Online monitoring method, system, equipment and medium for masterbatch screw extrusion granulation

Abstract

The present application relates to the technical field of masterbatches, and in particular to online monitoring methods, systems, equipment and media for masterbatch screw extrusion granulation. The present application first obtains an initial set of process parameters, then obtains historical operating data of each component of the equipment when producing similar products, and calculates a set of equipment performance coefficients that reflects the actual performance of the equipment; the performance status of the entire production system is judged based on the set of equipment performance coefficients that reflects the actual performance of the equipment, and the process parameters are adjusted accordingly; based on this, the intelligent update of the process parameters is realized, so that the updated process parameter set is more in line with the actual use status of the current equipment and the production requirements of new products; in addition, by real-time monitoring and adjustment of the production process, collecting real-time production data and product quality data, and using parameter optimization models to generate optimization feedback data, the process parameters and equipment performance evaluation can be continuously adjusted to further improve the stability of the production process and the consistency of product quality.

Description

Technical Field

[0001] The present application relates to the technical field of masterbatches, and in particular to an online monitoring method, system, equipment and medium for screw extrusion granulation of masterbatches. Background Art

[0002] Masterbatch screw extrusion granulation is a key process in the plastics industry and is used in the production of various plastic products. With the development of intelligent manufacturing, higher requirements are placed on the precise control and real-time monitoring of the masterbatch production process.

[0003] In the production of masterbatch screw extruder granulation, when it is necessary to produce new models of products or change raw materials, it is usually necessary to adjust the equipment parameters to adapt to the new production needs. However, this adjustment often affects the performance and stability of the entire production system.

[0004] In the prior art, the adjustment of process parameters mainly relies on manual adjustment based on the operator's experience. This method has two main problems: first, manual adjustment is inefficient and it is difficult to quickly respond to changes in production demand; second, over-reliance on manual experience can easily lead to inaccurate parameter settings, affecting product quality and equipment safety. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem of low efficiency and inaccuracy in adjusting process parameters in the existing masterbatch screw extrusion granulation production, the present application provides an online monitoring method, system, equipment and medium for masterbatch screw extrusion granulation, which adopts the following technical solutions:

[0006] In a first aspect, the present application provides an online monitoring method for masterbatch screw extrusion granulation, comprising the following steps:

[0007] In response to a new product production instruction, an initial process parameter value of each screw heating zone, a die head and a pelletizer is obtained to obtain an initial process parameter set;

[0008] Obtaining historical operation data of each component of the equipment when producing similar products, and calculating the equipment performance coefficient of each component based on the historical operation data to obtain an equipment performance coefficient set;

[0009] Calculating optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain an optimized process parameter set;

[0010] The initial process parameter set is updated according to the optimized process parameter set to obtain an updated process parameter set, and the masterbatch screw extrusion granulation process is monitored and adjusted in real time according to the updated process parameter set to obtain real-time production data;

[0011] Collecting the real-time production data and product quality data, and inputting the real-time production data and product quality data into a preset parameter optimization model to obtain optimization feedback data;

[0012] According to the optimization feedback data, the equipment performance coefficient set and the process parameter set are updated.

[0013] By adopting the above technical solution, when producing new products in the masterbatch screw extrusion granulation process, the present application first obtains the initial process parameter set, which is usually set based on the design stage or previous experience, and then obtains the historical operation data of each component of the equipment when producing similar products, and calculates the equipment performance coefficient set reflecting the actual performance of the equipment; since the new product has just started production, its optimal process parameters cannot be directly known, therefore, the present application judges the performance status of the entire production system according to the equipment performance coefficient set reflecting the actual performance of the equipment, and adjusts the process parameters accordingly; based on this, the intelligent update of the process parameters is realized, so that the updated process parameter set is more in line with the actual use status of the current equipment and the production requirements of new products; not only the efficiency and accuracy of parameter adjustment are improved, but also the dynamic changes of equipment performance can be adapted; in addition, by real-time monitoring and adjustment of the production process, collecting real-time production data and product quality data, and using the parameter optimization model to generate optimization feedback data, the process parameters and equipment performance evaluation can be continuously adjusted to further improve the stability of the production process and the consistency of product quality.

[0014] Optionally, according to the historical operation data, the equipment performance coefficient of each component is calculated to obtain an equipment performance coefficient set, which specifically includes the following steps:

[0015] Obtaining the working characteristics of each equipment component, and dividing the equipment components according to the working characteristics to obtain core components and auxiliary components;

[0016] According to the historical operation data, a historical temperature stability coefficient and a historical pressure stability coefficient are obtained;

[0017] Calculating a first performance coefficient corresponding to the core component according to the historical temperature stability coefficient and the historical pressure stability coefficient;

[0018] Calculating a second performance coefficient corresponding to the auxiliary component according to the historical temperature stability coefficient and the historical pressure stability coefficient;

[0019] A device performance coefficient set is obtained according to the first performance coefficient and the second performance coefficient.

