A polymer filter production quality tracking system and method

Abstract

The present application provides a polymer filter production quality tracking system and method, which relates to the field of production control technology, including: a production component disassembly unit disassembles the polymer filter production components and outputs multiple filter production parts; a real-time parameter acquisition unit tracks and identifies the filter production parts and obtains multiple real-time production parameter sets; the parameters are input into the filter simulation system through a parameter input unit; the filter simulation unit performs filter simulation and obtains a filter simulation data set; an adjustment parameter acquisition unit generates a filter performance index according to the filter simulation data set, and uses the difference between the filter performance index and the preset filter performance index as feedback to obtain the production line adjustment parameters; and a parameter adjustment unit adjusts the parameters of the production line. The present application solves the technical problem that the traditional production method lacks a real-time and accurate production control mechanism, resulting in poor product consistency, and improves the quality and efficiency of filter production.

Description

Technical Field

[0001] The present application relates to the technical field of production control, and in particular to a polymer filter production quality tracking system and method. Background Art

[0002] Polymer filters are a key component widely used in the chemical, food, and pharmaceutical industries, mainly used to filter and purify impurities in liquids or gases. As the industry's requirements for filtration efficiency and product quality continue to increase, how to ensure that polymer filters meet high quality standards during the production process has become a focus of attention for manufacturing companies.

[0003] Existing polymer filter production methods mainly rely on offline detection and empirical adjustment. Usually, after the initial setting, the production line relies on fixed process parameters for continuous production, and the quality control of each batch of products is mostly carried out through sampling inspection. On the one hand, due to the lack of real-time monitoring of the production process, this method cannot promptly discover and correct potential problems in the production process, resulting in increased uncertainty in product quality. On the other hand, due to the lack of specific tracking and real-time monitoring of each production part, it is difficult to carry out personalized control of each production part, which easily leads to poor product consistency. In addition, batch inspection and final inspection result in the adjustment of production line parameters often lagging behind the emergence of problems and unable to respond quickly to changes in the production process. These shortcomings make it difficult for existing polymer filter production control methods to meet the requirements of modern industry for high quality and high consistency. Summary of the invention

[0004] The present application provides a polymer filter production quality tracking system and method, which solves the technical problem that traditional polymer filter production methods usually rely on offline detection and experience-based adjustment, and cannot adjust the production line settings in time according to real-time production parameters during the production process, resulting in poor product consistency and high scrap rate. The application achieves the technical effect of dynamically adjusting production parameters according to the real-time status of the production line to improve the product quality and production efficiency of polymer filters.

[0005] In view of the above problems, on the one hand, the present application provides a polymer filter production quality tracking system, the system comprising: a production component disassembly unit, the production component disassembly unit is used to disassemble the production components of the polymer filter and output a plurality of filter production parts, each filter production part represents an independent part of the production process; a real-time parameter acquisition unit, the real-time parameter acquisition unit is used to track and identify the plurality of filter production parts and obtain a plurality of real-time production parameter sets, wherein each filter production part corresponds to a real-time production parameter set; a parameter input unit, the parameter input unit is used to input the plurality of real-time production parameter sets into a filter simulation system, the filter simulation system is obtained through finite element analysis; a filter simulation unit, the filter simulation unit is used to perform filter simulation according to the filter simulation system and obtain a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution and filter filtration efficiency; an adjustment parameter acquisition unit, the adjustment parameter acquisition unit is used to generate a filter performance index according to the filter simulation data set, and to obtain a production line adjustment parameter by feedback of the difference between the filter performance index and the preset filter performance index; a parameter adjustment unit, the parameter adjustment unit is used to adjust the parameters of the polymer filter production line according to the production line adjustment parameter.

[0006] On the other hand, the present application also provides a method for tracking the production quality of polymer filters, the method comprising: disassembling the production components of the polymer filter and outputting multiple filter production parts, each filter production part representing an independent part of the production process; tracking and identifying the multiple filter production parts and obtaining multiple real-time production parameter sets, wherein each filter production part corresponds to a real-time production parameter set; inputting the multiple real-time production parameter sets into a filter simulation system, the filter simulation system being obtained through finite element analysis; performing filter simulation according to the filter simulation system and obtaining a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution and filter filtration efficiency; generating a filter performance index based on the filter simulation data set, and obtaining a production line adjustment parameter by providing feedback based on the difference between the filter performance index and the preset filter performance index; and adjusting the parameters of the polymer filter production line according to the production line adjustment parameters.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The production component disassembly unit disassembles the production components of the polymer filter and outputs multiple filter production parts, so that the production process can be refined to each independent part, laying the foundation for subsequent precise tracking and control. The real-time parameter acquisition unit tracks and identifies multiple filter production parts, obtains multiple real-time production parameter sets, and monitors the production status of each production part in real time. The parameter input unit inputs multiple real-time production parameter sets into the filter simulation system; the filter simulation unit simulates the filter according to the filter simulation system and obtains the filter simulation data set. This process is simulated by finite element analysis to predict the flow characteristics, pressure distribution and filtration efficiency of the filter in advance, thereby providing a basis for subsequent performance evaluation. The adjustment parameter acquisition unit generates a filter performance index based on the filter simulation data set, and feeds back the difference between the filter performance index and the preset filter performance index to obtain the production line adjustment parameter. This feedback mechanism ensures that the production line can be accurately adjusted according to real-time data. Finally, the parameter adjustment unit adjusts the parameters of the polymer filter production line according to the production line adjustment parameters, realizing closed-loop control of the production process.

