Clamp damage prevention, detection and alarm system in lithium battery diaphragm double-pull equipment

Abstract

The invention relates to the technical field of lithium battery diaphragm production equipment, and discloses a clamp damage prevention, detection and alarm system in lithium battery diaphragm double-pull equipment. Comprising a sensor module, a data acquisition and processing module, an alarm and shutdown control module, a dynamic adjustment module, an environment adaptation module, a multi-dimensional environment monitoring module, an environment control and sharing module, an intelligent prompt module, a data visualization module, a virtual assistant module and a personalized customization module. The sensor module is used for monitoring key parts of the clamp in real time and collecting operation data; the sensor module comprises a strain sensor, a temperature and humidity sensor, a displacement sensor and a vibration sensor. Through the alarm and shutdown control module and the like, the fault prediction accuracy and adaptive capacity are improved, shutdown is reduced, data are shared through cross-device learning, the prediction capacity is improved, repeated faults are reduced, and therefore the intelligence, self-optimization capacity and operation efficiency of the system are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of lithium battery diaphragm production equipment, and in particular to a clip damage prevention detection and alarm system in a lithium battery diaphragm double-pull device. Background Art

[0002] Lithium battery separator is a key component in lithium-ion batteries. It is located between the positive and negative electrodes of the battery and plays the role of isolating and preventing electrode short circuit. The separator can not only isolate the positive and negative electrodes inside the battery to prevent them from direct contact and causing short circuit, but also allow lithium ions to pass freely in the electrolyte, thereby supporting the battery's charging and discharging process. In order to manufacture it, lithium battery separator double-pulling equipment is often used. It uses a biaxial stretching process (i.e., longitudinal and transverse stretching at the same time) to make the molecular structure of the separator material more uniform, thereby improving its mechanical strength and electrochemical properties. In the lithium battery separator double-pulling equipment, the clamp is one of the key components, which is used to fix and transport the separator material. Since the clamp operates for a long time in a high-load and high-frequency working environment, it is prone to wear and damage. In order to ensure the continuity of the production process and product quality, the status of the clamp needs to be monitored and preventively maintained.

[0003] First, the clip damage prevention detection and alarm system in the traditional lithium battery diaphragm double-pull equipment mainly relies on preset rules and simple statistical analysis methods. These methods have limited ability to identify complex fault patterns, which easily leads to insufficient accuracy in fault prediction. At the same time, these systems usually lack adaptive optimization capabilities and it is difficult to dynamically adjust detection parameters according to real-time data, resulting in low fault detection accuracy. In addition, the data isolation problem between different devices is serious, making it difficult to achieve cross-device learning and data sharing, resulting in frequent repeated failures. The intelligence and self-optimization capabilities of the system need to be improved urgently.

[0004] Secondly, the industrial production environment is complex and changeable. The clip damage prevention detection and alarm system in the traditional lithium battery diaphragm double-pull equipment can often only monitor a single or a few environmental parameters, and it is difficult to fully cope with the impact of different environmental conditions on the operation of the equipment. For example, environmental factors such as temperature, humidity, air quality and electromagnetic interference may have a significant impact on the performance and life of the equipment. Systems that lack multi-dimensional environmental monitoring and adaptive control capabilities are difficult to ensure the stable operation of equipment in the face of complex environmental changes. At the same time, the isolated use of environmental data leads to inefficient resource utilization, and it is difficult to identify potential environmental risks through data sharing and improve the safety and reliability of the system;

[0005] Finally, the interactive functions of the clip damage prevention detection alarm system in the traditional lithium battery diaphragm double-pull equipment are often complex in design and inconvenient to operate. Users need to spend a lot of time learning and adapting to the system, which reduces work efficiency. The traditional human-computer interaction system lacks intelligent and personalized support and cannot provide customized operation guidance and prompts based on the user's operating habits and needs. In addition, users often lack real-time operation guidance and feedback during the operation process, resulting in frequent operation errors, affecting the normal operation and maintenance efficiency of the equipment. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a clamp damage prevention detection and alarm system in a lithium battery diaphragm double-pull device, which solves the problems of low system operation efficiency and poor self-optimization capability.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a clip damage prevention detection alarm system in a lithium battery diaphragm double-pull device, including a sensor module, a data acquisition and processing module, an alarm and shutdown control module, a dynamic adjustment module, an environmental adaptation module, a multi-dimensional environmental monitoring module, an environmental control and sharing module, an intelligent prompt module, a data visualization module, a virtual assistant module and a personalized customization module;

[0008] The sensor module is used to monitor the key parts of the clamp in real time and collect operation data;

[0009] The sensor module includes strain sensors, temperature and humidity sensors, displacement sensors and vibration sensors, and they are evenly distributed at the clamping points and connection parts of the clamps in the lithium battery diaphragm double-pull device to ensure safety monitoring;

[0010] The strain sensor is used to monitor stress and strain changes on the clamp;

[0011] The temperature and humidity sensor is used to monitor the temperature and humidity changes of the clip;

[0012] The displacement sensor is used to detect the displacement of the clip;

[0013] The vibration sensor is used to monitor the vibration of the clamp;

[0014] The data acquisition and processing module is used to collect sensor data and perform preprocessing and analysis;

[0015] The alarm and shutdown control module is used to issue an early warning and control the equipment to shut down when a potential fault is detected;

[0016] The alarm and shutdown control module includes a fault detection unit, an early warning unit, a shutdown control unit and a communication unit;

