A functional film production whole-process digital management and control system and control method

Abstract

The application relates to the technical field of digital control of functional film production, and particularly provides a full-process digital control system and control method for functional film production, which comprises the following steps: an excitation module, which is used for applying a preset excitation disturbance signal to an execution unit of a functional film production device after receiving a product switching instruction; an acquisition module, which is used for collecting multi-source instantaneous response data in real time; an extraction module, which is used for performing feature extraction on the multi-source instantaneous response data to obtain a behavior feature mode; a parameter confirmation module, which is used for taking preset physical state parameters corresponding to a reference mode set with the highest matching degree with the behavior feature mode as real-time physical state parameters; and a correction module, which is used for compensating and correcting preset process parameters corresponding to a second product according to the real-time physical state parameters to obtain corrected process parameters, and then controlling the execution unit to execute the corrected process parameters; the system can perform prospective correction on the process parameters at the initial stage of product switching.

Description

Technical Field

[0001] This application relates to the field of digital control technology for functional membrane production, and more specifically, to a digital control system and control method for the entire process of functional membrane production. Background Technology

[0002] In the production of functional films, a comprehensive digital control system covering the entire process is crucial for ensuring product quality and production efficiency. This system typically manages every step from raw material input to final winding, controlling production equipment through preset process parameters and collecting data in real time for closed-loop control to ensure product quality. Under ideal continuous production conditions—that is, when producing the same model of product for extended periods—the existing system operates smoothly and efficiently.

[0003] However, actual market demand is often small-batch and diversified, requiring production lines to have the ability to quickly switch between product types. For example, after completing the production of type A optical base film, it may be necessary to immediately switch to the production of type B protective film. At this time, the operator will load the process parameter package for type B film into the system, and the system will then adjust the equipment according to the new parameter instructions. But the problem is that the instructions issued by the digital system are instantaneous, while the changes in the physical equipment status have a significant lag. The extruder barrel and die head that have just finished producing type A film still maintain a high internal temperature, and this thermal inertia will not instantly drop to the lower temperature required for type B film. At the same time, a small amount of high-viscosity melt of type A film will remain in the barrel screw and flow channel.

[0004] When the raw material for type B film is fed into the extruder, which is not yet fully cooled and contains residue, the production process enters an unstable transition phase. Due to the excessively high temperature, the raw material for type B film may undergo slight degradation; while the residual type A melt mixes with the type B melt, resulting in impurities in the produced film in terms of composition and defects in optical properties such as crystal points and streaks. These substandard films produced in the transition phase are judged as waste, causing raw material waste and increased production costs, i.e., the "switchover waste" problem.

[0005] To shorten this unstable transition phase and reduce waste, factory technicians face a challenge. Existing digital control systems are essentially "passive-response" models; they can only discard products after detecting defects, unable to proactively predict and shorten the process of defective product generation. The root cause is that while the system possesses historical production data for product A and target process parameters for product B, it is completely unaware of the "intermediate state" of the equipment during the switchover. For example, the system doesn't know the specific temperature distribution inside the extruder die, nor can it quantify the residual amount of type A raw material in the flow channel. The system has a wealth of information before and during the switchover, but lacks a mechanism to utilize this existing, indirect data to infer the internal state of the equipment and, based on this, intelligently and proactively adjust the initial control parameters for product B, allowing the physical equipment to transition from the state of product A to the stable production state of product B as quickly as possible.

[0006] There is currently no effective technical solution to the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a digital management and control system and method for the entire process of functional membrane production, which can proactively correct process parameters at the initial stage of product switching.

[0008] Firstly, this application provides a digital control system for the entire production process of functional films, applied in a functional film production line. The digital control system for the entire production process of functional films includes: The excitation module is used to apply a preset excitation disturbance signal to the execution unit of the functional film production equipment after receiving a product switching instruction to switch from the first product to the second product. The acquisition module is used to collect multi-source instantaneous response data of functional membrane production equipment in real time under the action of excitation disturbance signals; The extraction module is used to extract features from multi-source transient response data to obtain behavioral feature patterns; The parameter confirmation module is used to match the behavioral feature pattern with a pre-stored set of reference patterns, and to use the preset physical state parameters corresponding to the set of reference patterns with the highest matching degree as the real-time physical state parameters inside the functional membrane production equipment. The correction module is used to compensate and correct the preset process parameters corresponding to the second product based on the real-time physical state parameters to obtain the corrected process parameters, and then control the execution unit of the functional film production equipment to execute the corrected process parameters.

[0009] Secondly, this application also provides a control method for a digital management and control system for the entire process of functional film production, which is applied in functional film production equipment. The control method for the digital management and control system for the entire process of functional film production includes the following steps: S1. Upon receiving a product switching instruction to switch from the first product to the second product, a preset excitation disturbance signal is applied to the execution unit of the functional film production equipment. S2. Real-time acquisition of multi-source instantaneous response data of functional membrane production equipment under excitation disturbance signals; the instantaneous response data is acquired by sensing elements distributed on the production line; S3. Extract features from multi-source transient response data to obtain behavioral feature patterns; S4. Match the behavioral feature pattern with the pre-stored reference pattern set, and use the preset physical state parameter corresponding to the reference pattern set with the highest matching degree with the behavioral feature pattern as the real-time physical state parameter inside the functional membrane production equipment. S5. Based on the real-time physical state parameters, the preset process parameters corresponding to the second product are compensated and corrected to obtain the corrected process parameters. Then, the execution unit of the functional film production equipment is controlled to execute the corrected process parameters.

[0010] As can be seen from the above, the functional membrane production process digital management and control system and method provided in this application, by introducing excitation disturbance and response data analysis, can obtain key information such as temperature distribution and material residue inside the equipment in real time and accurately, thereby transforming "passive response" into "active prediction and adjustment". This enables the system to make forward-looking corrections to process parameters at the beginning of product switching, avoiding product non-conformity problems caused by equipment thermal inertia, material residue and other factors during the transition stage, significantly reducing switching waste, lowering production costs, and improving production efficiency and product quality. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a digital control system for the entire production process of functional membranes, provided as an embodiment of this application.

[0012] Figure 2 A flowchart illustrating a digital control system for the entire production process of functional membranes, provided in this application embodiment.

