Visual monitoring method and system of high-speed paper cutter control system

Abstract

The invention discloses a visual monitoring method and system for a high-speed paper cutter control system, and the method comprises the steps: collecting the data of a plurality of sensors disposed in a high-speed paper cutter in real time according to the operation state of the high-speed paper cutter; processing the sensor data by using an adaptive fuzzy logic algorithm to obtain the real-time change trend of each index, and generating a dynamic data model for intelligently evaluating the running state of the high-speed paper cutter; according to the dynamic data model, a real-time monitoring interface is generated through the augmented reality technology, the operation state, key parameters and alarm information of the high-speed paper cutter are presented in a three-dimensional visualization graph, an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface, and the high-speed paper cutter is monitored according to the operation state and fault information. And configuration parameters of the high-speed paper cutter are automatically adjusted. By utilizing the embodiment of the invention, the monitoring efficiency of the high-speed paper cutter can be improved, and rapid decision can be supported, so that the management level, the production efficiency and the equipment safety are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control, and particularly relates to a visualization monitoring method and system for a high-speed paper cutter control system. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, the high-speed paper cutter, as a key device in the production process of paper products, directly affects production efficiency and product quality. However, traditional high-speed paper cutter control systems often rely on static monitoring and simple alarm mechanisms, resulting in information lag, limited data processing capabilities, and poor visualization effects, making it difficult to quickly respond to faults and abnormal phenomena. At the same time, these systems lack an intelligent feedback mechanism and cannot actively adjust the device operating parameters when a fault occurs, thus increasing the risk of downtime and damage. Summary of the Invention

[0003] The purpose of the present invention is to provide a visualization monitoring method and system for a high-speed paper cutter control system to solve the deficiencies in the prior art, improve the monitoring efficiency of the high-speed paper cutter, support rapid decision-making, and further improve the management level, production efficiency, and equipment safety of the high-speed paper cutter.

[0004] An embodiment of the present application provides a visualization monitoring method for a high-speed paper cutter control system, the method comprising: According to the operating state of the high-speed paper cutter, multiple pieces of sensor data provided in the high-speed paper cutter are collected in real time, wherein the sensor data includes paper tension, speed, temperature, and humidity parameters; Based on the sensor data, an adaptive fuzzy logic algorithm is used to process the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter; According to the dynamic data model, an augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters, and alarm information of the high-speed paper cutter are presented in the form of three-dimensional visualization graphics, so that the operator can observe the operating state and fault information of the high-speed paper cutter, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

[0005] Optionally, the step of using an adaptive fuzzy logic algorithm to process the sensor data based on the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter includes: Normalize the sensor data, and with the help of expert knowledge, construct a set of fuzzy rules according to the operating environment and working conditions of the high-speed paper cutter; Using the constructed fuzzy rules, real-time inference is performed through a fuzzy inference engine. Among them, the standardized sensor data is input into the fuzzy inference system, converted into fuzzy sets, and the inference mechanism is applied to comprehensively evaluate each fuzzy set to generate a fuzzy output set, representing the operating state of the current high-speed paper cutter. The fuzzy output set is combined with historical data, and a dynamic data model is generated using a weighted neural network. Based on the dynamic data model, the change trends of various indicators of the high-speed paper cutter are calculated in real time, and a trend chart is generated. The trend chart not only provides real-time operating state information but also can demonstrate potential performance degradation or failure risks to the operator.

[0006] Optionally, according to the dynamic data model, an augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters, and alarm information of the high-speed paper cutter are presented in three-dimensional visualization graphics, including: Based on the physical model of the high-speed paper cutter, a corresponding three-dimensional visualization model is constructed using computer-aided design tools. The three-dimensional visualization model can truly reflect the structure and various functional components of the machine. The structured operating state, key parameters, and alarm information are mapped to the corresponding parts of the three-dimensional visualization model. Using WebGL technology, the operating state, key parameters, and alarm information are updated in real time and dynamically displayed in the augmented reality environment, so that the operator can directly observe the real-time changes of various indicators on the model, and an augmented reality interaction interface is designed as the monitoring interface, so that the operator can interact with the three-dimensional visualization model through gestures, touches, or voice commands.

[0007] Optionally, an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information, including: When the operating state is abnormal or a fault information is received, an adaptive feedback control mechanism is started to realize the real-time adjustment of the configuration parameters of the high-speed paper cutter by combining a fuzzy controller with a model predictive control MPC algorithm.

[0008] Another embodiment of the present application provides a visualization monitoring system for a high-speed paper cutter control system. The system includes: An acquisition module for real-time collecting a plurality of sensor data set in the high-speed paper cutter according to the operating state of the high-speed paper cutter. Among them, the sensor data includes paper tension, speed, temperature, and humidity parameters. A processing module for processing the sensor data using an adaptive fuzzy logic algorithm based on the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter. A monitoring module, which is used to generate a real-time monitoring interface by using augmented reality technology according to the dynamic data model, present the operating status, key parameters and alarm information of the high-speed paper cutter in the form of three-dimensional visualization graphics, so that operators can observe the operating status and fault information of the high-speed paper cutter, and embed an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating status and fault information.