[0009] In summary, this application has built a closed-loop quality control solution by disassembling production components, tracking production parameters in real time, simulating and analyzing filter performance, and adjusting the production line based on feedback. The real-time monitoring and feedback mechanism ensures that deviations in the production process are corrected in a timely manner, effectively reducing quality fluctuations and defective products in the production process; the refined tracking and identification of the production process enhances the traceability of the product and provides strong support for the rapid location and resolution of quality issues; through simulation analysis, production bottlenecks are predicted and resolved in advance, production stagnation and adjustment time are reduced, and overall production efficiency is improved. This application realizes precise monitoring of each production part and dynamic adjustment of the production line, significantly improving the stability, consistency and production efficiency of the polymer filter production process.

[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 in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of the structure of a polymer filter production quality tracking system provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a process for obtaining optimized filter performance indicators in a polymer filter production quality tracking system provided in an embodiment of the present application;

[0013] Figure 3A schematic diagram of a process for obtaining production line adjustment parameters in a polymer filter production quality tracking system provided in an embodiment of the present application;

[0014] Figure 4 A schematic flow chart of a polymer filter production quality tracking method provided in an embodiment of the present application.

[0015] Explanation of reference numerals: production component disassembling unit 10 , real-time parameter acquiring unit 20 , parameter input unit 30 , filter simulation unit 40 , adjustment parameter acquiring unit 50 , parameter adjusting unit 60 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a polymer filter production quality tracking system and method, which solves the technical problem that traditional polymer filter production methods usually rely on offline detection and experience-based adjustments, and cannot adjust production line settings in time according to real-time production parameters during the production process, resulting in poor product consistency and high scrap rates. The technical effect of dynamically adjusting production parameters according to the real-time status of the production line to improve the product quality and production efficiency of polymer filters is achieved.

[0017] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a polymer filter production quality tracking system, the system comprising:

[0018] The production component disassembly unit 10 is used to disassemble the production components of the polymer filter and output a plurality of filter production parts, each filter production part representing an independent part in the production process.

[0019] Specifically, during the production process, the polymer filter is usually composed of multiple complex components or production steps. The production component disassembly unit 10 disassembles a complete filter into multiple production parts that can be processed separately by decomposing the physical structure or production process of the filter. Each filter production part is an independent part of the polymer filter production process. These production parts can be physical components, such as filter membranes, support layers, sealing rings, etc., or they can be a specific stage in the production process, such as coating filter membranes, heating curing and cooling steps. This process can use automated mechanical tools and production control software to decompose the production process of polymer filters into multiple independently traceable production parts, efficiently and accurately decompose and identify each production part of the filter, so that each link in the production process can be more carefully monitored and controlled.

[0020] The real-time parameter acquisition unit 20 is used to track and identify the multiple filter production parts and acquire multiple real-time production parameter sets, wherein each filter production part corresponds to a real-time production parameter set.

[0021] Specifically, the real-time parameter acquisition unit 20 is responsible for tracking and monitoring each disassembled production part and acquiring all real-time production parameters related thereto. After each filter production part is disassembled by the production component disassembly unit 10, the real-time parameter acquisition unit 20 will assign it a unique identification code for tracking its status and parameters during the entire production process. For example, a QR code or RFID tag can be used to ensure that each production part can be accurately identified and located in the subsequent production process.

[0022] During the production process, the real-time parameter acquisition unit 20 tracks each production part through the identification code and collects the real-time production parameters related to the production part. The collection process is completed by a variety of sensors and data acquisition equipment, such as temperature sensors, pressure sensors, flow meters, etc., which are used to monitor the status of the production parts on the production line in real time. As the production parts move on the production line, the real-time parameter acquisition unit 20 continues to collect the real-time production parameters of each production part, and summarizes these data into a parameter set, associates them with the unique identifier of the production part, and generates multiple real-time production parameter sets. These real-time production parameter sets contain a series of production parameter sets related to the filter production parts, such as temperature, pressure, speed, etc., which change over time. For example, when the filter membrane is coated, the real-time parameter acquisition unit 20 can collect parameters such as the temperature of the coating solution, the coating pressure, the movement speed of the membrane, etc. in real time, and record these data in the parameter set of the production part.

