A sensor-based method and system for detecting the antistatic performance of a plastic wrapping film

Abstract

The application provides a sensor-based plastic wrapping film anti-static performance detection method and system, and relates to the technical field of anti-static performance detection. The method comprises the following steps: receiving original static signals collected by a plurality of static induction units arranged on a production path of a plastic wrapping film, the static induction units being used for independently monitoring a unit area on the surface of the film body; performing relative comparison between the original static signals and time trend analysis to obtain analysis results; identifying a local high signal mode meeting a preset condition on the surface of the film body according to the analysis results; determining a spatial position and a static strength at which static aggregation occurs on the surface of the film body according to the identified local high signal mode; and issuing a warning signal according to the spatial position and the static strength. The application can realize timely and accurate identification of the anti-static performance of the plastic wrapping film, thereby effectively preventing static discharge damage to sensitive electronic elements packaged in the finished film roll.

Description

Technical Field

[0001] This application relates to the field of antistatic performance testing technology, and in particular to a sensor-based method and system for testing the antistatic performance of plastic stretch film. Background Technology

[0002] Static electricity buildup is a common problem during the production and use of plastic stretch film. This not only causes the film to attract dust, but may also damage the packaged items. In particular, when packaging static-sensitive electronic components, static electricity problems can pose serious safety hazards.

[0003] Specifically, on plastic stretch film production lines, electrostatic sensor systems continuously monitor the amount of electrostatic charge on the surface of the stretch film in a non-contact manner. However, during the long-term continuous operation of the production line, minute friction between the stretch film and its surrounding environment, as well as trace amounts of process residues in the production environment, cause extremely fine particles to gradually adhere to and accumulate on the surface of the electrostatic sensor's measuring probe, forming a thin film-like deposit that is difficult to detect with the naked eye. This deposit acts as an insulating layer, altering the effective sensing distance between the sensor's measuring probe and the surface of the stretch film, and changing the distribution of local electric field lines. This results in a systematic attenuation of the intensity of the original electrical signal acquired by the sensor, and this attenuation is more pronounced in areas where the film velocity changes or where the electrostatic charge distribution is uneven.

[0004] However, deposits on the probe surface not only attenuate signal strength but also introduce additional insulation losses and response time delays, reducing the sensor's ability to detect rapid changes in the electric field. When transient, localized static charge accumulation occurs on the membrane surface, the sensor may not be able to fully sense its peak intensity within a sufficiently short time. This decrease in dynamic response capability cannot be detected during routine calibration. Therefore, when producing electrostatically sensitive membranes, if the detection system fails to identify and warn of these localized, transient high electrostatic risks in a timely and accurate manner, the values ​​displayed by the system may still be within the "normal" range or only show minor fluctuations, insufficient to trigger the warning mechanism. Ultimately, this could lead to electrostatic discharge damage to the sensitive electronic components packaged in the finished membrane roll. Summary of the Invention

[0005] Therefore, this application proposes a sensor-based method and system for detecting the antistatic performance of plastic stretch film, aiming to solve the technical problem that the detection system in the production process of plastic stretch film fails to identify and warn of these local and instantaneous high electrostatic risks in a timely and accurate manner, which may ultimately lead to electrostatic discharge damage to the packaged sensitive electronic components by the finished film roll.

[0006] In a first aspect, this application discloses a sensor-based method for testing the antistatic performance of plastic stretch film, comprising the following steps: The system receives raw electrostatic signals collected by multiple electrostatic induction units located on the production path of the plastic wrapping film, wherein the electrostatic induction units are used to independently monitor a unit area on the surface of the film. The analysis results are obtained by comparing the relative values ​​between units and analyzing the temporal variation trend of the original electrostatic signal. Based on the analysis results, a preset local high-frequency signal pattern was identified on the membrane surface; Based on the identified local high-frequency signal pattern, the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface are determined. A warning signal is issued based on the spatial location and electrostatic intensity.

[0007] According to some embodiments of this application, the step of performing relative comparisons between units and analyzing the temporal variation trend of the original electrostatic signal to obtain the analysis result includes: Based on the original electrostatic signals of each electrostatic induction unit, the energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted. A dynamic background spectrum baseline is established for each of the electrostatic induction units. The dynamic background spectrum baseline is used to distinguish between high-frequency signals and periodic micro-charge disturbance signals in the production environment. By comparing the energy characteristics with the dynamic background spectrum baseline between units, periodic micro-charge perturbation signals are identified. The periodic micro-charge perturbation signal is analyzed over time to obtain a periodic micro-charge perturbation pattern, which is then used as the analysis result.

[0008] According to some embodiments of this application, the step of extracting energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool based on the original electrostatic signals of each of the electrostatic induction units includes: The original electrostatic signals of each electrostatic induction unit are subjected to spectrum conversion within a rolling time window to obtain the real-time rolling spectrum of each electrostatic induction unit. Energy characteristics of preset high-frequency bands related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted from the real-time rolling spectrum of each of the aforementioned.

[0009] According to some embodiments of this application, the step of identifying a preset local high-frequency signal pattern on the membrane surface based on the analysis results includes: Based on the analysis results, the cumulative charge energy characteristic value of the original electrostatic signal of at least one adjacent electrostatic induction unit within the rolling time window is calculated. Determine whether the accumulated charge energy characteristic value exceeds a preset dynamic threshold, and obtain the determination result; Based on the judgment results, a preset local high-frequency signal pattern was identified on the membrane surface.

[0010] According to some embodiments of this application, the step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-frequency signal pattern includes: After identifying the local high-frequency signal pattern, the original electrostatic signals of the electrostatic induction unit that triggered the electrostatic accumulation and its adjacent electrostatic induction units are processed by local region signal separation to obtain the signal characteristics of each local high-frequency signal pattern after separation. Based on the signal characteristics, the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface are determined.

[0011] According to some embodiments of this application, the step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the signal characteristics includes: Based on the signal characteristics, combined with the physical location of each electrostatic induction unit and the membrane motion parameters, the spatial location of each electrostatic accumulation on the membrane surface is determined. Based on the signal characteristics, the charge density or equivalent voltage value of each electrostatic aggregate is estimated as the electrostatic intensity of each electrostatic aggregate on the membrane surface.

[0012] According to some embodiments of this application, the step of issuing a warning signal based on the spatial location and electrostatic intensity includes: The hazard level of the electrostatic accumulation is determined based on the electrostatic intensity. Based on the spatial location and hazard level, generate the content and triggering method of the early warning signal; The aforementioned warning signal was issued.

