Paper packaging box processing and production monitoring system and monitoring method thereof

Abstract

The invention belongs to the technical field of production monitoring systems, and discloses a paper packaging box processing and production monitoring system and a monitoring method thereof. Comprising a data acquisition module, a data processing and analysis module, a user interface and interaction module, a fault diagnosis and maintenance module, a security and privacy module, an investment and return evaluation module and a cloud platform and Internet of Things integration module, and the data acquisition module comprises a sensor module and a data standardization module. According to the invention, comprehensive improvement is carried out for many problems existing in an existing monitoring system, firstly, in the aspect of data acquisition, high-precision sensors are deployed and RESTful API connection is adopted, so that the comprehensiveness and the real-time performance of data are ensured, inaccurate production data caused by the precision problem of monitoring equipment is effectively avoided, and meanwhile, the production efficiency is improved. And due to application of data standardization processing and data cleaning tools, the data quality is further improved, and it is ensured that a user can easily master and fully utilize system functions.

Description

Technical Field

[0001] The invention belongs to the technical field of production monitoring systems, and in particular relates to a paper packaging box processing and production monitoring system and a monitoring method thereof. Background Art

[0002] The paper packaging box processing and production monitoring system is an efficient management tool designed specifically for paper packaging box manufacturers. It combines modern information technology and automation technology to monitor and optimize the entire production process in real time, improve production efficiency, ensure product quality, and reduce production costs. The paper packaging box processing and production monitoring system integrates various sensors, actuators, controllers and software platforms to achieve real-time monitoring and recording of key parameters such as material consumption, equipment status, production speed, and product quality on the production line. The system can automatically collect and analyze data to provide managers with comprehensive production information and decision support.

[0003] However, the current paper packaging box processing and production monitoring system still has many problems in actual use. For example, the monitoring equipment may have accuracy problems, resulting in inaccurate production data, thus affecting production decisions, lack of compatibility between old and new equipment, resulting in the inability to effectively integrate and use data, and the system may experience delays when processing large amounts of data, affecting real-time monitoring and timely decision-making. The system may lack intelligent analysis capabilities and cannot quickly identify and locate faults, resulting in extended downtime. Lack of timely technical support and maintenance services may cause the system to be in a faulty state for a long time. Operators have poor adaptability to the system, resulting in operational errors or inability to fully utilize system functions. The system may face the risk of data leakage, especially in the case of Internet connection. The high initial investment may be difficult to see a return in the short term, especially for small and medium-sized enterprises. In response to the above problems, it is urgent to improve the current processing and production monitoring system. Summary of the invention

[0004] The object of the present invention is to provide a paper packaging box processing and production monitoring system to solve the problems raised in the above background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a paper packaging box processing and production monitoring system and a monitoring method thereof, comprising a data acquisition module, a data processing and analysis module, a user interface and interaction module, a fault diagnosis and maintenance module, a security and privacy module, an investment and return evaluation module, and a cloud platform and Internet of Things integration module, wherein the data acquisition module comprises a sensor module and data standardization, the data processing and analysis module comprises real-time data processing and intelligent analysis tools, the user interface and interaction module comprises user interface design and training and support, the fault and maintenance module comprises automatic fault detection and remote maintenance functions, the security and privacy module comprises data encryption and access control, the investment and return evaluation module comprises a cost-benefit analysis tool and a long-term monitoring and feedback mechanism, the cloud platform and Internet of Things integration module comprises cloud data storage and Internet of Things connection, the sensor module comprises a temperature sensor, a humidity sensor, speed sensor, the data standardization includes data format specification and data cleaning tools, the real-time data processing includes edge computing nodes and data aggregation tools, the intelligent analysis tools include machine learning models and predictive maintenance algorithms, the user interface design includes dashboard design and customized settings, the training and support include user manuals and videos, and online support platforms, the automatic fault detection includes fault alarm systems and fault log records, the remote maintenance function includes remote diagnostic tools and maintenance plan management, the data encryption includes transport layer encryption and storage encryption, the access control includes multi-level user permissions and identity authentication mechanisms, the cost-benefit analysis tools include ROI computers and data visualization analysis, the long-term monitoring and feedback mechanisms include regular evaluation reports and user feedback channels, the cloud data storage includes centralized storage solutions and data backup solutions, the Internet of Things connections include device networking solutions and real-time data transmission.