[0009] Another embodiment of the present application provides a storage medium in which a computer program is stored. Wherein, the computer program is configured to execute the method described in any one of the above when running.

[0010] Another embodiment of the present application provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of the above.

[0011] Compared with the prior art, a visualization monitoring method for a high-speed paper cutter control system provided by the present invention collects sensor data of multiple sensors provided in the high-speed paper cutter in real time according to the operating status of the high-speed paper cutter; uses an adaptive fuzzy logic algorithm to process the sensor data, obtains the real-time change trends of various indicators, and generates a dynamic data model for intelligent evaluation of the operating status of the high-speed paper cutter; according to the dynamic data model, uses augmented reality technology to generate a real-time monitoring interface, presents the operating status, key parameters and alarm information of the high-speed paper cutter in the form of three-dimensional visualization graphics, and embeds an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating status and fault information, so as to improve the monitoring efficiency of the high-speed paper cutter and support rapid decision-making, and further improve the management level, production efficiency and equipment safety of the high-speed paper cutter. Description of the Drawings

[0012] Figure 1 It is a hardware structure block diagram of a computer terminal for a visualization monitoring method of a high-speed paper cutter control system provided by an embodiment of the present invention; Figure 2 It is a flowchart of a visualization monitoring method of a high-speed paper cutter control system provided by an embodiment of the present invention; Figure 3 It is a structure diagram of a visualization monitoring system of a high-speed paper cutter control system provided by an embodiment of the present invention. Detailed Embodiments

[0013] The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0014] An embodiment of the present invention first provides a visual monitoring method for a high-speed paper cutter control system. This method can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0015] The following takes the operation on a computer terminal as an example for detailed description. Figure 1 It is a hardware structure block diagram of a computer terminal for a visual monitoring method of a high-speed paper cutter control system provided by an embodiment of the present invention. As Figure 1 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory can include a non-volatile storage medium and an internal memory.

[0016] 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 visual monitoring method of a high-speed paper cutter control system.

[0017] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0018] 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 visual monitoring method of a high-speed paper cutter control system.

[0019] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 1 the structure shown in

[0020] is only a block diagram of some structures 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 some components, or have different component arrangements.

[0021] See Figure 2, an embodiment of the present invention provides a visualization monitoring method for a high-speed paper cutter control system, which may include the following steps: S201, according to the operating state of the high-speed paper cutter, collect sensor data of multiple sensors provided in the high-speed paper cutter in real time, wherein the sensor data includes paper tension, speed, temperature and humidity parameters; During the operation of the high-speed paper cutter, ensuring its normal and efficient operation depends on the real-time monitoring of various operating parameters. For this reason, this method collects key data in real time through multiple sensors installed in the high-speed paper cutter, including parameters such as paper tension, speed, temperature and humidity. The paper tension sensor can detect the change in the tension of the paper during processing to ensure the stability of the paper under different working conditions; the speed sensor monitors the running speed of the paper cutter in real time to prevent paper damage or printing quality problems caused by too fast or too slow speed; the temperature and humidity sensors are responsible for monitoring the physical state of the equipment and the surrounding environment, and these factors will directly affect the humidity and flexibility of the paper, thereby affecting the processing effect. By integrating these sensor data, for operators, they can not only obtain the running status of the machine in real time, but also give early warnings of potential risks and faults, improving the overall intelligent management level.

[0022] By collecting the sensor data set in the high-speed paper cutter in real time, operators can timely understand the running state of the machine, and thus make corresponding adjustments to ensure the efficient and stable operation of the equipment. For example, in the paper tension monitoring, if the sensor finds that the tension is too low, which may cause the paper to tear, the operator can immediately take measures to increase the tension setting to prevent production losses. At the same time, by monitoring parameters such as speed, temperature and humidity, the system can quickly identify the impact of environmental changes on the equipment performance and issue an alarm in time when an abnormality occurs. This real-time monitoring and feedback mechanism can greatly improve production efficiency, reduce the fault downtime, and ultimately improve the product quality and the comprehensive benefits of the production line.

[0023] In terms of specific implementation, first, various types of sensors need to be installed at key parts of the high-speed paper cutter. For example, the paper tension sensor can be installed in the transmission system of paper feeding and output to monitor the change in the tension of the paper during processing in real time. The speed sensor can be arranged in the motor drive part to ensure accurate measurement of the running speed of the paper cutter. The temperature and humidity sensors can be installed in the working environment of the machine, such as in the nearby control cabinet, to master the surrounding environmental changes.