[0020] By adopting the above technical solution, when calculating the performance coefficient of the equipment, the present application first divides the equipment into core components and auxiliary components according to the working characteristics of each component. This division method takes into account the importance and influence of different components in the masterbatch screw extrusion granulation process, so that the performance status of the entire system can be more accurately reflected; specifically, the core components (such as the screw body, heating partition, etc.) have a more direct and significant impact on the production process, while the auxiliary components (such as the pelletizer, cooling system, etc.) are also very important, but their influence is relatively indirect; by distinguishing the components, the performance of different components can be more refined and weighted, thereby improving the accuracy of process parameter adjustment; temperature and pressure are two key parameters in the masterbatch screw extrusion granulation process, therefore, by calculating the historical temperature stability coefficient and the historical pressure stability coefficient, the stability performance of the equipment in actual production is comprehensively evaluated, these two parameters directly affect product quality and production efficiency, so the performance coefficient calculated based on these two parameters can more accurately reflect the actual operating status of the equipment; by calculating the first performance coefficient of the core component and the second performance coefficient of the auxiliary component respectively, a hierarchical evaluation of the equipment performance is achieved, and the accuracy of the performance evaluation is improved.

[0021] Optionally, obtaining a historical temperature stability coefficient and a historical pressure stability coefficient according to the historical operation data specifically includes the following steps:

[0022] Extracting historical temperature monitoring data and historical pressure monitoring data from the historical operation data;

[0023] According to the preset initial process parameter threshold, the historical temperature monitoring data and the historical pressure monitoring data, a historical temperature stability coefficient and a historical pressure stability coefficient are calculated.

[0024] By adopting the above-mentioned technical scheme, the present application first specifically extracts historical temperature monitoring data and historical pressure monitoring data from the historical operating data, and then introduces a preset initial process parameter threshold as a reference standard. This threshold represents the optimal parameter range under normal operating conditions of the equipment. By comparing the actual monitoring data with this threshold, the corresponding stability coefficient is obtained; the historical temperature stability coefficient and historical pressure stability coefficient obtained by this method can accurately reflect the actual operating stability of the equipment before producing new products.

[0025] Optionally, updating the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set specifically includes the following steps:

[0026] Obtain the process parameter change data of masterbatch screw extrusion granulation process before and after optimization;

[0027] Calculate the third performance coefficient of each component of the equipment according to the process parameter change data;

[0028] An updated process parameter set is calculated based on the initial process parameter set, the third performance coefficient and the equipment performance coefficient set.

[0029] By adopting the above-mentioned technical scheme, the present application first captures the actual effect brought about by process optimization by obtaining the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization, and then calculates the third performance coefficient based on the process parameter change data, which represents the actual performance of each component of the equipment under the new process conditions. Finally, when calculating the updated process parameter set, the initial process parameter set, the third performance coefficient and the equipment performance coefficient set are combined to make the updated process parameter set more accurate.

[0030] Optionally, calculating an updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set specifically includes the following steps:

[0031] Get the equipment component type corresponding to each process parameter;

[0032] Determining the process parameter performance coefficient corresponding to each process parameter according to the equipment component type corresponding to each process parameter and the equipment performance coefficient set;

[0033] According to the process parameter performance coefficient corresponding to each process parameter, adjusting the initial process parameter value corresponding to each process parameter to obtain a preliminary adjusted process parameter value;

[0034] Adjust the preliminary adjustment process parameter value according to the third performance coefficient to obtain an updated process parameter value;

[0035] The updated process parameter values ​​are combined to form the updated process parameter set.

[0036] By adopting the above technical scheme, the present application first considers the specific role and importance of different equipment components in the masterbatch production process, obtains the equipment component type corresponding to each process parameter, and then judges the process parameter performance coefficient corresponding to each process parameter based on the equipment component type and equipment performance coefficient set corresponding to each process parameter. Different types of equipment components may correspond to different performance coefficients. For example, temperature control parameters may pay more attention to stability, while pressure control parameters may pay more attention to response speed. Then, further optimization is performed based on the third performance coefficient to obtain the final updated process parameter value, which can optimize the process parameters more comprehensively, taking into account the characteristics of each component of the equipment and the overall performance.

[0037] Optionally, obtaining process parameter change data of the masterbatch screw extrusion granulation process before and after optimization specifically includes the following steps:

[0038] Acquiring pre-optimization process parameter data within a first preset time period;

[0039] Acquiring optimized process parameter data within a second preset time period;

[0040] According to the process parameter data before optimization and the process parameter data after optimization, the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization are calculated.