[0023] The real-time parameter acquisition unit 20 uniquely identifies each filter production part and collects all production parameters related to the production part, thereby ensuring the transparency and traceability of the production process and providing key data support for subsequent production adjustments and quality control.

[0024] The parameter input unit 30 is used to input the plurality of real-time production parameter sets into the filter simulation system, wherein the filter simulation system is obtained through finite element analysis.

[0025] Specifically, the parameter input unit 30 inputs a plurality of real-time production parameter sets collected from the production line into the filter simulation system, so as to simulate and predict the performance of the filter through finite element analysis.

[0026] The filter simulation unit 40 is used to perform filter simulation according to the filter simulation system to obtain a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution and filter filtration efficiency.

[0027] Specifically, the filter simulation unit 40 obtains all necessary real-time production parameters from the parameter input unit 30, and uses the filter simulation system to perform detailed simulation analysis to generate a filter simulation data set. In the filter simulation system, the flow characteristics are first analyzed. The fluid flow in the filter is simulated using a fluid dynamics simulation tool to predict the flow velocity distribution, flow direction, vortex formation, etc. Next, the pressure distribution inside the filter is analyzed using a finite element analysis tool, and the pressure load at different locations is evaluated by calculating the pressure difference of the fluid inside the filter. Then, based on the structure and fluid characteristics of the filter, the filter's ability to remove specific particles or impurities is calculated to simulate its filtration efficiency. Finally, the filter simulation unit 40 will aggregate all simulation results into a filter simulation data set. This data set includes key indicators such as flow characteristics, pressure distribution, and filtration efficiency, which can be used to predict the performance of the filter under actual working conditions and optimize the production process and product design.

[0028] The adjustment parameter acquisition unit 50 is used to generate a filter performance index according to the filter simulation data set, and to obtain a production line adjustment parameter by providing feedback based on the difference between the filter performance index and a preset filter performance index.

[0029] Specifically, the adjustment parameter acquisition unit 50 acquires a simulation data set from the filter simulation unit 40. This data set contains key performance information such as the flow characteristics, pressure distribution and filtration efficiency of the filter. The adjustment parameter acquisition unit 50 processes this information into quantifiable performance indicators, namely, filter performance indicators. These filter performance indicators include filtration efficiency, pressure distribution, flow characteristics, etc., which are used to evaluate the performance of the filter. For example, the uniformity of the flow characteristics can be represented by the standard deviation of the velocity distribution, the pressure distribution can be measured by the maximum and minimum pressure differences, and the filtration efficiency may be in the form of a percentage.

[0030] The generated filter performance index is compared with the preset filter performance index. These preset filter performance indexes are performance parameters used as targets or standards in the production process, and are usually set based on design requirements or quality standards. Through comparison, the unit calculates the difference between the actual filter performance index and the preset filter performance index, and obtains the difference feedback. Based on the difference feedback, the adjustment parameter acquisition unit 50 generates a set of production line adjustment parameters. These production line adjustment parameters are used to adjust the operating conditions of the production line to ensure that the produced filters meet the preset performance standards. The adjustment parameters may include production process conditions such as temperature, pressure, and coating speed to narrow the gap and achieve the expected performance. For example, if the filtration efficiency is lower than expected, the system may suggest reducing the coating speed or increasing the membrane thickness to improve the filtration performance.

[0031] The adjustment parameter acquisition unit 50 generates corresponding production line adjustment parameters through a difference feedback mechanism to guide the production line to be optimized, ensuring that the production line can be continuously adjusted so that the production quality and efficiency of the polymer filter can reach the best state.

[0032] The parameter adjustment unit 60 is used to adjust the parameters of the production line of the polymer filter according to the production line adjustment parameters.

[0033] Specifically, the parameter adjustment unit 60 receives the production line adjustment parameters from the adjustment parameter acquisition unit 50, including temperature setting, pressure adjustment, flow rate control, material thickness, etc. The command is directly sent to the production line automation control system through the interface, such as PLC (programmable logic controller) or DCS (distributed control system), to automatically adjust the operating parameters of each device on the production line, such as temperature, pressure, speed, etc. After the adjustment is completed, the parameter adjustment unit 60 obtains the operating data of the production line in real time through sensors, data acquisition systems, etc., and continues to monitor the operating status of the production line to ensure that the adjusted parameters have indeed achieved the expected effect. If the ideal effect is still not achieved after the parameter adjustment, the parameter adjustment unit 60 will feed back this information to the adjustment parameter acquisition unit 50 to further optimize the production process, form a closed-loop control, and continuously improve the production process.

