Ointment online punching control method and device

Abstract

The invention provides an ointment on-line punching control method and device, and relates to the technical field of pharmaceutical machining, and the ointment on-line punching control method comprises the steps that the ointment on-line punching control device is built; monitoring an ointment production line in real time, and carrying out multi-channel shunting treatment to obtain ointment production characteristic information; a punching control record data set is collected, and retrieval matching optimization is carried out; punching control monitoring is conducted on the production line, and feature loss analysis is conducted; and performing optimization analysis on the ointment punching control parameters, determining punching optimization control parameters, and performing online punching control on the ointment production line. The technical problem that in the prior art, due to the fact that manual operation is usually needed or a fixed mechanical device is used in the existing ointment production process, the production efficiency is low can be solved, accurate control over the ointment punching process is achieved through real-time monitoring and accurate control, automatic analysis of feedback data and optimization of control parameters, and the production efficiency is improved. And the production efficiency is improved.

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 limits 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 of low production efficiency in the prior art due to the fact that the existing ointment production process usually requires manual operation or the use of fixed mechanical devices.

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

[0006] In the first aspect, the present application provides an online ointment punching control method, which is implemented by an online ointment punching control device, wherein the online ointment punching control method comprises: building an online ointment punching control device, wherein 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; 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 acquire 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; the ointment production line is monitored for punching control based on the ointment punching control parameters to obtain the ointment punching production feedback data stream, and the ointment punching production feedback data stream is analyzed for characteristic loss to obtain the ointment punching loss characteristic information; the ointment punching control parameters are optimized and analyzed based on the ointment punching loss characteristic information to determine the punching optimization control parameters, and the ointment production line is controlled online by using the punching optimization control parameters. That is to say, 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, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited 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 the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

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

[0014] Explanation of the reference numerals: 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] The present application provides an ointment online punching control method and device, which solves the technical problem in the prior art that the 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.

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

[0017] For example, 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, an online ointment punching control device is built, including laser punching equipment, sensor modules and control center. Laser punching equipment is a device that uses the high energy density characteristics of laser beams to accurately punch holes in materials. It has the advantages of fast speed, high precision and contactless processing. The sensor module is a system that integrates multiple sensors, including visual sensors, speed sensors, temperature and humidity sensors and position sensors, which are used to collect status information of the environment or equipment. By integrating multiple sensors, the ointment production status is fully monitored, and precise control of the ointment punching process is achieved.

[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, the sensor module is used to monitor the status of the ointment production line in real time and collect key parameters such as the flow rate, temperature, humidity, and position of the ointment. The data collected by the sensor module is combined into a continuous data stream, which contains the real-time status information of the ointment production line. The collected data is uploaded to the control center by wired or wireless means, and the control center calls the constructed data processing multi-channel to process the operation data flow of the ointment production line in real time. According to the type and characteristics of the data, the data is diverted to the corresponding processing channel and processed according to the predetermined preprocessing process and feature extraction process. For example, after the image data collected by the visual sensor is uploaded to the control center, it is diverted to the visual data channel, and after image preprocessing and feature extraction, the shape, size and other feature information of the ointment are obtained; after the speed data collected by the speed sensor is uploaded to the control center, it is diverted to the speed data channel, and after filtering and smoothing, the flow rate characteristics of the ointment are extracted. Through multi-channel diversion processing, multiple types of data are processed at the same time. Each channel is specially configured with a preprocessing process and feature extraction algorithm suitable for its data type, which enhances the data processing capability and accuracy.

[0022] Step three: acquiring the drilling control record data set of the laser drilling equipment through the control center, performing retrieval, matching and optimization based on the ointment production feature information and the drilling control record data set, and determining the ointment drilling control parameters.

[0023] Specifically, the control center collects the punching control record data set from the laser punching equipment, including the relevant attribute data of ointment production (such as ointment type, dosage form specification, production process), production feature data (such as ointment flow rate, temperature, humidity, position), punching control parameters (such as punching position, speed, depth) and punching effect data (such as aperture size, distribution uniformity, edge quality). The ointment production attribute information is obtained, including ointment type, dosage form specification, production process, etc., and the punching control record data set is classified to divide the data set into different categories. The ointment production line is attribute-classified according to the ointment production attribute information to determine the target ointment production attribute parameters. The punching control data related to the target attribute parameters are extracted from the data set, and a target punching control database is constructed, which contains the punching control data applicable to the current ointment attributes. Using the ointment production feature information, the target punching control database is searched and matched to find the punching control parameters that best suit the current production characteristics. Through retrieval, matching and tuning, it can be ensured that the best punching control parameters are found for the current ointment, and the accuracy of punching control can be improved.