[0041] By adopting the above-mentioned technical solution, the present application obtains the process parameter data before optimization within the first preset time period and the process parameter data after optimization within the second preset time period respectively, calculates the change data of the process parameters before and after optimization, and can accurately quantify the degree of change of each process parameter and objectively evaluate the optimization effect.

[0042] In a second aspect, the present application provides an online monitoring system for masterbatch screw extrusion granulation, comprising:

[0043] An initial process parameter set acquisition module is used to obtain the initial process parameter values ​​of each screw heating zone, die head and pelletizer in response to a new product production instruction to obtain an initial process parameter set;

[0044] The equipment performance coefficient set acquisition module is used to acquire the historical operation data of each component of the equipment when producing similar products, and calculate the equipment performance coefficient of each component according to the historical operation data to obtain the equipment performance coefficient set;

[0045] An optimized process parameter set acquisition module is used to calculate the optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain the optimized process parameter set;

[0046] A real-time production data acquisition module is used to update the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set, and to monitor and adjust the masterbatch screw extrusion granulation process in real time according to the updated process parameter set to obtain real-time production data;

[0047] An optimization feedback data acquisition module is used to collect the real-time production data and product quality data, input the real-time production data and product quality data into a preset parameter optimization model, and obtain optimization feedback data;

[0048] An updating module is used to update the equipment performance coefficient set and the process parameter set according to the optimization feedback data.

[0049] Optionally, the device performance coefficient set acquisition module includes:

[0050] An equipment component division unit is used to obtain the working characteristics of each equipment component, and divide the equipment components according to the working characteristics to obtain core components and auxiliary components;

[0051] A stability coefficient acquisition unit, used to acquire a historical temperature stability coefficient and a historical pressure stability coefficient according to the historical operation data;

[0052] A first performance coefficient calculation unit, used for calculating a first performance coefficient corresponding to the core component according to the historical temperature stability coefficient and the historical pressure stability coefficient;

[0053] A second performance coefficient calculation unit, used for calculating a second performance coefficient corresponding to the auxiliary component according to the historical temperature stability coefficient and the historical pressure stability coefficient;

[0054] The device performance coefficient set acquisition unit is used to obtain a device performance coefficient set according to the first performance coefficient and the second performance coefficient.

[0055] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned online monitoring method for screw extrusion granulation of masterbatch are implemented.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned online monitoring method for masterbatch screw extrusion granulation are implemented.

[0057] In summary, the present application includes at least one of the following beneficial technical effects:

[0058] When producing new products in the masterbatch screw extrusion granulation process, the present application first obtains an initial set of process parameters, which are usually set based on the design stage or previous experience, and then obtains the historical operating data of each component of the equipment when producing similar products, and calculates a set of equipment performance coefficients that reflects the actual performance of the equipment; since the new product has just started production, its optimal process parameters cannot be directly known, therefore, the present application judges the performance status of the entire production system based on the set of equipment performance coefficients that reflects the actual performance of the equipment, and adjusts the process parameters accordingly; based on this, an intelligent update of the process parameters is achieved, so that the updated process parameter set is more in line with the actual use status of the current equipment and the production requirements of new products; not only the efficiency and accuracy of parameter adjustment are improved, but also the dynamic changes of equipment performance can be adapted; in addition, by real-time monitoring and adjustment of the production process, collecting real-time production data and product quality data, and using parameter optimization models to generate optimization feedback data, the process parameters and equipment performance evaluation can be continuously adjusted to further improve the stability of the production process and the consistency of product quality;

[0059] When calculating the performance coefficient of the equipment, the present application first divides the equipment into core components and auxiliary components according to the working characteristics of each component. This division method takes into account the importance and influence of different components in the masterbatch screw extrusion granulation process, so that the performance status of the entire system can be more accurately reflected; specifically, the core components (such as the screw body, heating partition, etc.) have a more direct and significant impact on the production process, while the auxiliary components (such as pelletizers, cooling systems, etc.) are also very important, but their influence is relatively indirect; by distinguishing the components, the performance of different components can be more refined and weighted, thereby improving the accuracy of process parameter adjustment; temperature and pressure are two key parameters in the masterbatch screw extrusion granulation process. Therefore, by calculating the historical temperature stability coefficient and the historical pressure stability coefficient, the stability performance of the equipment in actual production is comprehensively evaluated. These two parameters directly affect product quality and production efficiency. Therefore, the performance coefficient calculated based on these two parameters can more accurately reflect the actual operating status of the equipment; by calculating the first performance coefficient of the core component and the second performance coefficient of the auxiliary component respectively, a hierarchical evaluation of the equipment performance is achieved, and the accuracy of the performance evaluation is improved;