[0034] Further, such as Figure 2 As shown, the system described in the embodiment of the present application is also used to perform the following steps:

[0035] Determine whether the number of filter layers of the polymer filter is greater than or equal to 2. If the number of filter layers of the polymer filter is greater than or equal to 2, perform local extraction based on the filter simulation system to construct a filter layer simulation system; perform filter layer simulation according to the filter layer simulation system to obtain a filter layer simulation data set; generate a filter layer performance index according to the filter layer simulation data set; optimize the filter performance index according to the filter layer performance index to obtain an optimized filter performance index.

[0036] Specifically, the system first checks the product specification sheet of the polymer filter to determine the number of its filter layers. The number of filter layers is the number of layers of filter media inside the polymer filter, which affects the filter efficiency and pressure loss of the filter. For polymer filters with 2 or more filter layers, the system extracts the data of each filter layer from the filter simulation system, including the fluid flow data and pressure data of the filter layer, and builds an independent filter layer simulation system for each filter layer based on finite element analysis or fluid dynamics model.

[0037] Run simulation analysis in this filter layer simulation system, use finite element analysis (FEA) and CFD tools to calculate the flow characteristics, pressure distribution and filtration efficiency of the filter layer, and obtain detailed performance data for each filter layer. Summarize these performance data to obtain a filter layer simulation data set. Analyze the simulation data set of each filter layer to generate filter layer performance indicators, and compare them with preset performance indicators to identify performance bottlenecks. According to the comparison results of the filter layer performance indicators, adjust the material, structure or process parameters of each layer to optimize the filter layer performance and ultimately improve the overall filter performance. Summarize the optimized performance indicators of each filter layer to generate new filter performance indicators. These optimized indicators reflect the expected performance of the improved filter.

[0038] Exemplarily, a double-layer polymer filter is produced, in which the first layer is used for coarse filtration and the second layer is used for fine filtration. The system first determines whether the number of filter layers is greater than or equal to 2. After confirmation, the two layers are locally extracted, and the simulation systems of the two layers are established separately using ANSYS Fluent. The first layer simulation found that the fluid resistance caused by the material thickness was too high, and the resistance was reduced by adjusting the material thickness; the second layer simulation found that the aperture design affects the filtration efficiency, and the filtration efficiency was improved by optimizing the aperture. After the adjustment, the system re-evaluated the overall performance and found that the pressure loss was reduced, the filtration efficiency was improved, and the overall performance indicators reached the optimization target. Finally, the improved filter performance indicators are generated based on the optimized filter layer performance indicators.

[0039] Through the above steps, the system conducts refined performance analysis and optimization of the polymer filter, and optimizes the performance of each layer in a targeted manner, thereby significantly improving the performance and production quality of the overall filter.

[0040] Furthermore, the system described in the embodiment of the present application is also used to perform the following steps:

[0041] According to the filter layer simulation data set, the filter performance index between two adjacent filter layers is calculated, including the matching degree, filter resistance and particle removal rate between the two adjacent filter layers; the filter performance index between the two adjacent filter layers is superimposed in sequence according to the filter guide, and the filter layer performance index is output.

[0042] Specifically, the process of obtaining the performance index of the filter layer includes: calculating multiple filtration performance indexes between two adjacent filter layers based on the filter layer simulation data set. These indicators include the degree of fit between adjacent filter layers, filtration resistance and particle removal rate. Among them, the degree of fit refers to the synergistic effect between two adjacent filter layers, which is used to measure how effectively they work together to improve the overall filtration performance. The degree of fit formula can be used to evaluate the coordination effect of the two layers. Commonly used fit indicators include flow uniformity and pressure matching. For example, the flow velocity difference between adjacent layers is calculated and compared with the ratio of the total flow velocity. Filtration resistance is the resistance encountered by the fluid when passing through the filter layer. It is used to measure the degree of obstruction of the filter layer to the fluid, and is usually expressed as the difference between the inlet and outlet pressures. The particle removal rate refers to the ability of the filter layer to remove specific particles. It is used to measure the filtration effect of the filter layer. It is usually calculated as the ratio of the inlet and outlet particle concentration difference to the feed particle concentration difference.

[0043] According to the filtration orientation, the performance data of each pair of adjacent filter layers are combined, and a comprehensive analysis tool or algorithm, such as weighted average or data fusion, is used to form an overall performance evaluation. For example, the filtration resistance data of the two filter layers are weighted and combined to form the comprehensive filtration resistance of the entire filter. The filtration resistance of the first layer is 1.2 bar, and the second layer is 1.0 bar. After superposition, the calculated comprehensive filtration resistance is 1.1 bar. After superposition, the system generates overall filtration layer performance indicators. These indicators integrate the performance data between all adjacent filter layers and are used to further optimize the design or production parameters of the filter. Among them, filtration orientation refers to the filtration logic or strategy followed when designing a multi-layer filter, which defines the order and goals of the fluid passing through each layer of filtration media, as well as the expected performance indicators. Take a three-layer polymer filter as an example: the first layer is the pre-filtration layer, which is designed to capture large particles and reduce the burden on subsequent layers. The filtration guide requires this layer to have lower filtration resistance and higher large particle removal rate; the second layer is the intermediate filtration layer, which is used to further remove smaller particles. The filtration guide requires this layer to have a higher small and medium particle removal rate while maintaining a certain filtration resistance; the third layer is the precision filtration layer, which is used to remove tiny particles to achieve the ultimate filtration accuracy. The filtration guide requires this layer to have an extremely high particle removal rate.