[0017] The fault detection unit is used to monitor sensor data in real time and identify potential faults, including mechanical faults, electrical faults, thermal management faults, sensor module faults, and temperature and humidity impact faults;

[0018] The warning unit is used to issue visual and audible alarms;

[0019] The shutdown control unit is used to control the safe shutdown of the equipment;

[0020] The communication unit is used to send alarm information to the control center or relevant personnel;

[0021] The dynamic adjustment module is used to dynamically adjust the sensitivity and monitoring frequency of the sensor according to real-time data;

[0022] The dynamic adjustment module includes a data analysis unit, a sensor control unit, and a self-learning unit;

[0023] The data analysis unit is used to analyze the current equipment status according to the real-time data, specifically including the operation status, stress status, temperature status, vibration status and position status;

[0024] The sensor control unit is used to dynamically adjust the response range and monitoring frequency of the sensor according to the device status;

[0025] The self-learning unit is used to learn and optimize the sensor adjustment strategy, and the self-learning unit optimization specifically includes the following steps:

[0026] S1. Data accumulation: collect and store historical and real-time data of sensor modules, including equipment status, environmental changes, and adjusted monitoring effects;

[0027] S2, pattern recognition: Use support vector machines to analyze the accumulated data, identify the patterns of equipment operation and environmental changes, and select the most effective sensor adjustment strategy;

[0028] S3, Optimization algorithm: Based on the identified patterns and historical data, the sensor adjustment algorithm is optimized and a new adjustment strategy is generated. Through continuous iterative learning and optimization, the sensor control unit can respond more intelligently to different device states and environmental changes;

[0029] S4, Apply new strategy: Apply the optimized adjustment strategy to the sensor control unit to achieve more accurate sensor dynamic adjustment;

[0030] S5. Continuous improvement: The self-learning unit continuously learns from new real-time data and adjustment effects, continuously optimizes the sensor adjustment strategy, and improves the system's adaptive ability and monitoring accuracy;

[0031] In step S2, the specific optimization algorithm formula of the support vector machine is as follows:

[0032]

[0033] Where w is the normal vector of the decision boundary, b is the bias of the decision boundary, ξ is the slack variable used to deal with inseparable data, and C is the regularization parameter that controls the trade-off between model complexity and error;

[0034] The environmental adaptation module is used to monitor and adapt to environmental changes and adjust system parameters to ensure stable operation;

[0035] The multi-dimensional environmental monitoring module is used to monitor various environmental parameters such as air quality and electromagnetic interference;

[0036] The environment control and sharing module is used to adjust the system working mode according to the environment data and share the environment data;

[0037] The intelligent prompt module is used to guide operators to perform operations and maintenance in real time through voice and screen prompts;

[0038] The data visualization module is used to display the monitoring data in the form of charts and reports to facilitate understanding and decision-making;

[0039] The virtual assistant module is used to provide intelligent voice and text interaction to assist operators in completing tasks;

[0040] The personalized customization module is used to provide a personalized operation interface and prompts according to user preferences.

[0041] Preferably, the environment adaptation module includes an environment sensing unit and a system adjustment unit;

[0042] The environmental sensing unit is used to monitor parameters such as temperature, humidity, and pressure in the working environment in real time;

[0043] The system adjustment unit is used to dynamically adjust device parameters according to the data collected by the environment sensing unit to adapt to environmental changes, thereby ensuring that the device can operate stably in various environments.

[0044] Preferably, the multi-dimensional environment monitoring module includes an air quality monitoring unit and an electromagnetic interference detection unit;

[0045] The air quality monitoring unit is used to monitor the air quality in the working environment, such as particulate matter PM2.5 / PM10, harmful gases including but not limited to CO2 and NOx;

[0046] The electromagnetic interference detection unit is used to monitor the electromagnetic interference level in the environment and evaluate its impact on the normal operation of the equipment.

[0047] Preferably, the intelligent prompt module includes a voice prompt unit and a screen display unit;

[0048] The voice prompt unit is used to provide voice instructions and warnings to operators to help them operate the equipment correctly or take measures when an abnormality occurs;

[0049] The screen display unit is used to display operation guides, maintenance prompts and alarm information on the screen of the device or console to ensure timely communication of information.

[0050] Preferably, the data visualization module includes a chart generation unit and a report generation unit;

[0051] The chart generation unit is used to convert the collected data into trend charts, pie charts and bar charts, which are convenient for operators and management to analyze and understand the data;

[0052] The report generation unit is used to automatically generate a detailed report containing key performance indicators and monitoring data for technical evaluation and decision support.

[0053] Preferably, the virtual assistant module includes an interaction processing unit and a task management unit;

[0054] The interactive processing unit is used to process the user's voice and text input, and provide intelligent feedback and operation guidance;

[0055] The task management unit is used to assist operators in completing complex task arrangements and daily maintenance plans, and to remind key operation steps and maintenance time points.

[0056] Preferably, the environment control and sharing module includes an environment adjustment unit and a data sharing unit;

[0057] The environmental adjustment unit is used to automatically adjust the air circulation, temperature and humidity of the working area according to the monitoring data;

[0058] The data sharing unit is used to transmit the environmental monitoring data to other devices and systems in the device network in a timely manner, so as to jointly optimize the environmental quality of the entire work area.