[0013] According to some embodiments of this application, the warning signal includes a description of the event type of electrostatic accumulation, the spatial location, the electrostatic intensity, and suggested measures for adjusting production process parameters corresponding to the hazard level.

[0014] According to some embodiments of this application, the steps for generating the proposed measures include: Obtain the material type of the membrane currently being produced; Based on the pre-stored material property database, query the sensitivity information to electrostatic damage corresponding to the material type; The hazard level is matched with the sensitivity information to generate recommended measures for adjusting production process parameters.

[0015] Secondly, this application discloses a sensor-based antistatic performance testing system for plastic stretch film, comprising: The receiving module is used to receive the original electrostatic signals collected by multiple electrostatic induction units set on the production path of the plastic wrapping film. The electrostatic induction units are used to independently monitor a unit area on the surface of the film. The analysis module is used to perform relative comparisons between units and analyze the temporal trends of the original electrostatic signal to obtain analysis results. The identification module is used to identify, based on the analysis results, a preset local high-frequency signal pattern on the membrane surface; The determination module is used to determine the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-frequency signal pattern. The early warning module is used to issue an early warning signal based on the spatial location and electrostatic intensity.

[0016] According to the technical solution of this application, at least the following beneficial effects are achieved: The sensor-based method for detecting the antistatic performance of plastic stretch film proposed in this application receives raw electrostatic signals collected by multiple electrostatic induction units, and performs relative comparison between these signals and analysis of their temporal trends. This effectively identifies local high-incidence signal patterns on the film surface that conform to a preset pattern. Furthermore, based on the identified local high-incidence signal patterns, the spatial location and intensity of electrostatic accumulation on the film surface can be accurately determined, and a warning signal can be issued in a timely manner. Thus, this application can overcome the signal attenuation and misjudgment problems caused by the attachment of sensor probes during the production of plastic stretch film, achieving timely and accurate identification of the antistatic performance of plastic stretch film, thereby effectively preventing electrostatic discharge damage to the packaged sensitive electronic components from the finished film roll.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0019] Figure 1 This is a schematic flowchart of a sensor-based method for testing the antistatic performance of plastic stretch film, provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the architecture of a sensor-based plastic stretch film antistatic performance testing system provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0023] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0024] In traditional plastic stretch film production, static electricity buildup is a common problem. This not only causes dust to adhere to the film but can also damage packaged goods, especially when packaging static-sensitive electronic components, posing a serious safety hazard. Currently, in fully automated, high-speed production lines, existing technology lacks an effective method to accurately identify instantaneous high-density static electricity buildup in localized areas on the film surface in real time. This makes it difficult to adjust process or environmental conditions promptly based on actual static risks during production. Specifically, in existing static electricity sensor systems, deposits accumulate on the probe surface over long-term operation, causing attenuation of the original electrical signal. This attenuation is more pronounced in areas with varying film velocity or uneven static charge distribution. When the detection system receives these attenuated signals, its output static electricity value is generally low. During calibration, maintenance personnel often interpret this as "zero-point drift" or "gain error" and "correct" the signal conversion parameters of the detection system, essentially forcibly adjusting the internal amplification factor or offset. This error-based "correction" causes the detection system to apply an incorrectly amplified conversion parameter when processing the original signal attenuated by subsequent deposits, thus masking the actual performance degradation of the probe. Furthermore, deposits on the probe surface introduce additional insulation losses and response time delays, reducing the sensor's ability to detect rapid changes in the electric field. When transient, localized static charge accumulation occurs on the membrane surface, the sensor may not be able to fully detect its peak intensity within a sufficiently short time. This causes the detection system to fail to identify and warn of these localized, transient high electrostatic risks in a timely and accurate manner, potentially leading to electrostatic discharge damage to the packaged sensitive electronic components from the finished membrane roll.

[0025] For this, please refer to Figure 1 This application proposes a sensor-based method for testing the antistatic performance of plastic stretch film, comprising the following steps: S110 receives raw electrostatic signals collected by multiple electrostatic induction units set on the production path of the plastic wrapping film. Each electrostatic induction unit is used to independently monitor a unit area on the surface of the film. S120, the analysis results are obtained by comparing the relative units of the original electrostatic signal and analyzing its change trend over time. S130, based on the analysis results, identifies a preset local high-frequency signal pattern on the membrane surface; S140, based on the identified local high-frequency signal pattern, determine the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface; S150 issues a warning signal based on spatial location and static electricity intensity.

[0026] To better understand the technical solution of this application, some key terms involved will be explained first.

[0027] "Electrostatic sensing units" refer to sensors used for non-contact measurement of static charge on the surface of objects, such as electrostatic voltmeter probes and electrostatic field meters. These units are strategically arranged along the production path of the plastic stretch film to achieve independent monitoring of different areas of the film surface. Each electrostatic sensing unit is responsible for monitoring a unit area of ​​the film surface, ensuring comprehensive coverage of the electrostatic distribution across the entire width of the film.

[0028] "Raw electrostatic signal" refers to the unprocessed electrical signal directly collected by the electrostatic induction unit, usually a voltage or current signal, whose intensity is related to the amount of static charge on the membrane surface.

[0029] "Local high-frequency signal pattern" refers to a pattern in which the electrostatic signal on the membrane surface is abnormally high or fluctuates violently in a certain area and for a certain period of time, indicating that there may be electrostatic accumulation.

[0030] "Electrostatic accumulation" refers to the abnormal accumulation of static charge in a localized area on the surface of a membrane, which may lead to electrostatic discharge.

[0031] "Spatial location" refers to the specific coordinates or area on the surface of the membrane where electrostatic charge accumulates.

[0032] "Electrostatic intensity" refers to the charge density or equivalent voltage value of static electricity accumulation, reflecting the severity of static electricity accumulation.

[0033] Specifically, the system first receives raw electrostatic signals collected by multiple electrostatic induction units positioned along the production path of the plastic wrapping film. These units are arranged on the production line, for example, in a closely spaced linear array along the width of the film at a center-to-center distance of 5 to 10 millimeters, or densely arranged in key areas according to the characteristics of the production process. Each electrostatic induction unit independently monitors a unit area on the film surface. For example, a non-contact electrostatic sensor with an electrode diameter of 2 to 5 millimeters can be used to acquire the raw electrostatic signal by sensing changes in the electric field. These units independently acquire signals at extremely high sampling rates, such as 100 kHz to 500 kHz, enabling the capture of instantaneous electrostatic changes at the millisecond or even microsecond level. For example, an array of electrostatic sensors can be used, with each sensor probe corresponding to a small area on the film surface, thereby achieving precise monitoring of the electrostatic distribution on the film surface.