[0006] As a further technical solution of the present invention, the temperature sensor is a thermocouple, and the interface adopts RS485, which is convenient for remote monitoring. The humidity sensor is a capacitive type with adjustable sensitivity. The speed sensor is a photoelectric type and is installed on a conveyor belt. The temperature sensor is used to monitor the temperature of the production environment to ensure that the material is processed at an appropriate temperature. The humidity sensor is used to monitor the air humidity in real time to prevent the paper from getting damp. The speed sensor is used to monitor the operating speed of the production line to ensure compliance with production standards. The data format specification adopts JSON format to facilitate cross-platform interaction. The data cleaning tool uses the Pandas library for data processing to remove outliers. The data format specification is used to formulate a unified data format and interface standard to ensure compatibility of sensor data. The data cleaning tool is used to remove noise and redundant data to improve data quality.

[0007] As a further technical solution of the present invention, the hardware of the edge computing node adopts NVIDIA Jetson, the data aggregation tool adopts Apache Kafka to process data streams and generate real-time reports, the edge computing node is used to deploy edge computing devices at the production site, quickly process local data, and reduce network latency, the data aggregation tool is used to aggregate real-time data and generate concise reports and charts, and its learning model adopts Scikit-learn to analyze historical data and identify anomalies, the predictive maintenance algorithm adopts a regression model to predict equipment life and optimize maintenance plans, the machine learning model is used to identify potential abnormal patterns in the production process, and the predictive maintenance algorithm is used to predict failures based on equipment status, perform maintenance in advance, and reduce downtime.

[0008] As a further technical solution of the present invention, the dashboard design adopts the React framework to create a dynamic data visualization interface, the custom settings allow users to select monitoring indicators and alarm thresholds, the dashboard is designed to display production status and key indicators in real time, the user manual and video provide step-by-step guidance on operations by creating concise PDF manuals and short videos, and the online support platform provides real-time help by setting up FAQ and online chat functions.

[0009] As a further technical solution of the present invention, the fault alarm system sets thresholds and uses text messages or emails for real-time alarms. The fault log records fault information in a database to facilitate later analysis. The remote diagnostic tool uses remote desktop technology to enable technicians to remotely access the system. The maintenance plan management automatically generates calendar reminders to ensure that maintenance is carried out on time.

[0010] As a further technical solution of the present invention, the transport layer encryption uses TLS1.2 version to ensure data transmission security, the storage encryption adopts AES-256 encryption technology to protect static data, the multi-level user permissions adopt role-based access control (RBAC) to ensure hierarchical management of data permissions, the identity authentication mechanism uses single sign-on (SSO) and multi-factor authentication (MFA) to improve security, the multi-level user permissions ensure that only authorized personnel can access sensitive data by setting different access permissions, and the identity authentication mechanism implements multi-factor authentication to improve system security.

[0011] As a further technical solution of the present invention, the ROI calculator helps users input cost and benefit data by designing a simple input interface, the data visualization analysis uses Power BI to display the return on investment, the ROI calculator helps enterprises calculate the return on investment and conduct financial evaluation by developing tools, the data visualization analysis displays the changing trends of investment and return through charts to assist decision-making, the regular evaluation report provides feedback and improvement suggestions by regularly generating system performance and economic benefit reports, and the user feedback channel collects usage experience and optimizes the system by establishing a user feedback system.

[0012] As a further technical solution of the present invention, the centralized storage solution uses Azure Blob Storage to centrally manage data, the data backup system uses regular automatic backup to cloud storage to ensure data security, the centralized storage solution uses cloud technology to centrally manage and store all production data for easy access and analysis, the data backup system ensures data security and reliability by regularly backing up data, the device networking solution uses the MQTT protocol to achieve communication between devices to ensure low latency, the real-time data transmission uses WebSocket to achieve real-time data push and enhance interactivity, the device networking solution is used to connect all devices through Internet of Things technology to support remote monitoring and management, and the real-time data transmission is used to ensure real-time transmission of data between devices and improve response speed.