[0013] Attached reference numerals: 1. Excitation module; 2. Acquisition module; 3. Extraction module; 4. Parameter confirmation module; 5. Correction module. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] Firstly, such as Figure 1 As shown, this application provides a digital control system for the entire production process of functional membranes, which includes: Excitation module 1 is used to apply a preset excitation disturbance signal to the execution unit of the functional film production equipment after receiving a product switching instruction to switch from the first product to the second product. Acquisition module 2 is used to collect multi-source instantaneous response data of functional membrane production equipment under the action of excitation disturbance signals in real time; Extraction module 3 is used to extract features from multi-source instantaneous response data to obtain behavioral feature patterns; The parameter confirmation module 4 is used to match the behavioral feature pattern with the pre-stored reference pattern set, and to use the preset physical state parameter corresponding to the reference pattern set with the highest matching degree with the behavioral feature pattern as the real-time physical state parameter inside the functional membrane production equipment. The correction module 5 is used to compensate and correct the preset process parameters corresponding to the second product based on the real-time physical state parameters to obtain the corrected process parameters, and then control the execution unit of the functional film production equipment to execute the corrected process parameters.

[0017] For ease of understanding, some key terms in this embodiment are explained below. The functional film production process digital control system of this embodiment is an integrated control and management platform. Its core function is to digitally monitor, collect data, assess the status, and optimize process parameters throughout the entire production process of functional films. This system achieves precise control of the execution units of the functional film production equipment through a combination of hardware and software, and can intelligently adjust based on real-time feedback data to ensure product quality and production efficiency. The excitation disturbance signal in this embodiment refers to a preset, minute, and instantaneous control parameter offset applied to the execution unit of the functional film production equipment at a specific moment. This offset aims to actively stimulate a transient response within the equipment, thereby inferring the actual physical state inside the equipment through data collected by external sensors. For example, it could be a small, instantaneous increase or decrease in the screw speed of the extruder, or a fine adjustment of the temperature in a certain heating section of the extruder. The multi-source instantaneous response data in this embodiment refers to the instantaneous data collected in real time by multiple sensors on the functional film production equipment under the action of excitation disturbance signals. This data can reflect the dynamic behavior of the equipment under disturbance from different dimensions. For example, it can include melt pressure information, extrusion current information, and die outlet melt temperature information. This data is the key basis for inferring the internal physical state of the equipment. The behavioral feature pattern in this embodiment refers to the structured data that reflects the unique "behavioral performance" of the equipment under a preset excitation disturbance signal after feature extraction of the multi-source instantaneous response data. Specifically, by filtering, time domain analysis, and frequency domain analysis of the raw instantaneous data, key features such as peak amplitude, peak time, main frequency components, and energy proportion can be extracted. These features together constitute a behavioral feature pattern vector. The reference pattern set in this embodiment is a pre-established database that stores patterns established during equipment commissioning or maintenance by applying the same micro-disturbance under various known internal states and recording the corresponding behavioral feature patterns. Each pattern in the set is precisely associated with a known internal physical state of the equipment and is used to match with the real-time extracted behavioral feature patterns to infer the current internal state of the equipment. The physical state parameters in this embodiment refer to the actual physical conditions inside the functional film production equipment. These conditions are typically difficult to obtain through direct measurement. In functional film production, these parameters may include temperature distribution information inside the equipment and material residue information corresponding to the preceding product. By matching behavioral feature patterns with a set of reference patterns, these physical state parameters can be inferred in real time and non-invasively. The corrected process parameters in this embodiment refer to the process parameters obtained after compensating and correcting the preset process parameters corresponding to the second product based on the real-time physical state parameters. These corrected parameters can more accurately adapt to the current actual internal state of the equipment, thereby guiding the execution unit to operate, shortening the unstable phase during product changeover, and reducing waste generation.

[0018] This application proposes a digital control system for the entire process of functional film production, which is applied to control functional film production equipment. The system optimizes the functional film production process through a series of steps.

[0019] First, upon receiving a product switching command to switch from the first product to the second product, the system applies a preset excitation disturbance signal to the execution unit of the functional film production equipment. This excitation disturbance signal can be set as an instantaneous offset of the control parameters in the time dimension. For example, the system can make a small instantaneous increase or decrease in the speed of the extruder screw, or fine-tune the temperature of a certain heating section of the extruder. The magnitude and duration of this instantaneous offset are small, aiming to actively stimulate the response signal of the actual internal state of the equipment without causing additional interference to the normal production transition.

[0020] Secondly, the system collects multi-source transient response data of the functional film production equipment in real time under the action of excitation disturbance signals. This multi-source transient response data can include melt pressure information, extrusion current information, and melt temperature information at the die outlet. For example, the system can collect data from melt pressure sensors installed inside the extruder adapter or die, motor current sensor data integrated in the extruder motor driver, and infrared thermometer data used to measure melt temperature near the die outlet at a very high frequency in real time. These data capture the changes in equipment status caused by disturbances from different dimensions, providing a basis for subsequent inference of the internal state.

[0021] Next, the system extracts features from the multi-source transient response data to obtain behavioral feature patterns. This step aims to refine and organize the raw, acquired transient operational data into structured features that reflect the actual behavior of the equipment. For example, the data processing may include applying digital filters to the raw sensor data to remove common low-frequency background noise and high-frequency electrical interference in the production process, thereby highlighting the transient response caused by micro-disturbances. Subsequently, key features are extracted from the filtered transient data. These features may include the peak amplitude of the pressure or current response, the time required for the response to reach its peak, the area under the response curve, and the main frequency components identified through methods such as Fourier transform. These extracted features together constitute a behavioral feature pattern vector.

[0022] The system then matches the behavioral feature patterns with a pre-stored set of reference patterns, and uses the preset physical state parameters corresponding to the reference pattern set with the highest matching degree as the real-time physical state parameters inside the functional film production equipment. For example, the system calculates the similarity (matching degree) between the real-time extracted behavioral feature patterns and each reference pattern in the pre-established set of reference response patterns, using Euclidean distance to measure the closeness of the feature vectors. By finding the item in the set of reference response patterns that is closest to the real-time behavioral feature pattern, the system can infer the current actual physical condition inside the extruder die head in real time and non-invasively. This method does not require additional intrusive detection sensors; it relies on information collected by existing external sensors combined with matching inference to obtain the actual state inside the equipment that cannot be directly measured.