[0034] After receiving the raw electrostatic signals, it is necessary to perform relative comparisons between units and temporal trend analysis on these signals to obtain the analysis results. Relative comparisons between units can be achieved by comparing the signal strength differences acquired by adjacent electrostatic induction units at the same time point; for example, calculating the difference or ratio of signals from adjacent units. Temporal trend analysis can be achieved by performing time-series analysis on the signals acquired by a single electrostatic induction unit over a period of time; for example, calculating the signal mean, variance, rate of change, or performing Fourier transform to analyze frequency components. For instance, the raw electrostatic signals of each electrostatic induction unit can be processed by a moving average, and then the moving averages of adjacent units can be compared, or the standard deviation of each unit's signal within a certain time window can be calculated to reflect its volatility.

[0035] Based on the above analysis, a preset local high-incidence signal pattern was identified on the membrane surface. This pattern can be a sudden increase in electrostatic signal in a certain area that persists for a period of time, or a periodic or non-periodic violent fluctuation in that area. The preset local high-incidence signal pattern can be determined through historical data analysis, expert experience, or simulation experiments. For example, a threshold can be set; if the signal strength of a certain electrostatic induction unit exceeds this threshold for N consecutive sampling points, and the signal strength of its adjacent units also increases simultaneously, then a local high-incidence signal pattern is considered to have been identified.

[0036] After identifying a localized high-intensity signal pattern, it is necessary to determine the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on this pattern. The spatial location can be determined by the physical location of the electrostatic induction unit that triggered the localized high-intensity signal pattern. For example, if an electrostatic induction unit and its adjacent units simultaneously trigger the high-intensity signal pattern, electrostatic accumulation may occur in the area covered by these units. The electrostatic intensity can be estimated by the signal amplitude, duration, or accumulated charge of the localized high-intensity signal pattern. For example, the lateral and longitudinal locations of the electrostatic accumulation can be determined based on the number of the electrostatic induction unit that triggered the localized high-intensity signal pattern and its coordinates on the production line. The electrostatic intensity can then be quantified based on the peak voltage or integrated charge of the signal.

[0037] Finally, based on the determined spatial location and electrostatic intensity, a warning signal is issued. The warning signal can include information on the location and intensity of the electrostatic accumulation, as well as the potential hazard level, and can be issued through audible and visual alarms, information push notifications, or linkage with the control system. For example, when the electrostatic intensity exceeds a certain safety threshold, the system can immediately issue an audible and visual alarm, display the specific location and intensity of the electrostatic accumulation on the user interface, and simultaneously send a text message or email notification to production management personnel.

[0038] The sensor-based method for detecting the antistatic performance of plastic stretch film proposed in this application receives raw electrostatic signals collected by multiple electrostatic induction units and performs relative comparisons between these signals and analysis of their temporal trends. This effectively identifies pre-defined local high-incidence signal patterns on the film surface. Furthermore, based on the identified local high-incidence signal patterns, the spatial location and intensity of electrostatic accumulation on the film surface can be accurately determined, and a timely warning signal can be issued. Therefore, this application overcomes the signal attenuation and misjudgment problems caused by deposits on the sensor probes during the production of plastic stretch film, achieving timely and accurate identification of the antistatic performance of plastic stretch film, thereby effectively preventing electrostatic discharge damage to the packaged sensitive electronic components from the finished film roll.

[0039] In some embodiments of this application, the steps of performing relative comparisons between units and analyzing the temporal trends of the original electrostatic signal to obtain the analysis results preferably include: Based on the original electrostatic signals of each electrostatic induction unit, the energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted. A dynamic background spectrum baseline is established for each electrostatic induction unit. The dynamic background spectrum baseline is used to distinguish between high-frequency signals and periodic micro-charge disturbance signals in the production environment. By comparing the energy characteristics with the dynamic background spectrum baseline between units, periodic micro-charge perturbation signals are identified. The periodic micro-charge perturbation signal is analyzed over time to obtain a periodic micro-charge perturbation pattern, which is then used as the analysis result.

[0040] Extracting the energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool involves performing spectral analysis on the original electrostatic signal to identify and quantify the energy of the preset high-frequency band associated with the vibration of the membrane material itself and the vibration of the cutting tool during the production process. These vibration characteristics may exhibit certain peaks or energy distributions in the spectrum, the purpose of which is to provide background information or potential interference source characteristics for subsequent signal differentiation.

[0041] Establishing a dynamic background spectrum baseline for each electrostatic induction unit can be understood as continuously monitoring the spectrum distribution of each unit under normal production conditions (i.e., when no significant electrostatic accumulation events occur), and dynamically adjusting the baseline according to environmental changes (such as temperature, humidity, equipment operating status, etc.). The main purpose of the dynamic background spectrum baseline is to provide a real-time reference to effectively distinguish between common high-frequency signals in the production environment (such as mechanical noise, power supply harmonics, etc.) and periodic micro-charge disturbance signals truly caused by electrostatic accumulation on the membrane surface.

[0042] Therefore, by comparing the energy characteristics with the dynamic background spectrum baseline between units, periodic micro-charge disturbance signals are identified. Specifically, this involves comparing the energy characteristics extracted from the original electrostatic signal acquired at the current moment with the real-time updated dynamic background spectrum baseline. When the intensity of the energy characteristics within a preset frequency range significantly exceeds the baseline (e.g., exceeding a preset threshold or a statistically significant deviation), a periodic micro-charge disturbance signal is determined to exist. The aim is to accurately separate signal components directly related to electrostatic accumulation from complex production environments.

[0043] Finally, the temporal trend analysis of the periodic micro-charge perturbation signal yields a periodic micro-charge perturbation pattern, which is then used as the analysis result. This means that it's not just about identifying individual perturbation events, but rather about tracing the evolution of these perturbation signals over time (e.g., changes in frequency, amplitude, and duration) to form a pattern with time-series characteristics. This pattern can more comprehensively reflect the dynamic process and potential regularity of electrostatic accumulation, thus serving as a reliable basis for subsequent identification of localized high-incidence signal patterns.