[0013] A monitoring method for a paper packaging box processing and production monitoring system comprises the following steps:

[0014] S1: Determine the monitoring target and make sure that the equipment failure rate does not exceed 2%;

[0015] S2: Design monitoring architecture, using a three-layer architecture including data collection layer, data processing layer, and user interface layer;

[0016] S3: Data collection, including sensor deployment, that is, installing temperature, humidity, and vibration sensors at key locations to ensure full coverage, and using RESTful APIs to connect sensors to the central control system;

[0017] S4: Data processing, including data cleaning, that is, real-time data cleaning, deletion of invalid data, and fault prediction using random forest algorithm;

[0018] S5: User interface design, creating a concise and clear dashboard that displays real-time data and alarm information, and users can view detailed data and historical records by clicking;

[0019] S6: Fault diagnosis, setting fault thresholds, and monthly equipment inspections to ensure that sensors are functioning properly;

[0020] S7: Security and privacy control, all data transmission is encrypted using AES-256, different user roles are set, and access to sensitive data is restricted;

[0021] S8: Deployment and testing: Install all hardware and configure software in the production environment, perform functional testing, and ensure that all modules work properly;

[0022] S9: Optimize feedback, collect user opinions every quarter, optimize the system, and update system functions every six months based on feedback.

[0023] The beneficial effects of the present invention are as follows:

[0024] 1. The present invention has made comprehensive improvements on many problems existing in the existing monitoring system. First, in terms of data collection, by deploying high-precision sensors and adopting RESTful API connections, the comprehensiveness and real-time nature of the data are ensured, and the inaccurate production data caused by the accuracy of the monitoring equipment is effectively avoided. At the same time, the application of data standardization processing and data cleaning tools further improves the data quality and provides reliable guarantees for subsequent data analysis and decision-making. In terms of data processing and analysis, the present invention introduces edge computing nodes and real-time data processing tools to achieve rapid processing of local data, significantly reduce network delays, and improve the response speed of the system. In addition, the application of intelligent analysis tools, such as machine learning models and predictive maintenance algorithms, can automatically identify abnormal patterns in the production process and predict equipment life, providing strong support for optimizing maintenance plans and reducing downtime. The design of the user interface and interactive modules also fully considers user experience and convenience. The dashboard design is concise and clear, and can display the production status and key indicators in real time. Users can also select monitoring indicators and alarm thresholds through custom settings, which further enhances the flexibility and practicality of the system. At the same time, training and support measures such as user manuals and videos, and online support platforms ensure that users can easily get started and make full use of system functions.

[0025] 2. The present invention realizes real-time monitoring and recording of equipment failures by setting fault thresholds and fault log records, providing technicians with timely and accurate fault information, remote diagnostic tools and maintenance plan management, further improving the efficiency and accuracy of fault handling, ensuring the stable operation of the production line, and the setting of the security and privacy module fully considers the security and privacy protection of data. The application of transport layer encryption and storage encryption technology ensures the security of data transmission and storage. At the same time, the implementation of multi-level user permissions and identity authentication mechanisms effectively prevents unauthorized access and data leakage risks, providing strong guarantees for the safe operation of the system. The investment and return evaluation module and the cloud platform and Internet of Things integration module also realize the evaluation of system investment benefits and remote monitoring and management. The application of ROI calculator and data visualization analysis provides enterprises with a comprehensive analysis of investment returns, which helps enterprises make more informed decisions, while the application of centralized storage solutions and data backup systems ensures the reliability and security of data, providing strong support for the long-term development of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is the overall system block diagram of the present invention;

[0027] Figure 2 is a system block diagram of the data acquisition module of the present invention;

[0028] Figure 3 is a system block diagram of the data processing and analysis module of the present invention;

[0029] Figure 4 A system block diagram of the user interface and interaction module of the present invention;

[0030] Figure 5 is a system block diagram of the fault and maintenance module of the present invention;

[0031] Figure 6 is a system block diagram of the security and privacy module of the present invention;

[0032] Figure 7 A system block diagram of the investment and return evaluation module of the present invention;

[0033] Figure 8 It is a system block diagram of the cloud platform and Internet of Things integration module of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments 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.

