State monitoring method and system of polyethylene film production system

Abstract

The invention relates to the technical field of production monitoring of polyethylene films, in particular to a state monitoring method and system of a polyethylene film production system. The method comprises the following steps: carrying out polyethylene film production data acquisition and pretreatment on polyethylene film production key nodes to generate standard polyethylene film production data; performing production state image conversion of the polyethylene film according to the standard polyethylene film production data to generate noise reduction production state image data; carrying out polyethylene film image block abnormal value calculation according to the standard polyethylene film production data, and generating a noise reduction production state image block abnormal value; and performing production parameter abnormal value optimization of the polyethylene film on the standard polyethylene film production data according to the noise reduction production state image block abnormal value to generate optimized polyethylene film production data. According to the invention, the monitoring of the polyethylene film production system is finer, and the abnormity possibly occurring in the polyethylene film production process can be monitored in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of production monitoring of polyethylene films, and particularly to a method and system for monitoring the state of a polyethylene film production system. Background Art

[0002] In a polyethylene film production system, linear polyethylene particles need to be processed by adjusting pressure, temperature, etc. to produce polyethylene films. It is particularly important to monitor the production process in real time and analyze the key parameters and changes in the production process, which can ensure the stable operation of the polyethylene film production system and guarantee the product quality of polyethylene films. However, the traditional method for monitoring the state of a polyethylene film production system can only detect problems in time when accidents occur during the production process of polyethylene films, cannot detect production problems in time, resulting in waste of resources, and is not intelligent and precise enough for monitoring the production process of polyethylene films, and fails to find and solve problems in the production process of polyethylene films in time. Summary of the Invention

[0003] Based on this, the present invention provides a method and system for monitoring the state of a polyethylene film production system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for monitoring the state of a polyethylene film production system includes the following steps:

[0005] Step S1: Obtain the key nodes of polyethylene film production; use sensors to collect production data of polyethylene films at the key nodes of polyethylene film production to generate production data of polyethylene film production; perform data preprocessing on the production data of polyethylene films to generate standard production data of polyethylene films;

[0006] Step S2: Analyze the production state of polyethylene films based on the standard production data of polyethylene films to generate production state data of polyethylene films; perform conversion and noise reduction processing on the production state data to generate noise-reduced production state image data;

[0007] Step S3: Divide the adjustment time period of polyethylene film production parameters based on the standard production data of polyethylene films to generate the adjustment time period of polyethylene film production parameters; perform image block segmentation and calculation of image block outliers on the noise-reduced production state image data according to the adjustment time period of production parameters to generate noise-reduced production state image block outliers;

[0008] Step S4: Divide the production state data into normal and abnormal production state data according to the noise-reduced production state image block outliers, and generate normal production state data and abnormal production state data respectively;

[0009] Step S5: Design the safety production parameter range for the standard polyethylene film production data based on the normal production status data to generate the safety production parameter range;

[0010] Step S6: Optimize the outlier of the production parameters of the standard polyethylene film production data according to the abnormal production status data and the safety production parameter range to generate the optimized polyethylene film production data.

[0011] The present invention collects data on key production nodes through sensors, realizes real-time monitoring of the production of polyethylene films and related production parameters. The data preprocessing process effectively eliminates data noise and outliers, ensuring the accuracy and reliability of the generated standard polyethylene film production data. This effective data acquisition and processing mechanism provides a reliable basis for subsequent analysis and optimization. Converting the standard production data into production status data, through the conversion and noise reduction processing of the production status identification image of the polyethylene film, endows the production process with visual characteristics. The generation of this production status data and the noise reduction processing can intuitively understand the production status, thus more quickly discovering potential problems and achieving the goal of early intervention and warning. The division of the production parameter adjustment time period provides a time basis for parameter adjustment in the production process, realizes the refined control of the production process, and through image block segmentation and outlier calculation, the polyethylene film system can accurately identify abnormal situations in the noise-reduced image data, providing an efficient means for anomaly detection. Dividing the production status data into normal and abnormal categories helps operators focus on abnormal situations, improving the efficiency of problem-solving. This division also provides a basis for subsequent safety parameter design and production optimization, helping to improve the overall production quality and efficiency. Designing the safety production parameter range through the normal production status data provides a reliable reference range for the production process. The design of this range helps prevent the production process from deviating from the normal range, ensuring the stability and sustainability of production. Using the abnormal production status data and the safety production parameter range to optimize the outliers of the standard production data helps the system automatically or assist the operator to correct abnormal situations in production, ultimately improving the product quality and production efficiency. Therefore, the state monitoring method of the polyethylene film production system of the present invention can detect abnormalities in the polyethylene film in advance and adjust and optimize its parameters, can timely discover production problems and solve them, thus saving resource waste, and for monitoring the polyethylene film production process, by analyzing the changes in the polyethylene film production status, more precisely discover potential problems in the polyethylene film production, so as to timely find out potential production problems and solve them.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Obtain the key production nodes of the polyethylene film;

[0014] Step S12: Deploy sensors at the key nodes of polyethylene film production, and use the sensors to collect production data of polyethylene film to generate production data of polyethylene film.

[0015] Step S13: Perform data cleaning on the production data of polyethylene film to generate cleaned production data of polyethylene film.

[0016] Step S14: Perform data standardization on the cleaned production data of polyethylene film using the min-max standardization method to generate standardized production data of polyethylene film.

[0017] The present invention obtains the key nodes of polyethylene film production, ensuring attention to the key nodes in the production process. These nodes are of great significance in the entire production process, can accurately capture the changes in production, and provide targeted data sources for subsequent monitoring and analysis. By deploying sensors at the key nodes, production data can be obtained in real time. This real-time nature provides an opportunity for operators to react quickly, so as to take appropriate measures before production anomalies occur, thereby avoiding production risks and losses. By removing noisy, abnormal, and incorrect data, the system obtains a cleaner and more accurate dataset, which helps to reduce misjudgments and incorrect decisions caused by inaccurate data, and ensures the quality of the basic data for subsequent analysis and processing. By performing data standardization on the data using the min-max standardization method, the data is normalized between different scales, eliminating the influence of different dimensions on the analysis, making the analysis results more stable, and helping with subsequent data comparison and model construction.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Analyze the production status of polyethylene film based on the standardized production data of polyethylene film to generate production status data of polyethylene film.

[0020] Step S22: Use Fourier transform to convert the production status data into an analog signal of production status data to generate a production status signal.

[0021] Step S23: Use the polyethylene film production status image conversion algorithm to convert the production status signal into a production status identification image of polyethylene film to generate production status image data.

[0022] Step S24: Use Gaussian filtering to perform noise reduction processing on the production status image data to produce noise-reduced production status image data.

[0023] The present invention conducts in-depth analysis of the production status based on the production data of standard polyethylene films. Through data mining and analysis, it can better understand the changes and trends in the production process, thereby revealing potential problems and opportunities, and providing more accurate decision-making support for operators and management. By converting the production status data into analog signals through Fourier transform, the time-domain data can be transformed into frequency-domain data, thus more comprehensively demonstrating the periodicity and trends of the production status. This conversion helps to deeply understand the characteristics of the production process from different perspectives and provides more dimensional information for anomaly detection and analysis. Using the polyethylene film production status image conversion algorithm, the production status signals are transformed into production status image data. This visualization method converts abstract signal data into images, making complex data easier to understand and analyze, and providing a more intuitive way for operators to recognize the production status. By applying Gaussian filtering to denoise the production status image data, the noise and interference in the image are effectively reduced. Such denoising helps to improve the clarity and readability of the image and provides a more reliable basis for subsequent image analysis and recognition.