[0023] Finally, the system compensates and corrects the preset process parameters corresponding to the second product based on real-time physical state parameters to obtain corrected process parameters. Then, it controls the execution unit of the functional film production equipment to execute the corrected process parameters. Based on accurate inferences about the current internal physical conditions of the equipment, the digital control system immediately calculates and proactively corrects the initial process parameters of the new product. For example, if the system infers that the die temperature is still too high and there is a significant amount of residual raw material from the previous process, it will proactively adjust the extruder heating temperature setpoint and screw speed of the new product. These adjustments are not simply made according to preset standard parameters, but are customized based on the actual internal conditions of the equipment, allowing the equipment to operate close to a steady state from the initial switching phase, effectively shortening the unstable transition stage and reducing waste generation.

[0024] The following example will provide a more detailed explanation of the above technical solution: Suppose a functional film production line needs to switch from producing a first product (e.g., type A optical base film) to producing a second product (e.g., type B protective film). Traditional digital control systems, upon receiving a product switchover command, would directly load the preset process parameters for type B protective film and issue them to the execution unit of the production equipment. However, due to thermal inertia and material residue within the equipment, the extruder die head may still maintain the high temperature used during the production of type A optical base film, and a small amount of molten type A optical base film may remain in the flow channel. In this situation, if production proceeds directly according to the preset parameters for type B protective film, the initial output is likely to suffer from quality problems due to excessively high temperatures causing raw material degradation or mixing with residues, resulting in a large amount of switchover waste.

[0025] To address this, this application proposes a digital control system for the entire functional film production process. When the system receives a product switching instruction from model A optical base film to model B protective film, it does not immediately issue all the preset process parameters for model B protective film. Instead, the system first applies a preset excitation disturbance signal to the execution unit of the functional film production equipment. For example, the system can momentarily increase the speed of the extruder screw by a small amount, such as 0.2% from the current speed, for a duration of approximately 100 milliseconds. Alternatively, the system can temporarily increase the temperature of the third heating section near the die outlet of the extruder by 0.8 degrees Celsius for 200 milliseconds. This small, momentary disturbance aims to gently probe the current internal state of the equipment.

[0026] While applying excitation disturbance signals, the system simultaneously acquires multi-source transient response data from the functional film production equipment at a very high frequency in real time. This data includes melt pressure information installed inside the extruder adapter or die, extrusion current information integrated into the extruder motor driver, and melt temperature information at the die exit, collected by an infrared thermometer located near the die exit to measure the melt temperature. For example, the melt pressure sensor can collect data 1000 times per second, the extrusion current sensor 500 times per second, and the infrared thermometer 100 times per second. This data captures the transient behavior of the equipment under micro-disturbances from different dimensions.

[0027] Subsequently, the system extracts features from these real-time acquired multi-source transient response data to obtain behavioral feature patterns. Specifically, the system first applies a digital filter (e.g., a Butterworth bandpass filter) to the raw sensor data to remove common low-frequency background noise and high-frequency electrical interference encountered during production, thereby highlighting the transient response caused by micro-perturbations. Next, key features are extracted from the filtered transient data (e.g., a 1-second data window consisting of 0.5 seconds before and after the perturbation). These features may include the peak amplitude of the pressure or current response, the time required for the response to reach its peak, the area under the response curve, and the main frequency components identified through methods such as Fourier transform. These extracted features collectively constitute a vector called a "behavioral feature pattern."

[0028] Next, the system matches this real-time extracted behavioral feature pattern with a pre-established set of reference patterns. This set of reference patterns is built during equipment commissioning or maintenance by repeatedly applying the same micro-perturbations under various known internal states (e.g., when the residual amount of preceding product raw material is 5%, 10%, and 15%, or when the internal temperature distribution of the die deviates from the target value by 2 degrees Celsius and 5 degrees Celsius), and recording the corresponding behavioral feature patterns. Each pattern in the set is precisely associated with a known internal state of the extruder die. The system calculates the similarity between the real-time pattern and each reference pattern in the set, for example, using Euclidean distance to measure the closeness of the feature vectors. By finding the item in the set of reference response patterns that is closest to the real-time behavioral feature pattern, the system can infer the current actual physical condition inside the extruder die in real-time and non-invasively. For example, if the real-time pattern best matches the pattern in the reference set labeled "the residual amount of preceding product raw material is approximately 12%, and the temperature in the center area of ​​the die is 6 degrees Celsius higher than the target value," then the system infers that the equipment is currently in this internal state. At this point, the system obtains real-time physical state parameters inside the functional film production equipment, including temperature distribution information inside the equipment and material residue information corresponding to the first product.

[0029] Finally, based on accurate inferences about the current internal physical conditions of the equipment, the digital control system immediately calculates and proactively compensates for the initial process parameters of the B-type protective film to obtain corrected process parameters. For example, if the system infers that the die temperature is still too high (6 degrees Celsius higher than the target value) and there is a significant amount of residual raw material (approximately 12%), it will proactively adjust the extruder heating temperature setpoint and screw speed for the B-type protective film. The system may temporarily reduce the initial set temperature of the extruder heating section by 3 degrees Celsius and reduce the screw speed by 1.5% initially for 8 minutes. These adjustments are not simply made according to preset standard parameters, but are customized based on the actual "body temperature" and "residue" of the equipment. The system then controls the execution unit of the functional film production equipment to execute these corrected process parameters.

[0030] As can be seen from the above examples, this application achieves accurate inference of the real-time physical state inside the functional film production equipment by actively applying excitation disturbance signals and combining feature extraction and reference pattern matching of multi-source instantaneous response data. This inference mechanism overcomes the limitation of traditional systems that cannot know the actual physical state inside the equipment. In traditional solutions, the system can only make passive adjustments after detecting product defects, resulting in a large amount of waste during the transition phase of product switching. However, this application proactively obtains the internal state of the equipment and compensates and corrects the process parameters of the new product accordingly, enabling the equipment to operate in a direction close to steady state from the initial switching. For example, in the above example, the system can know in advance that the die temperature is too high and the material residue is too large, and adjust the extruder heating temperature and screw speed accordingly, thereby avoiding initial product quality problems caused by excessive temperature or material mixing. This proactive prediction and correction mode significantly shortens the unstable transition phase, effectively reduces the generation of switching waste, and thus reduces raw material waste and production costs. The technical concept of this application, by introducing a closed-loop control of "excitation disturbance - response acquisition - state inference - parameter correction", realizes the transformation from passive response to active optimization, and provides an innovative solution for the digital management and control of functional membrane production.