[0024] S202, based on the sensor data, use the adaptive fuzzy logic algorithm to process the sensor data, obtain the real-time change trend of each index, and generate a dynamic data model for the intelligent evaluation of the running state of the high-speed paper cutter; This method aims to achieve intelligent monitoring and evaluation of the operating status of a high-speed paper cutter by collecting data from multiple sensors in real time (such as paper tension, speed, temperature, and humidity). By processing these sensor data through an adaptive fuzzy logic algorithm, the real-time change trends of various indicators can be obtained. This processing method can not only dynamically generate a data model but also effectively extract and estimate the key parameters of various operating states, thereby providing real-time data support for operators regarding the equipment performance, enabling them to take corresponding operation measures in a timely manner to ensure the normal operation of the equipment and production efficiency.

[0025] The implementation of this method can significantly improve the operating efficiency and reliability of the high-speed paper cutter. By real-time monitoring and predicting the operating status, operators can identify potential problems in advance, reduce the risk of failures, and optimize the production process. In addition, the dynamically generated data model and visual monitoring interface enable operators to more intuitively understand the equipment operating conditions, thereby making more accurate decisions and improving the intelligent level of the entire production line.

[0026] Specifically, the sensor data can be standardized, and with the help of expert knowledge, a set of fuzzy rules can be constructed according to the operating environment and working conditions of the high-speed paper cutter. In this step, by standardizing the collected sensor data, the influence between different dimensions is eliminated, and combined with the experience and knowledge of relevant experts, fuzzy rules for different working conditions are formulated. These rules provide the basis for fuzzy reasoning of the data, enabling the system to more flexibly adapt to different working condition changes. The standardization and construction of fuzzy rules make the data processing process professional and adaptable, improving the system's adaptability to complex environments and making the subsequent reasoning results more accurate and reliable.

[0027] In this implementation step, first, the data collected by various sensors used in the high-speed paper cutter (such as paper tension sensors, speed sensors, temperature sensors, and humidity sensors) need to be standardized. The core of standardization is to adjust the values from different sources and with different dimensions to the same scale for subsequent analysis and processing. Commonly used standardization methods include Z-Score standardization or Min-Max normalization. Under this processing, operators can more clearly compare and analyze the output results of each sensor, eliminating data deviations caused by different dimensions. After standardization, a set of consistent input data is formed, laying the foundation for the application of the fuzzy logic algorithm.

[0028] Next, combine expert knowledge and experience to construct fuzzy rules. Experts will formulate a set of fuzzy rules based on the operating characteristics of the high-speed paper cutter, past failure cases, and the equipment's response to environmental parameters. This process usually involves reviewing and analyzing the equipment's historical data to extract the relationships between key parameters and machine performance. The fuzzy rules not only include input variables (such as the high, medium, and low states of parameters like tension and speed), but also define the output responses under different states. For example, when the tension is too high, the machine needs to decelerate or reduce the tension. This rule base provides an important basis for subsequent fuzzy reasoning.

[0029] Finally, form the infrastructure of the fuzzy logic system by combining the standardized data with the fuzzy rules. After formulating the fuzzy rules, the encoding and logical deduction of the rules need to be implemented in the system, which is carried out through corresponding simulation software. The standardized data will be input into the fuzzy logic control system, and the system will apply the rules to fuzzify the data and generate relevant fuzzy sets. Ultimately, these fuzzy sets will be used for further reasoning and evaluation to form a preliminary judgment of the current equipment, assisting in grasping the overall operating state.

[0030] Utilize the constructed fuzzy rules to perform real-time reasoning through a fuzzy inference engine. Among them, the standardized sensor data is input into the fuzzy inference system, converted into fuzzy sets, and the inference mechanism is applied to comprehensively evaluate each fuzzy set to generate a fuzzy output set representing the operating state of the current high-speed paper cutter; In this process, the standardized sensor data in step one is input into the fuzzy inference engine. After being fuzzified and converted into fuzzy sets, the system applies the preset fuzzy rules for reasoning and finally outputs a fuzzy set. This fuzzy output set can be used to describe the operating state of the current high-speed paper cutter. Through fuzzy reasoning, the system can comprehensively consider the influence of multiple input variables and generate a comprehensive evaluation of the equipment's current state. This method improves the accuracy of judgment, can reflect the actual operating conditions of the equipment, and better supports subsequent intelligent decision-making.

[0031] During the process of performing real-time reasoning, the standardized sensor data will be sent into the fuzzy inference engine. This engine is one of the cores of the system, responsible for processing the input data and reasoning according to the pre-constructed fuzzy rules. The fuzzy inference engine converts the standardized data into fuzzy sets through the fuzzification process, representing the state of the input variables at a certain moment. The fuzzy set of each variable is evaluated according to the fuzzy rules defined by experts, and the specific value of the input is compared with the boundary of the fuzzy set to determine the degree of fuzziness of each variable.

[0032] After that, the fuzzy inference engine will perform inference based on these fuzzy sets and fuzzy rules, and use the inference mechanism to comprehensively evaluate the input fuzzy sets. In this process, through the "AND" and "OR" logical operations of the fuzzy rules, the influences of multiple input variables can be integrated to form a common fuzzy output set. This fuzzy output set will intuitively reflect the current operating state of the high-speed paper cutter, including information such as normal operation, maintenance requirements, or fault warnings. This comprehensive evaluation not only considers the real-time data of multiple sensors but also combines the laws in the historical operation of the system, greatly improving the accuracy of judgment.