[0024] Preferably, the polyethylene film production status image conversion algorithm in step S23 is as follows:

[0025]

[0026] In the formula, I(x,y) represents the gray value data of the production status image with the abscissa x and the ordinate y. x represents the abscissa extreme value of the generated image, y represents the ordinate extreme value of the generated image, T represents the total time length involved in the production status data, α represents the intensity adjustment value for controlling the generated image, k1 represents the texture information for controlling the abscissa of the image, k2 represents the texture information for controlling the ordinate of the image, β represents the amplitude size of the production status signal changing with time, γ represents the frequency size of the production status signal changing with time, t represents the time node corresponding to the total time length involved in the production status data, and τ represents the abnormal adjustment value of the gray value data.

[0027] The present invention utilizes a polyethylene film production status image conversion algorithm, which fully considers the interaction relationships among the abscissa extreme value x of the generated image, the ordinate extreme value y of the generated image, the total time length T involved in the production status data, the intensity adjustment value α for controlling the generated image, the texture information k1 for controlling the abscissa of the image, the texture information k2 for controlling the ordinate of the image, the amplitude size β of the production status signal changing with time, the frequency size γ of the production status signal changing with time, t representing the time node corresponding to the total time length involved in the production status data, and the functions to form a functional relationship:

[0028] That is, By converting the abstract production status data into the specific identification gray values of an image, the functional relationship makes the trends and changes in the production status visually visible. One can quickly understand the changes in the production status by observing the image, thus making it easier to detect abnormal situations or trends. Controlling the texture information of the abscissa of the image and the texture information used to control the ordinate of the image can make the image highlight the spatial features, thereby better demonstrating the status changes in different regions. Through the introduction of time nodes corresponding to the total length of time involved in the production status data, the image can also display the time characteristics of the production status. Controlling the amplitude of the production status signal that changes over time and the frequency of the production status signal that changes over time enables the image to capture the dynamic changes of the production status signal over time, which helps analyze periodic or gradually changing trends. Controlling the intensity adjustment value of the generated image can adjust the image intensity, making the image more clearly displayed, which helps operators quickly discover potential problems. This algorithm comprehensively demonstrates the spatial features, time characteristics, dynamic changes, and abnormal situations of the production status data, enabling the image to convey more comprehensive information. Through the collaborative action of multiple parameters, the abstract production status data is converted into a visual image, providing a more comprehensive and intuitive display of the production status, enabling operators to better understand and analyze the changes in the production status. Using the abnormal adjustment value τ of the gray value data to adjust and correct the functional relationship reduces the error impact caused by abnormal data or error terms, thereby more accurately generating the gray value data I(x, y) of the production status image, improving the accuracy and reliability of the conversion of the production status signal into the production status identification image of the polyethylene film. At the same time, the weight information and adjustment values in this formula can be adjusted according to the actual situation and applied to different production status signals, improving the flexibility and applicability of the algorithm.

[0029] Preferably, step S3 includes the following steps:

[0030] Step S31: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the production parameter adjustment time period of the polyethylene film;

[0031] Step S32: Extract the production rate change rate within the adjustment time node from the standard polyethylene film production data according to the production parameter adjustment time period to generate the production parameter adjustment change rate;

[0032] Step S33: Perform image block segmentation processing on the noise-reduced production status image data according to the production parameter adjustment time period to generate the noise-reduced production status image block data;

[0033] Step S34: Perform outlier detection processing on the denoised production status image block data according to the polyethylene film production status image anomaly detection algorithm and the production parameter adjustment change rate to generate denoised production status image block outliers.

[0034] In the present invention, by dividing the standard polyethylene film production data according to time periods, appropriate production parameter adjustment time periods are determined. This division makes the adjustment of production parameters more targeted, helps to adjust parameters at critical moments, and thus realizes more precise production control. Using the production parameter adjustment time periods, the production rate change rates within the time nodes are extracted. This extraction of change rates helps to capture the fluctuations and trends in the production process, thereby more accurately judging whether the production status is normal and making adjustments when necessary. The denoised production status image data is subjected to image block segmentation processing according to the production parameter adjustment time periods. The segmentation helps to divide the image into smaller blocks, provides more refined image features, makes subsequent anomaly detection more sensitive and accurate, and by analyzing the denoised production status image data under different adjustment time periods, the production parameters corresponding to the problematic time points can be found. Through the combination of the polyethylene film production status image anomaly detection algorithm and the production parameter adjustment change rate, outlier detection processing is performed on the denoised production status image block data. This combination enables the system to perform anomaly detection from different dimensions, improving the accuracy and reliability of anomaly detection.

[0035] Preferably, the polyethylene film production status image anomaly detection algorithm in step S34 is as follows:

[0036]

[0037] In the formula, P represents the outlier size of the denoised production status image block data, d represents the gray value intensity of the denoised production status image data, represents the corresponding production parameter adjustment time period of the denoised production status image data, w represents the number of gray value anomaly regions of the denoised production status image data, a represents the production parameter adjustment change rate data, A represents the expected gray value change rate of the denoised production status image block generated according to the production parameter adjustment change rate data, B represents the actual gray value change rate of the denoised production status image block, and δ represents the anomaly adjustment value of the outlier size.

[0038] The present invention utilizes a polyethylene film production status image anomaly detection algorithm that comprehensively considers the gray value intensity d of the denoised production status image data and the corresponding production parameter adjustment time period of the denoised production status image data The number w of gray - value abnormal regions in the noise - reduced production - state image data, the production - parameter adjustment change - rate data a, the expected gray - value change - rate A of the noise - reduced production - state image block generated from the production - parameter adjustment change - rate data, the actual gray - value change - rate B of the noise - reduced production - state image block, and the interaction relationship between functions are used to form a functional relationship:

[0039] That is, The gray - value intensity of the noise - reduced production - state image data is used to consider the information of gray values in the image, enabling the algorithm to judge abnormalities from the perspective of image intensity; the production - parameter adjustment time period corresponding to the noise - reduced production - state image data is related to the time range of anomaly detection, considering the time nature of the image - block data; the number w of gray - value abnormal regions in the noise - reduced production - state image data is used to consider the gray - scale abnormal conditions in the image. The more abnormal regions, the greater the possible abnormal value; the production - parameter adjustment change - rate data reflects the changes in the production state; the expected gray - value change - rate of the noise - reduced production - state image block generated from the production - parameter adjustment change - rate data, and the expected gray - value change - rate is used as a reference value to judge the expected state of the image block; the actual gray - value change - rate of the noise - reduced production - state image block, and the actual gray - value change - rate is compared with the expected value to judge the actual state of the image block. This algorithm can detect abnormal conditions from multiple dimensions such as gray values, time, and change - rates, and can finely detect abnormal conditions in the image - block data. It does not simply compare the numerical magnitudes, but comprehensively considers multiple factors, making the anomaly detection more accurate and reliable. Using the abnormal - adjustment value δ of the abnormal - value magnitude to adjust and correct the functional relationship, reducing the error impact brought by abnormal data or error terms, so as to more accurately generate the abnormal - value magnitude P of the noise - reduced production - state image - block data, improving the accuracy and reliability of the anomaly detection and processing of the noise - reduced production - state image - block data. At the same time, the adjustment value in this formula can be adjusted according to the actual situation and applied to different noise - reduced production - state image - block data, improving the flexibility and applicability of the algorithm.

[0040] Preferably, step S4 includes the following steps:

[0041] Step S41: Compare the abnormal value of the noise - reduced production - state image block with the preset abnormal threshold of the polyethylene - film production state. When the abnormal value of the noise - reduced production - state image block is not greater than the abnormal threshold of the polyethylene - film production state, mark the noise - reduced production - state image - block data as normal production - state image - block data;

[0042] Step S42: Compare the abnormal value of the noise - reduced production - state image block with the preset abnormal threshold of the polyethylene - film production state. When the abnormal value of the noise - reduced production - state image block is greater than the abnormal threshold of the polyethylene - film production state, mark the noise - reduced production - state image - block data as abnormal production - state image - block data;

[0043] Step S43: Perform regular production status data marking on the production status data based on the regular production status image block data to generate regular production status data;

[0044] Step S44: Perform abnormal production status data marking on the production status data based on the abnormal production status image block data to generate abnormal production status data.