[0060] This application first specifically extracts historical temperature monitoring data and historical pressure monitoring data from historical operating data, and then introduces a preset initial process parameter threshold as a reference standard. This threshold represents the optimal parameter range under normal operating conditions of the equipment. By comparing the actual monitoring data with this threshold, the corresponding stability coefficient is obtained; the historical temperature stability coefficient and historical pressure stability coefficient obtained by this method can accurately reflect the actual operating stability of the equipment before producing new products. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic flow chart of an online monitoring method for masterbatch screw extrusion granulation according to an embodiment of the present application;

[0062] Figure 2 It is a flow chart of step S200 in an online monitoring method for masterbatch screw extrusion granulation in an embodiment of the present application;

[0063] Figure 3 It is a flow chart of step S220 in an online monitoring method for masterbatch screw extrusion granulation in an embodiment of the present application;

[0064] Figure 4 It is a flow chart of step S300 in an online monitoring method for masterbatch screw extrusion granulation in an embodiment of the present application;

[0065] Figure 5 It is a flow chart of step S310 in an online monitoring method for masterbatch screw extrusion granulation in an embodiment of the present application;

[0066] Figure 6 It is a flow chart of step S330 in an online monitoring method for masterbatch screw extrusion granulation in an embodiment of the present application;

[0067] Figure 7 This is a module schematic diagram of an online monitoring system for masterbatch screw extrusion granulation in an embodiment of the present application;

[0068] Figure 8 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0069] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0070] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0071] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0072] In the first aspect, the present application provides an online monitoring method for masterbatch screw extrusion granulation, referring to Figure 1 , the method comprises the following steps:

[0073] S100, in response to a new product production instruction, obtaining initial process parameter values ​​of each screw heating zone, die head and pelletizer to obtain an initial process parameter set.

[0074] In this embodiment, the initial process parameter set refers to a set of initial process conditions set when starting to produce a new masterbatch product, including key production parameters such as the temperature of each heating zone of the screw, the screw speed, the die head temperature, and the pelletizer speed. It is understandable that the initial process parameters are usually set by process engineers based on the formula and expected performance characteristics of the new product.

[0075] Specifically, when receiving a new product production instruction, the system automatically retrieves the preset initial process parameters from the production management system. For example, for a new type of antistatic black masterbatch, the system may set the temperature of the first heating zone of the screw to 180°C, the temperature of the second heating zone to 190°C, the temperature of the third heating zone to 200°C, the die temperature to 210°C, the screw speed to 60rpm, the pelletizer speed to 1200rpm, etc. These parameter values ​​are combined together to form the initial process parameter set.

[0076] S200, obtaining historical operation data of each component of the equipment when producing similar products, and calculating the equipment performance coefficient of each component based on the historical operation data to obtain an equipment performance coefficient set.

[0077] Among them, the equipment performance coefficient refers to a set of values ​​that reflect the actual operating status of each component of the equipment.

[0078] In this embodiment, the system extracts the equipment operation data of the production of similar products in the past 6 months from the database. For example, for a screw extruder, the system analyzes its temperature control accuracy, the deviation between the actual output and the set value, etc. Assuming that the temperature control accuracy of a certain heating zone is ±2°C, its performance coefficient may be set to 0.95. For a pelletizer, if the average deviation between its actual speed and the set value is within 1%, its performance coefficient may be set to 0.98. In this way, the system calculates a performance coefficient for each key component, and finally forms a complete set of equipment performance coefficients.

[0079] S300, calculating optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain an optimized process parameter set.

[0080] Among them, the optimized process parameter set refers to the process parameter combination after preliminary optimization and adjustment.

[0081] Specifically, the system will calculate and adjust the initial process parameters and the corresponding equipment performance coefficient. For example, if the temperature control performance coefficient of a heating zone is 0.95 and the initial set temperature is 200°C, the system may adjust the actual control temperature to 210°C to compensate for possible temperature deviations.

[0082] S400, updating the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set, and monitoring and adjusting the masterbatch screw extrusion granulation process in real time according to the updated process parameter set to obtain real-time production data.

[0083] Among them, the updated process parameter set refers to the process parameter combination that is actually applied to the production process after optimization, and the real-time production data refers to the actual operation data collected by various sensors and control systems during the production process.

[0084] Specifically, the system will replace the initial process parameter set with the optimized process parameter set as the new production guidance parameter. During the production process, various sensors are used to monitor the production status in real time. For example, the system may record the actual temperature of each heating zone of the screw every 5 seconds, the actual speed and torque of the screw every minute, and the die pressure every 10 seconds, etc. Then, based on these real-time data and combined with the preset control algorithm, the process parameters are fine-tuned.

[0085] S500, collecting real-time production data and product quality data, inputting the real-time production data and product quality data into a preset parameter optimization model, and obtaining optimization feedback data.

[0086] Among them, product quality data refers to the quality index data of finished masterbatch obtained through online testing. The parameter optimization model is a pre-established mathematical model or algorithm used to analyze the relationship between production data and quality data and make optimization suggestions.