[0035] like Figures 1 to 8 As shown, in an embodiment of the present invention, a paper packaging box processing and production monitoring system includes a data acquisition module, a data processing and analysis module, a user interface and interaction module, a fault diagnosis and maintenance module, a security and privacy module, an investment and return evaluation module, and a cloud platform and Internet of Things integration module. The data acquisition module includes a sensor module and data standardization, the data processing and analysis module includes real-time data processing and intelligent analysis tools, the user interface and interaction module includes user interface design and training and support, the fault and maintenance module includes automatic fault detection and remote maintenance functions, the security and privacy module includes data encryption and access control, the investment and return evaluation module includes a cost-effectiveness analysis tool and a long-term monitoring and feedback mechanism, the cloud platform and Internet of Things integration module includes cloud data storage and Internet of Things connection, the sensor module includes a temperature sensor, a humidity sensor, a speed sensor Sensors, data standardization includes data format specifications and data cleaning tools, real-time data processing includes edge computing nodes and data aggregation tools, intelligent analysis tools include machine learning models and predictive maintenance algorithms, user interface design includes dashboard design and customized settings, training and support include user manuals and videos, and online support platforms, automatic fault detection includes fault alarm systems and fault log records, remote maintenance functions include remote diagnostic tools and maintenance plan management, data encryption includes transport layer encryption and storage encryption, access control includes multi-level user permissions and identity authentication mechanisms, cost-benefit analysis tools include ROI computers and data visualization analysis, long-term monitoring and feedback mechanisms include regular evaluation reports and user feedback channels, cloud data storage includes centralized storage solutions and data backup solutions, IoT connections include device networking solutions and real-time data transmission.

[0036] The system collects production data in real time through sensor modules and uses intelligent analysis tools to improve production efficiency. At the same time, it has a user-friendly interface and provides training and support. In addition, the system has automatic fault detection and remote maintenance functions to ensure production safety; data encryption and access control protect data security. The investment and return evaluation module helps optimize cost-effectiveness; the cloud platform and the Internet of Things are integrated to achieve efficient data storage and transmission. Overall, the system improves the intelligence and safety of paper packaging box processing and production.

[0037] like Figure 2As shown, the temperature sensor is a thermocouple, and the interface uses RS485 for remote monitoring. The humidity sensor is capacitive with adjustable sensitivity. The speed sensor is photoelectric and installed on the conveyor belt. The temperature sensor is used to monitor the temperature of the production environment to ensure that the material is processed at an appropriate temperature. The humidity sensor is used to monitor the air humidity in real time to prevent the paper from getting damp. The speed sensor is used to monitor the running speed of the production line to ensure compliance with production standards. The data format specification uses the JSON format to facilitate cross-platform interaction. The data cleaning tool uses the Pandas library for data processing and removal of outliers. The data format specification is used to formulate a unified data format and interface standard to ensure compatibility of sensor data. The data cleaning tool is used to remove noise and redundant data and improve data quality.

[0038] Thermocouple temperature sensors, capacitive humidity sensors and photoelectric speed sensors are used to monitor the temperature, humidity and production line speed of the production environment to ensure that the processing conditions are suitable. The RS485 interface and JSON data format specification are used to facilitate remote monitoring and cross-platform interaction. The Pandas library is used for data cleaning to improve data quality. Overall, the system effectively ensures the stability and efficiency of paper packaging box processing and production, and improves the intelligent level of production management.

[0039] like Figure 3 As shown, the hardware of the edge computing node uses NVIDIA Jetson, the data aggregation tool uses Apache Kafka for data stream processing and real-time report generation, the edge computing node is used to deploy edge computing devices at the production site, quickly process local data, and reduce network latency, the data aggregation tool is used to aggregate real-time data, generate concise reports and charts, and its learning model uses Scikit-learn to analyze historical data and identify anomalies, the predictive maintenance algorithm uses a regression model to predict equipment life and optimize maintenance plans, the machine learning model is used to identify potential abnormal patterns in the production process, and the predictive maintenance algorithm is used to predict failures based on equipment status, perform maintenance in advance, and reduce downtime.

[0040] NVIDIA Jetson edge computing nodes are used to quickly process local data and reduce network latency; Apache Kafka data aggregation tools generate real-time reports to improve management efficiency. At the same time, Scikit-learn machine learning models identify abnormal patterns, and regression models predict equipment life, enabling predictive maintenance, reducing downtime, and optimizing production processes. Overall, the system has significantly improved the intelligence level and maintenance efficiency of paper packaging box processing and production.

[0041] like Figure 4As shown in the figure, the dashboard design uses the React framework to create a dynamic data visualization interface. Custom settings allow users to select monitoring indicators and alarm thresholds. The dashboard is designed to display production status and key indicators in real time. The user manual and video provide step-by-step instructions by creating concise PDF manuals and short videos. The online support platform provides real-time help by setting up FAQ and online chat functions.