[0044] Furthermore, the system described in the embodiment of the present application is also used to perform the following steps:

[0045] Determine whether the filtering performance index between two adjacent filtering layers is less than a preset filtering performance index; if it is less than the preset filtering performance index, identify it as an abnormal filtering layer in the two adjacent filtering layers; identify the production line corresponding to the abnormal filtering layer, and output parameter adjustment for the production line corresponding to the abnormal filtering layer.

[0046] Specifically, after the system obtains the filtration performance index calculated in the above steps, it compares the filtration performance index between two adjacent filter layers with the preset filtration performance index. If the actual filter layer performance index is less than the preset filtration performance index, the filter layer that does not meet the performance index is marked as an abnormal filter layer, and the production line corresponding to the abnormal filter layer is traced back. These production lines need to be inspected and adjusted to correct the problem. The tracing and identification process can use a production management system or tracking tool. For example, the system can search the production batch and production line information related to the abnormal filter layer from the production database. For the production line corresponding to the identified abnormal filter layer, the system generates an adjustment plan based on the actual performance data. These adjustment plans include specific modifications to the parameters in the production process, such as adjusting the temperature, pressure or material ratio to improve the performance of the filter layer.

[0047] Through the above steps, the system identifies abnormal filter layers by comparing the actual filter performance indicators with the preset performance indicators. When anomalies are detected, the system identifies the production lines corresponding to these layers and generates adjustment suggestions for the production lines to ensure that the filter production process can be corrected and optimized in time to achieve the expected filter performance standards.

[0048] Furthermore, the filtering layer simulation system in the embodiment of the present application is connected to the hierarchical structure combination module, and the system is also used to perform the following steps:

[0049] If the filter layer performance index is less than the preset filter layer performance index, the hierarchical structure combination module is used to adaptively adjust the arrangement order of the filter layer components according to the filter layer performance index to obtain the hierarchical structure combination optimal solution; and feed back the hierarchical structure combination optimal solution to the filter layer simulation system.

[0050] Specifically, the hierarchical structure combination module is a system component for adjusting the arrangement order of the filter layers according to the performance indicators of the filter layers. The filter layer simulation system is connected to the hierarchical structure combination module and outputs the generated filter layer performance indicators to the hierarchical structure combination module.

[0051] After receiving the filter layer performance index, the hierarchical structure combination module compares it with the preset filter layer performance index. If the actual filter layer performance index is lower than the preset filter layer performance index, it indicates that the current filter layer configuration needs to be optimized. The hierarchical structure combination module uses multi-criteria decision analysis methods such as hierarchical analysis method based on these indicators to construct a judgment matrix and calculate the weight and ranking of each filter layer relative to the overall filtering effect. According to the calculated weight and ranking, the arrangement order of the filter layers is adaptively adjusted, including rearranging the order of the filter layers, adjusting the position of each layer or changing its relative order, so as to improve the overall performance of the polymer filter.

[0052] After adaptive adjustment, the module generates a hierarchical combination solution, that is, an optimized filter layer arrangement order. This optimal solution represents the best hierarchical configuration to achieve the best filtration performance. The optimized hierarchical combination solution is fed back to the filter layer simulation system to verify the performance of the new arrangement order to ensure that it meets or exceeds the preset filter layer performance indicators. In this way, the hierarchical combination module ensures the optimal combination of filter layers, thereby improving the overall filtration performance of the polymer filter.

[0053] Further, such as Figure 3 As shown, the adjustment parameter acquisition unit 50 described in the embodiment of the present application is also used to perform the following steps:

[0054] Acquire an initialization prediction model, wherein the initialization prediction model is constructed by a gradient boosting machine; acquire filter performance index training samples; iteratively train the initialization prediction model according to the filter performance index training samples, and update the initialization prediction model with the model residual training of the current round until a preset accuracy rate is met, thereby acquiring a converged filter performance prediction model; based on the converged filter performance prediction model, provide feedback with the difference between the filter performance index and the preset filter performance index to acquire production line adjustment parameters.