[0087] Specifically, the system collects real-time data during the production process, such as temperature curves, pressure changes, etc., and also collects product quality inspection data, such as color difference values, dispersion index, melt flow rate, etc., and inputs them into a pre-trained machine learning model. The model analyzes these data to find the relationship between production parameters and product quality. For example, the model may find that when the temperature of the second heating zone is 2°C higher than the current set value, the dispersion of the product will increase by 5%. Based on these analyses, optimization suggestions are generated, such as suggesting to increase the temperature of a heating zone by 2°C, or to increase the screw speed by 3rpm.

[0088] S600: Update the equipment performance coefficient set and the process parameter set according to the optimization feedback data.

[0089] Specifically, the system adjusts the equipment performance coefficient and process parameters accordingly based on the suggestions in the optimization feedback data. For example, if the optimization feedback data shows that the actual temperature control capability of a heating zone is better than the previous evaluation, the system may increase the performance coefficient of the heating zone from 0.95 to 0.97. For process parameters, if the feedback data recommends increasing the screw speed, the system will increase the screw speed.

[0090] In one embodiment, referring to Figure 2 In step S200, the equipment performance coefficient of each component is calculated based on the historical operation data to obtain an equipment performance coefficient set, which specifically includes the following steps:

[0091] S210. Obtain the working characteristics of each equipment component, divide the equipment components according to the working characteristics, and obtain core components and auxiliary components.

[0092] In this embodiment, the working characteristics refer to the functions and importance of each component of the equipment in the masterbatch production process. Core components usually refer to key components that have a direct and significant impact on product quality and production efficiency, while auxiliary components are components that support the production process but have a relatively small impact.

[0093] Specifically, the system will first analyze the function and importance of each component. For example, in a screw extruder, the screw body, heating zone, die head, etc. may be classified as core components because they directly determine the melting, mixing and molding quality of the material. The cooling system, feeding system, etc. may be classified as auxiliary components. Although they are also important, their direct impact on the quality of the final product is relatively small. The system sets an importance threshold and classifies components with an impact factor greater than 0.8 as core components, and those with an impact factor less than 0.8 as auxiliary components. In this way, the system can clearly identify the core components that need to be monitored and optimized.

[0094] S220. Obtain a historical temperature stability coefficient and a historical pressure stability coefficient according to historical operation data.

[0095] In this embodiment, the historical temperature stability coefficient refers to the stability of the temperature control of each component of the equipment during the past production process. The historical pressure stability coefficient reflects the stability of the pressure control of the equipment during operation. Both coefficients are important indicators for evaluating equipment performance, because the stability of temperature and pressure directly affects the quality and production efficiency of the masterbatch.

[0096] Specifically, the system will analyze the production data over the past period of time (such as the past 3 months). For temperature stability, the system calculates the average deviation and standard deviation between the actual temperature of each heating zone and the set temperature. For example, if the average temperature deviation of a heating zone is within ±1°C and the standard deviation is less than 0.5°C, then its temperature stability coefficient may be set to 0.95. For pressure stability, the system analyzes the fluctuation of the die pressure. If the fluctuation range of the die pressure is within ±2% of the set value, its pressure stability coefficient may be set to 0.93. The system will calculate these two coefficients for each relevant component to provide basic data for subsequent performance evaluation.

[0097] S230. Calculate a first performance coefficient of a corresponding core component according to a historical temperature stability coefficient and a historical pressure stability coefficient.

[0098] In this embodiment, the first performance coefficient is a comprehensive performance evaluation index calculated for the core components. The system pre-sets different calculation weights for different core components to calculate the first performance coefficient. For example, for the screw body, temperature stability and pressure stability may be given the same weight. For the die head, pressure stability may be more important and given a higher weight.

[0099] S240. Calculate a second performance coefficient of the corresponding auxiliary component according to the historical temperature stability coefficient and the historical pressure stability coefficient.

[0100] In this embodiment, the second performance coefficient is a performance evaluation index calculated for the auxiliary components. Although the auxiliary components have a relatively small impact on the production process, their performance still needs to be evaluated and monitored to ensure the stable operation of the entire production system. The system pre-sets different calculation weights for different auxiliary components to calculate the second performance coefficient. For example, for a cooling system, temperature stability may be more important, while pressure stability has a smaller impact.

[0101] S250: Obtain a device performance coefficient set according to the first performance coefficient and the second performance coefficient.

[0102] Specifically, the system aggregates all previously calculated first performance coefficients and second performance coefficients into a unified data set, which may be stored in the form of a table or structured data, including information such as the name, type (core or auxiliary), and corresponding performance coefficient of each component.

[0103] In one embodiment, referring to Figure 3 In step S220, according to the historical operation data, the historical temperature stability coefficient and the historical pressure stability coefficient are obtained, which specifically includes the following steps:

[0104] S221. Extract historical temperature monitoring data and historical pressure monitoring data from historical operation data.

