High-speed paper cutter control method and system

Abstract

The invention discloses a high-speed paper cutter control method and system, and the method comprises the steps: obtaining paper characteristic data of to-be-cut paper in real time, analyzing the paper characteristic data through a deep learning model, and generating a corresponding optimal cutting parameter set, the paper characteristic data including thickness, humidity and material component data; in the cutting process of the high-speed paper cutter, actual cutting data in the cutting process are monitored in real time through an embedded sensor; and according to the actual cutting data, cutting parameters of the high-speed paper cutter are dynamically adjusted through the self-adaptive control technology, so that the consistency and accuracy of the cutting quality are ensured. By utilizing the embodiment of the invention, the intelligence and automation of the cutting process can be realized, and the consistency and accuracy of the cutting quality are ensured.

Description

Technical Field

[0001] The invention belongs to the field of control technology, and in particular to a high-speed paper cutter control method and system. Background Art

[0002] In modern manufacturing, the cutting process involved in paper processing plays a vital role. High-speed paper cutters are widely used to cut various types of paper to meet the requirements of production efficiency and precision. However, the complexity of the paper cutting process and the diversity of paper itself make it difficult to operate. Different types of paper have significant differences in thickness, humidity, and material composition, which requires the formulation of corresponding cutting parameters for different papers during the cutting process to ensure the stability and consistency of cutting quality.

[0003] Traditional cutting control methods often rely on experience and static parameter settings, which are difficult to adapt to the impact of changes in paper characteristics. As the production environment changes, static parameters may not cover all possible cutting conditions, resulting in inconsistent cutting quality, sometimes even paper damage or inaccurate cutting. In addition, the lack of real-time monitoring and feedback mechanisms also makes it difficult for the production line to respond quickly when encountering problems, which in turn affects overall production efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a high-speed paper cutter control method and system to solve the deficiencies in the prior art, to realize the intelligence and automation of the cutting process, and to ensure the consistency and accuracy of the cutting quality.

[0005] An embodiment of the present application provides a high-speed paper cutter control method, the method comprising: Acquire paper property data of the paper to be cut in real time, analyze the paper property data through a deep learning model, and generate a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity, and material composition data; During the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time through embedded sensors; According to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted using adaptive control technology to ensure the consistency and accuracy of the cutting quality.

[0006] Optionally, the real-time acquisition of paper property data of the paper to be cut, the analysis of the paper property data by a deep learning model, and the generation of a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity and material composition data, includes: A multimodal deep learning model is used to fuse and analyze the thickness, humidity and material composition data. The thickness data in the paper characteristic data is used as a one-dimensional time series feature and input into a sub-model based on a convolutional neural network or a recurrent neural network to capture the variation pattern of paper thickness. The humidity data and material composition data are used as key static features and processed by a fully connected neural network to extract the influence of each material component on the physical properties under different humidity conditions. Finally, a feature fusion layer is used to fuse the data output by the sub-model to generate a high-dimensional vector that comprehensively represents the paper characteristics. The high-dimensional vector is input into the last fully connected layer of the multimodal deep learning model, and the corresponding multidimensional parameter vector is generated as the optimal cutting parameter set through weight mapping and nonlinear activation function. The optimal cutting parameter set includes at least cutting speed, tool pressure, and tool angle to achieve the stability of cutting quality, minimize tool wear, and maximize cutting efficiency.

[0007] Optionally, during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by an embedded sensor, including: During the cutting process, a distributed embedded sensor system is used to deploy multiple types of sensors. The data of the sensors are collected in real time by high-frequency sampling, including: The pressure sensor is installed on the tool carrier to collect the pressure changes of the tool on the paper in real time; the acceleration sensor is installed on the tool movement path to capture the tool's movement acceleration to analyze the physical vibration behavior during the cutting process; Acoustic sensors are installed around the cutting area to record the high-frequency noise generated during the cutting process to determine friction or cutting abnormalities; The optical sensor monitors the microscopic spectral changes in the cutting area in real time to capture the tiny physical or chemical changes caused by friction in the paper during the cutting process.

[0008] Optionally, the method of dynamically adjusting the cutting parameters of the high-speed paper cutter using adaptive control technology according to the actual cutting data to ensure the consistency and accuracy of the cutting quality includes: Integrate the multi-dimensional actual cutting data collected from the sensor into a state vector, wherein the state vector includes various indicators of the current cutting state; By formulating control rules based on fuzzy logic and defining the adjustment strategy of cutting parameters, the fuzzy logic controller automatically evaluates the cutting parameters that need to be adjusted based on the current state vector to optimize the cutting quality; Combine actual cutting data with historical cutting quality data and use machine learning algorithms to predict the cutting quality performance under the current cutting parameter settings; Based on the output of the fuzzy logic controller and machine learning model, the cutting parameters of the high-speed paper cutter are adjusted in real time. When it is detected that the cutting quality does not meet the set standards, the tool pressure or cutting speed is automatically adjusted to maintain the consistency and accuracy of the cutting.