[0045] The present invention realizes the precise classification and marking of the production status by comparing the preset abnormal threshold with the abnormal value of the noise-reduced production status image block, further enhancing the monitoring and analysis capabilities of the production process. By comparing with the preset abnormal threshold of the polyethylene film production status, the noise-reduced production status image block data is divided into two categories: regular and abnormal. This classification helps to focus attention on the parts that may have problems and achieves the goal of quickly identifying potential abnormal situations. According to the regular and abnormal production status image block data, corresponding markings are performed on the production status data, and regular production status data and abnormal production status data are respectively generated, which helps to organize and classify the data and provides a basis for subsequent safety parameter design and production optimization.

[0046] Preferably, step S5 includes the following steps:

[0047] Step S51: Perform safety production data marking of the polyethylene film on the standard polyethylene film production data according to the regular production status data to generate safety production data;

[0048] Step S52: Design the safety production parameter range of the polyethylene film according to the safety production data to generate the safety production parameter range.

[0049] The present invention performs safety production data marking on the standard polyethylene film production data according to the regular production status data. This marking is based on the data of the normal operating state and provides a reliable basis for the subsequent design of the safety parameter range. By using the safety production data, the system designs the safety production parameter range of the polyethylene film, relying on the statistical analysis of the safety production data, thereby determining a suitable set of parameter ranges to ensure that the production process operates under normal and safe conditions.

[0050] Preferably, step S6 includes the following steps:

[0051] Step S61: Perform abnormal production data marking of the polyethylene film on the standard polyethylene film production data according to the abnormal production status data to generate abnormal production data;

[0052] Step S62: Adjust the parameters of the abnormal production data according to the safety production parameter range to generate adjusted production data;

[0053] Step S63: Optimize the outlier production parameters of the standard polyethylene film production data using the adjusted production data to generate optimized polyethylene film production data.

[0054] The present invention marks the abnormal production data for the standard polyethylene film production data based on the abnormal production status data, which helps to identify and isolate abnormal situations, ensuring that the abnormal data does not affect the processing and analysis of normal production data. Adjust the parameters of the abnormal production data according to the safe production parameter range. This adjustment is based on a pre-determined safe parameter range. For example, if the production temperature is too high, it will be abnormal. The abnormal production data will be adjusted to safe temperature data within the safe production range to ensure that the abnormal data is adjusted within a safe range, avoiding possible risks and adverse effects. By using the adjusted production data to optimize the outlier production parameters, potential problems in the polyethylene film production process can be solved by adjusting the parameters to normal values, further improving the quality and stability of the production process.

[0055] This specification provides a state monitoring system for a polyethylene film production system, which is used to execute the state monitoring method of the polyethylene film production system as described above. The state monitoring system of the polyethylene film production system includes:

[0056] A polyethylene film production data acquisition module, which is used to obtain the key nodes of polyethylene film production; use sensors to collect the production data of polyethylene film at the key nodes of polyethylene film production to generate polyethylene film production data; perform data preprocessing on the polyethylene film production data to generate standard polyethylene film production data;

[0057] A polyethylene film production state image conversion module, which is used to analyze the production state of the polyethylene film according to the standard polyethylene film production data to generate the production state data of the polyethylene film; perform the production state identification image conversion and noise reduction processing on the production state data to generate noise-reduced production state image data;

[0058] A production state image outlier calculation module, which is used to divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the production parameter adjustment time period of the polyethylene film; perform image block segmentation and image block outlier calculation on the noise-reduced production state image data according to the production parameter adjustment time period to generate noise-reduced production state image block outliers;

[0059] A production state data division module, which is used to divide the production state data into normal and abnormal production state data according to the noise-reduced production state image block outliers, and generate normal production state data and abnormal production state data respectively;

[0060] A safety production parameter range design module, which is used to design the safety production parameter range of polyethylene film according to the conventional production status data for the standard polyethylene film production data, and generate a safety production parameter range;

[0061] A polyethylene film production data optimization module, which is used to optimize the abnormal values of the production parameters of polyethylene film according to the abnormal production status data and the safety production parameter range for the standard polyethylene film production data, and generate optimized polyethylene film production data.

[0062] The beneficial effect of this application is that through data collection, analysis and processing, the present invention converts a large amount of information in the polyethylene film production process into real-time and visual data, providing more and more accurate information for decision-makers. Based on the optimized polyethylene film production data, decisions can be made more wisely, thereby optimizing the production process, improving product quality, and achieving more efficient resource utilization. The analysis of production status data and anomaly detection can predict potential problems, help avoid machine failures and production interruptions, and implement predictive maintenance by identifying potential problems, reducing equipment downtime and maintenance costs, thereby improving production efficiency. Through the optimization and adjustment of polyethylene film production parameters, the production process is continuously iterated and improved, thereby continuously improving product quality, production efficiency, and the optimization of the work process. The polyethylene film production data and related data involved in this method enable the entire production process to have a high degree of traceability and transparency. Whether monitoring the production status or analyzing abnormal situations, operators can clearly understand the development and results of each stage, which helps to quickly locate the root cause when problems occur. Through the real-time monitoring and anomaly detection of the production process, this method helps to reduce human operation errors and improve work safety. At the same time, the safety parameter range set according to the safe polyethylene film production parameters and the adjustment of the abnormal parameters of the polyethylene film production parameters ensure that the production is carried out within a safe range, effectively avoiding potential safety risks. Description of the Drawings

[0063] Figure 1 It is a schematic step flow diagram of a method for monitoring the status of a polyethylene film production system according to the present invention;

[0064] Figure 2 is Figure 1 a detailed implementation step flow diagram of step S2 in

[0065] Figure 3 is Figure 1 a detailed implementation step flow diagram of step S3 in

[0066] Figure 4 is Figure 1 a detailed implementation step flow diagram of step S6 in

[0067] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0068] The technical method of the present invention for a patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts fall within the protection scope of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0071] To achieve the above object, please refer to Figures 1 to 4 , the present invention provides a method for monitoring the state of a polyethylene film production system, including the following steps:

[0072] Step S1: Obtain the key nodes of polyethylene film production; use sensors to collect production data of polyethylene film at the key nodes of polyethylene film production to generate production data of polyethylene film; perform data preprocessing on the production data of polyethylene film to generate standard production data of polyethylene film;

[0073] Step S2: Analyze the production state of the polyethylene film according to the standard production data of the polyethylene film to generate production state data of the polyethylene film; perform conversion and noise reduction processing on the production state data to generate noise-reduced production state image data;

[0074] Step S3: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the production parameter adjustment time period of the polyethylene film; segment the noise-reduced production status image data according to the production parameter adjustment time period and calculate the image block outliers to generate the noise-reduced production status image block outliers;

[0075] Step S4: Divide the production status data into normal and abnormal production status data according to the noise-reduced production status image block outliers, and generate normal production status data and abnormal production status data respectively;

[0076] Step S5: Design the safe production parameter interval of the polyethylene film according to the normal production status data for the standard polyethylene film production data to generate the safe production parameter interval;

[0077] Step S6: Optimize the production parameter outliers of the polyethylene film according to the abnormal production status data and the safe production parameter interval for the standard polyethylene film production data to generate the optimized polyethylene film production data.