[0044] This application's solution effectively addresses the noise interference issues that the aforementioned basic solution may face in complex production environments by introducing refined signal processing steps. Specifically, firstly, by extracting energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the tool vibration characteristics, a preliminary characterization and understanding of non-electrostatic signal interference that may occur during the production process can be achieved. Secondly, a dynamic background spectrum baseline is established for each electrostatic induction unit, enabling the system to adapt to changes in the production environment in real time, thereby accurately distinguishing between prevalent environmental high-frequency signals and periodic micro-charge disturbance signals caused by electrostatic accumulation. It is precisely because of the introduction of this dynamic baseline that the system can avoid misjudging conventional production noise as electrostatic anomalies. Based on this, by comparing the extracted energy characteristics with the dynamic background spectrum baseline, the system can accurately identify those periodic micro-charge disturbance signals that truly deviate from the background noise and indicate electrostatic accumulation. Finally, by performing temporal trend analysis on these identified periodic micro-charge disturbance signals, the dynamic evolution process of electrostatic accumulation can be captured, forming a periodic micro-charge disturbance pattern with higher confidence, thus providing more accurate and reliable analytical results for subsequent determination of the spatial location and intensity of electrostatic accumulation.

[0045] The following is a specific example to illustrate this.

[0046] On a plastic stretch film production line, multiple electrostatic induction units are evenly arranged along the film's movement path. During normal production, each unit continuously collects raw electrostatic signals. To accurately analyze these signals, a Fast Fourier Transform (FFT) within a rolling time window is first performed on the raw electrostatic signal collected by each unit to obtain the real-time spectrum. During this process, preset high-frequency bands (e.g., 500Hz-800Hz) related to the inherent vibration characteristics of the film material (e.g., polyethylene) and preset high-frequency bands (e.g., 1.2kHz-1.5kHz) related to the vibration characteristics of the cutting tool (e.g., a cutting tool) can be predetermined. The system then extracts the energy characteristics of the corresponding high-frequency bands from these real-time spectra.

[0047] Meanwhile, the system continuously establishes and updates a dynamic background spectrum baseline for each electrostatic induction unit. For example, during periods when the production line is running stably and no electrostatic anomalies are detected, the system records and averages the spectrum data of each electrostatic induction unit, and adaptively adjusts it according to changes in environmental parameters (such as temperature and humidity) to form a dynamic baseline that reflects the noise level of the normal production environment.

[0048] Once a new, raw electrostatic signal is acquired and its spectrum is analyzed, its extracted energy characteristics are compared in real time with the corresponding dynamic background spectrum baseline. If the energy characteristics of a certain electrostatic induction unit within a preset frequency range (e.g., 100Hz-300Hz, which is preset as the typical frequency of periodic micro-charge perturbation signals) are significantly higher than its dynamic background spectrum baseline (e.g., exceeding the baseline average by three standard deviations), then the signal is identified as a periodic micro-charge perturbation signal.

[0049] Subsequently, the system analyzes the temporal trends of these identified periodic micro-charge perturbation signals. For example, the system tracks parameters such as the duration, frequency drift, and amplitude changes of these perturbation signals, integrating them into a periodic micro-charge perturbation pattern. For instance, if an electrostatic induction unit in a certain area continuously detects a periodic perturbation signal with a frequency of approximately 150 Hz and a gradually increasing amplitude, this pattern will be recorded and used as the final analysis result, indicating a potential risk of electrostatic accumulation in that area. In this way, this application can provide a more accurate analysis result after noise filtering and pattern recognition, providing a reliable basis for subsequent electrostatic accumulation location and intensity assessment.

[0050] In a preferred embodiment of this application, the step of extracting energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool based on the original electrostatic signals of each electrostatic induction unit includes: The original electrostatic signals of each electrostatic induction unit are subjected to spectrum conversion within a rolling time window to obtain the real-time rolling spectrum of each electrostatic induction unit. Energy characteristics of preset high-frequency bands related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted from each real-time rolling spectrum.

[0051] The rolling time window spectral conversion of the raw electrostatic signals from each electrostatic induction unit refers to converting the time-domain raw electrostatic signals into frequency-domain spectral information using signal processing techniques such as Fourier transform. By employing a rolling time window, the continuously acquired raw electrostatic signals can be segmented for processing. Each segment undergoes spectral analysis within a fixed-length time window, which rolls forward over time, thus reflecting real-time changes in the signal's frequency components. The resulting real-time rolling spectrum of each electrostatic induction unit dynamically displays the frequency distribution of the electrostatic signal on the membrane surface and its changes over time.

[0052] Extracting energy characteristics from preset high-frequency bands related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool from various real-time rolling spectra refers to calculating the energy of predetermined high-frequency bands related to the vibration characteristics of the membrane material itself and the vibration characteristics of the cutting tool during production, after obtaining the real-time rolling spectrum. These high-frequency bands typically correspond to corresponding physical vibration modes, and their energy magnitude can serve as an indicator of the intensity and stability of these vibrations. For example, energy characteristics can be obtained by integrating or summing the spectral amplitudes within a preset frequency range.

[0053] The proposed solution captures the frequency components of the original electrostatic signal in real time and dynamically by performing a rolling time window spectrum conversion. This real-time rolling spectrum acquisition method allows the system to continuously monitor subtle changes in the electrostatic signal during membrane production, rather than just capturing static snapshots. This dynamic monitoring capability enables the precise extraction of preset high-frequency energy characteristics related to the inherent vibrational properties of the membrane material and the vibrational properties of the cutting tools from these real-time spectra. These energy characteristics are crucial for distinguishing high-frequency signals from periodic micro-charge disturbance signals in the production environment, as they reflect the mechanical or physical vibrations associated with the membrane and cutting tools. In this way, these interference signals directly related to the production process can be effectively separated from other background noise, laying the foundation for establishing a dynamic background spectrum baseline and identifying periodic micro-charge disturbance signals.

[0054] In a further embodiment of this application, the step of identifying a preset local high-frequency signal pattern on the membrane surface based on the analysis results preferably includes: Based on the analysis results, the cumulative charge energy characteristic value of the original electrostatic signal of at least one adjacent electrostatic induction unit within the rolling time window is calculated. Determine whether the accumulated charge energy characteristic value exceeds a preset dynamic threshold, and obtain the determination result; Based on the judgment results, a preset local high-frequency signal pattern was identified on the membrane surface.

[0055] Specifically, calculating the cumulative charge energy characteristic value aims to quantify the intensity and duration of electrostatic activity in certain regions on the membrane surface. The original electrostatic signal is acquired by electrostatic induction units, which independently monitor a unit area on the membrane surface. By processing the signals from at least one adjacent electrostatic induction unit, the electrostatic accumulation phenomenon in local areas can be better captured. The introduction of a rolling time window allows the system to continuously analyze the latest data, thereby reflecting the electrostatic state of the membrane surface in real time and avoiding delays or information lags that may occur with a fixed time window. The cumulative charge energy characteristic value can be understood as the integral of the amount of charge or its energy detected by the electrostatic induction unit over a certain period of time, aiming to smooth out instantaneous fluctuations and highlight persistent electrostatic accumulation effects.