[0009] Another embodiment of the present application provides a high-speed paper cutter control system, the system comprising: An analysis module, used to obtain the paper characteristic data of the paper to be cut in real time, analyze the paper characteristic data through a deep learning model, and generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; The monitoring module is used to monitor the actual cutting data in real time during the cutting process of the high-speed paper cutter through embedded sensors; The control module is used to dynamically adjust the cutting parameters of the high-speed paper cutter according to the actual cutting data by using adaptive control technology to ensure the consistency and accuracy of the cutting quality.

[0010] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0011] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0012] Compared with the prior art, the present invention provides a high-speed paper cutter control method, which obtains paper characteristic data of to-be-cut paper in real time, analyzes the paper characteristic data through a deep learning model, and generates a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time through an embedded sensor; according to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted using adaptive control technology to ensure the consistency and accuracy of the cutting quality, thereby realizing the intelligence and automation of the cutting process and ensuring the consistency and accuracy of the cutting quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A hardware structure block diagram of a computer terminal for a high-speed paper cutter control method provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a high-speed paper cutter control method provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a high-speed paper cutter control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0015] The embodiment of the present invention firstly provides a high-speed paper cutter control method, which can be applied to electronic equipment, such as a computer terminal, specifically a common computer, etc.

[0016] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a high-speed paper cutter control method provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0017] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any high-speed paper cutter control method.

[0018] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0019] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any high-speed paper cutter control method.

[0020] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0021] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0022] See also Figure 2 , an embodiment of the present invention provides a high-speed paper cutter control method, which may include the following steps: S201, acquiring paper property data of paper to be cut in real time, analyzing the paper property data through a deep learning model, and generating a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity, and material composition data; Real-time acquisition of paper property data for paper to be cut means that before the cutting process begins, the various physical and chemical properties of the paper to be cut can be fully detected and recorded through sensor technology and data acquisition systems. These characteristic data include paper thickness, humidity, and material composition, which can be obtained through different sensors, such as ultrasonic sensors to measure thickness, humidity sensors to measure paper moisture content, and chemical analysis instruments to obtain material composition information. Subsequently, these data will be input into the deep learning model, which will analyze these features and generate the best set of cutting parameters to ensure that it is adapted to the cutting needs of specific paper.

[0023] The significance of this process is to accurately analyze the characteristics of the paper to be cut through scientific methods, so as to provide reliable data support for the cutting process. By deeply understanding the physical and chemical properties of paper, more suitable cutting parameters can be formulated, which can not only improve the cutting quality, but also reduce the wear of the tool and improve the cutting efficiency. Ultimately, this precise cutting technology will ensure the consistency and high quality of the cut products to meet production needs.

[0024] Specifically, a multimodal deep learning model can be used to fuse and analyze the thickness, humidity and material composition data. The thickness data in the paper characteristic data is used as a one-dimensional time series feature and input into a sub-model based on a convolutional neural network or a recurrent neural network to capture the variation pattern of paper thickness. The humidity data and material composition data are used as key static features and processed by a fully connected neural network to extract the influence of the physical properties of each material component under different humidity conditions. Finally, a feature fusion layer is used to fuse the data output by the sub-model to generate a high-dimensional vector that comprehensively represents the characteristics of the paper. In this step, a multimodal deep learning model is used to fuse and analyze the data. The thickness data is a one-dimensional time series feature, and its changing pattern is captured through a convolutional neural network (CNN) or a recurrent neural network (RNN). For example, CNN is used to capture local features and focus on the short-term change trend of thickness. The humidity and material composition data are static features and are processed through a fully connected neural network (FCNN). This method can effectively extract the impact of material composition on paper properties under different humidity conditions, and finally integrate the outputs of each sub-model through the feature fusion layer to form a comprehensive high-dimensional vector.

[0025] The significance of this deep learning method is that it can analyze paper characteristics from multiple angles and in all directions, taking into account multiple factors, avoiding the limitations of traditional single feature analysis methods. Through this efficient feature extraction and fusion, it can more accurately reflect the actual cutting requirements of paper, laying a solid foundation for the subsequent generation of the best cutting parameter set.

[0026] In the process of implementing a multimodal deep learning model, the acquired paper property data must first be preprocessed. This includes standardizing and normalizing the thickness, humidity, and material composition data to eliminate the impact of different dimensions and ranges on model training. The preprocessed thickness data will be treated as a one-dimensional time series feature, segmented and labeled as samples to be input into the convolutional neural network (CNN) and recurrent neural network (RNN). CNN will extract local features of thickness data through local perception and weight sharing mechanisms to capture the changing trend of paper thickness at different time points. RNN will pay special attention to the long-range dependency of the time series, especially the sequence signal of thickness change, which will help the model learn the impact of historical thickness data on current cutting performance.

[0027] When processing humidity and material composition data, a fully connected neural network (FCNN) model is designed to extract these static features. At this stage, the humidity data and material composition data will be input into different neuron layers respectively, and FCNN will learn the influence of physical properties under various conditions (such as different humidity and different material combinations) through multiple layers of nonlinear transformation. The key to this process is to select the appropriate activation function and number of layers to ensure that the network can effectively capture complex feature relationships. In this process, the selection of training data sets and the use of data enhancement techniques are also particularly important to improve the generalization ability and robustness of the model.