[0042] By adopting the React framework design, dynamic data visualization is achieved, and customized settings enhance user flexibility. The user manual, video, and online support platform provide comprehensive operation guidance and real-time help, ensuring that users can easily master the use of the system, effectively monitor production status and key indicators, and improve the monitoring efficiency and user satisfaction of paper packaging box processing and production.

[0043] like Figure 5 As shown, the fault alarm system sets thresholds and uses text messages or emails for real-time alarms. The fault log uses a database to record fault information for later analysis. The remote diagnostic tool uses remote desktop technology to allow technicians to remotely access the system. The maintenance plan management automatically generates calendar reminders to ensure that maintenance is carried out on time.

[0044] The fault alarm system with set thresholds provides real-time notifications via SMS or email to ensure that problems are discovered in a timely manner; fault log records facilitate in-depth analysis at a later stage; remote diagnostic tools allow technicians to solve problems remotely; and maintenance plan management ensures that maintenance is carried out on time by automatically generating reminders, which improves the overall stability of the system and maintenance efficiency.

[0045] like Figure 6 As shown, the transport layer encryption uses TLS1.2 version to ensure data transmission security, storage encryption uses AES-256 encryption technology to protect static data, multi-level user permissions use role-based access control (RBAC) to ensure hierarchical management of data permissions, and the authentication mechanism uses single sign-on (SSO) and multi-factor authentication (MFA) to improve security. Multi-level user permissions ensure that only authorized personnel can access sensitive data by setting different access rights, and the authentication mechanism implements multi-factor authentication to improve system security.

[0046] Data security is ensured by adopting TLS1.2 transport layer encryption and AES-256 storage encryption. Multi-level user permissions and authentication mechanisms, including RBAC, SSO and MFA, implement hierarchical management of data permissions and multiple authentications, effectively prevent unauthorized access, and improve the overall security and data protection capabilities of the system, ensuring the stable operation of the paper packaging box processing and production monitoring system.

[0047] like Figure 7As shown, the ROI calculator helps users input cost and benefit data by designing a simple input interface. Data visualization analysis uses Power BI to display the return on investment. The ROI calculator helps companies calculate the return on investment and conduct financial evaluations through development tools. Data visualization analysis uses charts to display the changing trends of investment and returns to assist in decision-making. Regular evaluation reports provide feedback and improvement suggestions by regularly generating system performance and economic benefit reports. User feedback channels collect user experience and optimize the system by establishing a user feedback system.

[0048] Equipped with ROI calculator and data visualization analysis, it helps enterprises to accurately calculate the return on investment, intuitively display the trend of investment and return, and assist in financial decision-making. Regular evaluation reports and user feedback channels provide system performance feedback and improvement suggestions, continuously optimize user experience and system performance, and ensure the efficient operation of the paper packaging box processing and production monitoring system and maximize investment value.

[0049] like Figure 8 As shown, the centralized storage solution uses Azure Blob Storage to centrally manage data. The data backup system uses regular automatic backup to cloud storage to ensure data security. The centralized storage solution uses cloud technology to centrally manage and store all production data for easy access and analysis. The data backup system ensures data security and reliability by regularly backing up data. The device networking solution uses the MQTT protocol to achieve communication between devices to ensure low latency. Real-time data transmission uses WebSocket to achieve real-time data push and enhance interactivity. The device networking solution is used to connect all devices through the Internet of Things technology to support remote monitoring and management. Real-time data transmission is used to ensure real-time data transmission between devices and improve response speed.

[0050] Azure Blob Storage is used to centrally manage data and automatically back up data regularly to ensure data security. The MQTT protocol enables low-latency communication between devices, and WebSocket enables real-time data push and enhances interactivity. The overall solution supports remote monitoring and management of devices, ensures real-time data transmission, improves response speed, and provides efficient and reliable IoT technology support for paper packaging box processing and production, ensuring the stability and intelligence level of the production process.