[0033] Finally, the inference result will be presented in the form of a fuzzy output set. This output set contains the probability distribution under different states, representing the operating state of the equipment at this time. This fuzzy output set will be further defuzzified and converted into specific state information that is easy to interpret for the operator's reference. This process not only provides a basis for subsequent intelligent decision-making but also lays a foundation for the construction of a dynamic data model, ensuring that the real-time monitoring and evaluation of the equipment state always remain accurate and effective.

[0034] Combine the fuzzy output set with historical data, use a weighted neural network to generate a dynamic data model, and based on the dynamic data model, calculate the change trends of various indicators of the high-speed paper cutter in real time and generate a trend chart. The trend chart not only provides real-time operating state information but also can demonstrate potential performance degradation or fault risks to the operator.

[0035] This step combines the fuzzy output set with historical data and uses weighted neural network technology to construct a dynamic data model. This model can not only track the change trends of various operating indicators in real time but also visually show the equipment performance and potential risks to the operator through the generated trend chart, enabling the operator to take preventive measures in advance. The combination of this dynamic data model and the trend chart greatly improves the equipment's ability to handle emergencies during the production process, helps optimize equipment maintenance, reduces downtime, and ensures the continuity and stability of production.

[0036] In this step, the fuzzy output set is combined with historical data to form a comprehensive data set. Historical data usually includes the performance of the high-speed paper cutter under different working conditions, and collecting this data can be obtained through long-term monitoring and recording of the operation process. By combining the fuzzy output set, a weighted neural network (WNN) can be used to establish a dynamic model. WNN is a powerful data modeling tool that can handle nonlinear and high-dimensional problems. Its structure allows historical data to be introduced into the model as a learning basis, and then a prediction model of the current state is generated.

[0037] After introducing the weighted neural network, the system will use the training data to optimize the parameters of the neural network and achieve real-time tracking of the current state. Using the fuzzy output set as the input and combining historical data, the WNN will be trained and the network weights will be gradually adjusted to improve the prediction accuracy. The training process uses a neural network with a multi-layer structure and continuously optimizes the connection weights through the backpropagation algorithm to minimize the error between the model output and the true value. After sufficient training, the model will be able to timely reflect the change trends of various indicators (such as tension, speed, etc.) of the high-speed paper cutter and provide valuable real-time information for the operator.

[0038] Finally, based on the generated dynamic data model, calculate the change trends of various indicators in real time and generate a trend chart. The trend chart will visually display the law of data change, enabling the operator to quickly grasp the state of the equipment. During this process, the system will not only provide real-time operation status information, but also analyze the past operation data to predict potential performance degradation or failure risks. Such graphical information can help the operator make decisions in a short time and take timely measures to ensure production efficiency and equipment reliability.

[0039] S203, according to the dynamic data model, use augmented reality technology to generate a real-time monitoring interface, and present the operation status, key parameters, and alarm information of the high-speed paper cutter in three-dimensional visualization graphics, so that the operator can observe the operation status and fault information of the high-speed paper cutter, and embed an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operation status and fault information.

[0040] This method uses the dynamic data model combined with augmented reality technology to generate a real-time monitoring interface, aiming to present the operation status, key parameters, and alarm information of the high-speed paper cutter in three-dimensional visualization graphics. By constructing a highly interactive monitoring interface, the operator can intuitively observe the operation status and potential faults of the equipment. This augmented reality-based visualization not only improves the understandability of the data, but also enhances the operator's response ability through dynamic display. The system combines the real-time collected data with the three-dimensional model to ensure that the operator can quickly identify important anomalies in complex information and thus take effective measures in a timely manner.

[0041] The important significance of this monitoring method lies in its ability to improve the operator's intuitive understanding and operation efficiency of the high-speed paper cutter. Through three-dimensional visualization, the operator can quickly identify the key parameters of the equipment and any potential faults, thereby reducing the downtime caused by equipment failures. At the same time, embedding an intelligent feedback mechanism based on machine learning in the monitoring interface enables the system to automatically adjust the configuration parameters, achieve a higher level of intelligent management, and thus optimize production efficiency, save resources, and improve safety.

[0042] Specifically, according to the dynamic data model, a real-time monitoring interface is generated using augmented reality technology, and the operating status, key parameters, and alarm information of the high-speed paper cutter are presented in the form of three-dimensional visualization graphics. Based on the physical model of the high-speed paper cutter, a corresponding three-dimensional visualization model can be constructed using computer-aided design tools. The three-dimensional visualization model can truly reflect the structure of the machine and various functional components. In this step, a three-dimensional visualization model of the high-speed paper cutter is constructed through computer-aided design (CAD) tools. This model must accurately display the physical structure of the machine and the relative positions of its various functional components. Designers will refer to the technical drawings and specifications of the machine and gradually construct each component such as the frame, transmission system, sensors, and operation panel, ensuring that each component has a realistic scale and function in the model. This model is not limited to static display but also needs to consider dynamic functions for use in subsequent monitoring and real-time data display. Constructing an accurate three-dimensional visualization model is crucial for understanding the working principle of the machine. Through an intuitive visual display, operators can quickly identify each component of the machine and its functions. This intuitive display method can significantly improve operators' understanding of the equipment status, reduce error rates, and improve work efficiency. At the same time, this model provides the basis for the subsequent augmented reality (AR) monitoring interface, making real-time data update and monitoring more efficient and accurate.