[0078] The present invention collects data on key production nodes through sensors, achieving real-time monitoring of the production of polyethylene films and related production parameters. The process of data preprocessing effectively eliminates data noise and outliers, ensuring the accuracy and reliability of the generated standard polyethylene film production data. This effective data acquisition and processing mechanism provides a reliable basis for subsequent analysis and optimization. Converting the standard production data into production status data and performing image conversion and noise reduction processing on the production status identification images of polyethylene films endows the production process with visual characteristics. The generation of this production status data and the noise reduction processing enable an intuitive understanding of the production status, thereby more quickly discovering potential problems and achieving the goals of early intervention and warning. The division of the production parameter adjustment time period provides a time basis for parameter adjustment during the production process, realizing the refined control of the production process. Moreover, through image block segmentation and outlier calculation, the polyethylene film system can accurately identify abnormal situations in the noise-reduced image data, providing an efficient means for anomaly detection. Dividing the production status data into normal and abnormal categories helps operators focus on abnormal situations, improving the efficiency of problem-solving. This division also provides a basis for subsequent safety parameter design and production optimization, contributing to the improvement of overall production quality and efficiency. Designing a safe production parameter range through normal production status data provides a reliable reference range for the production process. The design of this range helps prevent the production process from deviating from the normal range, ensuring the stability and sustainability of production. Utilizing abnormal production status data and the safe production parameter range to optimize outliers in the standard production data helps the system automatically or assist the operator in correcting abnormal situations during production, ultimately improving product quality and production efficiency. Therefore, the method for monitoring the status of the polyethylene film production system of the present invention can detect abnormalities in polyethylene films in advance and adjust and optimize their parameters, enabling timely discovery and solution of production problems, thereby saving resource waste. By analyzing the changes in the production status of polyethylene films, potential problems in polyethylene film production can be more precisely discovered, and potential production problems can be promptly identified and solved.

[0079] In an embodiment of the present invention, referring to Figure 1 as described, it is a schematic flowchart of the steps of a method for monitoring the status of a polyethylene film production system of the present invention. In this embodiment, the method for monitoring the status of the polyethylene film production system includes the following steps:

[0080] Step S1: Obtain the key production nodes of polyethylene films; use sensors to collect production data of polyethylene films at the key production nodes of polyethylene films to generate polyethylene film production data; perform data preprocessing on the polyethylene film production data to generate standard polyethylene film production data;

[0081] In the embodiments of the present invention, during the production process of polyethylene films, the key nodes include raw material feeding, extrusion process, cooling stage, etc. Data of these key nodes in the production of polyethylene films are obtained. Sensors are deployed at the key nodes, such as a temperature sensor at the outlet of the extruder, a pressure sensor near the extrusion screw of the extruder, and temperature and pressure sensors on the cooling device. These sensors continuously collect data, including information such as temperature, pressure, and speed, to form a time-series standard polyethylene film production data.

[0082] Step S2: Analyze the production state of the polyethylene film based on the standard polyethylene film production data to generate production state data of the polyethylene film; perform conversion and noise reduction processing on the production state data to generate noise-reduced production state image data for the production state identification image of the polyethylene film;

[0083] In the embodiments of the present invention, a production state data matrix is formed based on the standard polyethylene film production data. For example, data related to the production status of the polyethylene film and temperature and pressure are mapped to the gray value range of 0 to 255. Then, specific gray value images are generated according to the production state data of different polyethylene films. To reduce noise, the Gaussian filtering algorithm is applied to smooth the image and reduce random fluctuations in the data to generate noise-reduced production state image data.

[0084] Step S3: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the adjustment time period of the polyethylene film production parameters; perform image block segmentation and calculation of image block outliers on the noise-reduced production state image data to generate noise-reduced production state image block outliers;

[0085] In the embodiments of the present invention, the noise-reduced production state image data is divided into non-overlapping small blocks, and each block represents the state within a time period. For each image block, the outlier of the gray value in the image is calculated. For example, a specific algorithm calculates the degree of abnormality of the image gray value, which is associated with the abnormal situation during the production of the polyethylene film. The image block with a high outlier value may indicate an abnormal situation in the production process, and noise-reduced production state image block outliers are generated.

[0086] Step S4: Divide the production state data into normal and abnormal production state data according to the noise-reduced production state image block outliers, and generate normal production state data and abnormal production state data respectively;

[0087] In the embodiments of the present invention, the image blocks are classified into normal and abnormal states according to the outlier value. For example, a threshold is set. If the outlier value of a certain image block exceeds the threshold, it is marked as an abnormal state; otherwise, it is marked as a normal state, so as to obtain normal and abnormal production state data within different time periods.

[0088] Step S5: Design the safety production parameter interval for the standard polyethylene film production data based on the normal production status data, and generate the safety production parameter interval.

[0089] In the embodiment of the present invention, in the normal production status data, analyze each production parameter, such as temperature, pressure, speed, etc., calculate the mean and standard deviation of these parameters, and then define the safety production parameter interval in the way of mean plus or minus the standard deviation. For example, the safety interval of the temperature parameter can be the mean plus or minus 2 times the standard deviation, and the safety production parameter interval is constructed in this way.

[0090] Step S6: Optimize the outlier of the production parameters of the standard polyethylene film production data according to the abnormal production status data and the safety production parameter interval, and generate the optimized polyethylene film production data.

[0091] In the embodiment of the present invention, for the abnormal production status data, analyze its abnormal situation, compare the abnormal parameters with the safety production parameter interval. If a certain parameter significantly exceeds the safety interval, it may be necessary to adjust parameters such as the speed of the extruder or the temperature of the cooling device. According to the adjusted parameters, regenerate the optimized polyethylene film production data to ensure that the production process is more stable and safe.

[0092] Preferably, step S1 includes the following steps:

[0093] Step S11: Obtain the key nodes of polyethylene film production;

[0094] Step S12: Deploy sensors to the key nodes of polyethylene film production by the sensors, and use the sensors to collect the production data of the polyethylene film to produce the polyethylene film production data;

[0095] Step S13: Perform data cleaning processing on the polyethylene film production data to generate the cleaned polyethylene film production data;

[0096] Step S14: Perform data standardization processing on the cleaned polyethylene film production data by using the min-max standardization method to generate the standard polyethylene film production data.

[0097] The present invention obtains the key nodes in the production of polyethylene films, ensuring attention to the key nodes in the production process. These nodes are of great significance in the entire production process, can accurately capture the changes in production, and provide targeted data sources for subsequent monitoring and analysis. By deploying sensors at the key nodes, production data can be obtained in real time. This real-time nature provides an opportunity for operators to react quickly and take appropriate measures before production anomalies occur, thereby avoiding production risks and losses. By removing noise, outliers, and incorrect data, the system obtains a cleaner and more accurate dataset, which helps to reduce misjudgments and incorrect decisions caused by inaccurate data and ensure the quality of the basic data for subsequent analysis and processing. By normalizing the data using the min-max normalization method, the data is standardized between different scales, eliminating the influence of different dimensions on the analysis, making the analysis results more stable, and facilitating subsequent data comparison and model construction.

[0098] In an embodiment of the present invention, during the production process of polyethylene films, the key nodes include raw material feeding, extrusion process, cooling stage, etc., and data of these key nodes in the production of polyethylene films is obtained. Sensors are deployed at the determined key node positions. For example, we install a temperature sensor at the outlet of the extruder to measure the temperature of the polyethylene film. At the same time, a pressure sensor is installed near the extrusion screw to monitor the pressure change during the extrusion process. Key node data is obtained in real time through precise sensor deployment. For example, during the raw material feeding stage, a weighing sensor is used to accurately measure the input amount of raw materials. During the extrusion process, the sensors continuously record the temperature and pressure of the extruder. During the cooling stage, temperature and pressure sensors are placed on the cooling device to monitor the effect of the cooling process. The data collected from the sensors may contain outliers and need to be data-cleaned. For example, it may be found that there are some accidental temperature spikes in the data, which may be caused by sensor errors or other interferences. By using techniques such as moving window averaging or median filtering, these abnormal data can be eliminated, resulting in smoother and more reliable data. The cleaned data is normalized using the min-max normalization method so that different types of data can be compared on the same scale. For data such as temperature and pressure with different magnitudes, through min-max normalization, they are mapped to the range of 0 to 1. In subsequent analysis, various data can be effectively compared and integrated.