[0055] Specifically, the adjustment parameter acquisition unit 50 uses the gradient boosting algorithm to construct an initialization prediction model, which serves as the basis for preliminary prediction of filter performance. The historical filter performance indicators in the production and experimental process are collected to establish training samples, and these sample data are processed and used to train the model. The filter performance indicator training samples are input into the model for iterative training. In each round of iteration, the difference between the predicted value and the actual value is calculated to obtain the model residual, and these residuals are used to update the gradient boosting model. By adjusting the model parameters, such as the learning rate, the tree depth, etc., the model residual is gradually reduced until the model prediction accuracy reaches the preset accuracy standard. After multiple rounds of training and optimization, a converged filter performance prediction model is obtained, and the prediction error of this model on the test data is lower than the set threshold, and the filter performance can be accurately predicted. The filter simulation data set obtained from the filter simulation unit 40 is input into the converged filter performance prediction model for performance prediction, and the filter performance indicator is output. The filter performance indicator and the preset filter performance indicator are compared, and the difference between the two is calculated. According to this difference, the corresponding production line adjustment suggestions are generated, such as adjusting the production temperature or changing the material ratio to reduce the performance difference.

[0056] Through iterative training and model optimization, the adjustment parameter acquisition unit 50 can accurately adjust the production parameters based on the feedback of the prediction model to ensure that the filter performance meets the standards.

[0057] Furthermore, the expression of the filter performance prediction model described in the embodiment of the present application includes:

[0058] ;

[0059] in, is the final model after M iterations, based on the input features Predicted production line adjustment parameters, To initialize the output of the prediction model, The new input feature set includes the current parameters of the production line, the actual performance indicators of the filter, the preset performance indicators and their differences, etc. is the number of iterations in the gradient boosting machine, To prevent the model from overfitting, the learning rate is the prediction model obtained by training the mth iteration, based on the input features Output.

[0060] The production line adjustment parameters are obtained by accumulating the output results of the prediction models in all iterations and combining them with the prediction values ​​of the initial model.

[0061] Specifically, when building a filter performance prediction model, the system does not rely solely on a single model prediction value, but instead accumulates and optimizes the prediction results generated in each iteration through multiple rounds of iterative training to improve the accuracy of the model.

[0062] First, get the initial prediction model , this model is based on the current input characteristics of the polymer filter However, the initial model may not be accurate enough, so in the subsequent M iterations, the system will gradually train and adjust the model. In each iteration, the system builds a new prediction model by calculating the residual of the current model. , these models will be based on the learning rate By accumulating the output results of each iteration, a comprehensive prediction model is finally obtained. The final prediction model is compared with the preset target performance indicators, the difference is calculated, and specific production line adjustment parameters are generated based on this difference to optimize the production process and ensure that the performance of the filter meets the design requirements.

[0063] In summary, the polymer filter production quality tracking system provided by the embodiment of the present application has the following technical effects:

[0064] The embodiment of the present application constructs a closed-loop quality control system by disassembling production components, real-time tracking of production parameters, simulation and analysis of filter performance, and feedback-based production line adjustment. The production component disassembly unit 10 disassembles the production components of the polymer filter and outputs multiple filter production parts, so that the production process can be refined to each independent part, laying the foundation for subsequent accurate tracking and control. The real-time parameter acquisition unit 20 tracks and identifies multiple filter production parts, obtains multiple real-time production parameter sets, and monitors the production status of each production part in real time. The parameter input unit 30 inputs multiple real-time production parameter sets into the filter simulation system; the filter simulation unit 40 performs filter simulation according to the filter simulation system to obtain a filter simulation data set. This process is simulated by finite element analysis to predict the flow characteristics, pressure distribution and filtration efficiency of the filter in advance, thereby providing a basis for subsequent performance evaluation. For filters with two or more filter layers, the filter layer is independently simulated by local extraction and simulation system, the filter layer performance index is generated, and the overall performance of the filter is optimized accordingly. In the case where the filter layer performance does not meet the standard, the arrangement order of the filter layer components is adaptively adjusted, and the overall performance of the filter is improved by optimizing the combined hierarchical structure. A more detailed performance analysis and optimization is performed on filters with complex structures, which enhances the flexibility of filter structure design. The final performance of the product is improved by optimizing the combination method, so that complex filters can show higher efficiency in different application scenarios. The adjustment parameter acquisition unit 50 trains and optimizes the filter performance prediction model through the gradient boosting method to generate a high-precision prediction model. According to the filter simulation data set, based on the filter performance prediction model, the filter performance index is generated, and the difference between the filter performance index and the preset filter performance index is used for feedback to obtain the production line adjustment parameters. This feedback mechanism enables the production line to be accurately adjusted and optimized before actual production, ensuring that the performance of the products produced meets the expected standards and reducing the rework or scrap rate after production. Finally, the parameter adjustment unit 60 adjusts the parameters of the production line of the polymer filter according to the production line adjustment parameters, realizing closed-loop control of the production process.