[0028] Finally, the feature fusion layer will combine the outputs of the above two sub-models to form a comprehensive high-dimensional vector. This vector will fully reflect the comprehensive characteristics of the paper, representing the synergistic effects of thickness, humidity and material composition. By setting a reasonable fusion strategy, such as weighted averaging or splicing, the feature information from different sources can be effectively integrated to generate a more comprehensive feature representation, thereby providing sufficient information support for subsequent processing stages.

[0029] The high-dimensional vector is input into the last fully connected layer of the multimodal deep learning model, and the corresponding multidimensional parameter vector is generated as the optimal cutting parameter set through weight mapping and nonlinear activation function. The optimal cutting parameter set includes at least cutting speed, tool pressure, and tool angle to achieve the stability of cutting quality, minimize tool wear, and maximize cutting efficiency.

[0030] In this step, the high-dimensional vector generated previously is input into the last fully connected layer of the multimodal deep learning model for processing. The function of the fully connected layer is to map the high-dimensional features to the cutting parameter space. While using weight mapping, nonlinear activation functions such as ReLU or Sigmoid are combined to make the output result have nonlinear characteristics to ensure the expressiveness and adaptability of the model. The multidimensional parameter vector finally generated is the optimal cutting parameter set, which includes key parameters such as cutting speed, tool pressure and tool angle. The significance of this process is to convert complex feature information into specific cutting parameters through a trained model, so as to achieve efficient control of the cutting process. By adaptively adjusting the cutting speed, tool pressure and angle, the cutting quality can be effectively improved and the service life of the tool can be extended, thereby maximizing the cutting efficiency and bringing higher economic benefits and lower failure rates to the production line.

[0031] In this step, we first need to design the last fully connected layer of the multimodal deep learning model. The role of this layer is to project the high-dimensional vector obtained from the feature fusion layer to the cutting parameter space. Specifically, a fully connected layer is constructed to associate each dimension of the feature vector with the output parameter (i.e., cutting speed, tool pressure, tool angle). In the design process, a reasonable number and size of layers are crucial. It is usually recommended to use multiple layers of fully connected layers to increase the nonlinear expression ability of the model and use appropriate regularization techniques (such as Dropout) to prevent overfitting.

[0032] Next, a weight mapping mechanism is used to map the high-dimensional feature vector to a multi-dimensional parameter vector. This process involves training the weights of each connection to ensure that the impact of different features on the final cutting parameters can reflect the actual situation. The weights are continuously optimized through the back-propagation algorithm to reduce the loss function of the model until a satisfactory level of accuracy is achieved. In addition, the choice of activation function for the output layer will also affect the results. For example, the ReLU or Softmax function can be selected to ensure the non-negativity and reasonable range of the output parameters.

[0033] Finally, once the high-dimensional vector is input into the fully connected layer through forward propagation, a corresponding multi-dimensional parameter vector will be generated. This vector will contain the optimal set of cutting parameters generated for different paper characteristics, which will be applied to the cutting process in real time. By comparing historical cutting data and quality standards, these parameters can be dynamically adjusted according to cutting requirements under different conditions to ensure consistent cutting quality, minimize tool wear, and maximize cutting efficiency. In this way, the entire cutting process will become more intelligent and adaptive, providing an important guarantee for further improving production efficiency.

[0034] S202, during the cutting process of the high-speed paper cutter, actual cutting data in the cutting process is monitored in real time by an embedded sensor; During the cutting process of a high-speed paper cutter, the process of real-time monitoring of actual cutting data during the cutting process through embedded sensors is designed to ensure that data collection and analysis during the cutting process can be closely integrated. This process involves multiple types of sensors that can monitor the pressure applied by the cutter to the paper, the acceleration changes of the cutter, the noise generated during the cutting process, and the spectral changes of the paper during cutting. This real-time monitoring can provide important feedback information for the cutting process and help understand the impact of different cutting conditions on paper quality.

[0035] The main function of this step is to provide real-time and dynamic status feedback for the cutting of the high-speed paper cutter to ensure the stability and accuracy of the cutting process. Through high-frequency data acquisition, abnormal conditions that may occur during the cutting process can be discovered and identified in a timely manner, such as insufficient cutting force, tool wear, or excessive cutting speed. This feedback mechanism will provide data support for subsequent adaptive control strategies, so that the cutting performance can be adjusted according to actual conditions, thereby improving the overall cutting quality and efficiency.