[0043] In the specific implementation process, designers first need to collect relevant technical information of the high-speed paper cutter, including design drawings, component lists, and material specifications, etc. These documents will provide detailed data support for modeling. On this basis, designers use computer-aided design (CAD) software, such as AutoCAD or SolidWorks, to construct the basic framework of the model. At this stage, designers will create the main structure of the machine, ensuring that the proportions and dimensions of all key components meet the real mechanical design standards. The design work is generally carried out in stages, from the whole to the part, using a hierarchical modeling method to gradually refine each component.

[0044] Through continuous iteration and feedback, designers gradually add details to the model, such as the gears of the transmission system, the panel of the control system, and the specific sensor configuration information. After the initial construction of the model, designers will simulate the kinematic characteristics of each component to ensure that the interaction between components can be verified in the virtual environment. This includes setting the movement paths to reflect the dynamic behavior of each component, such as the rotation angle of the motor and the movement trajectory of the paper. By dynamically testing the model, designers can timely discover and correct potential problems in the design to ensure that the final model can truly reflect the performance of the high-speed paper cutter.

[0045] After the model construction is completed, the designer will use appropriate rendering techniques to process the 3D visualization model to enhance the visual effect and readability. This includes simulating the material texture and adjusting the lighting effect to ensure that the model is more vivid and lively when displayed. At the same time, the designer will also output multiple perspectives and cross-sections of the model for subsequent display and analysis. This process will finally generate a complete set of 3D visualization model files, preparing for subsequent data integration and interactive operations.

[0046] Map the structured operating status, key parameters, and alarm information to the corresponding parts of the 3D visualization model. Utilize WebGL technology to update the operating status, key parameters, and alarm information in real-time and dynamically display them in the augmented reality environment, enabling the operator to directly observe the real-time changes of various indicators on the model, and design an augmented reality interaction interface as the monitoring interface, enabling the operator to interact with the 3D visualization model through gestures, touch, or voice commands.

[0047] This step focuses on mapping the real-time operating status, key parameters, and alarm information collected from sensors to the corresponding parts of the 3D visualization model. These information will be dynamically updated through WebGL technology, enabling the model to reflect the specific state of the machine in real-time. For example, when a certain parameter exceeds the set range, the relevant part of the model may change color to alert the operator. In addition, by designing an augmented reality interaction interface, the operator can interact with the 3D model through gestures, touch, or voice commands, facilitating the viewing of detailed data or adjustment of parameters. This ability of dynamic display not only enables the operator to obtain the operating status of the device in a timely manner, but also improves the readability and response speed of the information. The operator can quickly find the key data in the complex information, identify potential faults, and thus take necessary measures in a timely manner to prevent equipment damage or downtime, enhancing the overall production efficiency. At the same time, the interactivity of augmented reality technology makes the monitoring more intuitive and convenient, improving the operator's operation experience and work efficiency.

[0048] In this stage, the development team will first construct a data acquisition system to monitor the operating status of the high-speed paper cutter in real-time through sensors. The system will collect multiple key parameters such as temperature, pressure, speed, and tension, and organize these data in a structured manner, usually stored in JSON or XML format. Subsequently, this information will be sent to the central processing unit for real-time analysis and processing. To ensure the timeliness and accuracy of the data, the team will use efficient communication protocols such as MQTT or WebSocket to achieve low-latency data transmission between the device and the monitoring system.

[0049] Next, using WebGL technology, developers will build a dynamically updated 3D visualization environment. In this environment, the real-time collected data will be mapped to the corresponding parts of the 3D model. For example, when the parameters of a certain sensor are detected to exceed the set range, the component in the model may change color or add a flashing effect to attract the attention of the operator. In addition, to enhance the user experience, the development team will implement a browser-based visualization interface, allowing users to directly interact with the 3D model through the web page without the need to install additional plugins or software. This approach ensures user convenience and operational flexibility.

[0050] Finally, to achieve augmented reality interaction, the development team will design a user-friendly monitoring interface that allows operators to interact with the 3D visualization model through gestures, touch, or voice commands. The interface will include preset commands and a custom instruction function, enabling operators to scale the model, rotate the viewing angle, or select specific information for viewing with simple gestures. Meanwhile, the voice recognition function will allow operators to quickly obtain the device status or adjust operating parameters through voice commands. The integration of these functions will greatly improve the monitoring efficiency of operators, enabling them to more intuitively understand and control the operating conditions of the device.