[0056] The purpose of determining whether the accumulated charge energy characteristic value exceeds a preset dynamic threshold is to establish an adaptive judgment standard. The dynamic threshold can be adjusted in real time based on changes in the production environment, the characteristics of the membrane material, or historical data to adapt to different operating conditions, thereby improving the robustness of identification. For example, this dynamic threshold can be set and updated based on statistical analysis of historical data, machine learning models, or expert experience. When the accumulated charge energy characteristic value exceeds this dynamic threshold, it indicates that the electrostatic activity in that local area has reached or exceeded a level that may cause problems, requiring further attention.

[0057] Therefore, based on the judgment results, a preset local high-frequency signal pattern on the membrane surface can be identified. This pattern identification is based on quantified energy characteristics and dynamic threshold judgment, ensuring the accuracy and reliability of the identification and effectively distinguishing between normal fluctuations and abnormal electrostatic accumulation.

[0058] This application's solution effectively addresses the shortcomings in accuracy and robustness of traditional methods when identifying localized high-frequency electrostatic patterns by introducing the concepts of accumulated charge energy characteristic values ​​and dynamic thresholds. Specifically, by calculating the accumulated charge energy characteristic values ​​of the original electrostatic signals from at least one adjacent electrostatic induction unit within a rolling time window, instantaneous and random electrostatic disturbance signals can be effectively smoothed, thereby highlighting and quantifying persistent and potentially hazardous localized electrostatic accumulation phenomena. This cumulative analysis allows the system to more accurately capture the continuous accumulation of electrostatic energy, rather than just instantaneous peak values. Simultaneously, the introduction of dynamic thresholds enables the system to adaptively adjust the identification criteria according to changes in the actual production environment, avoiding false alarms or missed alarms that may occur with fixed thresholds, especially maintaining high identification accuracy when production conditions fluctuate significantly. It is precisely this judgment mechanism combining time accumulation and adaptive thresholds that enables this application to more accurately and reliably identify localized high-frequency signal patterns on the membrane surface that conform to a preset pattern.

[0059] The following is a specific example to illustrate this.

[0060] On the production line of plastic stretch film, multiple electrostatic induction units, such as electrostatic induction unit a, electrostatic induction unit b, and electrostatic induction unit c, are arranged along the direction of film movement. They are arranged at fixed intervals to monitor the electrostatic signals on the film surface in real time. After the system receives the raw electrostatic signals collected by these electrostatic induction units and completes preliminary analysis, in order to identify local high-incidence signal patterns, the system processes each electrostatic induction unit and its adjacent units (for example, for electrostatic induction unit b, the signals of electrostatic induction units a, b, and c are considered).

[0061] Specifically, the system sets a rolling time window, for example, 5 seconds. Within each 5-second rolling window, the system calculates the cumulative charge energy characteristic value of the original electrostatic signals collected by electrostatic induction unit b and its adjacent electrostatic induction units a and c. This characteristic value can be obtained by integrating or summing the squares of the signal amplitudes of these units within the window period, reflecting the total electrostatic energy of the local area over a period of time.

[0062] Meanwhile, the system dynamically adjusts the preset threshold based on parameters such as the type of membrane material being produced, the production speed, and the ambient temperature and humidity. For example, for a material that is prone to static electricity, the dynamic threshold may be set relatively low; while in a high-humidity environment, the threshold may be increased accordingly.

[0063] When the calculated cumulative charge energy characteristic value continuously exceeds the dynamic threshold, the system determines that the region (covered by electrostatic induction units a, b, and c) exhibits a preset local high-frequency signal pattern. For example, if the cumulative charge energy characteristic value of electrostatic induction unit b exceeds the dynamic threshold consecutively within three rolling windows, and the characteristic values ​​of its adjacent electrostatic induction units a and c also show a synchronous increasing trend, then it can be confirmed that there is persistent electrostatic accumulation in the local area. In this way, the system can effectively distinguish between instantaneous electrostatic fluctuations and genuine, concerning local electrostatic accumulation phenomena.

[0064] In a further embodiment of this application, the step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-incidence signal pattern includes: After identifying the local high-frequency signal pattern, the original electrostatic signals of the electrostatic induction unit that triggered the electrostatic accumulation and its adjacent electrostatic induction units are processed by local region signal separation to obtain the signal characteristics of each local high-frequency signal pattern after separation. Based on the signal characteristics, the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface are determined.

[0065] Specifically, local area signal separation processing refers to analyzing the original electrostatic signals collected by the electrostatic induction unit that triggered electrostatic aggregation and its adjacent units within a certain range when the signal of a certain electrostatic induction unit or group of units is identified as conforming to a local high-incidence signal pattern. This processing aims to decompose complex signals that may be formed by multiple electrostatic aggregation points or superimposed background noise, thereby extracting the true signal features of each independent electrostatic aggregation event. For example, blind source separation (BSS) techniques, such as Independent Component Analysis (ICA) or Non-negative Matrix Factorization (NFF), can be used to process multiple original electrostatic signals within a local area to separate potential independent signal sources. Alternatively, wavelet decomposition, Empirical Mode Decomposition (EMD), and other signal processing methods can be used to decompose the original electrostatic signal into components of different frequencies or scales, thereby identifying and separating the signal components related to electrostatic aggregation.

[0066] The signal characteristics of each local high-incidence signal pattern after separation can be understood as the unique attribute information that can independently characterize each electrostatic accumulation event after signal separation processing. These signal characteristics may include, but are not limited to, the amplitude, frequency, duration, energy distribution, and waveform shape of the separated signal. The purpose is to provide a purer and more representative data basis for subsequent accurate determination of the spatial location and electrostatic intensity of electrostatic accumulation, avoiding errors caused by signal aliasing.

[0067] This application's solution effectively addresses the inaccuracy of traditional methods in locating and quantifying electrostatic aggregation in complex signal environments by introducing localized signal separation processing. When multiple adjacent electrostatic aggregation points exist on the membrane surface, or when electrostatic signals are affected by other electromagnetic interferences in the production environment, the signals from a single electrostatic induction unit or simple aggregation may not clearly distinguish individual electrostatic events. By performing localized signal separation processing on the original electrostatic signals of the electrostatic induction unit that triggered the aggregation and its adjacent units, individual electrostatic aggregation signal sources can be extracted from the aliased signals. Each separated signal source corresponds to a locally prevalent signal pattern, and its signal characteristics more accurately reflect the true situation of the electrostatic aggregation event. Based on these separated signal characteristics, combined with the physical layout of the electrostatic induction units and membrane motion parameters, the spatial location of each electrostatic aggregation can be determined more accurately, and its true electrostatic intensity can be estimated, thus overcoming the limitations of positioning ambiguity and inaccurate intensity assessment caused by signal aliasing.