[0036] Specifically, during the cutting process, a distributed embedded sensor system can be used to deploy multiple types of sensors, and the data of the sensors are collected in real time in a high-frequency sampling manner; In this step, a distributed system consisting of multiple embedded sensors was built to achieve comprehensive monitoring of the cutting process. The system uses high-frequency sampling technology to ensure that all sensors can capture the key physical parameters of the cutting process in real time. Through different types of sensors, such as pressure sensors, accelerometers, acoustic sensors, and optical sensors, multiple cutting parameters can be monitored simultaneously. Such a system design enables the capture of rich data during the cutting process, which helps to conduct comprehensive state analysis. The significance of this step is to monitor the cutting conditions in real time through high-frequency, multi-dimensional data acquisition, so that the operator can obtain dynamic feedback of the cutting process in a timely manner. This comprehensive monitoring capability can detect problems in advance, evaluate the cutting status, and provide a solid data foundation for subsequent adaptive control, thereby improving cutting efficiency and quality.

[0037] When implementing a distributed embedded sensor system, you first need to select suitable sensors and arrange multiple sensors according to the actual design of the cutting machine. Each sensor needs to be calibrated to ensure data accuracy. Each sensor is connected to the central processing unit wirelessly or wired, and data is transmitted in real time. The system also needs to have strong data processing capabilities to support the collection and storage of high-frequency data to ensure real-time analysis and feedback.

[0038] Among them, the pressure sensor is installed on the tool carrier to collect the pressure changes of the tool on the paper in real time; At this stage, the pressure sensor is installed on the carrier of the cutter to monitor the pressure changes applied by the cutter to the paper during the cutting process. Through high-frequency data acquisition, the pressure sensor can record the pressure size and change trend in real time, providing key pressure parameters during the cutting process. These data help analyze whether the cutting force is maintained at the optimal state so that timely adjustments can be made.

[0039] The significance of this step is to ensure that the pressure applied by the cutter during the cutting process remains within a reasonable range, thereby improving the cutting quality and material integrity. Monitoring pressure changes can help to detect cutter wear or other potential problems in a timely manner, thereby preventing cutting failure or paper damage caused by improper pressure and improving overall cutting efficiency and quality.

[0040] In order to achieve real-time pressure monitoring, it is necessary to select a suitable pressure sensor and position it on the tool carrier to ensure that it can accurately reflect the pressure of the tool on the paper. The sensor needs to undergo rigorous testing and calibration to ensure accuracy under different cutting conditions. The data is transmitted to the central control unit in real time through the acquisition system, and the system will process and analyze the data and issue adjustment instructions to the control system when necessary.

[0041] The acceleration sensor is installed on the tool motion path to capture the tool motion acceleration to analyze the physical vibration behavior during the cutting process; In this step, an acceleration sensor is installed on the tool's motion path to monitor the tool's acceleration during the cutting process in real time. This monitoring can capture the acceleration and deceleration of the tool during movement, thereby analyzing the physical vibration behavior generated during cutting. By analyzing the acceleration data, it can help determine whether the tool maintains a stable cutting state and whether there is a risk of reduced cutting quality due to vibration.

[0042] The significance of this process is that by analyzing the acceleration of the tool movement, abnormal vibrations that may occur during the cutting process can be identified, which may affect the cutting quality or even cause tool damage. Real-time monitoring of acceleration can effectively warn, so as to adjust the cutting parameters in a targeted manner to maintain the stability and consistency of cutting, and improve production efficiency and cutting effect.

[0043] In the specific implementation, a high-sensitivity acceleration sensor should be selected and installed at an appropriate position on the tool motion path. The sensor should have the ability to sample at high frequency in order to capture rapidly changing acceleration data. The data is transmitted to the control system in real time through the data acquisition module. The system will analyze the motion acceleration in real time and make a comprehensive judgment based on other monitoring data (such as pressure, noise, etc.), so as to timely discover and adjust the cutting state to ensure the stability of the cutting process.

[0044] Acoustic sensors are installed around the cutting area to record the high-frequency noise generated during the cutting process to determine friction or cutting abnormalities; In this step, acoustic sensors are installed around the cutting area to monitor and record audio signals occurring during the cutting process in real time, especially high-frequency noise. These noise signals are usually associated with the friction between the cutter and the paper, and the cutting state of the cut material. By analyzing the data collected by the acoustic sensor, it can be determined whether there are problems such as excessive friction, poor contact of the cutter, or abnormal cutting during the cutting process. The significance of acoustic monitoring is to provide a non-contact detection method that can analyze the stability of the cutting process through sound signals. Changes in noise generated during the cutting process can indicate potential quality problems. Through timely feedback, the operator can quickly adjust the cutting parameters to prevent material loss, tool damage, and production delays caused by abnormal cutting.

[0045] In the specific implementation, it is necessary to select suitable acoustic sensors and arrange them in multiple locations around the cutting area to ensure that sufficient acoustic signals are obtained. The system should have the ability of high-frequency sampling and real-time data processing, and analyze the collected acoustic data through algorithms to identify potential abnormal noise patterns. In addition, the acoustic data is fused and analyzed with other sensor data to provide a more comprehensive cutting status assessment to ensure the smooth progress of the cutting process.

[0046] The optical sensor monitors the microscopic spectral changes in the cutting area in real time to capture the tiny physical or chemical changes caused by friction in the paper during the cutting process.