[0051] Specifically, an intelligent feedback mechanism based on machine learning will be embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating status and fault information. When the operating status is abnormal or a fault message is received, the adaptive feedback control mechanism can be activated to combine the fuzzy controller with the model predictive control (MPC) algorithm to achieve real-time adjustment of the configuration parameters of the high-speed paper cutter.

[0052] When the abnormal operating status of the high-speed paper cutter is detected or a fault message is received, the system will automatically activate the adaptive feedback control mechanism. This mechanism combines a fuzzy controller and a model predictive control (MPC) algorithm to achieve immediate adjustment of the device configuration parameters. Specifically, the fuzzy controller evaluates the current operating status of the device based on the real-time acquired sensor data and preset fuzzy rules, and generates corresponding control instructions. The MPC algorithm predicts based on the dynamic model of the system, analyzes the potential performance and response under different configuration parameters, and thus selects the most appropriate control strategy. This combination ensures that in a complex and changing operating environment, it can flexibly respond to various state changes, timely adjust the device operating parameters, and maintain its stability and efficiency.

[0053] Implementing this adaptive feedback control mechanism greatly enhances the intelligence level and operational safety of high-speed paper cutters. When the device detects abnormal conditions or fault information, it can respond quickly, automatically adjust relevant configuration parameters, thereby reducing the dependence on human intervention. This real-time adjustment can not only prevent potential equipment damage, but also maintain production efficiency and reduce downtime. In addition, through the combination of fuzzy control and MPC, the system can make more accurate predictions and decisions under complex working conditions, thereby optimizing the overall production process, reducing maintenance costs, and improving the user experience.

[0054] During the operation of the high-speed paper cutter, the system first monitors the operating states of the machine in real time through a variety of sensors, including paper tension, speed, temperature, and humidity, etc. These sensors continuously transmit data to the central control unit, and during this period, the system sets multiple safety thresholds. When any parameter exceeds its predetermined range, the system will immediately identify a possible abnormal state and automatically issue an alarm to notify the operator. At the same time, the system also activates the adaptive feedback control mechanism to ensure the operating stability of the machine.

[0055] Once the feedback control mechanism is activated, the fuzzy controller will first analyze the real-time sensor data. First, the system standardizes the collected raw data to ensure data consistency. Then, the fuzzy controller combines the preset fuzzy rules to perform fuzzy reasoning on various parameters. For example, if it detects that the paper tension has increased abnormally, the controller will convert this data into a fuzzy set and perform reasoning with the keyword "high tension". This reasoning process uses fuzzy logic and can comprehensively consider the influence of multiple factors (such as temperature and speed) on the paper tension, thereby generating a fuzzy output set reflecting the current operating state.

[0056] Meanwhile, the model predictive control (MPC) algorithm is also running in the background. It combines historical data with the current operating state, predicts the performance of the machine in the next few seconds through a dynamic model, including possible faults or performance degradation. The MPC algorithm uses the dynamic model of the system to perform multi-step predictions and calculates the optimal control input under the current conditions. Combining the output of the fuzzy controller, the system can generate a comprehensive control instruction to adjust the configuration parameters of the high-speed paper cutter to ensure its restoration to a safe and stable working state. For example, if the paper tension is too high, the system may automatically reduce the motor speed or adjust the tension setting of the paper feed to reduce the pressure; if the temperature rises, the system will adjust the operating speed of the cooling fan to quickly reduce the temperature. The entire adjustment process is real-time and dynamically corrected according to the feedback signal to ensure continuous adaptation to different working environments and conditions.

[0057] Finally, when the adaptive feedback control mechanism finishes parameter adjustment, the system will monitor the adjusted effects through sensors again and generate real-time monitoring data. These data not only help operators understand the current state of the equipment but also provide valuable information for the system's future learning and optimization. Through such a complete feedback control mechanism, the high-speed paper cutter can effectively handle various abnormal situations during operation, ensuring production continuity and equipment safety.

[0058] It can be seen that according to the operating state of the high-speed paper cutter, sensor data of multiple sensors provided in the high-speed paper cutter are collected in real time; the sensor data are processed by using an adaptive fuzzy logic algorithm to obtain the real-time change trends of various indicators and generate a dynamic data model for intelligent evaluation of the operating state of the high-speed paper cutter; according to the dynamic data model, an augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters, and alarm information of the high-speed paper cutter are presented in a three-dimensional visualization graph, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information, so that the monitoring efficiency of the high-speed paper cutter can be improved and rapid decision-making can be supported, thereby improving the management level, production efficiency, and equipment safety of the high-speed paper cutter.