[0031] In some preferred embodiments, the excitation disturbance signal is an instantaneous shift of the control parameter in the time dimension. The excitation disturbance signal refers to a signal, distinct from normal production control commands, actively applied during the operation of functional film production equipment to detect the internal state of the equipment. Its function is to indirectly obtain internal physical state information that is difficult to measure directly by observing the equipment's response to the signal. The application of this signal should effectively stimulate the dynamic response inside the equipment without affecting or minimizing the impact on normal production. The instantaneous shift of the control parameter in the time dimension refers to a brief, controllable, and non-continuous change to the control parameters used to adjust the operating state of the functional film production equipment, such as the screw speed of the extruder or the temperature of each heating zone. This shift can be a small increase or decrease in the parameter value, and its duration is usually extremely short, such as on the order of milliseconds to seconds. This form of disturbance aims to stimulate a transient response inside the equipment with minimal intervention cost and minimal impact on the production process, thereby providing effective data for subsequent state inference.

[0032] In some preferred embodiments, the physical state parameters include at least the temperature distribution information inside the functional film production equipment and the material residue information corresponding to the first product. The temperature distribution information inside the functional film production equipment refers to the real-time temperature values ​​and spatial distribution of different areas within the equipment (e.g., extruder, die, casting roller, etc.). This information reflects the heat transfer and thermal balance state inside the equipment, and its changes exhibit significant lag, especially during product switching. The material residue information corresponding to the first product refers to the quantity, distribution, and state of the material belonging to the previous product (i.e., the first product) remaining inside the functional film production equipment (e.g., screw, barrel, flow channel, die) after product switching. This information is one of the main reasons for product quality defects in the initial stage of product switching, and its quantitative information is crucial for process adjustment.

[0033] In some preferred embodiments, the multi-source transient response data includes melt pressure information, extrusion current information, and die outlet melt temperature information. Melt pressure information refers to real-time data on the pressure exerted on the molten polymer inside the functional film production equipment. This information directly reflects the flow state, filling degree, and resistance experienced by the melt in the equipment's flow channels. Its changes are extremely sensitive to uneven temperature distribution and residual preceding materials within the equipment. By collecting melt pressure information, the transient response of changes in the internal state of the equipment in the pressure dimension can be captured. Melt pressure information can be collected by a piezoelectric pressure sensor installed inside the extruder adapter or die, or obtained through a pressure sensor based on the strain gauge principle. Extrusion current information refers to real-time data on the current consumed by the motor driving the extrusion screw of the functional film production equipment. This information directly reflects the mechanical load of the extrusion screw when pushing the melt. The mechanical load is closely related to the melt viscosity, which is affected by the internal temperature of the equipment, residual preceding materials, and the rheological properties after mixing with the current material. Therefore, collecting extrusion current information can reflect the transient response of the internal state of the equipment from the perspective of mechanical load. Extrusion current information can be acquired by a Hall effect current sensor integrated into the extruder motor driver, or by a current transformer monitoring the motor power line. The die exit melt temperature information refers to the real-time temperature data of the molten polymer flowing out of the die exit. The die exit is a critical location for functional film forming; the melt temperature there directly reflects the final effect of heat transfer and the actual temperature state within the equipment, and is closely related to the overall temperature distribution inside the equipment. Acquiring this information allows for direct acquisition of the instantaneous response in the temperature dimension. The die exit melt temperature information can be measured by a non-contact infrared thermometer of the melt curtain at the die exit, or acquired through a precision thermocouple array embedded near the die lip.

[0034] In some preferred embodiments, the step of extracting features from multi-source transient response data to obtain behavioral feature patterns reflecting the current performance of the functional membrane production equipment includes: A1. Obtain the preset material characteristics corresponding to the second product, and obtain the type and intensity of the excitation disturbance signal; A2. Based on the preset material characteristics and the type and intensity of the excitation disturbance signal, extract the corresponding feature extraction algorithm parameters from the preset feature extraction algorithm parameter set as the parameters of the feature extraction algorithm; A3. Use the selected feature extraction algorithm to extract features from multi-source instantaneous response data to obtain behavioral feature patterns.

[0035] In the production of functional films, different grades of secondary products typically use materials with different physicochemical properties. Obtaining these pre-defined material characteristics (e.g., melt viscosity, heat capacity, thermal conductivity, glass transition temperature, etc.) is fundamental to understanding the material's response behavior within the equipment. These characteristics can be pre-stored in a product database and automatically retrieved by the system after a product switching command is issued. Acquiring the type (e.g., whether the disturbance is related to the extruder screw speed or the temperature of a specific heating zone) and intensity (e.g., the magnitude of speed change, temperature change, and duration) of the excitation disturbance signal is crucial. This information is usually directly recorded and provided by the system control module when the disturbance is generated and applied, ensuring an accurate understanding of the disturbance source and enabling precise analysis of the equipment's response to that specific disturbance.

[0036] Step A2 aims to achieve the adaptability of the feature extraction algorithm. Specifically, since the response characteristics of different materials vary significantly under different disturbances, fixed feature extraction parameters may not effectively capture key information. Therefore, the system intelligently selects the most suitable parameters from a pre-established set of feature extraction algorithm parameters based on the material characteristics of the current second product and the specific type and intensity of the applied excitation disturbance signal. For example, this parameter set may include filter cutoff frequencies and time-domain analysis window sizes optimized for high-viscosity materials and low-frequency disturbances, as well as frequency-domain analysis parameters optimized for low-viscosity materials and high-frequency disturbances. This parameter selection mechanism can be achieved by using the feature extraction algorithm parameters that have the highest matching degree with the preset material characteristics and the type and intensity of the excitation disturbance signal from the feature extraction algorithm parameter set as the final feature extraction algorithm parameters. After determining the most suitable feature extraction algorithm parameters for the current scenario, the system will use these parameters to configure the feature extraction algorithm and process the real-time multi-source transient response data, which may include melt pressure information, extrusion current information, and die outlet melt temperature information, etc. Feature extraction algorithms analyze these raw data, such as performing time-domain analysis (e.g., peak amplitude, time to peak, area under the response curve) and frequency-domain analysis (e.g., main frequency components, energy percentage), to extract behavioral feature patterns that characterize the dynamic response of a device under specific disturbances. This behavioral feature pattern is a concise yet information-rich vector that serves as a fingerprint of the device's real-time physical state for subsequent state matching and inference.