[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 characteristic loss analysis is performed on the ointment punching production feedback data stream to obtain ointment punching loss characteristic information.

[0025] Specifically, the control center applies the punching control parameters on the production line according to the ointment punching control parameters to perform punching operations on the ointment patch. The ointment production line is monitored in real time through the sensor module and the punching operation feedback data on the production line is collected. These data reflect the actual effect of the punching process, such as the actual aperture size, aperture distribution, and hole wall smoothness. The ointment punching production feedback data stream is subjected to characteristic loss analysis to evaluate the actual effect of the punching parameters and identify the loss characteristics in the punching process, including aperture deviation, hole edge roughness, and ointment loss. By real-time monitoring of the punching operation on the production line, problems in the punching process are discovered in a timely manner, and characteristic loss analysis of the feedback data stream is performed to understand the loss situation in the punching process, and the punching control parameters are continuously optimized to 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 punching control parameters are optimized and analyzed using the characteristic information of the ointment punching loss to find out which control parameters are most related to the punching loss, so as to determine the parameters that need to be optimized. Through the association optimization analysis, a set of possible punching control parameter values ​​are determined to form a solution space. The positions and velocities of a group of particles are randomly initialized. The particle positions represent possible combinations of punching control parameters, and the particle velocities are used to control the movement of particles. 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. By evaluating each particle position, a set of fitness values ​​of the punching control parameters are obtained. According to the fitness set, the particle swarm parameters are iteratively updated, including the update of particle positions and the adjustment of particle velocities. The iterative update process will continue until the preset convergence conditions are met, including reaching the maximum number of iterations, the fitness change is less than a certain threshold, etc. After the iterative update is completed, the particle with the largest fitness is found, that is, the punching optimization control parameter. According to the punching optimization control parameters, the ointment production line is online punched to ensure that the punching process is carried out according to the optimized control parameters to achieve the expected punching effect. The control center monitors and adjusts the punching process in real time based on the optimized punching control parameters to ensure that the equipment operates according to the parameter requirements. Based on the real-time feedback data, the punching control parameters are fine-tuned to maintain the best punching effect. Through optimization analysis, the optimal punching control parameters are found, thereby optimizing the punching process and improving the punching effect.

[0028] Further, 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, according to the capabilities and requirements of the sensor module, determine the data type that needs to be collected, such as visual sensors collecting image data, speed sensors collecting speed data, temperature and humidity sensors collecting temperature and humidity data, and position sensors collecting position data. Perform preprocessing analysis on each data type to remove noise, correct errors, standardize data formats, etc. to ensure data quality. Data preprocessing is to improve data quality and facilitate subsequent feature extraction and analysis. Since different sensors collect different types of data, their preprocessing steps will also be different. For example, for image data collected by visual sensors, preprocessing such as denoising, contrast enhancement, and segmentation is required; while for speed data collected by speed sensors, preprocessing such as filtering and smoothing is required. According to different data types, formulate corresponding preprocessing processes to form a process set.

[0031] According to different aspects of ointment production, a series of feature extraction algorithms are collected and sorted 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 speed data uses time series analysis algorithms. Through feature matching, a feature extraction algorithm set for multi-type data is obtained, and each algorithm corresponds to one or more data types. According to the preprocessing process and feature extraction algorithm, a multi-channel data processing system is constructed. Each channel is responsible for processing one type of data, from data acquisition to preprocessing, and then to feature extraction, forming a complete data processing chain. Each channel needs to be configured with corresponding sensors, preprocessing processes and feature extraction algorithms. For example, the visual data channel needs to be configured with image sensors, image preprocessing processes and image feature extraction algorithms. The purpose of multi-channel configuration is to effectively combine the preprocessing process and feature extraction algorithm to form an efficient system that can process multiple types of data at the same time. Through multi-channel configuration, the data processing process can be optimized and the efficiency and accuracy of data processing can be improved.