[0059] Preferably, the data acquisition and processing module includes a data receiving unit, a preprocessing unit, a data analysis unit and a decision support unit;

[0060] The data receiving unit is responsible for receiving data from each sensor;

[0061] The preprocessing unit performs preliminary processing on the collected raw data, specifically including filtering noise and standardizing data format;

[0062] The data analysis unit performs in-depth analysis on the pre-processed data using linear regression analysis to identify patterns, trends or potential abnormal conditions;

[0063] The specific formula of the linear regression analysis is as follows:

[0064] y=β0+β1x+∈

[0065] Where y represents the response variable, x represents the explanatory variable, β0 represents the intercept, β1 represents the slope, and ∈ represents the error term;

[0066] The decision support unit provides operation and maintenance decision support based on the data analysis results, specifically including fault prediction and performance optimization suggestions.

[0067] Preferably, the personalized customization module includes a user configuration unit, a configuration management unit, an adaptive learning unit and an interactive optimization unit;

[0068] The user configuration unit is used for the user to input personalized parameters, such as equipment operation preferences, maintenance cycles, alarm sensitivity settings, etc., and adjust the behavior of the system according to these parameters;

[0069] The configuration management unit is used to store and manage the user's personalized settings, ensuring that the system applies these configurations in future operations and can easily modify or restore default settings when necessary;

[0070] The adaptive learning unit is used to analyze the user's operating habits and feedback, and automatically adjust the system settings to better suit the user's specific needs and preferences;

[0071] The interaction optimization unit is used to optimize the user interface and interaction experience, adjust the interface layout and function access method according to user feedback and usage patterns, and improve the convenience and efficiency of operation.

[0072] Preferably, the sensor module is electrically connected to the data acquisition and processing module, the data acquisition and processing module is electrically connected to the alarm and shutdown control module, the data acquisition and processing module is electrically connected to the dynamic adjustment module, the alarm and shutdown control module is electrically connected to the intelligent prompt module, the dynamic adjustment module is electrically connected to the adaptive optimization module, the environmental adaptation module is electrically connected to the multi-dimensional environmental monitoring module, the environmental adaptation module is electrically connected to the environmental control and sharing module, and the multi-dimensional environmental monitoring module is electrically connected to the environmental control and sharing module; the intelligent prompt module is electrically connected to the data visualization module; the environmental control and sharing module is electrically connected to the virtual assistant module, the virtual assistant module is electrically connected to the personalized customization module, and the data visualization module is electrically connected to the personalized customization module.

[0073] The present invention provides a detection and alarm system for preventing damage to clips in a double-pull device for lithium battery diaphragms. It has the following beneficial effects:

[0074] 1. The present invention improves the fault prediction accuracy and adaptability through alarm and shutdown control modules, etc., accurately identifies complex faults through deep learning, reduces shutdowns, updates models in real time, adaptively optimizes and dynamically adjusts parameters to adapt to the environment, improves detection accuracy, learns and shares data across devices, improves prediction capabilities, and reduces repeated faults. These improvements significantly improve the intelligence, self-optimization capabilities and operating efficiency of the system.

[0075] 2. The environmental adaptation module of the present invention enhances the stability and adaptability of the system in various environments. The multi-dimensional environmental monitoring module ensures that the system operates stably in complex environments. The adaptive control dynamically adjusts the working mode and optimizes performance. Data sharing improves utilization efficiency, identifies risks, and improves safety and reliability. These improvements ensure that the system operates with high performance in different environments and reduces negative environmental impacts.

[0076] 3. The human-computer interaction optimization module of the present invention improves user experience and operational efficiency. The virtual assistant module provides intelligent interaction and assists tasks. Augmented reality provides real-time data and operational guidance to improve work efficiency. The personalized customization module improves system usability and user satisfaction, making the system more friendly and easy to use, significantly improving operational efficiency and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a system module diagram of the present invention;

[0078] Figure 2 is a schematic diagram of a sensor module of the present invention;

[0079] Figure 3 It is a schematic diagram of the alarm and shutdown control module of the present invention;

[0080] Figure 4 It is a schematic diagram of a dynamic adjustment module of the present invention;

[0081] Figure 5 This is a schematic diagram of the environmental adaptation module of the present invention;

[0082] Figure 6 It is a schematic diagram of a multi-dimensional environment monitoring module of the present invention;

[0083] Figure 7 This is a schematic diagram of the intelligent prompt module of the present invention;

[0084] Figure 8 It is a schematic diagram of a data visualization module of the present invention;

[0085] Fig. 9is a schematic diagram of a virtual assistant module of the present invention;

[0086] Fig.10 It is a schematic diagram of the environment control and sharing module of the present invention;

[0087] Fig.11 It is a schematic diagram of the data acquisition and processing module of the present invention;

[0088] Fig.12 It is a schematic diagram of the personalized customization module of the present invention. DETAILED DESCRIPTION

[0089] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] Example:

[0091] Please refer to the attached Figure 1 -Attached Figure 4 , an embodiment of the present invention provides a clamp damage prevention detection alarm system in a lithium battery diaphragm double-pull device, including a sensor module, a data acquisition and processing module, an alarm and shutdown control module, a dynamic adjustment module, an environmental adaptation module, a multi-dimensional environmental monitoring module, an environmental control and sharing module, an intelligent prompt module, a data visualization module, a virtual assistant module and a personalized customization module;

[0092] The sensor module is used to monitor the key parts of the clamp in real time and collect operating data;

[0093] The sensor module includes strain sensors, temperature and humidity sensors, displacement sensors and vibration sensors, and they are evenly distributed at the clamping points and connection parts of the clamps in the lithium battery diaphragm double-pull device to ensure safety monitoring;