[0037] This solution applies a preset excitation disturbance signal to the execution unit of the functional film production equipment after receiving a product switching command to switch from the first product to the second product, and collects multi-source instantaneous response data of the functional film production equipment under the action of the excitation disturbance signal in real time. Based on this, to more accurately infer the real-time physical state inside the equipment, this application further optimizes the feature extraction step of the multi-source instantaneous response data. Specifically, in the process of extracting features from the multi-source instantaneous response data to obtain behavioral feature patterns, the system first obtains the preset material characteristics corresponding to the second product to be produced, as well as the type and intensity of the currently applied excitation disturbance signal. This information is a unique identifier of the current product switching scenario. Given that the response characteristics of different materials under different disturbances vary significantly, if a feature extraction method with fixed parameters is used, it will be impossible to accurately capture the actual behavior of the equipment in the current scenario. Therefore, this solution intelligently extracts the most suitable feature extraction algorithm parameters for the current scenario from a preset feature extraction algorithm parameter set based on these obtained preset material characteristics and the type and intensity of the excitation disturbance signal. Subsequently, the system utilizes these adaptively selected feature extraction algorithms to process the real-time multi-source transient response data, thereby obtaining behavioral feature patterns that accurately reflect the current behavior of the functional film production equipment. This adaptive feature extraction mechanism enables the extracted behavioral feature patterns to more accurately capture the dynamic response characteristics of the equipment under specific material and disturbance conditions, thus overcoming the limitations of fixed-parameter feature extraction methods in variable production scenarios. This optimization of the feature extraction process allows for higher matching accuracy when the behavioral feature patterns are subsequently matched with a pre-stored set of reference patterns, thus more reliably inferring the real-time physical state parameters inside the functional film production equipment, such as internal temperature distribution and material residue information. Finally, based on these accurate real-time physical state parameters, the system can more precisely compensate and correct the preset process parameters corresponding to the second product, obtaining corrected process parameters and controlling the execution unit to execute them. This effectively shortens the unstable transition phase during product switching, significantly reduces switching waste, and improves production efficiency and economic benefits.

[0038] In some preferred embodiments, the feature extraction algorithm includes a time-domain analysis algorithm and a frequency-domain analysis algorithm, the multi-source transient response data includes different types of transient response data, and step A3 includes: A31. Filter the multi-source transient response data; A32. For each type of instantaneous response data, use a time-domain analysis algorithm to obtain the peak amplitude and peak time based on the instantaneous response data, and use a frequency-domain analysis algorithm to obtain the main frequency components and energy proportions of the instantaneous response data. Then, use the peak amplitude, peak time, main frequency components, and energy proportions as the single-type data feature pattern corresponding to the instantaneous response data. A33. Integrate all single-type data feature patterns into behavioral feature patterns.

[0039] Time-domain analysis algorithms are methods that directly analyze signals on the time axis, focusing primarily on the characteristics of signals changing over time. Their purpose is to capture information such as instantaneous changes, duration, and amplitude. Possible implementations of time-domain analysis algorithms include, but are not limited to: analysis based on statistical characteristics, such as mean, variance, kurtosis, and skewness; or analysis based on waveform characteristics, such as peak detection, zero-crossing rate, and rise / fall time calculation. Frequency-domain analysis algorithms are methods that convert signals from the time domain to the frequency domain for analysis. They primarily focus on the various frequency components and their intensities contained in the signal, revealing the signal's periodicity, harmonic components, and energy distribution at different frequencies. Possible implementations of frequency-domain analysis algorithms include, but are not limited to: Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), wavelet transform, and power spectral density estimation. Multi-source transient response data includes different types of transient response data, which refers to the transient change data generated in real time by different sensors or monitoring points of the functional film production equipment under the action of excitation disturbance signals. These data reflect the dynamic response of the equipment after being subjected to minor external stimuli. The melt pressure information, extrusion current information and die outlet melt temperature information mentioned in the above embodiments are three types of transient response data.

[0040] Filtering multi-source transient response data is a signal preprocessing technique aimed at removing unnecessary noise or interference from the raw data to improve signal quality and the accuracy of subsequent analysis. Its role is to ensure that feature extraction is based on clean, valid signals, avoiding the misleading influence of noise on behavioral feature patterns. Possible implementations of filtering include, but are not limited to: digital filters, such as Butterworth filters, Chebyshev filters, and elliptic filters, which can be low-pass, high-pass, band-pass, or band-stop filters based on the frequency characteristics of the noise; or nonlinear filtering methods such as moving average filtering and median filtering.

[0041] Obtaining peak amplitude and time to peak are key features of time-domain analysis. Peak amplitude reflects the maximum intensity of change in the response signal over time, while time to peak reflects the time required for the response signal to reach its maximum intensity from the onset of the disturbance, i.e., the response speed. These features can intuitively quantify the instantaneous response and dynamic characteristics of a device to disturbances. Possible implementation methods include, but are not limited to: directly finding the maximum value and its corresponding time point by traversing the time series data; or extracting peak information after envelope detection of the signal. Obtaining the dominant frequency components and energy proportions are key features of frequency-domain analysis. Dominant frequency components reveal the dominant periodic changes in the signal, while energy proportions quantify the contribution of these frequency components to the total signal energy. These features can reveal the response pattern of the device's internal state to disturbances from another dimension, such as whether there are specific vibration modes or resonant frequencies. Possible implementation methods include, but are not limited to: performing a Fourier transform on the filtered data, then identifying the frequency points with the largest amplitudes in the spectrum as dominant frequency components, and calculating their proportion in the total energy; or using wavelet analysis and other methods to simultaneously extract energy distribution features in the time and frequency domains. A single-type data feature pattern refers to a comprehensive set of features extracted from instantaneous response data of a specific type, such as melt pressure data, through time-domain and frequency-domain analysis. Its purpose is to comprehensively and concisely characterize the internal equipment state information contained in this type of data. Possible implementation methods include, but are not limited to, combining values ​​or vectors such as peak amplitude, peak time, a list of main frequency components, and an energy percentage list into a single feature vector (single-type data feature pattern).