[0032] The control center calls the ointment production data processing multi-channel to process the data flow on the production line in real time. The data flow is assigned to the corresponding processing channel according to the data type. Each channel is configured with a preprocessing process and feature extraction algorithm suitable for its data type. After entering each channel, the data flow will go through the corresponding preprocessing and feature extraction steps. Through multi-channel diversion processing, key feature information is extracted from the ointment production data, such as the ointment's flow rate, temperature, humidity, and location. Through multi-channel configuration, different types of data can be processed in parallel, which improves the overall data processing speed.

[0033] Further, 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; perform search, matching and optimization on 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), etc. According to the ointment production attribute information, the punching control record data set is classified and identified to find the best punching parameters for each ointment type. The data set is divided into different categories, each category corresponding 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] According to the ointment production attribute information, the ointments on the production line are classified to find the most suitable production parameters for each type of ointment. According to the type of ointment, dosage form specifications and production process information, the ointment production line is divided into different categories, and the target ointment production attribute parameters are obtained, including production speed, temperature control, humidity control, pressure setting, etc., to determine the optimal parameter settings for each category of ointment production line. The punching control parameters related to the target ointment production attribute parameters are extracted from the punching attribute calibration data set. The purpose of association extraction is to match the target ointment production attribute parameters with the optimal punching parameters in the punching attribute calibration data set. Through association extraction, a database containing punching control data matching the target ointment production attribute parameters is obtained to ensure that the punching parameters match the production requirements of the ointment. For example, for an ointment with a specific production process, the database contains the corresponding temperature control and humidity control parameters. The control center compares and matches the current ointment production feature information with the parameters in the database to find the punching control parameters that best suit the current production features. Through attribute classification and matching, it is ensured 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, thereby improving the intelligence level of the system.

[0037] Furthermore, the present application also includes 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 according to 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, the ointment production feature information is used to search in the target perforation control database, the current ointment production feature information is compared with the parameters in the database, and the matching degree is calculated through similarity algorithms such as cosine similarity, Euclidean distance, etc. Through matching analysis, a perforation data matching set is obtained, which contains the matching score of each perforation control parameter and the current ointment production feature. A similarity threshold is set in advance, and only records with a matching degree higher than this threshold will be considered. From the perforation data matching set, perforation parameters that highly match the ointment production feature are selected, and the selected perforation parameters are associated with their matching scores through mapping to form an ordered data set, which is the perforation control data set. It is obtained through experimental or historical data, usually based on the physical properties and production requirements of the ointment, to obtain a set of evaluation indicators for the perforation effect of the ointment, including pore size, pore size distribution uniformity, hole wall smoothness, ointment integrity, etc., which reflects the quality of the perforation effect.

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

[0041] For example, suppose 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 preset similarity threshold is 0.80, and record 1 and record 3 are screened 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 selection of the best punching parameters is ensured, and the punching parameters are automatically selected and optimized, thereby improving the intelligence and automation level of the system.

[0042] Furthermore, the present application also includes 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 optimal punching control parameters; based on the optimal punching control parameters, the production feature matching deviation parameters are determined; the ointment production feature deviation data in the punching control record data set and the corresponding punching control parameter variables 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, i.e., the optimal punching control parameters. According to the optimal punching control parameters, analyze their matching degree with the current ointment production characteristic information 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 applications, such as whether it will cause ointment loss, pore size non-uniformity, 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 to make them more consistent with the ointment production characteristics.

[0045] The ointment production feature deviation data refers to the deviation that occurs during the production process, such as the difference between the actual position of the ointment patch and the expected position. The ointment production feature deviation data in the punching control record data set is mapped, associated and fitted with the corresponding punching control parameter variables. The punching control parameter variables include control parameters that affect the punching effect, such as punching speed, depth, position, etc. The ointment production feature deviation data is associated with the corresponding punching control parameter variables, and a mapping relationship is constructed through statistical methods or machine learning algorithms. Through mapping, association and fitting, a regression model is constructed to predict or fine-tune the punching control parameters according to the change deviation of the ointment production feature data. The constructed regression model is used to tune and update the production feature matching deviation parameters so that the control parameters are more accurately adapted to the actual production conditions. According to the prediction of the ointment punching parameter regression model, the production feature matching deviation parameters are adjusted to reduce the deviation from the ointment production characteristics. Through tuning and updating, the precise ointment punching control parameters suitable for the current production conditions are finally 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 aperture 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 the actual production conditions, and the punching parameters are automatically adjusted according to the changes in production characteristics, which improves the adaptability and flexibility of the system.