[0068] The following is a specific example to illustrate this.

[0069] In the production path of plastic stretch film, electrostatic induction units d, e, and f are arranged continuously. When electrostatic induction unit e detects that the original electrostatic signal exceeds a preset threshold and is identified as a local high-incidence signal pattern, the system will select the original electrostatic signals collected by electrostatic induction units d, e, and f as a local region for analysis, centered on electrostatic induction unit e. Specifically, the Independent Component Analysis (ICA) algorithm can be used to process these three original electrostatic signals. The ICA algorithm can decompose a mixed signal into statistically independent source signals, thereby separating multiple independent electrostatic aggregation signals that may exist. For example, if there are two independent electrostatic aggregation points, ICA may separate two independent signal components, each representing an electrostatic aggregation event. Subsequently, feature extraction is performed on each separated signal component, such as calculating its peak value, energy, and duration as signal features of the separated local high-incidence signal pattern. For example, one separated signal has a peak voltage of 5kV and a duration of 10ms, while another separated signal has a peak voltage of 3kV and a duration of 5ms. Based on these signal characteristics, combined with the precise physical location of the electrostatic induction unit and the real-time movement velocity of the membrane, the specific coordinates of each electrostatic accumulation point on the membrane can be accurately calculated, and its corresponding charge density or equivalent voltage value can be estimated as the electrostatic intensity. For example, the first electrostatic accumulation point is located at position X1 in the width direction of the membrane, with an intensity of 5kV; the second electrostatic accumulation point is located at position X2 in the width direction of the membrane, with an intensity of 3kV.

[0070] In some embodiments of this application, the step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on signal characteristics preferably includes: Based on the signal characteristics, combined with the physical location of each electrostatic induction unit and the membrane motion parameters, the spatial location of each electrostatic accumulation on the membrane surface is determined. Based on the signal characteristics, the charge density or equivalent voltage value of each electrostatic aggregate is estimated as the electrostatic intensity of each electrostatic aggregate on the membrane surface.

[0071] The signal characteristics refer to data characterizing electrostatic accumulation, obtained by localized signal separation processing of the original electrostatic signals from the electrostatic induction unit that triggers electrostatic accumulation and its adjacent units. These signal characteristics may include, but are not limited to, information such as signal amplitude, frequency, duration, and energy distribution. The physical location of the electrostatic induction unit refers to the precise coordinates or relative position information of each electrostatic induction unit in space along the plastic wrapping film production path. This position information is pre-recorded and stored during system installation. The film motion parameters refer to the speed, direction, and dynamic information such as jitter or offset that the plastic wrapping film moves during production. These parameters can be acquired in real time using additional sensors (e.g., encoders, vision sensors). The charge density refers to the amount of charge per unit area, a physical quantity for measuring the intensity of electrostatic accumulation. The equivalent voltage value refers to the voltage value equivalent to the electric field strength generated in the electrostatic accumulation area over a certain distance, and is also a commonly used indicator for measuring the intensity of electrostatic accumulation.

[0072] This application's solution combines the signal characteristics of the separated local high-incidence signal modes with the physical location of the electrostatic induction units and the membrane motion parameters, enabling precise calculation of the specific spatial coordinates of electrostatic accumulation on the membrane surface. For example, by analyzing the propagation delay or intensity differences of signal characteristics on different electrostatic induction units and combining this with the real-time motion state of the membrane, the precise point of electrostatic accumulation can be deduced. Simultaneously, by performing quantitative analysis of the signal characteristics, such as extracting the signal peak value, integral area, or spectral energy, and comparing them with a preset calibration model, the charge density or equivalent voltage value of the electrostatic accumulation region can be estimated, thereby accurately characterizing the intensity of electrostatic accumulation. This method makes the localization and quantification of electrostatic accumulation more precise and reliable.

[0073] In a specific embodiment of this application, the step of issuing a warning signal includes: The hazard level of the electrostatic accumulation is determined based on the electrostatic intensity. Based on the spatial location and hazard level, generate the content and triggering method of the early warning signal; The aforementioned warning signal was issued.

[0074] Determining the hazard level of electrostatic accumulation refers to classifying the potential risks posed by electrostatic accumulation based on the detected electrostatic intensity and a preset hazard assessment standard. For example, different electrostatic intensity thresholds can be set; when the electrostatic intensity exceeds a certain threshold, it is considered low hazard; when it exceeds a higher threshold, it is considered medium hazard; and when it exceeds the highest threshold, it is considered high hazard. The hazard level classification can be based on a comprehensive consideration of factors such as the material properties of the plastic stretch film, the production environment, and potential production safety or product quality problems caused by electrostatic discharge.

[0075] The generation of early warning signals involves customizing the information based on the determined hazard level and spatial location of static electricity accumulation. The content of the early warning signal can include the precise location of the static electricity accumulation, the current static electricity intensity, the determined hazard level, and recommended handling measures for that hazard level and location. The triggering method can vary depending on the hazard level. For example, low-hazard situations may only be detected through system logs or screen notifications; medium-hazard situations may trigger audible and visual alarms and send notifications to operators; and high-hazard situations may trigger emergency shutdowns or automatic adjustments to production parameters.

[0076] Therefore, issuing the warning signal means sending the generated warning signal, which contains detailed information and triggering method, to the corresponding receiving end, such as the control system, operator interface, mobile device, or automated actuator.

[0077] This application provides more accurate and intelligent early warnings based on the actual degree of harm caused by electrostatic accumulation. Compared to basic solutions that issue general early warnings based solely on spatial location and electrostatic intensity, this application introduces a hazard level assessment, enabling the early warning signal to not only indicate the location and intensity of the problem but also to clearly define its potential risk level. This, in turn, guides operators or automated systems to take more appropriate and timely countermeasures.

[0078] The following is a specific example to illustrate this.