[0042] Finally, all single-type data feature patterns are integrated into a behavioral feature pattern. This integration refers to fusing the single-type data feature patterns extracted from different types of instantaneous response data to form a unified feature set that comprehensively reflects the overall behavioral performance of the functional membrane production equipment. Its purpose is to provide a comprehensive perspective for understanding the complex dynamic response of the equipment under disturbances, providing a complete data foundation for subsequent physical state parameter matching. Possible implementation methods include, but are not limited to, concatenating the feature vectors of all single-type data feature patterns to form a higher-dimensional comprehensive feature vector.

[0043] Upon receiving a product switching command and applying an excitation disturbance signal, this solution collects multi-source transient response data from the functional film production equipment in real time. This data may include various types such as melt pressure information, extrusion current information, and melt temperature information at the die outlet. Each type of data may carry different aspects of the equipment's internal state. To ensure the accuracy of subsequent analysis, this multi-source transient response data is first filtered to eliminate noise and interference that may be introduced during the acquisition process, ensuring data purity. Subsequently, for each type of transient response data, the system uses time-domain analysis algorithms and frequency-domain analysis algorithms for feature extraction. Time-domain analysis algorithms capture the dynamic characteristics of the data in the time dimension; for example, by obtaining the peak amplitude and the time to reach the peak, the intensity and speed of the equipment's response to disturbances can be intuitively reflected. Simultaneously, frequency-domain analysis algorithms reveal the compositional structure of the data in the frequency dimension; for example, by identifying the main frequency components and energy proportions, periodic or vibrational patterns that are difficult to detect in time-domain analysis can be discovered. Combining these time-domain and frequency-domain features forms a single-type data feature pattern corresponding to that type of data, ensuring that the information extracted from a single data source is comprehensive and multi-dimensional. Ultimately, all different types of single-type data feature patterns are integrated to form a unified behavioral feature pattern. This integration not only avoids information omissions that may result from a single data source or a single analysis dimension, but also, through multi-source and multi-dimensional feature fusion, enables the final behavioral feature pattern to more comprehensively and accurately reflect the unique behavioral performance of functional membrane production equipment in response to excitation and disturbance signals under its current internal physical state. This method of categorizing and extracting and integrating features across multiple dimensions allows the system to fully mine the internal state information of the equipment contained in different data, thereby improving the accuracy and completeness of the behavioral feature pattern. In this way, the solution proposed in this application can overcome the limitations that traditional methods may have when processing multi-source heterogeneous data, providing a more reliable foundation for accurately matching the real-time physical state parameters of the equipment, and thus supporting precise compensation and correction of the preset process parameters corresponding to the second product.

[0044] In some preferred embodiments, the step of matching behavioral feature patterns with a pre-stored set of reference patterns and using the preset physical state parameters corresponding to the set of reference patterns with the highest matching degree as the real-time physical state parameters inside the functional membrane production equipment includes: B1. Calculate the matching degree between the behavioral feature pattern and each reference pattern in the preset reference pattern set; B2. Calculate the difference in matching degree based on any two matching degrees, and analyze whether all the differences in matching degree are less than the preset threshold. If yes, proceed to step B4; otherwise, proceed to step B3. B3. Use the preset physical state parameters corresponding to the reference mode with the highest matching degree as the real-time physical state parameters inside the functional membrane production equipment. B4. Obtain the preset process characteristics corresponding to the second product; the preset process characteristics include the sensitivity of the second product to the temperature distribution inside the functional membrane production equipment; B5. Based on the preset process characteristics, assign weighted weights to the features in the behavioral feature pattern that are related to the preset process characteristics; B6. Based on the weighted weights, recalculate the weighted matching degree between the behavioral feature patterns and multiple reference patterns; B7. Use the preset physical state parameters corresponding to the reference mode with the highest weighted matching degree as the real-time physical state parameters inside the functional membrane production equipment.

[0045] The scheme first obtains preliminary matching results by calculating the matching degree between behavioral feature patterns and each reference pattern in a preset set of reference patterns. This step quantifies the similarity between real-time device behavior and known device states; for example, it can be calculated using methods such as Euclidean distance or cosine similarity.

[0046] The system then further analyzes the differences between these initial matching scores. Specifically, it calculates the difference between any two matching scores and determines whether all these differences are less than a preset threshold. The preset threshold can be set based on historical data analysis or expert experience to define whether the matching scores are close enough. This judgment mechanism is one of the key innovations of this solution, enabling the system to intelligently identify scenarios where the matching results are ambiguous and it is difficult to make an accurate judgment directly.

[0047] When the matching degree difference is sufficiently large (a matching degree difference greater than or equal to a preset threshold, equivalent to the initial matching result having clear distinguishability), the system will directly use the preset physical state parameters corresponding to the reference mode with the highest matching degree as the real-time physical state parameters inside the functional film production equipment. In this case, no additional complex calculations are required, ensuring processing efficiency. The preset physical state parameters correspond one-to-one with the preset reference modes, and may include, for example, the temperature distribution curve inside the die head, the temperature value of a specific area, and the residual amount of material from the preceding product.

[0048] When the system determines that all matching differences are less than a preset threshold, indicating that multiple reference patterns are very close to the current behavioral feature pattern and it is difficult to accurately select based on the simple "highest matching degree" principle, the system will initiate a weighted matching process based on product process characteristics. First, the system will obtain the preset process characteristics corresponding to the second product to be produced (e.g., the product's sensitivity to the internal temperature distribution of the functional film production equipment). These preset process characteristics can be stored in the product database and loaded by the system when a product switching command is issued. Besides sensitivity to temperature distribution, they can also include sensitivity to material residues, etc. Based on these process characteristics, the system will dynamically assign higher weights to features in the behavioral feature pattern that are related to these preset process characteristics. For example, if the second product is temperature-sensitive, then in the behavioral characteristic pattern, features related to temperature response (such as the peak amplitude and peak time of melt temperature information at the die exit) will be assigned higher weights. Conversely, if it is more sensitive to material residue, features related to extrusion current information may be assigned higher weights. The assigned weights are preferably extracted from a pre-built mapping table of process characteristics and weights based on preset process characteristics. For example, in this mapping table, the higher the temperature sensitivity of the second product according to preset process characteristics, the greater the weight assigned to the peak amplitude and peak time of melt temperature information at the die exit. After the features are assigned weighted weights, the system recalculates the matching degree. The weighted matching degree calculation method is similar to the initial matching, but the weight of each feature is taken into account during the calculation. For example, when using Euclidean distance, it can be modified to weighted Euclidean distance. This weighted calculation allows features more relevant to the current product's process characteristics to dominate the matching results, thereby improving the accuracy of the matching. Ultimately, the system will select the preset physical state parameters corresponding to the reference mode with the highest weighted matching degree as the real-time physical state parameters inside the functional membrane production equipment.