[0099] Preferably, step S2 includes the following steps:

[0100] Step S21: Analyze the production status of the polyethylene film based on the standard polyethylene film production data to generate production status data of the polyethylene film;

[0101] Step S22: Use Fourier transform to perform analog signal conversion of the production status data to generate a production status signal;

[0102] Step S23: Use the polyethylene film production status image conversion algorithm to perform polyethylene film production status identification image conversion on the production status signal to generate production status image data;

[0103] Step S24: Use Gaussian filtering to perform noise reduction processing on the production status image data to produce noise-reduced production status image data.

[0104] The present invention conducts in-depth production status analysis based on standard polyethylene film production data. Through data mining and analysis, it can better understand the changes and trends in the production process, thereby revealing potential problems and opportunities, and providing more accurate decision-making support for operators and management. By converting production status data into analog signals through Fourier transform, time-domain data can be transformed into frequency-domain data, thus more comprehensively showing the periodicity and trends of the production status. This conversion helps to deeply understand the characteristics of the production process from different perspectives and provides more dimensions of information for anomaly detection and analysis. Using the polyethylene film production status image conversion algorithm to convert production status signals into production status image data, this visualization method transforms abstract signal data into images, making complex data easier to understand and analyze, and providing a more intuitive way for operators to recognize the production status. By applying Gaussian filtering to perform noise reduction processing on the production status image data, the noise and interference in the image are effectively reduced. Such noise reduction helps to improve the clarity and readability of the image and provides a more reliable basis for subsequent image analysis and recognition.

[0105] As an example of the present invention, refer to Figure 2 shown in Figure 1 is a schematic diagram of the detailed implementation steps of step S2 in

[0106] Step S21: Perform production status analysis of the polyethylene film based on the standard polyethylene film production data to generate production status data of the polyethylene film;

[0107] In the embodiment of the present invention, based on the standard polyethylene film production data obtained by preprocessing in the previous steps, in-depth status analysis is carried out. Using statistical methods, the change trends of parameters such as temperature and pressure in different time periods are correlated with the quality status of the produced polyethylene film, as well as their correlation with the film quality. These analyses can reveal potential problems or changes in the production process and generate production status data of the polyethylene film.

[0108] Step S22: Use Fourier transform to perform analog signal conversion of the production status data to generate a production status signal;

[0109] In the embodiment of the present invention, the production status data is subjected to Fourier transform and converted into a frequency-domain signal. Through Fourier transform, the time-domain data is converted into a frequency-domain spectrum distribution. Further analyzing the influence of different frequency components in the production status, it is found that the oscillations of certain frequencies are related to the fluctuations of the film quality, and a production status signal is generated.

[0110] Step S23: Use the production status image conversion algorithm for polyethylene film to perform production status identification image conversion of the polyethylene film on the production status signal to generate production status image data;

[0111] In the embodiment of the present invention, using the production status image conversion algorithm for polyethylene film to perform production status identification image conversion of the production status signal, by mapping the spectrum information to the horizontal and vertical coordinates of the image, we can generate a production status image, where the gray value at different coordinate positions reflects the presence of different frequency components in the production process, thereby establishing a unique identification image of the production status data and generating production status image data.

[0112] Step S24: Use Gaussian filtering to perform image data noise reduction processing on the production status image data to produce noise-reduced production status image data.

[0113] In the embodiment of the present invention, in order to reduce the noise interference in the image, image processing techniques such as Gaussian filtering can be applied. By adjusting the filter parameters, the main features in the production status image can be retained while removing the noise, and the obtained noise-reduced image can more accurately reflect the state characteristics of the production process.

[0114] Preferably, the production status image conversion algorithm for polyethylene film in step S23 is as follows:

[0115]

[0116] In the formula, I(x,y) represents the gray value data of the production status image with the abscissa x and the ordinate y, x represents the abscissa extreme value of the generated image, y represents the ordinate extreme value of the generated image, T represents the total time length involved in the production status data, α represents the intensity adjustment value for controlling the generated image, k1 represents the texture information for controlling the abscissa of the image, k2 represents the texture information for controlling the ordinate of the image, β represents the amplitude size of the production status signal varying with time, γ represents the frequency size of the production status signal varying with time, t represents the time node corresponding to the total time length involved in the production status data, and τ represents the abnormal adjustment value of the gray value data.

[0117] The present invention utilizes an algorithm for converting the production state image of a polyethylene film. This algorithm fully considers the extreme value x of the abscissa of the generated image, the extreme value y of the ordinate of the generated image, the total time length T of the production state data involved, the intensity adjustment value α for controlling the generated image, the texture information k1 for controlling the abscissa of the image, the texture information k2 for controlling the ordinate of the image, the amplitude β of the production state signal varying with time, the frequency γ of the production state signal varying with time, t represents the time node corresponding to the total time length of the production state data involved, and the interaction relationships between functions, so as to form a functional relationship:

[0118] That is, By converting the abstract production state data into the specific identification gray value of the image, this functional relationship makes the trend and changes of the production state become intuitively visible. One can quickly understand the changes in the production state by observing the image, thus making it easier to discover abnormal situations or trends. The texture information for controlling the abscissa of the image and the texture information for controlling the ordinate of the image can make the image highlight the spatial characteristics, thereby better showing the state changes in different regions. By introducing the time node corresponding to the total time length of the production state data involved, the image can also show the time characteristics of the production state. Controlling the amplitude of the production state signal varying with time and the frequency of the production state signal varying with time enables the image to capture the dynamic changes of the production state signal over time, which helps analyze periodic or gradually changing trends. Controlling the intensity adjustment value of the generated image can adjust the image intensity, making the image more clearly displayed, which helps the operator quickly discover potential problems. This algorithm comprehensively displays the spatial characteristics, time characteristics, dynamic changes, and abnormal situations of the production state data, enabling the image to transmit more comprehensive information. Through the synergistic effect of multiple parameters, the abstract production state data is converted into a visual image, providing a more comprehensive and intuitive display of the production state, enabling the operator to better understand and analyze the changes in the production state. Using the abnormal adjustment value τ of the gray value data to adjust and correct the functional relationship, reducing the error influence caused by abnormal data or error terms, thus more accurately generating the gray value data I(x, y) of the production state image, improving the accuracy and reliability of converting the production state signal into the production state identification image of the polyethylene film. At the same time, the weight information and adjustment value in this formula can be adjusted according to the actual situation and applied to different production state signals, improving the flexibility and applicability of the algorithm.

[0119] Preferably, step S3 includes the following steps:

[0120] Step S31: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the production parameter adjustment time period of the polyethylene film;

[0121] Step S32: Extract the production rate change rate within the adjustment time node from the standard polyethylene film production data according to the production parameter adjustment time period, and generate the production parameter adjustment change rate;

[0122] Step S33: Perform image block segmentation processing on the noise-reduced production status image data according to the production parameter adjustment time period, and generate the noise-reduced production status image block data;

[0123] Step S34: Perform production status image block outlier detection processing on the noise-reduced production status image block data according to the polyethylene film production status image anomaly detection algorithm and the production parameter adjustment change rate, and generate the noise-reduced production status image block outliers.

