particle swarm optimization algorithm (PSO) is an optimization algorithm based on swarm intelligence that simulates the foraging behavior of a flock of birds to find the optimal solution to a problem. In an optimization problem, each particle represents a potential solution. The particle position usually refers to a point in the solution space that contains the values ​​of all decision variables of the optimization problem. The particle velocity determines the direction and distance the particle moves in the solution space. In the PSO algorithm, the particle velocity determines how the particle updates its position. The purpose of initializing the particle swarm parameters is to provide each particle with an initial solution and initial velocity. These parameters will be used in the iterative process of the PSO algorithm.

[0055] The performance of each particle is evaluated using the ointment punching effect fitness function. The fitness function calculates the fitness value of each particle based on preset indicators (such as pore size consistency, hole edge smoothness, etc.). Through iterative search and evaluation, the fitness values ​​of a group of particles are obtained to form a fitness set. According to the fitness value of each particle, the position and velocity of the particle are updated, and the fitness of each particle is evaluated using the ointment punching effect fitness function in each iteration. After iterative search and evaluation of the particle swarm parameters using the ointment punching effect fitness function, the punching control parameter fitness set is obtained, which contains the fitness values ​​of all particles and reflects the pros and cons in the current solution space.

[0056] Particles are iteratively updated based on the fitness set of the punching control parameters. Each particle will track its current best position (individual best position) and the best position of the entire group (global best position), and then adjust its position and speed based on this information. Set a preset convergence condition, such as the fitness change is less than a certain threshold, or the number of iterations reaches a preset maximum number. Meeting the convergence condition means that the particle swarm has found a relatively stable optimal solution, or the solution has converged to a local optimal solution. When the convergence condition is met, the particle with the largest fitness is found, and its position represents the optimal combination of punching control parameters. Through the particle swarm optimization algorithm, better punching control parameters are found within a shorter number of iterations. Continuous iteration and optimization adapt to different production conditions and improve overall performance.

[0057] In summary, the ointment online punching control method provided in this application has the following technical effects:

[0058] An online ointment punching control device is constructed, which includes a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor; the sensor module is used to monitor the ointment production line in real time, obtain the ointment production line operation data stream, and upload the ointment production line operation data stream to the control center for multi-channel diversion processing to obtain ointment production characteristic information; the control center collects and obtains the punching control record data set of the laser punching device, and performs retrieval, matching and optimization based on the ointment production characteristic information and the punching control record data set to determine the ointment punching control parameters; based on the ointment punching control parameters, the ointment production line is subjected to punching control monitoring to obtain the ointment punching production feedback data stream, and the ointment punching production feedback data stream is subjected to feature loss analysis to obtain ointment punching loss feature information; based on the ointment punching loss feature information, the ointment punching control parameters are optimized and analyzed to determine the punching optimization control parameters, and the ointment production line is subjected to online punching control using the punching optimization control parameters. In other words, through real-time monitoring and precise control, automatic analysis of feedback data, and optimization of control parameters, precise control of the ointment punching process is achieved, thereby improving production efficiency.

[0059] Example 2: Based on the same inventive concept as the ointment online punching control method in the above embodiment, this application also provides an ointment online punching control device, please refer to the attached Figure 2 , the ointment online punching control device comprises:

[0060] The data acquisition module 11 is used to build an online ointment punching control device, which includes a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor.

[0061] The real-time monitoring module 12 is used to use the sensor module to monitor the ointment production line in real time, obtain the ointment production line operation data flow, and upload the ointment production line operation data flow to the control center for multi-channel diversion processing to obtain ointment production characteristic information.

[0062] The control parameter determination module 13 is used to collect the punching control record data set of the laser punching equipment through the control center, perform retrieval matching and optimization based on the ointment production feature information and the punching control record data set, and determine the ointment punching control parameters.

[0063] The punching control monitoring module 14 is used to perform punching control monitoring on the ointment production line based on the ointment punching control parameters, obtain the ointment punching production feedback data stream, perform feature loss analysis on the ointment punching production feedback data stream, and obtain ointment punching loss feature information.

[0064] The control parameter updating module 15 is used to optimize and analyze the ointment punching control parameters based on the ointment punching loss characteristic information, determine the punching optimization control parameters, and use the punching optimization control parameters to perform online punching control on the ointment production line.

[0065] Furthermore, the real-time monitoring module 12 in the ointment online punching control device is also used for:

[0066] Determine a data acquisition type set according to the sensor module, and perform a preprocessing step analysis on the data acquisition type set to obtain a multi-type data preprocessing process set; obtain an ointment production feature extraction algorithm set, perform feature matching based on the data acquisition type set and the ointment production feature extraction algorithm set, and obtain a multi-type data feature extraction algorithm set; perform multi-channel configuration based on the multi-type data preprocessing process set and the multi-type data feature extraction algorithm set, and construct an ointment production data processing multi-channel; call the ointment production data processing multi-channel through the control center, perform multi-channel diversion processing on the ointment production line operation data flow based on the ointment production data processing multi-channel, and obtain the ointment production feature information.