[0049] This solution, through the aforementioned process, enables the digital control system for the entire functional film production process to more accurately infer the real-time physical state parameters within the equipment after receiving a product switching command. It not only considers the instantaneous response of the equipment under excitation disturbances but also incorporates the specific process requirements of the product to be produced during the matching process. This makes the inferred physical state parameters more consistent with reality, providing a more precise basis for subsequent compensation and correction of the preset process parameters for the second product. This forward-looking and intelligent state perception capability effectively solves the problem of misjudgment in traditional systems when the matching degree is close, thus more effectively guiding process adjustments, shortening the product switching transition phase, and reducing waste generation.

[0050] In some preferred embodiments, the preset process characteristics include the sensitivity of the second product to the temperature distribution inside the functional film production equipment, and step B5 includes: B51. Based on the preset process characteristics, assign weighted weights to the features in the behavioral feature pattern that are related to the temperature distribution inside the functional membrane production equipment.

[0051] The higher the product's sensitivity to the internal temperature distribution of the equipment, the greater the impact of accurate temperature distribution parameters on subsequent process compensation and final product quality. Therefore, determining the corresponding weighting object based on preset process characteristics, rather than blindly selecting weighting features, can specifically amplify the influence of temperature-related features throughout the matching process, avoid interference from other irrelevant features, and help select a reference mode that better matches the actual temperature state of the equipment. This ensures that the obtained real-time physical state parameters inside the equipment are more accurate, thus providing a reliable basis for the compensation and correction of the process parameters of the second product, better meeting the process requirements of the current product, and effectively helping to shorten the unstable transition phase of product switching and significantly reduce the generation of switching waste.

[0052] In some preferred embodiments, the step of compensating and correcting the preset process parameters corresponding to the second product based on real-time physical state parameters to obtain corrected process parameters includes: C1. Calculate the parameter deviation between the real-time physical state parameters and the target physical state parameters corresponding to the second product; C2. Based on the parameter deviation and the preset adjustment gain, calculate the compensation correction amount for the preset process parameters corresponding to the second product. C3. Apply the compensation correction amount to the preset process parameters corresponding to the second product to obtain the corrected process parameters.

[0053] The target physical state parameters corresponding to the second product refer to the ideal or standard physical state that the functional film production equipment should achieve when producing the second product. For example, for the second product, the target temperature distribution curve inside the die head should be present, and ideally, the material residue should be zero. Their function is to serve as a reference benchmark, compared with real-time physical state parameters, to determine the degree and direction of deviation from the equipment state (parameter deviation). The preset adjustment gain refers to one or a set of pre-set coefficients used to convert the deviation of the physical state parameters into a correction amount for the process parameters. These gains can be constants, and their function is to establish a mapping relationship between physical state deviation and process parameter adjustment, ensuring that the compensation correction amount can accurately and effectively guide the equipment state towards the target state. The compensation correction amount refers to the adjustment amount required to the preset process parameters corresponding to the second product in order to eliminate parameter deviation. Its function is to provide specific, executable process parameter adjustment instructions to correct non-ideal states within the equipment. Applying the compensation correction to the preset process parameters corresponding to the second product means applying the calculated compensation correction to the standard process parameters used by the second product during normal production using mathematical operations such as addition, subtraction, multiplication, and division. Its function is to transform the theoretically calculated adjustment into practically usable control commands, forming new process parameters that are more adapted to the current equipment state. The corrected process parameters are the final process parameters used to control the functional film production equipment after compensation correction. These parameters are directly issued to the execution unit to guide the equipment's operation. Their function is to serve as direct input to the equipment execution unit, guiding the equipment to quickly and smoothly transition from the current state to the stable production state of the second product, thereby shortening the transition time and reducing waste.

[0054] This solution clarifies the gap between the current state of the equipment and the ideal target state by calculating the parameter deviation between the real-time physical state parameters and the target physical state parameters corresponding to the second product. Subsequently, based on the calculated parameter deviation and the preset adjustment gain, the system calculates the compensation correction amount for the preset process parameters corresponding to the second product. The preset adjustment gain plays a crucial role here, transforming the abstract physical state deviation into a specific, operable adjustment range for the process parameters, ensuring the accuracy and effectiveness of the adjustment and avoiding over- or under-adjustment. Finally, the calculated compensation correction amount is applied to the preset process parameters corresponding to the second product, thus obtaining the final corrected process parameters. These corrected process parameters are then used to control the execution unit of the functional film production equipment, enabling the equipment to make more intelligent and proactive adjustments based on its current actual internal state, rather than simply using standard preset parameters. This closed-loop control mechanism based on real-time state inference and precise compensation correction significantly shortens the unstable transition phase during product switching, effectively reduces switching waste, and improves production efficiency and raw material utilization.

[0055] Secondly, such as Figure 2 As shown, this application also provides a control method for a digital management and control system for the entire process of functional film production, applied in functional film production equipment, which includes the following steps: S1. Upon receiving a product switching instruction to switch from the first product to the second product, a preset excitation disturbance signal is applied to the execution unit of the functional film production equipment. S2. Real-time acquisition of multi-source instantaneous response data of functional membrane production equipment under excitation disturbance signals; the instantaneous response data is acquired by sensing elements distributed on the production line; S3. Extract features from multi-source transient response data to obtain behavioral feature patterns; S4. Match the behavioral feature pattern with the pre-stored reference pattern set, and use the preset physical state parameter corresponding to the reference pattern set with the highest matching degree with the behavioral feature pattern as the real-time physical state parameter inside the functional membrane production equipment. S5. Based on the real-time physical state parameters, the preset process parameters corresponding to the second product are compensated and corrected to obtain the corrected process parameters. Then, the execution unit of the functional film production equipment is controlled to execute the corrected process parameters.

[0056] The steps of the control method of the full-process digital management and control system for functional film production provided in this application correspond to the steps executed by each module in the full-process digital management and control system for functional film production provided in the first aspect above. The principle of the control method of the full-process digital management and control system for functional film production provided in this embodiment is the same as the principle of the full-process digital management and control system for functional film production provided in the first aspect above, and will not be repeated here.