[0124] In the present invention, by dividing the time period according to the standard polyethylene film production data, a suitable production parameter adjustment time period is determined. This division makes the adjustment of production parameters more targeted, helps to adjust the parameters at critical moments, and thus realizes more precise production control. Using the production parameter adjustment time period, the production rate change rate within the time node is extracted. This change rate extraction helps to capture the fluctuations and trends in the production process, so as to more accurately judge whether the production status is normal and make adjustments when necessary. Perform image block segmentation processing on the noise-reduced production status image data according to the production parameter adjustment time period. The segmentation helps to divide the image into smaller blocks, provides more refined image features, makes the subsequent anomaly detection more sensitive and accurate, and by analyzing the noise-reduced production status image data under different adjustment time periods, the production parameters corresponding to the problematic time points can be found. Through the combination of the polyethylene film production status image anomaly detection algorithm and the production parameter adjustment change rate, outlier detection processing is performed on the noise-reduced production status image block data. This combination enables the system to perform anomaly detection from different dimensions, improving the accuracy and reliability of anomaly detection.

[0125] As an example of the present invention, refer to Figure 3 shown, which is Figure 1 the detailed implementation step flow diagram of step S3 in

[0126] Step S31: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data, and generate the production parameter adjustment time period of the polyethylene film;

[0127] In the embodiments of the present invention, based on the production data of standard polyethylene films, the changing trends of production parameters can be identified, and the production process can be segmented into different time periods according to these trends. The changes in production parameters within each time period are relatively stable. For example, the production process can be divided into different stages according to the change amplitude and frequency of parameters such as temperature, pressure, and rate.

[0128] Step S32: Extract the production rate change rate within the adjustment time node from the standard polyethylene film production data according to the production parameter adjustment time period to generate the production parameter adjustment change rate;

[0129] In the embodiments of the present invention, within each adjustment time period, by analyzing the rate change situation in the standard production data, the change rate of the production rate is calculated. This can be achieved by calculating the slope of the rate within the time period. For example, if the rate shows a gradually increasing trend within a certain time period, the corresponding change rate will be positive, and vice versa.

[0130] Step S33: Perform image block segmentation processing on the noise-reduced production state image data according to the production parameter adjustment time period to generate the noise-reduced production state image block data;

[0131] In the embodiments of the present invention, within each adjustment time period, the noise-reduced production state image data is segmented into multiple image blocks. Through image segmentation techniques, such as region-based segmentation or threshold-based segmentation, the image is segmented into small blocks with similar characteristics, and each image block will represent a local production state.

[0132] Step S34: Perform production state image block outlier detection processing on the noise-reduced production state image block data according to the polyethylene film production state image anomaly detection algorithm and the production parameter adjustment change rate to generate the noise-reduced production state image block outliers.

[0133] In the embodiments of the present invention, for each image block, combining the previously extracted production parameter adjustment change rate information and using the polyethylene film production state image anomaly detection algorithm to calculate the outlier value of the image block helps to determine which local production states are abnormal within a specific adjustment time period. For example, if the outlier value of an image block is significantly higher than other blocks, it may indicate that there is a production anomaly during this time period.

[0134] Preferably, the polyethylene film production state image anomaly detection algorithm in step S34 is as follows:

[0135]

[0136] In the formula, P represents the outlier value size of the noise-reduced production state image block data, and d represents the gray value intensity of the noise-reduced production state image data. The corresponding production parameter adjustment time period represented as the noise reduction production state image data, w represents the number of gray value abnormal regions in the noise reduction production state image data, a represents the production parameter adjustment change rate data, A represents the expected gray value change rate of the noise reduction production state image block generated according to the production parameter adjustment change rate data, B represents the actual gray value change rate of the noise reduction production state image block, and δ represents the abnormal adjustment value of the abnormal value size.

[0137] The present invention utilizes an abnormal detection algorithm for the production state image of a polyethylene film. This algorithm comprehensively considers the gray value intensity d of the noise reduction production state image data and the corresponding production parameter adjustment time period of the noise reduction production state image data The number w of gray value abnormal regions in the noise reduction production state image data, the production parameter adjustment change rate data a, the expected gray value change rate A of the noise reduction production state image block generated according to the production parameter adjustment change rate data, the actual gray value change rate B of the noise reduction production state image block, and the interaction relationship between functions to form a functional relationship:

[0138] That is, The gray value intensity of the noise reduction production state image data is used to consider the information of the gray values in the image, enabling the algorithm to judge abnormalities from the perspective of image intensity; the corresponding production parameter adjustment time period of the noise reduction production state image data is related to the time range of abnormal detection and considers the time nature of the image block data; the number w of gray value abnormal regions in the noise reduction production state image data is used to consider the gray abnormalities in the image. The more abnormal regions there are, the greater the possible abnormal value; the production parameter adjustment change rate data reflects the changes in the production state; the expected gray value change rate of the noise reduction production state image block generated according to the production parameter adjustment change rate data, with the expected gray value change rate as a reference value, is used to judge the expected state of the image block; the actual gray value change rate of the noise reduction production state image block, with the actual gray value change rate compared with the expected value, is used to judge the actual state of the image block. This algorithm can detect abnormal conditions from multiple dimensions such as gray values, time, and change rates, and can finely detect abnormal conditions in the image block data. It does not simply compare the numerical sizes but comprehensively considers multiple factors, making the abnormal detection more accurate and reliable. Using the abnormal adjustment value δ of the abnormal value size to adjust and correct the functional relationship reduces the error influence brought by abnormal data or error terms, thereby more accurately generating the abnormal value size P of the noise reduction production state image block data, improving the accuracy and reliability of the abnormal value detection process for the production state image block of the noise reduction production state image block data. At the same time, the adjustment value in this formula can be adjusted according to the actual situation and applied to different noise reduction production state image block data, improving the flexibility and applicability of the algorithm.

[0139] Preferably, step S4 includes the following steps:

[0140] Step S41: Compare the abnormal value of the noise-reduced production status image block with the preset abnormal threshold of the polyethylene film production status. When the abnormal value of the noise-reduced production status image block is not greater than the abnormal threshold of the polyethylene film production status, mark the data of the noise-reduced production status image block as the data of the normal production status image block;

[0141] Step S42: Compare the abnormal value of the noise-reduced production status image block with the preset abnormal threshold of the polyethylene film production status. When the abnormal value of the noise-reduced production status image block is greater than the abnormal threshold of the polyethylene film production status, mark the data of the noise-reduced production status image block as the data of the abnormal production status image block;

[0142] Step S43: Mark the production status data as the normal production status data according to the data of the normal production status image block to generate the normal production status data;

[0143] Step S44: Mark the production status data as the abnormal production status data according to the data of the abnormal production status image block to generate the abnormal production status data.

[0144] In the present invention, by comparing the preset abnormal threshold with the abnormal value of the noise-reduced production status image block, the accurate classification and marking of the production status are realized, and the monitoring and analysis capabilities of the production process are further enhanced. By comparing with the preset abnormal threshold of the polyethylene film production status, the data of the noise-reduced production status image block is divided into two categories: normal and abnormal. This classification helps to focus on the parts that may have problems and achieves the goal of quickly identifying potential abnormal situations. According to the data of the normal and abnormal production status image blocks, the production status data is marked accordingly, and the normal production status data and the abnormal production status data are generated respectively, which helps to organize and classify the data and provides a basis for the subsequent safety parameter design and production optimization.

[0145] In an embodiment of the present invention, an abnormal threshold for the production state of a polyethylene film is preset in advance. This threshold is used to judge the degree of abnormality of an image block. By comparing the abnormal value of the image block of the noise-reduced production state with the preset threshold, if the abnormal value is not greater than the threshold, the image block is marked as a normal production state. For example, when the abnormal value of the image block is small and lower than the preset threshold, it indicates that the image block is in a normal state and belongs to the normal production state. According to the preset abnormal threshold for the production state of the polyethylene film, the abnormal value of the image block of the noise-reduced production state is compared again. If the abnormal value is greater than the preset threshold, the image block is marked as an abnormal production state. For example, when the abnormal value of the image block significantly exceeds the preset threshold, it may indicate that there is a production abnormality in the image block, and further analysis and processing are required. According to the image blocks marked as the normal production state, the production state data corresponding to these image blocks are marked as normal production state data. For example, within a specific time period, if the image blocks are all determined to be in the normal state, then the corresponding production state data will also be marked as the normal state. Similarly, the image blocks marked as the abnormal production state mark the corresponding production state data as the abnormal production state. For example, if some image blocks are determined to be in the abnormal state, then the corresponding production state data will also be marked as the abnormal state.