[0079] During the production of plastic stretch film, the electrostatic induction unit detected an electrostatic intensity of 8kV in a localized area. The system first determines that the 8kV electrostatic intensity falls under the "medium hazard" level based on preset electrostatic intensity thresholds (e.g., 0-5kV is low hazard, 5-10kV is medium hazard, and above 10kV is high hazard). Then, considering the spatial location of the electrostatic accumulation (e.g., located at the left edge of the production line, 5 meters from the winding machine), the system generates a warning signal. This warning signal may include: "Medium hazard electrostatic accumulation: Location - left edge, 5 meters from the winding machine; electrostatic intensity - 8kV; recommendation to check and activate the local ion fan for dissipation." Simultaneously, the warning is triggered by a pop-up warning window on the operator's control panel, accompanied by a slight audible alert, and a text message notification to the engineer responsible for the area. In this way, operators can quickly understand the severity of the problem, its specific location, and initial response suggestions, enabling timely action and effective control of electrostatic risks.

[0080] It is worth mentioning that, in the embodiments of this application, the content of the warning signal preferably includes a description of the event type of electrostatic accumulation, the spatial location, the electrostatic intensity, and suggested measures for adjusting production process parameters corresponding to the hazard level.

[0081] The description of the event type of electrostatic accumulation refers to the classification and naming of detected electrostatic accumulation phenomena, such as "local high-voltage discharge," "continuous surface charge accumulation," or "periodic electrostatic pulse," to help operators quickly understand the nature of the electrostatic problem. The spatial location refers to the specific coordinates or area markers of the electrostatic accumulation on the membrane surface, such as "left edge of the production line," "central area," or "XY position of the sensor array," to accurately guide operators to locate the source of the problem. The electrostatic intensity can be understood as a quantitative indicator of electrostatic accumulation, such as charge density (nC / cm²) or equivalent voltage value (kV), to assess the severity of the electrostatic problem. In practical applications, the suggested measures for adjusting production process parameters corresponding to the hazard level are specifically optimization suggestions for the current production process automatically generated by the system or matched from a preset database based on the hazard level of electrostatic accumulation. These suggestions may include "reduce production line speed by 10%," "increase ambient humidity to 60%," "adjust corona treatment intensity," or "check material formulation," to provide operators with direct and actionable solutions to effectively alleviate or eliminate electrostatic problems.

[0082] Specifically, the steps for generating the proposed measures preferably include: Obtain the material type of the membrane currently being produced; Based on the pre-stored material property database, query the sensitivity information to electrostatic damage corresponding to the material type; The hazard level is matched with the sensitivity information to generate recommended measures for adjusting production process parameters.

[0083] Specifically, obtaining the material type of the currently produced membrane refers to identifying the specific material of the plastic wrapping film being produced, such as polyethylene (PE) film, polypropylene (PP) film, or polyvinyl chloride (PVC) film, through methods such as manual input, automatic reading from the production management system, or sensor identification. This step is the foundation for generating accurate recommendations. Specifically, querying the sensitivity information to electrostatic damage corresponding to the material type based on a pre-stored material property database can be understood as the system maintaining a dataset containing various membrane materials and their sensitivity to electrostatic damage. This sensitivity information may include parameters such as the material's dielectric constant, surface resistivity, breakdown voltage, and electrostatic decay time, as well as the types and degrees of damage that may occur under different electrostatic intensities. Its purpose is to provide a quantitative basis for the generation of subsequent recommendations. In practical applications, matching the hazard level with the sensitivity information generates recommendations for adjusting production process parameters. Specifically, the system generates a set of targeted production process parameter adjustment recommendations based on the currently detected hazard level of electrostatic accumulation (e.g., low, medium, high) and the queried membrane material's sensitivity information to electrostatic damage, using preset matching rules or algorithms. For example, for highly sensitive materials, even for moderate levels of electrostatic accumulation, more proactive antistatic measures may be recommended, such as increasing humidity, adjusting additive formulations, or reducing production speed.

[0084] Through the above technical solution, the generated recommendations for adjusting production process parameters are more precise and personalized, fully considering the varying sensitivities of different plastic stretch film materials to electrostatic damage. This significantly enhances the practical value and guiding significance of the early warning signals, enabling production personnel to take the most appropriate and effective anti-static measures based on specific material characteristics and the level of electrostatic hazard. This effectively avoids film quality problems, decreased production efficiency, and even safety hazards caused by static electricity accumulation, further optimizing the overall effect of anti-static performance testing.

[0085] The following is a specific example to illustrate this.

[0086] The production line is producing two different types of plastic stretch film: one is polyethylene (PE) film, which is less sensitive to electrostatic damage; the other is polypropylene (PP) film, which is more sensitive to electrostatic damage.

[0087] When the system detects static electricity buildup on the surface of a PE film and determines its hazard level to be "moderate," the system first identifies the film material type as PE. Then, it queries the material properties database and finds that the PE film has low sensitivity to electrostatic damage. After matching the "moderate" hazard level with the "low" sensitivity, the system may generate recommended measures such as: "Increase the humidity of the production environment by 5% and check whether the amount of antistatic agent added meets the standard."

[0088] However, when the system detects electrostatic accumulation on the PP film surface at a hazard level of "moderate," it identifies the film material as PP. A search of the material properties database reveals that PP films are highly sensitive to electrostatic damage. Matching the "moderate" hazard level with the "high" sensitivity, the system may generate more stringent recommendations, such as: "Immediately reduce the production line speed by 10%, check and adjust the antistatic agent formulation, and consider increasing the power or number of ion fans."

[0089] Please refer to Figure 2 This application also proposes a sensor-based antistatic performance testing system 200 for plastic stretch film, comprising: The receiving module 210 is used to receive the original electrostatic signals collected by multiple electrostatic induction units set on the production path of the plastic wrapping film, wherein the electrostatic induction units are used to independently monitor a unit area on the surface of the film. Analysis module 220 is used to perform relative comparisons between units and analyze the time-varying trends of the original electrostatic signal to obtain analysis results; The identification module 230 is used to identify, based on the analysis results, a preset local high-frequency signal pattern on the membrane surface; The determining module 240 is used to determine the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-frequency signal pattern. The early warning module 250 is used to issue an early warning signal based on the spatial location and electrostatic intensity.

[0090] Specifically, the receiving module 210 can be understood as a data acquisition interface or hardware unit configured to connect to multiple electrostatic induction units to receive raw electrostatic signals. For example, the receiving module can be a data acquisition card, an embedded controller, or a dedicated signal conditioning circuit with analog-to-digital conversion capabilities, capable of converting the analog signals output by the electrostatic induction units into digital signals and performing preliminary filtering and amplification.