[0057] As can be seen from the above, the digital management and control system and method for the entire process of functional membrane production provided in this application, by introducing excitation disturbance and response data analysis, can obtain key information such as temperature distribution and material residue inside the equipment in real time and accurately, thereby transforming "passive response" into "active prediction and adjustment". This enables the system to make forward-looking corrections to process parameters at the initial stage of product switching, avoiding product non-conformity problems caused by factors such as equipment thermal inertia and material residue during the transition stage, significantly reducing switching waste, lowering production costs, and improving production efficiency and product quality.

[0058] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0060] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0061] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A digital control system for the entire production process of functional membranes, characterized in that, The digital control system for the entire functional film production process, used in functional film production equipment, includes: The excitation module is used to apply a preset excitation disturbance signal to the execution unit of the functional film production equipment after receiving a product switching instruction to switch from the first product to the second product. The acquisition module is used to collect multi-source instantaneous response data of the functional membrane production equipment under the action of the excitation disturbance signal in real time; The extraction module is used to extract features from the multi-source instantaneous response data to obtain behavioral feature patterns; The parameter confirmation module is used to match the behavior feature pattern with a pre-stored set of reference patterns, and to use the preset physical state parameter corresponding to the set of reference patterns with the highest matching degree with the behavior feature pattern as the real-time physical state parameter inside the functional membrane production equipment. The correction module is used to compensate and correct the preset process parameters corresponding to the second product based on the real-time physical state parameters to obtain corrected process parameters, and then control the execution unit of the functional film production equipment to execute the corrected process parameters.

2. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The excitation disturbance signal is the instantaneous offset of the control parameter in the time dimension.

3. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The physical state parameters include at least the temperature distribution information inside the functional membrane production equipment and the material residue information corresponding to the first product.

4. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The multi-source instantaneous response data includes melt pressure information, extrusion current information, and melt temperature information at the die exit.

5. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The step of extracting features from the multi-source transient response data to obtain behavioral feature patterns reflecting the current behavior of the functional membrane production equipment includes: A1. Obtain the preset material characteristics corresponding to the second product, and obtain the type and intensity of the excitation disturbance signal; A2. Based on the preset material characteristics and the type and intensity of the excitation disturbance signal, extract the corresponding feature extraction algorithm parameters from the preset feature extraction algorithm parameter set as the parameters of the feature extraction algorithm; A3. Use the selected feature extraction algorithm to extract features from the multi-source instantaneous response data to obtain behavioral feature patterns.

6. The digital control system for the entire production process of functional membranes according to claim 5, characterized in that, The feature extraction algorithm includes a time-domain analysis algorithm and a frequency-domain analysis algorithm. The multi-source instantaneous response data includes different types of instantaneous response data. Step A3 includes: A31. Filter the multi-source instantaneous response data; A32. For each type of instantaneous response data, the time-domain analysis algorithm is used to obtain the peak amplitude and peak time based on the instantaneous response data, and the frequency-domain analysis algorithm is used to obtain the main frequency components and energy proportion of the instantaneous response data. Then, the peak amplitude, peak time, main frequency components and energy proportion are used as the single type of data feature pattern corresponding to the instantaneous response data. A33. Integrate all the single-type data feature patterns into a behavioral feature pattern.

7. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The step of matching the behavioral feature pattern with a pre-stored set of reference patterns and using the preset physical state parameter corresponding to the set of reference patterns with the highest matching degree as the real-time physical state parameter inside the functional membrane production equipment includes: B1. Calculate the matching degree between the behavioral feature pattern and each reference pattern in the preset reference pattern set; B2. Calculate the difference in matching degree based on any two of the matching degrees, and analyze whether all the differences in matching degree are less than a preset threshold. If yes, proceed to step B4; otherwise, proceed to step B3. B3. The preset physical state parameters corresponding to the reference mode with the highest matching degree are used as the real-time physical state parameters inside the functional membrane production equipment. B4. Obtain the preset process characteristics corresponding to the second product; the preset process characteristics include the sensitivity of the second product to the temperature distribution inside the functional membrane production equipment; B5. Based on the preset process characteristics, assign weighted weights to the features in the behavioral feature pattern that are related to the preset process characteristics; B6. Based on the weighted weights, recalculate the weighted matching degree between the behavioral feature pattern and multiple reference patterns; B7. The preset physical state parameters corresponding to the reference mode with the highest weighted matching degree are used as the real-time physical state parameters inside the functional membrane production equipment.

8. The digital control system for the entire production process of functional membranes according to claim 7, characterized in that, The preset process characteristics include the sensitivity of the second product to the temperature distribution inside the functional film production equipment. Step B5 includes: B51. Based on the preset process characteristics, assign weighted weights to the features in the behavioral feature pattern that are related to the temperature distribution inside the functional membrane production equipment.

9. The digital control system for the entire production process of functional membranes according to claim 1, characterized in that, The step of compensating and correcting the preset process parameters corresponding to the second product based on the real-time physical state parameters to obtain the corrected process parameters includes: C1. Calculate the parameter deviation between the real-time physical state parameters and the target physical state parameters corresponding to the second product; C2. Based on the parameter deviation and the preset adjustment gain, calculate the compensation correction amount for the preset process parameters corresponding to the second product. C3. Apply the compensation correction amount to the preset process parameters corresponding to the second product to obtain the corrected process parameters.

10. A control method for a digital management and control system for the entire production process of functional membranes, characterized in that, The control method of the digital management and control system for the entire functional film production process, applied in functional film production equipment, includes the following steps: S1. Upon receiving a product switching instruction to switch from the first product to the second product, a preset excitation disturbance signal is applied to the execution unit of the functional film production equipment. S2. Real-time acquisition of multi-source instantaneous response data of the functional membrane production equipment under the action of the excitation disturbance signal; the instantaneous response data is acquired by sensing elements distributed on the production line; S3. Perform feature extraction on the multi-source transient response data to obtain behavioral feature patterns; S4. The behavioral feature pattern is matched with a pre-stored set of reference patterns, and the preset physical state parameter corresponding to the set of reference patterns with the highest matching degree with the behavioral feature pattern is used as the real-time physical state parameter inside the functional membrane production equipment. S5. Based on the real-time physical state parameters, the preset process parameters corresponding to the second product are compensated and corrected to obtain corrected process parameters, and then the execution unit of the functional film production equipment is controlled to execute the corrected process parameters.

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