[0146] Preferably, step S5 includes the following steps:

[0147] Step S51: Mark the safe production data of the polyethylene film for the standard polyethylene film production data according to the normal production state data to generate safe production data;

[0148] Step S52: Design the safe production parameter range of the polyethylene film according to the safe production data to generate the safe production parameter range.

[0149] In the present invention, by marking the safe production data for the standard polyethylene film production data according to the normal production state data, this marking is based on the data in the normal operation state, providing a reliable basis for the subsequent design of the safe parameter range. By using the safe production data, the system designs the safe production parameter range of the polyethylene film, relying on the statistical analysis of the safe production data, thereby determining a suitable set of parameter ranges to ensure that the production process operates under normal and safe conditions.

[0150] In the embodiments of the present invention, the safe production data of polyethylene films is marked based on the conventional production status data for the standard polyethylene film production data. For example, if there are no problems indicated by the conventional production status data within a specific time period, such as the produced polyethylene films meeting the regulations, then the production data of the corresponding polyethylene films can be considered relatively safe, such as parameters like temperature and pressure. These data can be used to establish a benchmark for safe production and provide a basis for the subsequent design of parameter ranges. The key parameters of polyethylene film production are analyzed and designed using the safe production data. By statistically analyzing information such as the data range and volatility under the conventional production status, an appropriate parameter range, i.e., the safe production parameter range, can be determined. For example, based on historical data and experience, the reasonable ranges of parameters such as production rate and temperature can be determined to ensure that the production process proceeds in a safe state.

[0151] Preferably, step S6 includes the following steps:

[0152] Step S61: Mark the abnormal production data of polyethylene films for the standard polyethylene film production data according to the abnormal production status data to generate abnormal production data;

[0153] Step S62: Adjust the parameters of the abnormal production data according to the safe production parameter range to generate adjusted production data;

[0154] Step S63: Optimize the abnormal values of the production parameters of the polyethylene films for the standard polyethylene film production data using the adjusted production data to generate optimized polyethylene film production data.

[0155] The present invention marks the abnormal production data for the standard polyethylene film production data according to the abnormal production status data, which helps to identify and isolate abnormal situations and ensure that abnormal data does not affect the processing and analysis of normal production data. Adjust the parameters of the abnormal production data according to the safe production parameter range. This adjustment is based on the previously determined safe parameter range. For example, if the production temperature is too high, it will be abnormal, and the abnormal production data will be adjusted to safe temperature data within the safe production range to ensure that the abnormal data is adjusted within a safe range and avoid possible risks and adverse effects. By using the adjusted production data to optimize the abnormal values of the production parameters, potential problems in the polyethylene film production process can be solved by adjusting the parameters to normal values, further improving the quality and stability of the production process.

[0156] As an example of the present invention, referring to Figure 4 shown, it is Figure 1 a schematic diagram of the detailed implementation steps of step S6 in

[0157] Step S61: Mark the abnormal production data of the polyethylene film on the standard polyethylene film production data according to the abnormal production status data, and generate abnormal production data;

[0158] In the embodiment of the present invention, through the data of the abnormal production status, such as the production status of the polyethylene film not meeting the requirements, or the possible production status not meeting the requirements, etc., the standard polyethylene film production data corresponding to the abnormal production status data is marked as abnormal production data, such as marking the corresponding standard polyethylene film production data as too high or too low temperature, too large or too small pressure, etc. These abnormal production data record the problems that occur during the production process and provide a basis for subsequent processing.

[0159] Step S62: Adjust the parameters of the abnormal production data according to the safe production parameter range to generate adjusted production data;

[0160] In the embodiment of the present invention, the parameters of the abnormal production data are adjusted according to the safe production parameter range. For example, if it is found that a certain parameter exceeds the safe production parameter range, the parameter can be adjusted to a reasonable range within the range to avoid further abnormal situations. This can ensure that the production process is carried out within a safe range and reduce the possibility of production abnormalities.

[0161] Step S63: Optimize the abnormal values of the production parameters of the polyethylene film on the standard polyethylene film production data by using the adjusted production data to generate optimized polyethylene film production data.

[0162] In the embodiment of the present invention, the abnormal values of the production parameters of the polyethylene film are optimized on the standard polyethylene film production data by using the adjusted production data. By adjusting the abnormal data, the settings of the production parameters can be improved, and the abnormal situations during the production process can be reduced. These optimized production parameters can improve the production efficiency, stability and quality, and generate optimized polyethylene film production data.

[0163] This specification provides a status monitoring system for a polyethylene film production system, which is used to execute the status monitoring method of the polyethylene film production system as described above. The status monitoring system of the polyethylene film production system includes:

[0164] A polyethylene film production data acquisition module, which is used to obtain the key nodes of polyethylene film production; use sensors to collect the production data of the polyethylene film at the key nodes of polyethylene film production to produce polyethylene film production data; perform data preprocessing on the polyethylene film production data to generate standard polyethylene film production data;

[0165] The polyethylene film production status image conversion module is used to analyze the production status of polyethylene films based on standard polyethylene film production data, generating production status data of polyethylene films; performing polyethylene film production status identification image conversion and noise reduction processing on the production status data to generate noise-reduced production status image data;

[0166] The production status image outlier calculation module is used to divide the adjustment time period of polyethylene film production parameters based on standard polyethylene film production data, generating the production parameter adjustment time period of polyethylene films; performing image block segmentation and image block outlier calculation on the noise-reduced production status image data according to the production parameter adjustment time period, generating noise-reduced production status image block outliers;

[0167] The production status data division module is used to divide the production status data into normal and abnormal production status data according to the noise-reduced production status image block outliers, respectively generating normal production status data and abnormal production status data;

[0168] The safe production parameter interval design module is used to design the safe production parameter interval of polyethylene films based on the normal production status data for the standard polyethylene film production data, generating the safe production parameter interval;

[0169] The polyethylene film production data optimization module is used to optimize the production parameter outliers of polyethylene films based on the abnormal production status data and the safe production parameter interval for the standard polyethylene film production data, generating optimized polyethylene film production data.

[0170] The beneficial effects of this application are as follows. Through data collection, analysis, and processing, the present invention converts a large amount of information in the polyethylene film production process into real-time and visual data, providing more and more accurate information for decision-makers. Based on the optimized polyethylene film production data, decisions can be made more wisely, thereby optimizing the production process, improving product quality, and achieving more efficient resource utilization. The analysis of production status data and anomaly detection can predict potential problems, help avoid machine failures and production interruptions. By identifying potential problems, predictive maintenance can be implemented, reducing equipment downtime and maintenance costs, thus improving production efficiency. Through the optimization and adjustment of polyethylene film production parameters, the production process is continuously iterated and improved, thereby continuously improving product quality, production efficiency, and workflow optimization. The polyethylene film production data and related data involved in this method enable the entire production process to have a high degree of traceability and transparency. Whether monitoring the production status or analyzing abnormal situations, operators can clearly understand the development and results of each stage, which helps to quickly locate the root cause when problems occur. Through real-time monitoring and anomaly detection of the production process, this method helps to reduce human operation errors and improve work safety. At the same time, by setting the safety parameter range according to the safe polyethylene film production parameters and adjusting the abnormal parameters of the polyethylene film production parameters, it is ensured that the production is carried out within a safe range, effectively avoiding potential safety risks.