[0059] Another embodiment of the present invention provides a visualization monitoring system for a high-speed paper cutter control system. Refer to Figure 3 , the system may include: An acquisition module 301, configured to collect sensor data of multiple sensors provided in the high-speed paper cutter in real time according to the operating state of the high-speed paper cutter, where the sensor data includes paper tension, speed, temperature, and humidity parameters; A processing module 302, configured to process the sensor data by using an adaptive fuzzy logic algorithm based on the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for intelligent evaluation of the operating state of the high-speed paper cutter; A monitoring module 303, configured to generate a real-time monitoring interface by using augmented reality technology according to the dynamic data model, and present the operating state, key parameters, and alarm information of the high-speed paper cutter in a three-dimensional visualization graph, so that an operator can observe the operating state and fault information of the high-speed paper cutter, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

[0060] It can be seen that according to the operating state of the high-speed paper cutter, sensor data of multiple sensors disposed in the high-speed paper cutter are collected in real time; an adaptive fuzzy logic algorithm is used to process the sensor data to obtain the real-time change trends of various indicators, and a dynamic data model is generated for intelligent evaluation of the operating state of the high-speed paper cutter; according to the dynamic data model, an augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters and alarm information of the high-speed paper cutter are presented in a three-dimensional visualization graph, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information, so as to improve the monitoring efficiency of the high-speed paper cutter and support rapid decision-making, and further improve the management level, production efficiency and equipment safety of the high-speed paper cutter.

[0061] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, and the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0062] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for executing the following steps: S201, according to the operating state of the high-speed paper cutter, sensor data of multiple sensors disposed in the high-speed paper cutter are collected in real time, where the sensor data includes paper tension, speed, temperature and humidity parameters; S202, based on the sensor data, an adaptive fuzzy logic algorithm is used to process the sensor data to obtain the real-time change trends of various indicators, and a dynamic data model is generated for intelligent evaluation of the operating state of the high-speed paper cutter; S203, according to the dynamic data model, an augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters and alarm information of the high-speed paper cutter are presented in a three-dimensional visualization graph, so that an operator can observe the operating state and fault information of the high-speed paper cutter, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

[0063] It can be seen that according to the operating state of the high-speed paper cutter, sensor data of multiple sensors provided in the high-speed paper cutter is collected in real time; the adaptive fuzzy logic algorithm is used to process the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter; according to the dynamic data model, the augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters and alarm information of the high-speed paper cutter are presented in the form of three-dimensional visualization graphics, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information, so as to improve the monitoring efficiency of the high-speed paper cutter and support rapid decision-making, and further improve the management level, production efficiency and equipment safety of the high-speed paper cutter.

[0064] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0065] Specifically, the above electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0066] Specifically, in this embodiment, the above processor may be configured to execute the following steps through a computer program: S201, according to the operating state of the high-speed paper cutter, collect sensor data of multiple sensors provided in the high-speed paper cutter in real time, wherein the sensor data includes paper tension, speed, temperature and humidity parameters; S202, based on the sensor data, use the adaptive fuzzy logic algorithm to process the sensor data to obtain the real-time change trends of various indicators and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter; S203, according to the dynamic data model, use the augmented reality technology to generate a real-time monitoring interface, and present the operating state, key parameters and alarm information of the high-speed paper cutter in the form of three-dimensional visualization graphics, so that the operator can observe the operating state and fault information of the high-speed paper cutter, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

[0067] It can be seen that according to the operating state of the high-speed paper cutter, sensor data of multiple sensors provided in the high-speed paper cutter are collected in real time; the adaptive fuzzy logic algorithm is used to process the sensor data to obtain the real-time change trends of various indicators, and a dynamic data model is generated for the intelligent evaluation of the operating state of the high-speed paper cutter; according to the dynamic data model, the augmented reality technology is used to generate a real-time monitoring interface, and the operating state, key parameters and alarm information of the high-speed paper cutter are presented in three-dimensional visualization graphics, and an intelligent feedback mechanism based on machine learning is embedded in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information, so as to improve the monitoring efficiency of the high-speed paper cutter and support rapid decision-making, and further improve the management level, production efficiency and equipment safety of the high-speed paper cutter.

[0068] The structure, features and effects of the present invention have been described in detail based on the embodiments shown in the drawings. The above is only the preferred embodiment of the present invention, but the present invention is not limited to the scope defined by the drawings. Any changes made according to the concept of the present invention, or equivalent embodiments modified into equivalent changes, still within the spirit covered by the specification and drawings, shall be within the protection scope of the present invention.

Claims

1. A visualization monitoring method for a control system of a high-speed paper cutter, characterized in that, The method includes: According to the operating state of the high-speed paper cutter, collect sensor data of multiple sensors installed in the high-speed paper cutter in real time, where the sensor data includes paper tension, speed, temperature, and humidity parameters; Based on the sensor data, use the adaptive fuzzy logic algorithm to process the sensor data, obtain the real-time change trends of various indicators, and generate a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter; According to the dynamic data model, use augmented reality technology to generate a real-time monitoring interface, and present the operating state, key parameters, and alarm information of the high-speed paper cutter in three-dimensional visualization graphics, so that the operator can observe the operating state and fault information of the high-speed paper cutter, and embed an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