[0047] In this step, optical sensors are used to monitor spectral changes in the cutting area in real time. By analyzing the interaction of different wavelengths of light with the paper material, tiny physical or chemical changes caused by friction and cutting can be captured. This monitoring provides deep insights into the state of the paper and can help identify quality issues that may arise during the cutting process, such as surface damage or changes in material properties.

[0048] The significance of optical monitoring is to provide a detailed assessment of the quality of the cut product. By capturing spectral changes, it is possible to identify potential physical and chemical changes during the cutting process and take timely measures to reduce losses. At the same time, this technology can provide rich data for later analysis, support continuous improvement of the cutting process, and improve the consistency and stability of cutting quality.

[0049] When implementing optical monitoring, it is necessary to select suitable optical sensors that can cover the important wavelength range and ensure that the sensor can respond quickly to spectral changes. Sensors are arranged in the cutting area so that they can monitor the reflection, transmission and scattering of light at any time. The system needs to have real-time data collection and analysis capabilities, and judge the state changes of paper through spectral analysis algorithms. Combined with the multi-dimensional data of the sensor, it provides comprehensive monitoring of cutting quality and provides a basis for subsequent adaptive control.

[0050] S203, dynamically adjusting the cutting parameters of the high-speed paper cutter using adaptive control technology according to the actual cutting data to ensure the consistency and accuracy of the cutting quality.

[0051] According to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted using adaptive control technology to ensure the consistency and accuracy of the cutting quality. This involves acquiring multi-dimensional cutting data collected from sensors in real time, such as pressure changes, acceleration, sound and spectral changes, to form a state vector. This state vector is used to comprehensively evaluate the state of the current cutting process. Subsequently, the cutting parameters are optimized and adjusted by using fuzzy logic control rules and machine learning models to achieve effective monitoring and regulation of the cutting quality. This dynamic adjustment mechanism ensures that during the cutting process, the operation of the tool is always in the best state to adapt to the characteristics and cutting conditions of different papers.

[0052] The significance of this process is that through adaptive control technology, the high-speed paper cutter can respond to changes in real time during the cutting process, improving cutting quality and efficiency. When the actual cutting data reflects a trend of declining cutting quality, the system can automatically adjust the tool pressure or cutting speed to ensure the stability and consistency of the cutting process. Such dynamic adjustments can reduce resource waste and maintenance costs, while improving production efficiency, allowing the high-speed paper cutter to continue to maintain efficient cutting performance in a changing working environment.

[0053] Specifically, the multi-dimensional actual cutting data collected from the sensor can be integrated into a state vector, and the state vector includes various indicators of the current cutting state; In this step, the multi-dimensional cutting data (such as pressure, acceleration, acoustic and optical data) collected from the embedded sensor system described above are integrated into a comprehensive state vector. This state vector not only contains the real-time data obtained by each sensor, but also integrates other key indicators of the cutting process, such as the current degree of tool wear, cutting speed and temperature. Through this integration, the current cutting state can be fully reflected, providing a basis for subsequent control decisions. The significance of integrating multi-dimensional data into a state vector is to ensure that various parameters in the cutting process can be observed from a unified perspective. This comprehensive state representation not only enhances the ability to understand the cutting process, but also provides accurate information support for subsequent adaptive control, making the adjustment of cutting parameters more scientific and accurate, thereby improving the stability and reliability of cutting quality.

[0054] In this step, a data acquisition system needs to be designed first to ensure that the data obtained from multiple embedded sensors can be effectively integrated. Each sensor will collect different types of cutting parameters, such as tool pressure provided by pressure sensors, motion acceleration recorded by accelerometers, noise data from acoustic sensors, and spectral changes from optical sensors. These data will be transmitted to a central processing unit in real time at a high frequency, and the central processing unit needs to process and filter these data in real time to ensure the accuracy and integrity of the data.

[0055] Next, based on these real-time collected data, a state vector needs to be constructed, which should contain all the key characteristic indicators of the current cutting state. This may include cutting speed, tool pressure, acceleration of tool movement, noise level during cutting, and changes in spectral monitoring. In order to ensure the validity of the state vector, standardization or normalization can be performed so that the values ​​of different data sources are at the same level, which is convenient for subsequent analysis. The construction of this comprehensive state vector lays the foundation for the subsequent analysis of optimizing cutting parameters.

[0056] Finally, this state vector will be updated in real time to reflect the current cutting status. Through the integrated algorithm, it can quickly adapt to various dynamic changes in the cutting process, ensuring that when the state vector is updated, it can promptly reflect any abnormalities in the cutting quality.

[0057] By formulating control rules based on fuzzy logic and defining the adjustment strategy of cutting parameters, the fuzzy logic controller automatically evaluates the cutting parameters that need to be adjusted based on the current state vector to optimize the cutting quality; In this step, the fuzzy logic controller automatically evaluates the need to adjust the cutting parameters according to the preset control rules based on the state vector obtained from the sensor integration. These control rules are based on empirical knowledge and expert judgment in the cutting process, covering the cutting parameter adjustment strategies under different cutting states, and can handle uncertainty and ambiguity. Through fuzzy reasoning, the controller can determine the adjustment range and direction of parameters such as tool pressure, speed, and angle. The significance of formulating control rules based on fuzzy logic is that the system can still make reasonable cutting parameter adjustment decisions in the face of uncertain environments. Fuzzy logic can effectively reduce the complexity of the cutting process, dynamically respond to problems caused by different conditions, thereby optimizing cutting quality, reducing errors and failures, and improving the efficiency and reliability of the cutting process.