[0067] Furthermore, the control parameter determination module 13 in the ointment online punching control device is further configured to:

[0068] Obtain ointment production attribute information, classify and identify the punching control record data set according to the ointment production attribute information, and obtain a punching attribute calibration data set; classify the ointment production line according to the ointment production attribute information to obtain target ointment production attribute parameters; perform association extraction on the punching attribute calibration data set based on the target ointment production attribute parameters to obtain a target punching control database; search and match the target punching control database based on the ointment production feature information to determine the ointment punching control parameters.

[0069] Furthermore, the control parameter determination module 13 in the ointment online punching control device is further configured to:

[0070] Based on the ointment production characteristic information, the target punching control database is searched and matched for analysis to obtain a punching data matching set; according to the punching data matching set, the data within a preset similarity threshold is screened, mapped and divided to obtain an available punching control data set; a set of ointment punching effect evaluation indicators is obtained, and an ointment punching effect fitness function is constructed based on the ointment punching effect evaluation indicator set; based on the ointment punching effect fitness function, the available punching control data set is evaluated, optimized and adjusted to determine the ointment punching control parameters.

[0071] Furthermore, the control parameter determination module 13 in the ointment online punching control device is further configured to:

[0072] Based on the ointment punching effect fitness function, the available punching control data set is evaluated for fitness and optimized to obtain the punching optimization control parameters; based on the punching optimization control parameters, the production feature matching deviation parameters are determined; the ointment production feature deviation data and the corresponding punching control parameter variables in the punching control record data set are mapped and associated with each other to construct an ointment punching parameter regression model; based on the ointment punching parameter regression model, the production feature matching deviation parameters are tuned and updated to determine the ointment punching control parameters.

[0073] Furthermore, the punching control monitoring module 14 in the ointment online punching control device is also used for:

[0074] According to the ointment punching control parameters, the preset ointment punching effect information is determined; the preset ointment punching effect information and the ointment punching production feedback data stream are respectively subjected to feature twin extraction processing to obtain a preset ointment punching feature set and an ointment feedback punching feature set; the punching feature analysis target is obtained, the mean square error metric analysis is performed on the punching feature analysis target, and a punching feature loss function is constructed; the punching feature loss function is used to perform loss calculation analysis on the preset ointment punching feature set and the ointment feedback punching feature set to obtain the ointment punching loss feature information.

[0075] Furthermore, the control parameter updating module 15 in the ointment online punching control device is further used for:

[0076] Based on the ointment punching loss characteristic information, the ointment punching control parameters are associated optimized and analyzed to obtain a punching control parameter selection solution space; the particle swarm parameters are initialized, and the particle swarm parameters include particle position and particle velocity; the ointment punching effect fitness function is used to iteratively search and evaluate the particle swarm parameters in the punching control parameter selection solution space to obtain a punching control parameter fitness set; based on the punching control parameter fitness set, particles are iteratively updated until a preset convergence condition is met, and the punching optimization control parameters are determined by optimization, and the punching optimization control parameters are the particles with the largest fitness.

[0077] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The ointment online punching control method and specific examples in Example 1 are also applicable to the ointment online punching control device in this embodiment. Through the detailed description of the ointment online punching control method above, those skilled in the art can clearly understand the ointment online punching control device in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0078] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for controlling online hole punching of ointment, characterized in that: include: Build an online ointment punching control device, which includes a laser punching device, a sensor module and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor; The sensor module is used to monitor the ointment production line in real time, obtain the ointment production line operation data stream, and upload the ointment production line operation data stream to the control center for multi-channel diversion processing to obtain ointment production characteristic information; Acquiring a drilling control record data set of the laser drilling device through the control center, performing retrieval, matching, and optimization based on the ointment production feature information and the drilling control record data set to determine ointment drilling control parameters; Performing punching control monitoring on the ointment production line based on the ointment punching control parameters to obtain an ointment punching production feedback data stream, performing feature loss analysis on the ointment punching production feedback data stream to obtain ointment punching loss feature information; Optimizing and analyzing the ointment punching control parameters based on the ointment punching loss characteristic information, determining the punching optimization control parameters, and performing online punching control on the ointment production line using the punching optimization control parameters; The obtaining of ointment production characteristic information includes: Determining a data acquisition type set according to the sensor module, and performing preprocessing step analysis on the data acquisition type set to obtain a multi-type data preprocessing process set; Obtaining an ointment production feature extraction algorithm set, performing feature matching based on the data collection type set and the ointment production feature extraction algorithm set to obtain a multi-type data feature extraction algorithm set; Perform multi-channel configuration based on the multi-type data preprocessing process set and the multi-type data feature extraction algorithm set to build a multi-channel for ointment production data processing; The ointment production data processing multi-channel is called by the control center, and the ointment production line operation data flow is subjected to multi-channel diversion processing based on the ointment production data processing multi-channel to obtain the ointment production characteristic information.