2. The method according to claim 1, wherein The using the adaptive fuzzy logic algorithm to process the sensor data based on the sensor data, obtaining the real-time change trends of various indicators, and generating a dynamic data model for the intelligent evaluation of the operating state of the high-speed paper cutter includes: Perform standardization processing on the sensor data, and with the help of expert knowledge, construct a set of fuzzy rules according to the operating environment and working conditions of the high-speed paper cutter; Use the constructed fuzzy rules to perform real-time reasoning through a fuzzy inference engine. Among them, input the standardized sensor data into the fuzzy inference system, convert it into a fuzzy set, and apply the inference mechanism to comprehensively evaluate each fuzzy set to generate a fuzzy output set representing the current operating state of the high-speed paper cutter; Combine the fuzzy output set with historical data, use a weighted neural network to generate a dynamic data model, and based on the dynamic data model, calculate the change trends of various indicators of the high-speed paper cutter in real time and generate a trend chart. The trend chart not only provides real-time operating state information but also can demonstrate potential performance degradation or fault risks to the operator.

3. The method according to claim 2, wherein The using augmented reality technology to generate a real-time monitoring interface according to the dynamic data model and presenting the operating state, key parameters, and alarm information of the high-speed paper cutter in three-dimensional visualization graphics includes: Based on the physical model of the high-speed paper cutter, use computer-aided design tools to construct a corresponding three-dimensional visualization model, which can truly reflect the structure and various functional components of the machine; Map the structured operating state, key parameters, and alarm information to the corresponding parts of the three-dimensional visualization model, use WebGL technology to update the operating state, key parameters, and alarm information in real time and dynamically display them in the augmented reality environment, so that the operator can directly observe the real-time changes of various indicators on the model, and design an augmented reality interaction interface as the monitoring interface, so that the operator can interact with the three-dimensional visualization model through gestures, touches, or voice commands.

4. The method according to claim 3, wherein The embedding an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information includes: When the operating state is abnormal or a fault message is received, start the adaptive feedback control mechanism to realize real-time adjustment of the configuration parameters of the high-speed paper cutter by combining a fuzzy controller with a Model Predictive Control (MPC) algorithm.

5. A visualization monitoring system for a high-speed paper cutter control system, characterized in that, The system includes: An acquisition module, which is used to collect in real time multiple sensor data set in the high-speed paper cutter according to the operating state of the high-speed paper cutter. Among them, the sensor data includes paper tension, speed, temperature, and humidity parameters; A processing module, which is used to process the sensor data by using an adaptive fuzzy logic algorithm based on the sensor data, obtain the real-time change trends of various indicators, and generate a dynamic data model for intelligent evaluation of the operating state of the high-speed paper cutter; A monitoring module, which is used to generate a real-time monitoring interface by using augmented reality technology according to the dynamic data model, and present the operating state, key parameters, and alarm information of the high-speed paper cutter in the form of three-dimensional visualization graphics, so that the operator can observe the operating state and fault information of the high-speed paper cutter, and embed an intelligent feedback mechanism based on machine learning in the monitoring interface to automatically adjust the configuration parameters of the high-speed paper cutter according to the operating state and fault information.

6. The system according to claim 5, wherein The processing module is specifically used for: Perform standardization processing on the sensor data, and construct a set of fuzzy rules according to the operating environment and working conditions of the high-speed paper cutter with the help of expert knowledge; Use the constructed fuzzy rules to perform real-time reasoning through a fuzzy inference engine. Among them, input the standardized sensor data into the fuzzy inference system, convert it into a fuzzy set, and apply the inference mechanism to comprehensively evaluate each fuzzy set to generate a fuzzy output set, representing the current operating state of the high-speed paper cutter; Combine the fuzzy output set with historical data, generate a dynamic data model by using a weighted neural network, calculate the change trends of various indicators of the high-speed paper cutter in real time based on the dynamic data model, and generate a trend chart. The trend chart not only provides real-time operating state information but also can demonstrate potential performance degradation or fault risks to the operator.

7. The system according to claim 6, wherein The monitoring module is specifically used for: Based on the physical model of the high-speed paper cutter, use computer-aided design tools to construct a corresponding three-dimensional visualization model, which can truly reflect the structure and various functional components of the machine; Map the structured operating state, key parameters, and alarm information to the corresponding parts of the three-dimensional visualization model, use WebGL technology to update the operating state, key parameters, and alarm information in real time and dynamically display them in the augmented reality environment, so that the operator can directly observe the real-time changes of various indicators on the model, and design an augmented reality interaction interface as the monitoring interface, so that the operator can interact with the three-dimensional visualization model through gestures, touches, or voice commands.

8. The system according to claim 7, wherein The monitoring module is specifically used for: When the operating state is abnormal or a fault message is received, start the adaptive feedback control mechanism to realize real-time adjustment of the configuration parameters of the high-speed paper cutter by combining a fuzzy controller with a Model Predictive Control (MPC) algorithm.

9. A storage medium, characterized in that, A computer program is stored in the storage medium, wherein the computer program is configured to execute the method according to any one of claims 1-4 when running.

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 execute the method according to any one of claims 1-4.

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