[0105] In this embodiment, the historical operation data refers to the complete operation record of the masterbatch production equipment in the past period of time, including various parameter information of various components of the equipment at different time points, among which temperature and pressure are two key parameters.

[0106] Specifically, the system pre-sets a data extraction cycle, such as the operating data of the last three months. Within this time range, the system will extract relevant temperature and pressure data for each equipment component.

[0107] S222. Calculate a historical temperature stability coefficient and a historical pressure stability coefficient based on a preset initial process parameter threshold, historical temperature monitoring data, and historical pressure monitoring data.

[0108] In this embodiment, the preset initial process parameter threshold refers to the range within which each parameter is considered normal or acceptable during the masterbatch production process, and is pre-set by engineers based on equipment specifications, production requirements and industry standards.

[0109] Specifically, the system first defines the acceptable fluctuation range of temperature and pressure. For example, for a certain heating zone, the initial process parameter threshold may be set to the target temperature ±5°C; for the die pressure, the threshold may be set to the target pressure ±3%. The system then analyzes historical data and calculates the deviation between the actual parameter value and the set value. For the temperature stability coefficient, the system uses the following method: calculate the percentage of all temperature readings that fall within the threshold range. If 95% of the temperature readings are within the target temperature ±5°C range, then the temperature stability coefficient may be set to 0.95. For the pressure stability coefficient, the system calculates the standard deviation of the pressure fluctuation and compares it with the preset threshold. For example, if the standard deviation of the pressure fluctuation is 2% of the target pressure and the preset threshold is 3%, then the pressure stability coefficient may be set to 0.93.

[0110] In one embodiment, referring to Figure 4 In step S300, the initial process parameter set is updated according to the optimized process parameter set to obtain an updated process parameter set, which specifically includes the following steps:

[0111] S310, obtaining process parameter change data of the masterbatch screw extrusion granulation process before and after optimization.

[0112] In this embodiment, the process parameter change data refers to the changes in various key parameters in the production process after applying the optimized process parameter set.

[0113] Specifically, the system continuously monitors and records the production process for a period of time after applying the optimized process parameters. During the process, data is collected at fixed time intervals to form a time series data set.

[0114] S320. Calculate the third performance coefficient of each component of the equipment according to the process parameter change data.

[0115] In this embodiment, the third performance coefficient is an indicator reflecting the performance change of each component of the equipment after applying the optimized process parameters.

[0116] Specifically, the system calculates the third performance coefficient for each equipment component separately, which varies according to the characteristics and importance of the component. For example, for the heating zone, the system optimizes the accuracy and stability of temperature control before and after.

[0117] S330. Calculate an updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set.

[0118] Specifically, the system uses a weight calculation method to obtain an updated process parameter set. First, the system sets a basic adjustment range for each parameter, and then adjusts this range based on the equipment performance coefficient set and the third performance coefficient.

[0119] In one embodiment, referring to Figure 5 In step S310, obtaining the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization specifically includes the following steps:

[0120] S311, obtaining pre-optimization process parameter data within a first preset time period.

[0121] S312, obtaining optimized process parameter data within a second preset time period.

[0122] In this embodiment, the second preset time period refers to a specific period of time after the optimized process parameters are applied, which matches the first preset time period to ensure the comparability of data.

[0123] S313. According to the process parameter data before and after optimization, the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization are calculated.

[0124] In one embodiment, referring to Figure 6 In step S330, an updated process parameter set is calculated based on the initial process parameter set, the third performance coefficient and the equipment performance coefficient set, which specifically includes the following steps:

[0125] S331. Obtain the equipment component type corresponding to each process parameter.

[0126] In this embodiment, each process parameter is generally associated with a specific equipment component.

[0127] Specifically, the system maintains a mapping table of process parameters and equipment components in advance. For example, temperature parameters are associated with heating zones, pressure parameters are associated with die heads, and screw speed is associated with main motors. When the system needs to adjust a process parameter, it will first query the mapping table to determine the type of equipment component that corresponds to the parameter.

[0128] S332. Determine the process parameter performance coefficient corresponding to each process parameter according to the equipment component type and equipment performance coefficient set corresponding to each process parameter.

[0129] Specifically, the system first searches for the performance coefficient of the equipment component corresponding to each process parameter, and then weights or adjusts the equipment performance coefficient according to the characteristics and importance of the process parameter to obtain the process parameter performance coefficient.

[0130] S333. According to the process parameter performance coefficient corresponding to each process parameter, adjust the initial process parameter value corresponding to each process parameter to obtain a preliminary adjusted process parameter value.

[0131] In this embodiment, the preliminary adjustment of the process parameter value is the first round of adjustment of the initial process parameter value after considering the process parameter performance coefficient.