[0091] The analysis module 220 can be understood as a data processing unit configured to perform complex algorithmic processing on the raw electrostatic signal received by the receiving module. For example, the analysis module can be a high-performance microprocessor, digital signal processor (DSP), or a general-purpose computer system running specialized software programs. This module extracts valuable feature information from the raw electrostatic signal and generates analysis results by executing relative comparison algorithms between units and temporal trend analysis algorithms. To achieve this function, the analysis module is typically equipped with sufficient memory and storage space to store the algorithm program, intermediate processing data, and historical data.

[0092] The identification module 230 can be understood as a pattern matching or decision-making unit, configured to identify pre-defined local high-incidence signal patterns on the membrane surface based on the analysis results output by the analysis module. For example, the identification module can be a processor running a pattern recognition algorithm, which can be a rule-based expert system, a machine learning model, or a statistical analysis model. This module determines whether there is a risk of electrostatic accumulation by comparing the real-time analysis results with a pre-defined library of local high-incidence signal patterns.

[0093] The determination module 240 can be understood as a localization and quantization unit, configured to further determine the spatial location and electrostatic intensity of electrostatic accumulation after the identification module identifies a local high-frequency signal pattern. For example, the determination module could be a processor running a localization algorithm and an intensity estimation algorithm. This module combines the physical layout information of the electrostatic induction unit, membrane motion parameters, and signal characteristics to accurately calculate the specific location of electrostatic accumulation on the membrane and estimate its charge density or equivalent voltage value.

[0094] The early warning module 250 can be understood as an information output and control unit configured to issue an early warning signal based on the spatial location and electrostatic intensity determined by the module's output. For example, the early warning module could be a controller integrating an audible and visual alarm, a display interface, a network communication interface, and / or relay outputs. Based on the severity of the electrostatic accumulation, the module generates corresponding early warning information and sends alerts to operators or the automated control system through various means (such as audible and visual alarms, screen displays, SMS notifications, emails, or triggering production line shutdowns / deceleration).

[0095] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0096] 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 sensor-based method for testing the antistatic performance of plastic stretch film, characterized in that, Includes the following steps: The system receives raw electrostatic signals collected by multiple electrostatic induction units located on the production path of the plastic wrapping film, wherein the electrostatic induction units are used to independently monitor a unit area on the surface of the film. The analysis results are obtained by comparing the relative values ​​between units and analyzing the temporal variation trend of the original electrostatic signal. Based on the analysis results, a preset local high-frequency signal pattern was identified on the membrane surface; Based on the identified local high-frequency signal pattern, the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface are determined. A warning signal is issued based on the spatial location and electrostatic intensity.

2. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 1, characterized in that, The steps for comparing the original electrostatic signal between units and analyzing its temporal trend to obtain the analysis results include: Based on the original electrostatic signals of each electrostatic induction unit, the energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted. A dynamic background spectrum baseline is established for each of the electrostatic induction units. The dynamic background spectrum baseline is used to distinguish between high-frequency signals and periodic micro-charge disturbance signals in the production environment. By comparing the energy characteristics with the dynamic background spectrum baseline between units, periodic micro-charge perturbation signals are identified. The periodic micro-charge perturbation signal is analyzed over time to obtain a periodic micro-charge perturbation pattern, which is then used as the analysis result.

3. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 2, characterized in that, The step of extracting the energy characteristics of a preset high-frequency band related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool based on the original electrostatic signals of each of the electrostatic induction units includes: The original electrostatic signals of each electrostatic induction unit are subjected to spectrum conversion within a rolling time window to obtain the real-time rolling spectrum of each electrostatic induction unit. Energy characteristics of preset high-frequency bands related to the inherent vibration characteristics of the membrane material and the vibration characteristics of the cutting tool are extracted from the real-time rolling spectrum of each of the aforementioned.

4. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 1, characterized in that, The step of identifying a preset local high-frequency signal pattern on the membrane surface based on the analysis results includes: Based on the analysis results, the cumulative charge energy characteristic value of the original electrostatic signal of at least one adjacent electrostatic induction unit within the rolling time window is calculated. Determine whether the accumulated charge energy characteristic value exceeds a preset dynamic threshold, and obtain the determination result; Based on the judgment results, a preset local high-frequency signal pattern was identified on the membrane surface.

5. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 1, characterized in that, The step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-frequency signal pattern includes: After identifying the local high-frequency signal pattern, the original electrostatic signals of the electrostatic induction unit that triggered the electrostatic accumulation and its adjacent electrostatic induction units are processed by local region signal separation to obtain the signal characteristics of each local high-frequency signal pattern after separation. Based on the signal characteristics, the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface are determined.

6. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 5, characterized in that, The step of determining the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the signal characteristics includes: Based on the signal characteristics, combined with the physical location of each electrostatic induction unit and the membrane motion parameters, the spatial location of each electrostatic accumulation on the membrane surface is determined. Based on the signal characteristics, the charge density or equivalent voltage value of each electrostatic aggregate is estimated as the electrostatic intensity of each electrostatic aggregate on the membrane surface.

7. The sensor-based method for detecting the antistatic performance of plastic stretch film according to claim 1, characterized in that, The step of issuing a warning signal based on the spatial location and electrostatic intensity includes: The hazard level of the electrostatic accumulation is determined based on the electrostatic intensity. Based on the spatial location and hazard level, generate the content and triggering method of the early warning signal; The aforementioned warning signal was issued.

8. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 7, characterized in that, The warning signal includes a description of the event type of electrostatic accumulation, the spatial location, the electrostatic intensity, and recommended measures for adjusting production process parameters corresponding to the hazard level.

9. The sensor-based method for testing the antistatic performance of plastic stretch film according to claim 8, characterized in that, The steps for generating the proposed measures include: Obtain the material type of the membrane currently being produced; Based on the pre-stored material property database, query the sensitivity information to electrostatic damage corresponding to the material type; The hazard level is matched with the sensitivity information to generate recommended measures for adjusting production process parameters.

10. A sensor-based system for testing the antistatic performance of plastic stretch film, characterized in that, include: The receiving module is used to receive the original electrostatic signals collected by multiple electrostatic induction units set on the production path of the plastic wrapping film. The electrostatic induction units are used to independently monitor a unit area on the surface of the film. The analysis module is used to perform relative comparisons between units and analyze the temporal trends of the original electrostatic signal to obtain analysis results. The identification module is used to identify, based on the analysis results, a preset local high-frequency signal pattern on the membrane surface; The determination module is used to determine the spatial location and electrostatic intensity of electrostatic accumulation on the membrane surface based on the identified local high-frequency signal pattern. The early warning module is used to issue an early warning signal based on the spatial location and electrostatic intensity.

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