[0051] A monitoring method for a paper packaging box processing and production monitoring system comprises the following steps:

[0052] S1: Determine the monitoring target and make sure that the equipment failure rate does not exceed 2%;

[0053] S2: Design monitoring architecture, using a three-layer architecture including data collection layer, data processing layer, and user interface layer;

[0054] S3: Data collection, including sensor deployment, that is, installing temperature, humidity, and vibration sensors at key locations to ensure full coverage, and using RESTful APIs to connect sensors to the central control system;

[0055] S4: Data processing, including data cleaning, that is, real-time data cleaning, deletion of invalid data, and fault prediction using random forest algorithm;

[0056] S5: User interface design, creating a concise and clear dashboard that displays real-time data and alarm information, and users can view detailed data and historical records by clicking;

[0057] S6: Fault diagnosis, setting fault thresholds, and monthly equipment inspections to ensure that sensors are functioning properly;

[0058] S7: Security and privacy control, all data transmission is encrypted using AES-256, different user roles are set, and access to sensitive data is restricted;

[0059] S8: Deployment and testing: Install all hardware and configure software in the production environment, perform functional testing, and ensure that all modules work properly;

[0060] S9: Optimize feedback, collect user opinions every quarter, optimize the system, and update system functions every six months based on feedback.

[0061] By clarifying the monitoring objectives and designing a three-layer monitoring architecture, comprehensive monitoring of the production process is achieved. In the data collection stage, sensors deployed at key locations are connected to the central control system through RESTful APIs to ensure the comprehensiveness and real-time nature of the data. In the data processing stage, the random forest algorithm is used for fault prediction, which improves the accuracy and timeliness of fault detection. The user interface design is concise and clear, which makes it convenient for users to view real-time data and alarm information, and improves monitoring efficiency. At the same time, the settings of fault diagnosis, security and privacy control, etc., further ensure the safety of production and the privacy of data. Finally, through deployment and testing, optimization feedback and other steps, the stability and continuous optimization of the system are ensured. The application of this method has effectively improved the monitoring level and production efficiency of paper packaging box processing and production, and provided strong support for the intelligent transformation of enterprises.

[0062] 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 paper packaging box processing and production monitoring system, including a data acquisition module, a data processing and analysis module, a user interface and interaction module, a fault diagnosis and maintenance module, a security and privacy module, an investment and return evaluation module, and a cloud platform and Internet of Things integration module. The data acquisition module includes a sensor module and data standardization. The data processing and analysis module includes real-time data processing and intelligent analysis tools. The user interface and interaction module includes user interface design, training and support. The fault and maintenance module includes automatic fault detection and remote maintenance functions. The security and privacy module includes data encryption and access control. The investment and return evaluation module includes a cost-benefit analysis tool and a long-term monitoring and feedback mechanism. The cloud platform and Internet of Things integration module includes cloud data storage and Internet of Things connection. The sensor module includes a temperature sensor, a humidity sensor, and a speed sensor. The data standardization module includes a temperature sensor, a humidity sensor, and a speed sensor. It includes data format specifications and data cleaning tools. The real-time data processing includes edge computing nodes and data aggregation tools. The intelligent analysis tools include machine learning models and predictive maintenance algorithms. The user interface design includes dashboard design and customized settings. The training and support include user manuals and videos, and online support platforms. The automatic fault detection includes fault alarm system and fault log records. The remote maintenance function includes remote diagnostic tools and maintenance plan management. The data encryption includes transport layer encryption and storage encryption. The access control includes multi-level user permissions and identity authentication mechanism. The cost-benefit analysis tool includes ROI computer and data visualization analysis. The long-term monitoring and feedback mechanism includes regular evaluation reports and user feedback channels. The cloud data storage includes centralized storage solutions and data backup solutions. The Internet of Things connection includes device networking solutions and real-time data transmission.

2. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The temperature sensor is of the type of thermocouple, and the interface adopts RS485, which is convenient for remote monitoring. The humidity sensor is of the type of capacitive type, and the sensitivity is adjustable. The speed sensor is of the type of photoelectric type and is installed on the conveyor belt. The temperature sensor is used to monitor the temperature of the production environment to ensure that the material is processed at an appropriate temperature. The humidity sensor is used to monitor the air humidity in real time to prevent the paper from getting damp. The speed sensor is used to monitor the running speed of the production line to ensure compliance with production standards. The data format specification adopts the JSON format to facilitate cross-platform interaction. The data cleaning tool uses the Pandas library for data processing and removes outliers. The data format specification is used to formulate a unified data format and interface standard to ensure compatibility of sensor data. The data cleaning tool is used to remove noise and redundant data to improve data quality.

3. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The hardware of the edge computing node adopts NVIDIA Jetson, the data aggregation tool adopts Apache Kafka for data stream processing and generates real-time reports, the edge computing node is used to deploy edge computing devices at the production site, quickly process local data, and reduce network latency, the data aggregation tool is used to aggregate real-time data and generate concise reports and charts, and its learning model adopts Scikit-learn to analyze historical data and identify anomalies, the predictive maintenance algorithm adopts a regression model to predict equipment life and optimize maintenance plans, the machine learning model is used to identify potential abnormal patterns in the production process, and the predictive maintenance algorithm is used to predict failures based on equipment status, perform maintenance in advance, and reduce downtime.

4. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The dashboard design adopts the React framework to create a dynamic data visualization interface. The custom settings allow users to select monitoring indicators and alarm thresholds. The dashboard is designed to display production status and key indicators in real time. The user manual and video provide step-by-step instructions for operation by creating concise PDF manuals and short videos. The online support platform provides real-time help by setting up FAQ and online chat functions.

5. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The fault alarm system sets thresholds and uses text messages or emails for real-time alarms. The fault log records fault information in a database to facilitate later analysis. The remote diagnostic tool uses remote desktop technology to allow technicians to remotely access the system. The maintenance plan management automatically generates calendar reminders to ensure that maintenance is carried out on time.

6. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The transport layer encryption uses TLS1.2 version to ensure data transmission security, the storage encryption adopts AES-256 encryption technology to protect static data, the multi-level user permissions adopt role-based access control (RBAC) to ensure hierarchical management of data permissions, the identity authentication mechanism uses single sign-on (SSO) and multi-factor authentication (MFA) to improve security, the multi-level user permissions ensure that only authorized personnel can access sensitive data by setting different access permissions, and the identity authentication mechanism implements multi-factor authentication to improve system security.

7. A paper packaging box processing and production monitoring system according to claim 1, characterized in that: The ROI calculator helps users input cost and benefit data by designing a simple input interface. The data visualization analysis uses Power BI to display the return on investment. The ROI calculator helps enterprises calculate the return on investment and conduct financial evaluation by developing tools. The data visualization analysis uses charts to display the changing trends of investment and return to assist in decision-making. The regular evaluation report provides feedback and improvement suggestions by regularly generating system performance and economic benefit reports. The user feedback channel collects usage experience and optimizes the system by establishing a user feedback system.

8. The paper packaging box processing and production monitoring system according to claim 1, characterized in that: The centralized storage solution uses Azure Blob Storage to centrally manage data. The data backup system uses regular automatic backup to cloud storage to ensure data security. The centralized storage solution uses cloud technology to centrally manage and store all production data for easy access and analysis. The data backup system ensures data security and reliability by regularly backing up data. The device networking solution uses the MQTT protocol to achieve communication between devices to ensure low latency. The real-time data transmission uses WebSocket to achieve real-time data push and enhance interactivity. The device networking solution is used to connect all devices through the Internet of Things technology to support remote monitoring and management. The real-time data transmission is used to ensure real-time transmission of data between devices and improve response speed.

9. The monitoring method of the paper packaging box processing and production monitoring system according to claim 1, characterized in that: The following steps are involved: S1: Determine the monitoring target and make sure that the equipment failure rate does not exceed 2%; S2: Design monitoring architecture, using a three-layer architecture including data collection layer, data processing layer, and user interface layer; S3: Data collection, including sensor deployment, that is, installing temperature, humidity, and vibration sensors at key locations to ensure full coverage, and using RESTful APIs to connect sensors to the central control system; S4: Data processing, including data cleaning, that is, real-time data cleaning, deletion of invalid data, and fault prediction using random forest algorithm; S5: User interface design, creating a concise and clear dashboard that displays real-time data and alarm information, and users can view detailed data and historical records by clicking; S6: Fault diagnosis, setting fault thresholds, and monthly equipment inspections to ensure that sensors are functioning properly; S7: Security and privacy control, all data transmission is encrypted using AES-256, different user roles are set, and access to sensitive data is restricted; S8: Deployment and testing: Install all hardware and configure software in the production environment, perform functional testing, and ensure that all modules work properly; S9: Optimize feedback, collect user opinions every quarter, optimize the system, and update system functions every six months based on feedback.

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