[0065] Overall, the embodiments of the present application achieve precise monitoring of each production part of the polymer filter and adaptive adjustment of the production line. It can predict product performance before actual production, predict and solve production bottlenecks in advance, ensure that deviations in the production process are corrected in a timely manner, effectively reduce quality fluctuations and defective products in the production process, and significantly improve the stability, consistency and production efficiency of the polymer filter production process.

[0066] Embodiment 2, as Figure 4 As shown, an embodiment of the present application provides a method for tracing the production quality of a polymer filter, the method comprising:

[0067] The polymer filter is disassembled into production components, and a plurality of filter production parts are output, each filter production part represents an independent part of the production process; the plurality of filter production parts are tracked and identified, and a plurality of real-time production parameter sets are obtained, wherein each filter production part corresponds to a real-time production parameter set; the plurality of real-time production parameter sets are input into a filter simulation system, and the filter simulation system is obtained through finite element analysis; a filter simulation is performed according to the filter simulation system to obtain a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution and filter filtration efficiency; a filter performance index is generated according to the filter simulation data set, and the difference between the filter performance index and the preset filter performance index is fed back to obtain a production line adjustment parameter; and parameters of the polymer filter production line are adjusted according to the production line adjustment parameters.

[0068] Furthermore, the method described in the embodiment of the present application also includes:

[0069] Determine whether the number of filter layers of the polymer filter is greater than or equal to 2. If the number of filter layers of the polymer filter is greater than or equal to 2, perform local extraction based on the filter simulation system to construct a filter layer simulation system; perform filter layer simulation according to the filter layer simulation system to obtain a filter layer simulation data set; generate a filter layer performance index according to the filter layer simulation data set; optimize the filter performance index according to the filter layer performance index to obtain an optimized filter performance index.

[0070] Furthermore, the embodiment of the present application generates a filter layer performance index according to the filter layer simulation data set, including:

[0071] According to the filter layer simulation data set, the filter performance index between two adjacent filter layers is calculated, including the matching degree, filter resistance and particle removal rate between the two adjacent filter layers; the filter performance index between the two adjacent filter layers is superimposed in sequence according to the filter guide, and the filter layer performance index is output.

[0072] Furthermore, the method described in the embodiment of the present application also includes:

[0073] Determine whether the filtering performance index between two adjacent filtering layers is less than a preset filtering performance index; if it is less than the preset filtering performance index, identify it as an abnormal filtering layer in the two adjacent filtering layers; identify the production line corresponding to the abnormal filtering layer, and output parameter adjustment for the production line corresponding to the abnormal filtering layer.

[0074] Furthermore, the filtering layer simulation system described in the embodiment of the present application is connected with the hierarchical structure combination module, including:

[0075] If the filter layer performance index is less than the preset filter layer performance index, the hierarchical structure combination module is used to adaptively adjust the arrangement order of the filter layer components according to the filter layer performance index to obtain the hierarchical structure combination optimal solution; and feed back the hierarchical structure combination optimal solution to the filter layer simulation system.

[0076] Furthermore, the embodiment of the present application uses the difference between the filter performance index and the preset filter performance index as feedback to obtain the production line adjustment parameters, including:

[0077] Acquire an initialization prediction model, wherein the initialization prediction model is constructed by a gradient boosting machine; acquire filter performance index training samples; iteratively train the initialization prediction model according to the filter performance index training samples, and update the initialization prediction model with the model residual training of the current round until a preset accuracy rate is met, thereby acquiring a converged filter performance prediction model; based on the converged filter performance prediction model, provide feedback with the difference between the filter performance index and the preset filter performance index to acquire production line adjustment parameters.

[0078] Furthermore, the expression of the filter performance prediction model described in the embodiment of the present application includes:

[0079] ;

[0080] in, is the final model after M iterations, based on the input features Predicted production line adjustment parameters, To initialize the output of the prediction model, The new input feature set includes the current parameters of the production line, the actual performance indicators of the filter, the preset performance indicators and their differences, etc. is the number of iterations in the gradient boosting machine, To prevent the model from overfitting, the learning rate is the prediction model obtained by training the mth iteration, based on the input features Output.

[0081] The production line adjustment parameters are obtained by accumulating the output results of the prediction models in all iterations and combining them with the prediction values ​​of the initial model.

[0082] Through the above-mentioned detailed description of a polymer filter production quality tracking system in this specification, those skilled in the art can clearly understand a polymer filter production quality tracking method in this embodiment. As for the method disclosed in Example 2, since it corresponds to the system disclosed in Example 1 and has corresponding execution steps and beneficial effects, the relevant parts can be referred to the system part description.

[0083] 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.