[0058] In this step, a set of control rules based on fuzzy logic should be developed first. These control rules should take into account the relationship between various cutting parameters and cutting quality. For example, by analyzing historical data, a rule set can be created to define the thresholds at which the cutting quality will be affected when the tool pressure is too high, the cutting speed is too fast, or the noise generated exceeds the normal level. These rules are usually expressed in the form of "if...then...", which specifically reflects the logical relationship of the cutting parameter adjustment.

[0059] Next, the fuzzy logic controller will use the data in the current state vector to evaluate the deviation between the current cutting condition and the preset standard. The fuzzy logic controller can convert the input real value into a fuzzy set and make inferences based on the set rules to determine which cutting parameters need to be adjusted. In this process, different weight values ​​can be set to reflect the importance of different parameters to the cutting quality, thereby optimizing the controller's decision.

[0060] Finally, based on the output of the fuzzy logic controller, a set of cutting parameter adjustment recommendations will be generated. These recommendations will be expressed in the form of specific numerical values, for example, it is recommended to reduce the tool pressure by 2% or increase the cutting speed by 5%. This process ensures that in a dynamic cutting environment, appropriate adjustments can be made based on real-time data feedback to optimize cutting quality.

[0061] Combine actual cutting data with historical cutting quality data and use machine learning algorithms to predict the cutting quality performance under the current cutting parameter settings; In this step, the system combines the real-time actual cutting data obtained from the sensor with the historical cutting quality data, and performs data analysis and model training through machine learning algorithms. By establishing a predictive model, the system can identify the relationship between cutting parameters and cutting quality, and predict the performance of cutting quality under the current cutting parameter settings. Such predictions provide an important basis for subsequent cutting parameter adjustments. The significance of combining real-time data with historical data is that the system can not only make decisions based on the current status, but also learn from past experience to improve the accuracy of predictions. Through the application of machine learning, potential problems in the cutting process can be identified in advance, so that necessary adjustments can be made before cutting to ensure that high cutting quality and production efficiency are always maintained.

[0062] In this step, first of all, it is necessary to extract valuable information from the historical cutting quality data. These historical data can include the correlation analysis between different paper types, cutting parameter settings and cutting results. Therefore, it is necessary to preprocess the historical data, including data cleaning, missing value processing and feature selection, so that the data can be effectively used for training machine learning algorithms.

[0063] Next, compare the current actual cutting data with the historical cutting data, and build a prediction model in combination with the machine learning algorithm used. You can choose algorithms such as regression analysis, random forest, or support vector machine, use the current cutting parameters as input features, and output the cutting quality prediction value under the current cutting settings through the trained model. At this point, the model will be able to learn the patterns in the historical data, so as to more accurately evaluate the cutting quality performance under the current parameter settings.

[0064] Finally, based on the output of the machine learning model, the system will be able to identify potential cutting quality problems in a timely manner. If the predicted cutting quality performance does not meet expectations, the system will immediately issue an alarm and provide a basis for the subsequent automatic adjustment of cutting parameters. This process not only improves the timeliness and accuracy of cutting quality monitoring, but also provides data support for the implementation of adaptive control technology.

[0065] Based on the output of the fuzzy logic controller and machine learning model, the cutting parameters of the high-speed paper cutter are adjusted in real time. When it is detected that the cutting quality does not meet the set standards, the tool pressure or cutting speed is automatically adjusted to maintain the consistency and accuracy of the cutting.

[0066] In this step, the system integrates the output of the fuzzy logic controller and the machine learning model to adjust the cutting parameters in real time. When it is detected that the cutting quality monitored by the sensor does not meet the set standard, the system will automatically adjust the pressure or cutting speed of the tool based on the adjustment suggestions provided by the fuzzy logic controller and the prediction results of the machine learning model to ensure that the cutting process is always in an ideal state. The significance of this process is to ensure that the high-speed paper cutter can flexibly adapt to different cutting conditions and paper characteristics through real-time feedback and adjustment mechanisms, and always maintain the consistency and accuracy of cutting quality. The automated adjustment mechanism reduces the need for human intervention, improves production efficiency, and reduces material waste caused by fluctuations in cutting quality. In addition, through continuous adjustment and optimization, the service life of the tool can be extended, maintenance costs can be reduced, and ultimately the overall production efficiency can be improved.

[0067] In this step, the system will make real-time cutting parameter adjustments based on the outputs of the fuzzy logic controller and the machine learning model. First, it is necessary to integrate the recommendations of the fuzzy logic controller and the predictions of the machine learning model to develop a comprehensive parameter adjustment plan. For example, if the fuzzy logic controller recommends reducing the tool pressure, and the machine learning model shows that the cutting quality is declining, the system can process this information comprehensively.