[0132] Specifically, the system pre-sets a baseline adjustment range and then scales this range based on the process parameter performance coefficient.

[0133] S334. Preliminarily adjust the process parameter value according to the third performance coefficient to obtain an updated process parameter value.

[0134] In this embodiment, the process parameter values ​​are updated based on the preliminary adjustment, with further consideration of the optimization effect.

[0135] Specifically, the system uses the third performance coefficient to further fine-tune the initial adjustment process parameter value. For example, if the third performance coefficient of a heating zone is 1.15 (indicating a 15% performance improvement), the system will make additional positive adjustments based on the initial adjustment.

[0136] S335. Combine the updated process parameter values ​​to form an updated process parameter set.

[0137] In this embodiment, the updated process parameter set is a complete parameter set formed by summarizing all adjusted process parameter values, which helps operators understand the reasons and magnitudes of parameter changes.

[0138] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0139] In the second aspect, the present application provides an online monitoring system for masterbatch screw extrusion granulation. The online monitoring system for masterbatch screw extrusion granulation of the present application is described below in combination with the above-mentioned online monitoring method for masterbatch screw extrusion granulation.

[0140] Reference Figure 7 , an online monitoring system for masterbatch screw extrusion granulation, comprising:

[0141] An initial process parameter set acquisition module is used to obtain the initial process parameter values ​​of each screw heating zone, die head and pelletizer in response to a new product production instruction to obtain an initial process parameter set;

[0142] The equipment performance coefficient set acquisition module is used to obtain the historical operation data of each component of the equipment when producing similar products, and calculate the equipment performance coefficient of each component based on the historical operation data to obtain the equipment performance coefficient set;

[0143] The optimized process parameter set acquisition module is used to calculate the optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain the optimized process parameter set;

[0144] A real-time production data acquisition module is used to update the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set, and to monitor and adjust the masterbatch screw extrusion granulation process in real time according to the updated process parameter set to obtain real-time production data;

[0145] The optimization feedback data acquisition module is used to collect real-time production data and product quality data, input the real-time production data and product quality data into a preset parameter optimization model, and obtain optimization feedback data;

[0146] The updating module is used to update the equipment performance coefficient set and the process parameter set according to the optimization feedback data.

[0147] In one embodiment, the device performance coefficient set acquisition module includes:

[0148] The equipment component division unit is used to obtain the working characteristics of each equipment component, and divide the equipment components according to the working characteristics to obtain core components and auxiliary components;

[0149] A stability coefficient acquisition unit, used for acquiring a historical temperature stability coefficient and a historical pressure stability coefficient according to historical operation data;

[0150] A first performance coefficient calculation unit, used for calculating a first performance coefficient of a corresponding core component according to a historical temperature stability coefficient and a historical pressure stability coefficient;

[0151] A second performance coefficient calculation unit, used for calculating a second performance coefficient of a corresponding auxiliary component according to a historical temperature stability coefficient and a historical pressure stability coefficient;

[0152] The device performance coefficient set acquisition unit is used to obtain the device performance coefficient set according to the first performance coefficient and the second performance coefficient.

[0153] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an online monitoring method for masterbatch screw extrusion granulation is implemented.

[0154] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0156] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0157] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An online monitoring method for masterbatch screw extrusion granulation, characterized in that: The steps include: In response to a new product production instruction, an initial process parameter value of each screw heating zone, a die head and a pelletizer is obtained to obtain an initial process parameter set; Obtaining historical operation data of each component of the equipment when producing similar products, and calculating the equipment performance coefficient of each component based on the historical operation data to obtain an equipment performance coefficient set; Calculating optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain an optimized process parameter set; The initial process parameter set is updated according to the optimized process parameter set to obtain an updated process parameter set, and the masterbatch screw extrusion granulation process is monitored and adjusted in real time according to the updated process parameter set to obtain real-time production data; Collecting the real-time production data and product quality data, and inputting the real-time production data and product quality data into a preset parameter optimization model to obtain optimization feedback data; According to the optimization feedback data, the equipment performance coefficient set and the process parameter set are updated; The process of calculating the equipment performance coefficient of each component according to the historical operation data to obtain an equipment performance coefficient set specifically includes the following steps: Obtaining the working characteristics of each equipment component, dividing the equipment components according to the working characteristics, and obtaining core components and auxiliary components; According to the historical operation data, a historical temperature stability coefficient and a historical pressure stability coefficient are obtained; Calculating a first performance coefficient corresponding to the core component according to the historical temperature stability coefficient and the historical pressure stability coefficient; Calculating a second performance coefficient corresponding to the auxiliary component according to the historical temperature stability coefficient and the historical pressure stability coefficient; Obtaining a device performance coefficient set according to the first performance coefficient and the second performance coefficient; Wherein, according to the historical operation data, obtaining the historical temperature stability coefficient and the historical pressure stability coefficient specifically includes the following steps: Extracting historical temperature monitoring data and historical pressure monitoring data from the historical operation data; Calculate a historical temperature stability coefficient and a historical pressure stability coefficient according to a preset initial process parameter threshold, the historical temperature monitoring data, and the historical pressure monitoring data; Wherein, updating the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set specifically includes the following steps: Obtain the process parameter change data of masterbatch screw extrusion granulation process before and after optimization; Calculate the third performance coefficient of each component of the equipment according to the process parameter change data; Calculating an updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set; The step of calculating the updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set specifically includes the following steps: Get the equipment component type corresponding to each process parameter; Determining the process parameter performance coefficient corresponding to each process parameter according to the equipment component type corresponding to each process parameter and the equipment performance coefficient set; According to the process parameter performance coefficient corresponding to each process parameter, adjusting the initial process parameter value corresponding to each process parameter to obtain a preliminary adjusted process parameter value; Adjust the preliminary adjustment process parameter value according to the third performance coefficient to obtain an updated process parameter value; The updated process parameter values ​​are combined to form the updated process parameter set.