[0171] Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0172] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the state of a polyethylene film production system, characterized in that, Including the following steps: Step S1: Obtain the key nodes in the production of polyethylene film; Use sensors to collect production data of polyethylene film at the key nodes in the production of polyethylene film, and generate production data of polyethylene film; Perform data preprocessing on the production data of polyethylene film to generate standard production data of polyethylene film; Step S2: Analyze the production status of polyethylene film based on the standard production data of polyethylene film to generate production status data of polyethylene film; Perform conversion and noise reduction processing on the production status data for the production status identification image of polyethylene film to generate noise-reduced production status image data; Step S3: Divide the adjustment time period of the production parameters of polyethylene film according to the standard production data of polyethylene film to generate the adjustment time period of the production parameters of polyethylene film; Perform image block segmentation and calculation of image block outliers on the noise-reduced production status image data according to the adjustment time period of the production parameters to generate noise-reduced production status image block outliers; Step S4: Divide the production status data into normal and abnormal production status data according to the noise-reduced production status image block outliers, and generate normal production status data and abnormal production status data respectively; Step S5: Design the safe production parameter interval of polyethylene film based on the normal production status data for the standard production data of polyethylene film to generate the safe production parameter interval; Step S6: Optimize the production parameter outliers of polyethylene film based on the abnormal production status data and the safe production parameter interval for the standard production data of polyethylene film to generate optimized production data of polyethylene film.

2. The method for monitoring the state of the polyethylene film production system according to claim 1, wherein, Step S1 includes the following steps: Step S11: Obtain the key nodes in the production of polyethylene film; Step S12: Deploy sensors to the key nodes in the production of polyethylene film by the sensors, and use the sensors to collect production data of polyethylene film to generate production data of polyethylene film; Step S13: Perform data cleaning on the production data of polyethylene film to generate cleaned production data of polyethylene film; Step S14: Perform data standardization on the cleaned production data of polyethylene film using the min-max standardization method to generate standard production data of polyethylene film.

3. The method for monitoring the state of the polyethylene film production system according to claim 2, characterized in that, Step S2 includes the following steps: Step S21: Analyze the production status of polyethylene film based on the standard production data of polyethylene film to generate production status data of polyethylene film; Step S22: Use Fourier transform to perform analog signal conversion on the production status data to generate production status signals; Step S23: Use the polyethylene film production status image conversion algorithm to perform production status identification image conversion of polyethylene film on the production status signals to generate production status image data; Step S24: Use Gaussian filtering to perform noise reduction processing on the production status image data to produce noise-reduced production status image data.

4. The method for monitoring the state of the polyethylene film production system according to claim 2, wherein, The polyethylene film production status image conversion algorithm in Step S23 is as follows: In the formula, I(x, y) represents the grayscale value data of the production status image with the abscissa x and the ordinate y. x represents the abscissa extreme value of the generated image, y represents the ordinate extreme value of the generated image, T represents the total time length involved in the production status data, α represents the intensity adjustment value for controlling the generated image, k1 represents the texture information for controlling the abscissa of the image, k2 represents the texture information for controlling the ordinate of the image, β represents the amplitude size of the production status signal varying with time, γ represents the frequency size of the production status signal varying with time, t represents the time node corresponding to the total time length involved in the production status data, and τ represents the abnormal adjustment value of the grayscale value data.

5. The method for monitoring the state of the polyethylene film production system according to claim 4, wherein Step S3 includes the following steps: Step S31: Divide the adjustment time period of the polyethylene film production parameters according to the standard polyethylene film production data to generate the production parameter adjustment time period of the polyethylene film; Step S32: Extract the production rate change rate within the adjustment time node from the standard polyethylene film production data according to the production parameter adjustment time period to generate the production parameter adjustment change rate; Step S33: Perform image block segmentation processing on the noise-reduced production status image data according to the production parameter adjustment time period to generate the noise-reduced production status image block data; Step S34: Perform production status image block outlier detection processing on the noise-reduced production status image block data according to the polyethylene film production status image anomaly detection algorithm and the production parameter adjustment change rate to generate the noise-reduced production status image block outliers.

6. The method for monitoring the state of the polyethylene film production system according to claim 5, wherein The polyethylene film production status image anomaly detection algorithm in Step S34 is as follows: Wherein, P represents the outlier size of the image block data in the noise reduction production state, and d represents the gray value intensity of the image data in the noise reduction production state. represents the corresponding production parameter adjustment time period of the image data in the noise reduction production state, w represents the number of gray value abnormal regions of the image data in the noise reduction production state, a represents the production parameter adjustment change rate data, A represents the expected gray value change rate of the image block in the noise reduction production state generated according to the production parameter adjustment change rate data, B represents the actual gray value change rate of the image block in the noise reduction production state, and δ represents the abnormal adjustment value of the outlier size.

7. The method for monitoring the state of the polyethylene film production system according to claim 6, characterized in that, Step S4 includes the following steps: Step S41: Compare the preset polyethylene film production status anomaly threshold with the noise-reduced production status image block outliers. When the noise-reduced production status image block outliers are not greater than the polyethylene film production status anomaly threshold, mark the noise-reduced production status image block data as normal production status image block data; Step S42: Compare the preset polyethylene film production status anomaly threshold with the noise-reduced production status image block outliers. When the noise-reduced production status image block outliers are greater than the polyethylene film production status anomaly threshold, mark the noise-reduced production status image block data as abnormal production status image block data; Step S43: Mark the production status data as normal production status data according to the normal production status image block data to generate normal production status data; Step S44: Mark the production status data as abnormal production status data according to the abnormal production status image block data to generate abnormal production status data.

8. The method for monitoring the state of the polyethylene film production system according to claim 7, wherein, Step S5 includes the following steps: Step S51: Mark the safe production data of the polyethylene film for the standard polyethylene film production data according to the normal production status data to generate safe production data; Step S52: Design the safe production parameter interval of the polyethylene film according to the safe production data to generate the safe production parameter interval.

9. The method for monitoring the state of the polyethylene film production system according to claim 8, characterized in that, Step S6 includes the following steps: Step S61: Mark the abnormal production data of the polyethylene film on the standard polyethylene film production data according to the abnormal production status data to generate abnormal production data; Step S62: Adjust the parameters of the abnormal production data according to the safe production parameter range to generate adjusted production data; Step S63: Optimize the abnormal values of the production parameters of the polyethylene film using the adjusted production data on the standard polyethylene film production data to generate optimized polyethylene film production data.

10. A state monitoring system for a polyethylene film production system, characterized in that, A state monitoring method for a polyethylene film production system for implementing the method as claimed in claim 1, the state monitoring system of the polyethylene film production system comprising: A polyethylene film production data acquisition module, configured to obtain key nodes of polyethylene film production; collect production data of the polyethylene film at the key nodes of polyethylene film production using sensors to generate polyethylene film production data; perform data preprocessing on the polyethylene film production data to generate standard polyethylene film production data; A polyethylene film production state image conversion module, configured to perform production state analysis of the polyethylene film according to the standard polyethylene film production data to generate production state data of the polyethylene film; perform production state identification image conversion and noise reduction processing on the production state data to generate noise-reduced production state image data; A production state image abnormal value calculation module, configured to divide the adjustment time period of the production parameters of the polyethylene film according to the standard polyethylene film production data to generate an adjustment time period of the production parameters of the polyethylene film; perform image block segmentation and image block abnormal value calculation on the noise-reduced production state image data according to the production parameter adjustment time period to generate noise-reduced production state image block abnormal values; A production state data division module, configured to divide the production state data into normal and abnormal production state data according to the noise-reduced production state image block abnormal values, and generate normal production state data and abnormal production state data respectively; A safe production parameter range design module, configured to design a safe production parameter range of the polyethylene film on the standard polyethylene film production data according to the normal production state data to generate a safe production parameter range; A polyethylene film production data optimization module, configured to optimize the abnormal values of the production parameters of the polyethylene film on the standard polyethylene film production data according to the abnormal production state data and the safe production parameter range to generate optimized polyethylene film production data.

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