[0068] These parameter adjustments are then quickly executed through the control system. Specifically, the system will automatically adjust the tool pressure, cutting speed and other related parameters to ensure the stability and consistency of the cutting process. This adjustment process must have a high response speed to cope with the rapid changes in cutting conditions. A certain adjustment range can be set to avoid cutting abnormalities caused by over-adjustment.

[0069] Finally, in order to confirm the effect of the adjustment, the system needs to continuously monitor the cutting quality and re-evaluate the actual results under the new cutting parameters. This feedback mechanism ensures the effectiveness of the adjustment measures and the continuous improvement of the cutting quality. If the cutting quality still does not meet the standard after adjustment, the system will restart the evaluation and adjustment process to achieve highly automated and intelligent cutting management.

[0070] It can be seen that the paper characteristic data of the paper to be cut is acquired in real time, and the paper characteristic data is analyzed by a deep learning model to generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by an embedded sensor; according to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted by using adaptive control technology to ensure the consistency and accuracy of the cutting quality, thereby realizing the intelligence and automation of the cutting process and ensuring the consistency and accuracy of the cutting quality.

[0071] Another embodiment of the present invention provides a high-speed paper cutter control system. Figure 3 , the system may include: The analysis module 301 is used to obtain the paper characteristic data of the paper to be cut in real time, analyze the paper characteristic data through the deep learning model, and generate the corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; The monitoring module 302 is used to monitor the actual cutting data in the cutting process in real time through the embedded sensor during the cutting process of the high-speed paper cutter; The control module 303 is used to dynamically adjust the cutting parameters of the high-speed paper cutter according to the actual cutting data by using the adaptive control technology to ensure the consistency and accuracy of the cutting quality.

[0072] It can be seen that the paper characteristic data of the paper to be cut is acquired in real time, and the paper characteristic data is analyzed by a deep learning model to generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by an embedded sensor; according to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted by using adaptive control technology to ensure the consistency and accuracy of the cutting quality, thereby realizing the intelligence and automation of the cutting process and ensuring the consistency and accuracy of the cutting quality.

[0073] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0074] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, acquiring paper property data of paper to be cut in real time, analyzing the paper property data through a deep learning model, and generating a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity, and material composition data; S202, during the cutting process of the high-speed paper cutter, actual cutting data in the cutting process is monitored in real time by an embedded sensor; S203, dynamically adjusting the cutting parameters of the high-speed paper cutter using adaptive control technology according to the actual cutting data to ensure the consistency and accuracy of the cutting quality.

[0075] It can be seen that the paper characteristic data of the paper to be cut is acquired in real time, and the paper characteristic data is analyzed by a deep learning model to generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by an embedded sensor; according to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted by using adaptive control technology to ensure the consistency and accuracy of the cutting quality, thereby realizing the intelligence and automation of the cutting process and ensuring the consistency and accuracy of the cutting quality.

[0076] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0077] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0078] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, acquiring paper property data of paper to be cut in real time, analyzing the paper property data through a deep learning model, and generating a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity, and material composition data; S202, during the cutting process of the high-speed paper cutter, actual cutting data in the cutting process is monitored in real time by an embedded sensor; S203, dynamically adjusting the cutting parameters of the high-speed paper cutter using adaptive control technology according to the actual cutting data to ensure the consistency and accuracy of the cutting quality.

[0079] It can be seen that the paper characteristic data of the paper to be cut is acquired in real time, and the paper characteristic data is analyzed by a deep learning model to generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; during the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by an embedded sensor; according to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted by using adaptive control technology to ensure the consistency and accuracy of the cutting quality, thereby realizing the intelligence and automation of the cutting process and ensuring the consistency and accuracy of the cutting quality.

[0080] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A high-speed paper cutter control method, characterized in that: The method comprises: Acquire paper property data of the paper to be cut in real time, analyze the paper property data through a deep learning model, and generate a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity, and material composition data; During the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time through embedded sensors; According to the actual cutting data, the cutting parameters of the high-speed paper cutter are dynamically adjusted using adaptive control technology to ensure the consistency and accuracy of the cutting quality.

2. The method according to claim 1, characterized in that The method of acquiring the paper property data of the paper to be cut in real time, analyzing the paper property data through a deep learning model, and generating a corresponding optimal cutting parameter set, wherein the paper property data includes thickness, humidity and material composition data, includes: A multimodal deep learning model is used to fuse and analyze the thickness, humidity and material composition data. The thickness data in the paper characteristic data is used as a one-dimensional time series feature and input into a sub-model based on a convolutional neural network or a recurrent neural network to capture the variation pattern of paper thickness. The humidity data and material composition data are used as key static features and processed by a fully connected neural network to extract the influence of each material component on the physical properties under different humidity conditions. Finally, a feature fusion layer is used to fuse the data output by the sub-model to generate a high-dimensional vector that comprehensively represents the paper characteristics. The high-dimensional vector is input into the last fully connected layer of the multimodal deep learning model, and the corresponding multidimensional parameter vector is generated as the optimal cutting parameter set through weight mapping and nonlinear activation function. The optimal cutting parameter set includes at least cutting speed, tool pressure, and tool angle to achieve the stability of cutting quality, minimize tool wear, and maximize cutting efficiency.