2. The online monitoring method for masterbatch screw extrusion granulation according to claim 1, characterized in that: Obtaining the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization includes the following steps: Acquiring pre-optimization process parameter data within a first preset time period; Acquiring optimized process parameter data within a second preset time period; According to the process parameter data before optimization and the process parameter data after optimization, the process parameter change data of the masterbatch screw extrusion granulation process before and after optimization are calculated.

3. An online monitoring system for masterbatch screw extrusion granulation, characterized in that: include: An initial process parameter set acquisition module is used to obtain the initial process parameter values ​​of each screw heating zone, die head and pelletizer in response to a new product production instruction to obtain an initial process parameter set; The equipment performance coefficient set acquisition module is used to acquire the historical operation data of each component of the equipment when producing similar products, and calculate the equipment performance coefficient of each component according to the historical operation data to obtain the equipment performance coefficient set; An optimized process parameter set acquisition module is used to calculate the optimized process parameter values ​​according to the equipment performance coefficient set and the initial process parameter set to obtain the optimized process parameter set; A real-time production data acquisition module is used to update the initial process parameter set according to the optimized process parameter set to obtain an updated process parameter set, and to monitor and adjust the masterbatch screw extrusion granulation process in real time according to the updated process parameter set to obtain real-time production data; An optimization feedback data acquisition module is used to collect the real-time production data and product quality data, input the real-time production data and product quality data into a preset parameter optimization model, and obtain optimization feedback data; An updating module, used for updating the equipment performance coefficient set and updating the process parameter set according to the optimization feedback data; Wherein, the equipment performance coefficient set acquisition module includes: An equipment component division unit is used to obtain the working characteristics of each equipment component, and divide the equipment components according to the working characteristics to obtain core components and auxiliary components; A stability coefficient acquisition unit, used to acquire a historical temperature stability coefficient and a historical pressure stability coefficient according to the historical operation data; A first performance coefficient calculation unit, used for calculating a first performance coefficient corresponding to the core component according to the historical temperature stability coefficient and the historical pressure stability coefficient; A second performance coefficient calculation unit, used for calculating a second performance coefficient corresponding to the auxiliary component according to the historical temperature stability coefficient and the historical pressure stability coefficient; A device performance coefficient set acquisition unit, configured to obtain a device performance coefficient set according to the first performance coefficient and the second performance coefficient; The stability coefficient acquisition unit performs the following steps: Extracting historical temperature monitoring data and historical pressure monitoring data from the historical operation data; Calculate a historical temperature stability coefficient and a historical pressure stability coefficient according to a preset initial process parameter threshold, the historical temperature monitoring data, and the historical pressure monitoring data; The real-time production data acquisition module performs the following steps: Obtain the process parameter change data of masterbatch screw extrusion granulation process before and after optimization; Calculate the third performance coefficient of each component of the equipment according to the process parameter change data; Calculating an updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set; The step of calculating the updated process parameter set according to the initial process parameter set, the third performance coefficient and the equipment performance coefficient set specifically includes the following steps: Get the equipment component type corresponding to each process parameter; Determining the process parameter performance coefficient corresponding to each process parameter according to the equipment component type corresponding to each process parameter and the equipment performance coefficient set; According to the process parameter performance coefficient corresponding to each process parameter, adjusting the initial process parameter value corresponding to each process parameter to obtain a preliminary adjusted process parameter value; Adjust the preliminary adjustment process parameter value according to the third performance coefficient to obtain an updated process parameter value; The updated process parameter values ​​are combined to form the updated process parameter set.

4. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for online monitoring of masterbatch screw extrusion granulation described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the on-line monitoring method for masterbatch screw extrusion granulation according to any one of claims 1-2 are implemented.

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