[0094] Strain sensors are used to monitor stress and strain changes on the clamp;

[0095] The temperature and humidity sensor is used to monitor the temperature and humidity changes of the clip;

[0096] The displacement sensor is used to detect the displacement of the clip;

[0097] The vibration sensor is used to monitor the vibration of the clamp;

[0098] The data acquisition and processing module is used to collect sensor data and perform preprocessing and analysis;

[0099] The alarm and shutdown control module is used to issue an early warning and control the equipment to shut down when a potential fault is detected;

[0100] The alarm and shutdown control module includes a fault detection unit, an early warning unit, a shutdown control unit and a communication unit;

[0101] The fault detection unit is used to monitor sensor data in real time and identify potential faults, including mechanical faults, electrical faults, thermal management faults, sensor module faults, and faults affected by temperature and humidity;

[0102] The early warning unit is used to sound visual and audible alarms;

[0103] The shutdown control unit is used to control the safe shutdown of the equipment;

[0104] The communication unit is used to send alarm information to the control center or relevant personnel;

[0105] The dynamic adjustment module is used to dynamically adjust the sensitivity and monitoring frequency of the sensor according to real-time data;

[0106] The dynamic adjustment module includes a data analysis unit, a sensor control unit, and a self-learning unit;

[0107] The data analysis unit is used to analyze the current equipment status according to real-time data, including operation status, stress status, temperature status, vibration status and position status;

[0108] The sensor control unit is used to dynamically adjust the sensor's response range and monitoring frequency according to the device status;

[0109] The self-learning unit is used to learn and optimize the sensor adjustment strategy. The self-learning unit optimization specifically includes the following steps:

[0110] S1. Data accumulation: collect and store historical and real-time data of sensor modules, including equipment status, environmental changes, and adjusted monitoring effects;

[0111] S2, pattern recognition: Use support vector machines to analyze the accumulated data, identify the patterns of equipment operation and environmental changes, and select the most effective sensor adjustment strategy;

[0112] S3, Optimization algorithm: Based on the identified patterns and historical data, the sensor adjustment algorithm is optimized and a new adjustment strategy is generated. Through continuous iterative learning and optimization, the sensor control unit can respond more intelligently to different device states and environmental changes;

[0113] S4, Apply new strategy: Apply the optimized adjustment strategy to the sensor control unit to achieve more accurate sensor dynamic adjustment;

[0114] S5. Continuous improvement: The self-learning unit continuously learns from new real-time data and adjustment effects, continuously optimizes the sensor adjustment strategy, and improves the system's adaptive ability and monitoring accuracy;

[0115] In step S2, the specific optimization algorithm formula of the support vector machine is as follows:

[0116]

[0117] Where w is the normal vector of the decision boundary, b is the bias of the decision boundary, ξ is the slack variable used to deal with inseparable data, and C is the regularization parameter that controls the trade-off between model complexity and error;

[0118] The environmental adaptation module is used to monitor and adapt to environmental changes and adjust system parameters to ensure stable operation;

[0119] The multi-dimensional environmental monitoring module is used to monitor various environmental parameters such as air quality and electromagnetic interference;

[0120] The environment control and sharing module is used to adjust the system working mode according to the environment data and share the environment data;

[0121] The intelligent prompt module is used to guide operators to perform operations and maintenance in real time through voice and screen prompts;

[0122] The data visualization module is used to display monitoring data in the form of charts and reports to facilitate understanding and decision-making;

[0123] The virtual assistant module is used to provide intelligent voice and text interaction to assist operators in completing tasks;

[0124] The personalized customization module is used to provide personalized operation interface and prompts according to user preferences;

[0125] The sensor module is electrically connected to the data acquisition and processing module, the data acquisition and processing module is electrically connected to the alarm and shutdown control module, the data acquisition and processing module is electrically connected to the dynamic adjustment module, the alarm and shutdown control module is electrically connected to the intelligent prompt module, the dynamic adjustment module is electrically connected to the adaptive optimization module, the environmental adaptation module is electrically connected to the multi-dimensional environmental monitoring module, the environmental adaptation module is electrically connected to the environmental control and sharing module, and the multi-dimensional environmental monitoring module is electrically connected to the environmental control and sharing module; the intelligent prompt module is electrically connected to the data visualization module; the environmental control and sharing module is electrically connected to the virtual assistant module, the virtual assistant module is electrically connected to the personalized customization module, and the data visualization module is electrically connected to the personalized customization module.

[0126] Specifically, the sensor module ensures monitoring of equipment status from multiple dimensions and angles through evenly distributed sensors, improves the accuracy and timeliness of fault detection, and reduces production delays and costs caused by equipment failures; the data acquisition and analysis module can timely discover and warn of potential equipment problems through effective data processing and analysis, thereby preventing possible failures or performance degradation and ensuring production efficiency and equipment safety; the alarm and shutdown control module can respond quickly at the beginning of the problem through a comprehensive monitoring and response mechanism, minimizing the impact of failures on production and possible equipment damage; the dynamic adjustment module enables the system to dynamically adjust the monitoring strategy according to the actual operating conditions, improving the efficiency and accuracy of monitoring. At the same time, through continuous learning, the system adaptability and intelligence level continue to improve, and the dynamic adjustment module classifies the equipment status through its internal as follows:

[0127] f(x)=w1·x1+w2·x2+w3·x3+w4·x4+b

[0128] Where x1 represents device stress, x2 represents device temperature, x3 represents ambient temperature, x4 represents ambient humidity, and the corresponding label f(x) represents the device status (1 represents normal, -1 represents abnormal). By training the SVM model, we get the decision boundary w and bias b.