Claims

1. A polymer filter production quality tracking system, It is characterized in that The system comprises: A production component disassembly unit, the production component disassembly unit is used to disassemble the polymer filter into production components and output a plurality of filter production parts, each filter production part represents an independent part of the production process; A real-time parameter acquisition unit, the real-time parameter acquisition unit is used to track and identify the multiple filter production parts, and acquire multiple real-time production parameter sets, wherein each filter production part corresponds to a real-time production parameter set; a parameter input unit, the parameter input unit being used to input the plurality of real-time production parameter sets into a filter simulation system, the filter simulation system being obtained through finite element analysis; A filter simulation unit, the filter simulation unit is used to perform filter simulation according to the filter simulation system to obtain a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution and filter filtration efficiency; An adjustment parameter acquisition unit, the adjustment parameter acquisition unit is used to generate a filter performance index according to the filter simulation data set, and to obtain a production line adjustment parameter by providing feedback based on the difference between the filter performance index and a preset filter performance index; A parameter adjustment unit is used to adjust the parameters of the production line of the polymer filter according to the production line adjustment parameters.

2. A polymer filter production quality tracking system as claimed in claim 1, It is characterized in that The system is also configured to perform the following steps: Determine whether the number of filtration layers of the polymer filter is greater than or equal to 2, and if the number of filtration layers of the polymer filter is greater than or equal to 2, perform local extraction based on the filter simulation system to construct a filtration layer simulation system; Perform filter layer simulation according to the filter layer simulation system to obtain a filter layer simulation data set; generating a filter layer performance index according to the filter layer simulation data set; The filter performance index is optimized according to the filter layer performance index to obtain an optimized filter performance index.

3. A polymer filter production quality tracking system as claimed in claim 2, It is characterized in that The system is also configured to perform the following steps: Calculating the filtration performance indexes between two adjacent filtration layers according to the filtration layer simulation data set, including the matching degree, filtration resistance and particle removal rate between the two adjacent filtration layers; The filtering performance indicators between two adjacent filtering layers are superimposed in sequence according to the filtering guide, and the filtering layer performance indicators are output.

4. A polymer filter production quality tracking system as claimed in claim 3, It is characterized in that The system is also configured to perform the following steps: Determine whether the filtration performance index between two adjacent filtration layers is less than a preset filtration performance index; If it is less than the preset filtering performance index, it is marked as an abnormal filtering layer in two adjacent filtering layers; The production line corresponding to the abnormal filter layer is identified, and parameter adjustment is performed on the production line corresponding to the abnormal filter layer.

5. A polymer filter production quality tracking system as claimed in claim 2, It is characterized in that The filter layer simulation system is connected to the hierarchical structure combination module, and the system is also used to perform the following steps: If the filter layer performance index is less than the preset filter layer performance index, the hierarchical structure combination module is used to adaptively adjust the arrangement order of the filter layer components according to the filter layer performance index to obtain the hierarchical structure combination optimal solution; The optimal solutions are combined according to the hierarchical structure and fed back to the filtering layer simulation system.

6. A polymer filter production quality tracking system as claimed in claim 1, It is characterized in that The adjustment parameter acquisition unit is further configured to perform the following steps: Obtaining an initialization prediction model, wherein the initialization prediction model is constructed by a gradient boosting machine; Obtain filter performance indicator training samples; Iteratively train the initialization prediction model according to the filter performance index training samples, update the initialization prediction model with the model residual training of the current round until the preset accuracy is met, and obtain a converged filter performance prediction model; Based on the converged filter performance prediction model, the difference between the filter performance index and the preset filter performance index is fed back to obtain the production line adjustment parameters.

7. A polymer filter production quality tracking system as claimed in claim 6, It is characterized in that The expression of the filter performance prediction model includes: ； in, is the final model after M iterations, based on the input features Predicted production line adjustment parameters, To initialize the output of the prediction model, The new input feature set includes the current parameters of the production line, the actual performance indicators of the filter, the preset performance indicators and their differences. is the number of iterations in the gradient boosting machine, To prevent the model from overfitting, the learning rate is the prediction model obtained by training the mth iteration, based on the input features Output: The production line adjustment parameters are obtained by accumulating the output results of the prediction models in all iterations and combining them with the prediction values ​​of the initial model.

8. A method for tracking the production quality of polymer filters, It is characterized in that The method is performed by a polymer filter production quality tracking system according to any one of claims 1 to 7, and the method comprises: Disassemble the polymer filter into production components and output multiple filter production parts, each filter production part represents an independent part of the production process; Tracking and identifying the plurality of filter production parts, and obtaining a plurality of real-time production parameter sets, wherein each filter production part corresponds to a real-time production parameter set; inputting the plurality of sets of real-time production parameters into a filter simulation system, the filter simulation system being obtained through finite element analysis; Performing filter simulation according to the filter simulation system to obtain a filter simulation data set, wherein the filter simulation data set includes filter flow characteristics, filter pressure distribution, and filter filtration efficiency; Generate a filter performance index according to the filter simulation data set, and use the difference between the filter performance index and the preset filter performance index as feedback to obtain a production line adjustment parameter; The production line of the polymer filter is adjusted according to the production line adjustment parameters.

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