2. The ointment online punching control method according to claim 1, characterized in that: The step of determining the ointment punching control parameters includes: Obtaining ointment production attribute information, classifying and identifying the punching control record data set according to the ointment production attribute information, and obtaining a punching attribute calibration data set; Classify the ointment production line according to the ointment production attribute information to obtain target ointment production attribute parameters; Performing correlation extraction on the punching attribute calibration data set based on the target ointment production attribute parameters to obtain a target punching control database; The target punching control database is searched and matched based on the ointment production characteristic information to determine the ointment punching control parameters.

3. The ointment online punching control method according to claim 2, characterized in that: The step of determining the ointment punching control parameters includes: Performing a search and matching analysis on the target punching control database based on the ointment production characteristic information to obtain a punching data matching set; According to the punching data matching degree set, the data within the preset similarity threshold is screened, mapped and divided to obtain an available punching control data set; Obtaining a set of evaluation indicators for ointment perforation effect, and constructing an ointment perforation effect fitness function based on the set of evaluation indicators for ointment perforation effect; The available perforation control data set is evaluated and optimized based on the ointment perforation effect fitness function to determine the ointment perforation control parameters.

4. The ointment online punching control method according to claim 3, characterized in that: The step of determining the ointment punching control parameters includes: Performing fitness evaluation and optimization on the available perforation control data set based on the ointment perforation effect fitness function to obtain the optimal perforation control parameters; Determining a production feature matching deviation parameter based on the punching priority control parameter; Performing mapping association fitting on the ointment production characteristic deviation data and the corresponding punching control parameter variables in the punching control record data set to construct an ointment punching parameter regression model; The production feature matching deviation parameters are tuned and updated based on the ointment punching parameter regression model to determine the ointment punching control parameters.

5. The ointment online punching control method according to claim 1, characterized in that: The obtaining of ointment perforation loss characteristic information includes: Determining preset ointment punching effect information according to the ointment punching control parameters; Performing feature twin extraction processing on the preset ointment punching effect information and the ointment punching production feedback data stream respectively to obtain a preset ointment punching feature set and an ointment feedback punching feature set; Obtaining a perforation feature analysis target, performing a mean square error metric analysis on the perforation feature analysis target, and constructing a perforation feature loss function; The punching feature loss function is used to perform loss calculation analysis on the preset ointment punching feature set and the ointment feedback punching feature set to obtain the ointment punching loss feature information.

6. The ointment online punching control method according to claim 3, characterized in that: The determining of the punching optimization control parameters includes: Performing correlation optimization analysis on the ointment punching control parameters based on the ointment punching loss characteristic information to obtain a solution space for selecting the ointment punching control parameters; Initializing particle swarm parameters, including particle positions and particle velocities; Using the ointment punching effect fitness function, iteratively search and evaluate the particle swarm parameters in the punching control parameter selection solution space to obtain a punching control parameter fitness set; Particles are iteratively updated based on the fitness set of the punching control parameters until a preset convergence condition is met, and the punching optimization control parameters are determined by optimization, and the punching optimization control parameters are the particles with the largest fitness.

7. An ointment online punching control device, characterized in that: The steps for implementing the ointment online punching control method according to any one of claims 1 to 6, wherein the ointment online punching control device comprises: A data acquisition module, which is used to build an online ointment punching control device, which includes a laser punching device, a sensor module, and a control center, wherein the sensor module integrates a visual sensor, a speed sensor, a temperature and humidity sensor, and a position sensor; A real-time monitoring module, which is used to use the sensor module to monitor the ointment production line in real time, obtain the ointment production line operation data stream, and upload the ointment production line operation data stream to the control center for multi-channel diversion processing to obtain ointment production characteristic information; a control parameter determination module, the control parameter determination module being configured to acquire a drilling control record data set of the laser drilling device through the control center, perform retrieval, matching, and optimization based on the ointment production characteristic information and the drilling control record data set, and determine the ointment drilling control parameters; a punching control monitoring module, the punching control monitoring module being used to perform punching control monitoring on the ointment production line based on the ointment punching control parameters, obtain an ointment punching production feedback data stream, perform feature loss analysis on the ointment punching production feedback data stream, and obtain ointment punching loss feature information; A control parameter updating module is used to optimize and analyze the ointment punching control parameters based on the ointment punching loss characteristic information, determine the punching optimization control parameters, and use the punching optimization control parameters to perform online punching control on the ointment production line.

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