[0129] According to the above content, if f(x)>0, the equipment status is normal; if f(x)<0, the equipment status is abnormal; the environmental adaptation module enables the equipment to adapt to complex and changeable working environments, reduce the impact of environmental factors on equipment performance, and improve the reliability and stability of the system; the multi-dimensional environmental detection module monitors environmental factors in real time, and the system can prevent equipment failures caused by environmental changes, ensuring that the equipment can operate efficiently and stably even in an undesirable environment. The environmental control and sharing module not only optimizes the working environment of the equipment itself, but also enhances the environmental management capabilities of the entire production system through data sharing. The intelligent prompt module enhances operational safety and convenience, reduces operational errors, and improves the work efficiency of operators. The data visualization module improves the transparency and accessibility of data, supports more effective monitoring and management decisions, and the virtual assistant module enhances user interaction experience, reduces work complexity, and enhances the user-friendliness of the system; the personalized customization module enables users to adjust system settings according to personal preferences and operating habits, increasing user satisfaction and system applicability.

[0130] Please refer to the attached Figure 5 -Attached Figure 7 ,The environment adaptation module includes an environment sensing unit and a system adjustment unit;

[0131] The environmental sensing unit is used to monitor the temperature, humidity, pressure and other parameters in the working environment in real time;

[0132] The system adjustment unit is used to dynamically adjust the device parameters to adapt to environmental changes based on the data collected by the environmental sensing unit, ensuring that the device can operate stably in various environments;

[0133] The multi-dimensional environmental monitoring module includes an air quality monitoring unit and an electromagnetic interference detection unit;

[0134] The air quality monitoring unit is used to monitor the air quality in the working environment, such as particulate matter PM2.5 / PM10, harmful gases including but not limited to CO2 and NOx;

[0135] The electromagnetic interference detection unit is used to monitor the electromagnetic interference level in the environment and evaluate its impact on the normal operation of the equipment;

[0136] The intelligent prompt module includes a voice prompt unit and a screen display unit;

[0137] The voice prompt unit is used to provide voice instructions and warnings to operators to help them operate the equipment correctly or take measures when abnormalities occur;

[0138] The screen display unit is used to display operation guides, maintenance reminders and alarm information on the screen of the equipment or console to ensure timely communication of information;

[0139] The data visualization module includes a chart generation unit and a report generation unit;

[0140] The chart generation unit is used to convert the collected data into trend charts, pie charts and bar charts, which are convenient for operators and management to analyze and understand the data;

[0141] The report generation unit is used to automatically generate detailed reports containing key performance indicators and monitoring data for technical evaluation and decision support;

[0142] Please refer to the attached Fig.11 ,The data acquisition and processing module includes a data receiving unit, a preprocessing unit, a data analysis unit and a decision support unit;

[0143] The data receiving unit is responsible for receiving data from each sensor;

[0144] The preprocessing unit performs preliminary processing on the collected raw data, including noise filtering and standardizing the data format;

[0145] The data analysis unit uses linear regression analysis to conduct in-depth analysis of the pre-processed data to identify patterns, trends or potential abnormal conditions;

[0146] The specific formula of linear regression analysis is as follows:

[0147] y=β0+β1x+∈

[0148] Where y represents the response variable, x represents the explanatory variable, β0 represents the intercept, β1 represents the slope, and ∈ represents the error term;

[0149] The decision support unit provides operation and maintenance decision support based on data analysis results, including fault prediction and performance optimization suggestions.

[0150] Specifically, the environmental sensing unit monitors environmental parameters in real time, detects anomalies in a timely manner, and prevents adverse effects of environmental factors on equipment operation, thereby improving the adaptability and stability of the equipment under different environmental conditions and reducing the number of shutdowns and maintenance caused by environmental changes; the system adjustment unit dynamically adjusts equipment parameters to ensure that the equipment can operate stably and efficiently under various environmental conditions, thereby reducing the impact of environmental changes on equipment performance and improving the reliability and service life of the equipment; the air quality monitoring unit monitors the air quality of the working environment in real time, takes timely measures to improve the air quality, and ensures the health and safety of equipment and personnel, thereby providing air quality data to help formulate environmental improvement measures and improve the working environment. The electromagnetic interference detection unit detects and evaluates the impact of electromagnetic interference on equipment operation in advance, prevents equipment failure or misoperation caused by electromagnetic interference, and provides electromagnetic interference data to help formulate electromagnetic environment improvement measures to ensure stable operation of equipment in a low-interference environment; the voice prompt unit provides real-time voice instructions and warnings to help operators understand the equipment status in a timely manner, operate the equipment correctly, and prevent misoperation. When an abnormal situation occurs, a voice warning is issued in time to remind the operator to take prompt measures to reduce the impact of the failure. Through an intuitive graphical interface, detailed operation and maintenance information is provided to the operator to improve operation efficiency and accuracy. When a failure occurs, the prompt information is displayed in time. The alarm information ensures that the operator quickly understands the problem and takes appropriate measures; the data receiving unit ensures the real-time and accuracy of the sensor data, providing a reliable data basis for subsequent data processing and analysis, thereby improving the overall data collection efficiency through an efficient data transmission system; the preprocessing unit converts the data into a unified format to ensure the consistency and comparability of the data, thereby improving the data quality and ensuring the accuracy and reliability of data analysis, and through standardized processing, the data of different sensors can be processed uniformly to simplify the subsequent analysis process; the data analysis unit uses linear regression analysis to identify the laws and trends in the operation of the equipment and discover potential abnormal conditions in advance, thereby providing data The analysis results of the driver support fault prediction and performance optimization. The decision support unit helps operators make smart decisions based on data to improve the safety and efficiency of equipment operation, thereby providing specific performance optimization suggestions to help extend equipment life and reduce maintenance costs. The key performance indicators in the report generation unit are production efficiency, mean time between failures, mean time to repair, failure rate, energy consumption and utilization rate, which measure the utilization rate of the equipment and indicate the actual operating time ratio of the equipment within the planned operating time. The monitoring data specifically includes temperature data, stress data, vibration data, environmental data and equipment status data. The corresponding data analysis unit uses linear regression analysis. The specific steps are as follows:

[0151] A. Collect data on ambient temperature and equipment failure rate;

[0152] B. Use linear regression model to fit and get intercept β0 and slope β1;

[0153] C. Analyze the relationship between ambient temperature and equipment failure rate based on the fitting results;

[0154] D. Propose corresponding temperature control measures to reduce equipment failure rate;

[0155] Through the above detailed description and formula explanation, the report generation unit can automatically generate a detailed report containing key performance indicators and monitoring data, providing a reliable data basis and analysis results for technical evaluation and decision support.

[0156] Please refer to the attached Figure 8 -Attached Fig.10 ,The data visualization module includes a chart generating unit and a report generating unit;

[0157] The chart generation unit is used to convert the collected data into trend charts, pie charts and bar charts, which are convenient for operators and management to analyze and understand the data;

[0158] The report generation unit is used to automatically generate detailed reports containing key performance indicators and monitoring data for technical evaluation and decision support;

[0159] The virtual assistant module includes an interaction processing unit and a task management unit;

[0160] The interactive processing unit is used to process the user's voice and text input, and provide intelligent feedback and operation guidance;

[0161] The task management unit is used to assist operators in completing complex task arrangements and daily maintenance plans, and remind key operation steps and maintenance time points;

[0162] The environment control and sharing module includes an environment adjustment unit and a data sharing unit;

[0163] The environmental conditioning unit is used to automatically adjust the air circulation, temperature and humidity of the working area according to the monitoring data;

[0164] The data sharing unit is used to transmit environmental monitoring data to other devices and systems in the equipment network in a timely manner to jointly optimize the environmental quality of the entire work area;

[0165] Please refer to the attached Fig.12 ,The personalized customization module includes a user configuration unit, a ,configuration management unit, an adaptive learning unit and an interactive ,optimization unit;

[0166] The user configuration unit is used for users to input personalized parameters, such as equipment operation preferences, maintenance cycles, alarm sensitivity settings, etc., and adjust the system behavior according to these parameters;

[0167] The configuration management unit is used to store and manage the user's personalized settings, ensuring that the system applies these configurations in future operations and can easily modify or restore default settings when necessary;

[0168] The adaptive learning unit is used to analyze user operating habits and feedback, and automatically adjust system settings to better suit the user's specific needs and preferences;

[0169] The interaction optimization unit is used to optimize the user interface and interactive experience, adjust the interface layout and function access method according to user feedback and usage patterns, and improve the convenience and efficiency of operation.

[0170] Specifically, the chart generation unit uses charts to intuitively display data trends and distributions, making it easier for operators and management to quickly understand and analyze data, thereby providing a variety of chart types to meet different analysis needs and improve data interpretation and decision-making efficiency; the report generation unit improves work efficiency and reduces errors in manual reports by automatically generating detailed reports, where the reports contain key indicators and data analysis results, providing a reliable basis for technical evaluation and decision support; the interactive processing unit provides intelligent operation guidance to reduce the learning cost and operation errors of operators, thereby processing user input in real time, providing fast and accurate feedback, and improving operation efficiency, and its intelligent feedback includes real-time status feedback, fault diagnosis feedback and operation confirmation feedback, and the corresponding operation guidance specifically includes

[0171] 1. Equipment startup and shutdown instructions, that is, please press the start button, then check all sensor data in turn, and then before shutting down the equipment, make sure all production materials have been cleared and press the red shutdown button

[0172] 2. Maintenance operation instructions: When preparing for routine maintenance, please disconnect the power supply first, ensure safety, then open the rear cover of the equipment, and lubricate the transmission belt with lubricating oil to ensure even coverage;

[0173] 3. Troubleshooting Guide: If a transmission component is abnormal, please follow the following steps to check: stop the machine, open the equipment cover, and check whether the transmission belt is worn or loose; if the temperature is too high, please follow the following steps to cool it down: increase the coolant flow, check whether the cooling system is blocked, and restart the equipment to monitor the temperature change;

[0174] The task management unit helps operators to effectively manage and arrange tasks, reduce omissions and delays, and provide key steps and maintenance reminders to ensure that equipment is maintained on time and extend equipment life. The environmental adjustment unit ensures stable operation of equipment by keeping the environmental conditions in the working area within an appropriate range, thereby improving the comfort of the working environment and reducing the negative impact of the environment on equipment and operators. The data sharing unit promotes collaborative work between equipment and systems by sharing environmental monitoring data in real time, thereby improving the environmental quality of the entire working area and reducing the failure of a single device due to environmental changes. The equipment involved includes collaborative production equipment and automated robots, and the systems involved include environmental control systems, air purification systems, security monitoring systems, and fire alarm systems. The user configuration unit improves the applicability and user satisfaction of the system by meeting the personalized needs of different users, thereby adjusting the system according to user preferences and providing a more customized operating experience. The configuration management unit ensures the persistence of personalized settings by securely storing user configurations, thereby providing convenient configuration management functions, and users can adjust or restore settings at any time. The adaptive learning unit improves the ease of use and operating efficiency of the system by dynamically adapting to the user's operating habits, thereby providing a more personalized experience that meets user needs. The adjustment includes the following steps:

[0175] 1. Data collection: collect data on users’ operation frequency, time and mode, common functions and operation paths, users’ response speed to the system, usage of interface layout, users’ evaluation and suggestions on the system, difficulties and problems encountered by users during operation, and improvement requirements and preference settings submitted by users;

[0176] 2. Data analysis: Identify the operation patterns of different user groups through cluster analysis (such as K-means), identify the high-frequency time periods and common operation sequences of users through time series analysis, and finally analyze the positive and negative emotions of user feedback through statistical analysis and sentiment analysis (such as natural language processing NLP); and then identify the user's preference trends in terms of system functions, interface layout, and response speed;

[0177] 3. Model training: Use the supervised learning model decision tree to train the system to predict user operation habits and feedback;

[0178] 4. Settings adjustment: dynamically adjust the interface layout according to the user's frequently used functions and operation paths, place high-frequency functions in conspicuous and convenient locations, and provide personalized quick operation buttons and menus to reduce user operation steps. Then, based on the user's operation history and preferences, recommend related functions and setting options; for example, users who frequently use certain functions will have these functions displayed first on the interface to optimize system response speed, reduce user waiting time, and improve operation experience. In this way, the alarm sensitivity and maintenance cycle are adjusted. Then, according to the user's operation habits and historical data, the alarm sensitivity settings are automatically adjusted to ensure that users are reminded in appropriate situations. Finally, according to the user's maintenance records and feedback, the maintenance cycle and plan are dynamically adjusted to ensure that the equipment operates in the best condition.

[0179] 5. User feedback loop, collect user feedback on new adjustment settings, evaluate the adjustment effect, and then continuously update and optimize the machine learning model to make the system settings more in line with the actual needs and preferences of users; the interaction optimization unit optimizes the user interface to improve the convenience and efficiency of operation, thereby improving the user experience and reducing the complexity of operation by improving the interaction design;

[0180] The interaction optimization unit improves the convenience and efficiency of operations by optimizing the user interface, and enhances the user experience and reduces the complexity of operations by improving the interaction design.

[0181] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A clamp damage prevention detection alarm system in a lithium battery diaphragm double-pull device, characterized in that: It includes sensor module, data acquisition and processing module, alarm and shutdown control module, dynamic adjustment module, environmental adaptation module, multi-dimensional environmental monitoring module, environmental control and sharing module, intelligent prompt module, data visualization module, virtual assistant module and personalized customization module; The sensor module is used to monitor the key parts of the clamp in real time and collect operation data; The sensor module includes strain sensors, temperature and humidity sensors, displacement sensors and vibration sensors, and they are evenly distributed at the clamping points and connection parts of the clamps in the lithium battery diaphragm double-pull device to ensure safety monitoring; The strain sensor is used to monitor stress and strain changes on the clamp; The temperature and humidity sensor is used to monitor the temperature and humidity changes of the clip; The displacement sensor is used to detect the displacement of the clip; The vibration sensor is used to monitor the vibration of the clamp; The data acquisition and processing module is used to collect sensor data and perform preprocessing and analysis; The alarm and shutdown control module is used to issue an early warning and control the equipment to shut down when a potential fault is detected; The alarm and shutdown control module includes a fault detection unit, an early warning unit, a shutdown control unit and a communication unit; The fault detection unit is used to monitor sensor data in real time and identify potential faults, including mechanical faults, electrical faults, thermal management faults, sensor module faults, and temperature and humidity impact faults; The warning unit is used to issue visual and audible alarms; The shutdown control unit is used to control the safe shutdown of the equipment; The communication unit is used to send alarm information to the control center or relevant personnel; The dynamic adjustment module is used to dynamically adjust the sensitivity and monitoring frequency of the sensor according to real-time data; The dynamic adjustment module includes a data analysis unit, a sensor control unit, and a self-learning unit; The data analysis unit is used to analyze the current equipment status according to the real-time data, specifically including the operation status, stress status, temperature status, vibration status and position status; The sensor control unit is used to dynamically adjust the response range and monitoring frequency of the sensor according to the device status; The self-learning unit is used to learn and optimize the sensor adjustment strategy, and the self-learning unit optimization specifically includes the following steps: S1. Data accumulation: collect and store historical and real-time data of sensor modules, including equipment status, environmental changes, and adjusted monitoring effects; S2, pattern recognition: Use support vector machines to analyze the accumulated data, identify the patterns of equipment operation and environmental changes, and select the most effective sensor adjustment strategy; S3, Optimization algorithm: Based on the identified patterns and historical data, the sensor adjustment algorithm is optimized and a new adjustment strategy is generated. Through continuous iterative learning and optimization, the sensor control unit can respond more intelligently to different device states and environmental changes; S4, Apply new strategy: Apply the optimized adjustment strategy to the sensor control unit to achieve more accurate sensor dynamic adjustment; S5. Continuous improvement: The self-learning unit continuously learns from new real-time data and adjustment effects, continuously optimizes the sensor adjustment strategy, and improves the system's adaptive ability and monitoring accuracy; In step S2, the specific optimization algorithm formula of the support vector machine is as follows: Where w is the normal vector of the decision boundary, b is the bias of the decision boundary, ξ is the slack variable used to deal with inseparable data, and C is the regularization parameter that controls the trade-off between model complexity and error; The environmental adaptation module is used to monitor and adapt to environmental changes and adjust system parameters to ensure stable operation; The multi-dimensional environmental monitoring module is used to monitor various environmental parameters such as air quality and electromagnetic interference; The environment control and sharing module is used to adjust the system working mode according to the environment data and share the environment data; The intelligent prompt module is used to guide operators to perform operations and maintenance in real time through voice and screen prompts; The data visualization module is used to display the monitoring data in the form of charts and reports to facilitate understanding and decision-making; The virtual assistant module is used to provide intelligent voice and text interaction to assist operators in completing tasks; The personalized customization module is used to provide a personalized operation interface and prompts according to user preferences.

2. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The environmental adaptation module includes an environmental sensing unit and a system adjustment unit; The environmental sensing unit is used to monitor parameters such as temperature, humidity, and pressure in the working environment in real time; The system adjustment unit is used to dynamically adjust device parameters according to the data collected by the environment sensing unit to adapt to environmental changes, thereby ensuring that the device can operate stably in various environments.

3. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The multi-dimensional environment monitoring module includes an air quality monitoring unit and an electromagnetic interference detection unit; The air quality monitoring unit is used to monitor the air quality in the working environment, such as particulate matter PM2.5 / PM10, harmful gases including but not limited to CO2 and NOx; The electromagnetic interference detection unit is used to monitor the electromagnetic interference level in the environment and evaluate its impact on the normal operation of the equipment.

4. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The intelligent prompt module includes a voice prompt unit and a screen display unit; The voice prompt unit is used to provide voice instructions and warnings to operators to help them operate the equipment correctly or take measures when an abnormality occurs; The screen display unit is used to display operation guides, maintenance prompts and alarm information on the screen of the device or console to ensure timely communication of information.

5. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The data visualization module includes a chart generation unit and a report generation unit; The chart generation unit is used to convert the collected data into trend charts, pie charts and bar charts, which are convenient for operators and management to analyze and understand the data; The report generation unit is used to automatically generate a detailed report containing key performance indicators and monitoring data for technical evaluation and decision support.

6. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The virtual assistant module includes an interaction processing unit and a task management unit; The interactive processing unit is used to process the user's voice and text input, and provide intelligent feedback and operation guidance; The task management unit is used to assist operators in completing complex task arrangements and daily maintenance plans, and to remind key operation steps and maintenance time points.

7. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The environment control and sharing module includes an environment adjustment unit and a data sharing unit; The environmental adjustment unit is used to automatically adjust the air circulation, temperature and humidity of the working area according to the monitoring data; The data sharing unit is used to transmit the environmental monitoring data to other devices and systems in the device network in a timely manner, so as to jointly optimize the environmental quality of the entire work area.

8. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The data acquisition and processing module includes a data receiving unit, a preprocessing unit, a data analysis unit and a decision support unit; The data receiving unit is responsible for receiving data from each sensor; The preprocessing unit performs preliminary processing on the collected raw data, specifically including filtering noise and standardizing the data format; The data analysis unit performs in-depth analysis on the pre-processed data using linear regression analysis to identify patterns, trends or potential abnormal conditions; The specific formula of the linear regression analysis is as follows: y=β0+β1x+∈ Where y represents the response variable, x represents the explanatory variable, β0 represents the intercept, β1 represents the slope, and ∈ represents the error term; The decision support unit provides operation and maintenance decision support based on the data analysis results, specifically including fault prediction and performance optimization suggestions.

9. The clip damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1 is characterized in that: The personalized customization module includes a user configuration unit, a configuration management unit, an adaptive learning unit and an interactive optimization unit; The user configuration unit is used for the user to input personalized parameters, such as equipment operation preferences, maintenance cycles, alarm sensitivity settings, etc., and adjust the behavior of the system according to these parameters; The configuration management unit is used to store and manage the user's personalized settings, ensuring that the system applies these configurations in future operations and can easily modify or restore default settings when necessary; The adaptive learning unit is used to analyze the user's operating habits and feedback, and automatically adjust the system settings to better suit the user's specific needs and preferences; The interaction optimization unit is used to optimize the user interface and interaction experience, adjust the interface layout and function access method according to user feedback and usage patterns, and improve the convenience and efficiency of operation.

10. The clamp damage prevention detection alarm system in the lithium battery diaphragm double pulling equipment according to claim 1, characterized in that: The sensor module is electrically connected to the data acquisition and processing module, the data acquisition and processing module is electrically connected to the alarm and shutdown control module, the data acquisition and processing module is electrically connected to the dynamic adjustment module, the alarm and shutdown control module is electrically connected to the intelligent prompt module, the dynamic adjustment module is electrically connected to the adaptive optimization module, the environmental adaptation module is electrically connected to the multi-dimensional environmental monitoring module, the environmental adaptation module is electrically connected to the environmental control and sharing module, and the multi-dimensional environmental monitoring module is electrically connected to the environmental control and sharing module; the intelligent prompt module is electrically connected to the data visualization module; the environmental control and sharing module is electrically connected to the virtual assistant module, the virtual assistant module is electrically connected to the personalized customization module, and the data visualization module is electrically connected to the personalized customization module.

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