3. The method according to claim 2, characterized in that During the cutting process of the high-speed paper cutter, the actual cutting data in the cutting process is monitored in real time by the embedded sensor, including: During the cutting process, a distributed embedded sensor system is used to deploy multiple types of sensors. The data of the sensors are collected in real time by high-frequency sampling, including: The pressure sensor is installed on the tool carrier to collect the pressure changes of the tool on the paper in real time; the acceleration sensor is installed on the tool movement path to capture the tool's movement acceleration to analyze the physical vibration behavior during the cutting process; Acoustic sensors are installed around the cutting area to record the high-frequency noise generated during the cutting process to determine friction or cutting abnormalities; The optical sensor monitors the microscopic spectral changes in the cutting area in real time to capture the tiny physical or chemical changes caused by friction in the paper during the cutting process.

4. The method according to claim 3, characterized in that The method of dynamically adjusting the cutting parameters of the high-speed paper cutter using adaptive control technology according to the actual cutting data to ensure the consistency and accuracy of the cutting quality includes: Integrate the multi-dimensional actual cutting data collected from the sensor into a state vector, wherein the state vector includes various indicators of the current cutting state; By formulating control rules based on fuzzy logic and defining the adjustment strategy of cutting parameters, the fuzzy logic controller automatically evaluates the cutting parameters that need to be adjusted based on the current state vector to optimize the cutting quality; Combine actual cutting data with historical cutting quality data and use machine learning algorithms to predict the cutting quality performance under the current cutting parameter settings; Based on the output of the fuzzy logic controller and machine learning model, the cutting parameters of the high-speed paper cutter are adjusted in real time. When it is detected that the cutting quality does not meet the set standards, the tool pressure or cutting speed is automatically adjusted to maintain the consistency and accuracy of the cutting.

5. A high-speed paper cutter control system, characterized in that: The system comprises: An analysis module, used to obtain the paper characteristic data of the paper to be cut in real time, analyze the paper characteristic data through a deep learning model, and generate a corresponding optimal cutting parameter set, wherein the paper characteristic data includes thickness, humidity and material composition data; The monitoring module is used to monitor the actual cutting data in real time during the cutting process of the high-speed paper cutter through embedded sensors; The control module is used to dynamically adjust the cutting parameters of the high-speed paper cutter according to the actual cutting data by using adaptive control technology to ensure the consistency and accuracy of the cutting quality.

6. The system according to claim 5, characterized in that The analysis module is specifically used for: A multimodal deep learning model is used to fuse and analyze the thickness, humidity and material composition data. The thickness data in the paper characteristic data is used as a one-dimensional time series feature and input into a sub-model based on a convolutional neural network or a recurrent neural network to capture the variation pattern of paper thickness. The humidity data and material composition data are used as key static features and processed by a fully connected neural network to extract the influence of each material component on the physical properties under different humidity conditions. Finally, a feature fusion layer is used to fuse the data output by the sub-model to generate a high-dimensional vector that comprehensively represents the paper characteristics. The high-dimensional vector is input into the last fully connected layer of the multimodal deep learning model, and the corresponding multidimensional parameter vector is generated as the optimal cutting parameter set through weight mapping and nonlinear activation function. The optimal cutting parameter set includes at least cutting speed, tool pressure, and tool angle to achieve the stability of cutting quality, minimize tool wear, and maximize cutting efficiency.

7. The system according to claim 6, characterized in that The monitoring module is specifically used for: During the cutting process, a distributed embedded sensor system is used to deploy multiple types of sensors. The data of the sensors are collected in real time by high-frequency sampling, including: The pressure sensor is installed on the tool carrier to collect the pressure changes of the tool on the paper in real time; the acceleration sensor is installed on the tool movement path to capture the tool's movement acceleration to analyze the physical vibration behavior during the cutting process; Acoustic sensors are installed around the cutting area to record the high-frequency noise generated during the cutting process to determine friction or cutting abnormalities; The optical sensor monitors the microscopic spectral changes in the cutting area in real time to capture the tiny physical or chemical changes caused by friction in the paper during the cutting process.

8. The system according to claim 7, characterized in that The control module is specifically used for: Integrate the multi-dimensional actual cutting data collected from the sensor into a state vector, wherein the state vector includes various indicators of the current cutting state; By formulating control rules based on fuzzy logic and defining the adjustment strategy of cutting parameters, the fuzzy logic controller automatically evaluates the cutting parameters that need to be adjusted based on the current state vector to optimize the cutting quality; Combine actual cutting data with historical cutting quality data and use machine learning algorithms to predict the cutting quality performance under the current cutting parameter settings; Based on the output of the fuzzy logic controller and machine learning model, the cutting parameters of the high-speed paper cutter are adjusted in real time. When it is detected that the cutting quality does not meet the set standards, the tool pressure or cutting speed is automatically adjusted to maintain the consistency and accuracy of the cutting.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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