[0047] Further, 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, according to the ointment punching control parameters, the expected punching effect is predicted and set, including the aperture size, hole edge quality, punching time, ointment loss rate, etc., reflecting the ideal punching result. The features related to the punching loss, such as aperture deviation, hole edge roughness, ointment loss, etc., 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 effect loss in the actual punching process, 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. The punching feature analysis target is obtained, including the key features in the ointment punching process, such as aperture deviation, hole edge roughness, ointment loss, etc. The mean square error metric analysis is performed on the punching feature analysis target. The mean square error is a commonly used method to evaluate the prediction accuracy of the model, which is used to measure the difference between the predicted value and the actual value. Based on the mean square error metric analysis, a loss function is constructed to quantify the effect loss in the punching process.

[0050] Using the constructed punching feature loss function, the loss calculation analysis is performed on the preset ointment punching feature set and the ointment feedback punching feature set, and the loss value between the preset feature set and the feedback feature set is calculated to obtain the ointment punching loss feature information, including the loss characteristics in the punching process, such as aperture deviation, hole edge roughness, ointment loss, etc. This information reflects the difference between the actual punching effect and the expected effect, which helps to identify and solve problems in the punching process. Through the loss function, the effect loss in the punching process is quantified, and the specific situation of the punching loss is understood, which helps to optimize the production process in a targeted manner and reduce losses.

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

[0052] Based on the ointment punching loss characteristic information, the ointment punching control parameters are associated and optimized 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 particles with the largest fitness.

[0053] Specifically, the punching control parameters are optimized and analyzed using the ointment punching loss feature information. The relationship between the loss feature information and the control parameters is analyzed using statistical analysis or machine learning methods to find out which control parameters are most relevant to the punching loss, thereby determining the parameters that need to be optimized, reducing the loss, and determining the best combination of parameters. Through the association optimization analysis, a set of possible punching control parameter values ​​is determined to form a solution space. The solution space contains all possible parameter combinations that can minimize the punching loss.

[0054] Initialize the particle swarm parameters, which are the basic elements used in the particle swarm optimization algorithm. Particle swarm optimization (PSO) is an optimization algorithm based on swarm intelligence that simulates the foraging behavior of bird flocks to find the optimal solution to the problem. In the optimization problem, each particle represents a potential solution. The particle position usually refers to a point in the solution space, which contains the values ​​of all decision variables of the optimization problem. The particle velocity determines the direction and distance that 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, which 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 will calculate 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 speed 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 through 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 according to 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 the 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 in a shorter number of iterations. Continuously iterate and optimize to adapt to different production conditions and improve overall performance.

[0057] In summary, the ointment online punching control method provided by the present 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; the ointment production line is monitored for punching control based on the ointment punching control parameters to obtain the ointment punching production feedback data stream, and the ointment punching production feedback data stream is analyzed for characteristic loss to obtain the ointment punching loss characteristic information; the ointment punching control parameters are optimized and analyzed based on the ointment punching loss characteristic information to determine the punching optimization control parameters, and the ointment production line is controlled online by using the punching optimization control parameters. That is to say, 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] Embodiment 2: Based on the same inventive concept as the ointment online perforation control method in the above embodiment, the present application also provides an ointment online perforation control device, see 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 perform real-time monitoring on the ointment production line, 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.

[0062] The control parameter determination module 13 is used to acquire 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 characteristic loss analysis on the ointment punching production feedback data stream, and obtain ointment punching loss characteristic 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 perforation 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] Further, the control parameter determination module 13 in the ointment online perforation control device is also used for:

[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; perform search, matching and optimization on the target punching control database based on the ointment production feature information to determine the ointment punching control parameters.

[0069] Further, the control parameter determination module 13 in the ointment online perforation control device is also used for:

[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 according to 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] Further, the control parameter determination module 13 in the ointment online perforation control device is also used for:

[0072] Based on the ointment punching effect fitness function, the available punching control data set is evaluated for fitness and optimized to obtain the optimal punching control parameters; based on the optimal punching control parameters, the production feature matching deviation parameters are determined; the ointment production feature deviation data in the punching control record data set and the corresponding punching control parameter variables 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 perforation control monitoring module 14 in the ointment online perforation 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] Further, the control parameter updating module 15 in the ointment online perforation control device is also used for:

[0076] Based on the ointment punching loss characteristic information, the ointment punching control parameters are associated and optimized 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 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 the first embodiment are also applicable to the ointment online punching control device in the present embodiment. Through the above detailed description of the ointment online punching control method, those skilled in the art can clearly know the ointment online punching control device in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those 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 will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can 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 belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for controlling online hole punching of ointment, characterized in that: include: Build an ointment online 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 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; Acquiring a drilling control record data set of the laser drilling device through the control center, performing retrieval, matching and tuning based on the ointment production feature information and the drilling control record data set, and determining the ointment drilling control parameters; Based on the ointment punching control parameters, the ointment production line is monitored for punching control, a production feedback data stream of ointment punching is obtained, and characteristic loss analysis is performed on the ointment punching production feedback data stream to obtain ointment punching loss characteristic information; The ointment punching control parameters are optimized and analyzed based on the ointment punching loss characteristic information, the punching optimization control parameters are determined, and the ointment production line is online punched using the punching optimization control parameters.

2. A method for controlling online hole punching of ointment as claimed in claim 1, characterized in that: The step of obtaining ointment production characteristic information includes: Determine a data acquisition type set according to the sensor module, and perform preprocessing step analysis on the data acquisition type set to obtain a multi-type data preprocessing process set; Acquire a set of ointment production feature extraction algorithms, perform feature matching based on the data collection 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 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.

3. The method for controlling the online hole punching of ointment according to claim 1, characterized in that: The step of determining the ointment perforation control parameters comprises: 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; Based on the target ointment production attribute parameters, the perforation attribute calibration data set is associated and extracted to obtain a target perforation control database; The target perforation control database is searched and matched based on the ointment production characteristic information to determine the ointment perforation control parameters.

4. A method for controlling online hole punching of ointment as claimed in claim 3, characterized in that: The step of determining the ointment perforation control parameters comprises: Based on the ointment production characteristic information, a search matching degree analysis is performed on the target punching control database to obtain a punching data matching degree 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 the effect of ointment perforation, and constructing an ointment perforation effect fitness function according to the set of evaluation indicators for the effect of ointment perforation; Based on the ointment perforation effect fitness function, the available perforation control data set is evaluated and optimized to determine the ointment perforation control parameters.

5. A method for controlling online hole punching of ointment as claimed in claim 4, characterized in that: The step of determining the ointment perforation control parameters comprises: Based on the ointment perforation effect fitness function, the available perforation control data set is evaluated and optimized to obtain the perforation optimal control parameters; Determining a production feature matching deviation parameter according to the punching priority control parameter; Mapping and associating 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.

6. The method for controlling the online hole punching of ointment according to claim 1, characterized in that: The step of obtaining the ointment perforation loss characteristic information comprises: Determining preset ointment punching effect information according to the ointment punching control parameter; 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; Obtain a perforation feature analysis target, perform a mean square error metric analysis on the perforation feature analysis target, and construct 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.

7. A method for controlling online hole punching of ointment as claimed in claim 4, characterized in that: The step of determining the optimal control parameters for punching includes: Based on the ointment perforation loss characteristic information, the ointment perforation control parameters are subjected to correlation optimization analysis to obtain a perforation control parameter selection solution space; Initializing particle swarm parameters, wherein the particle swarm parameters include 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.

8. An ointment online drilling control device, characterized in that: Steps for implementing the method for controlling online ointment perforation according to any one of claims 1 to 7, wherein the device for controlling online ointment perforation comprises: A data acquisition module, wherein the data acquisition module is used to build an online ointment punching control device, wherein 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, wherein the real-time monitoring module is used to use the sensor module to perform real-time monitoring on the ointment production line, obtain the ointment production line operation data stream, and upload the ointment production line operation data stream to the control center for multi-channel shunting processing to obtain ointment production characteristic information; A control parameter determination module, the control parameter determination module is used 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 feature 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 is 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 characteristic loss analysis on the ointment punching production feedback data stream, and obtain ointment punching loss characteristic 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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