Intelligent roller maintenance system

Abstract

The invention relates to the technical field of mechanical equipment maintenance, in particular to an intelligent roller maintenance system which comprises a data acquisition module, a model simulation module, an analysis and decision module and a user interaction module. The model simulation module obtains running state data of a sorting equipment roller and a mechanical structure of the sorting equipment roller in real time based on the data acquisition module to construct a digital twinborn model of the roller, and the analysis and decision module performs deep analysis based on dynamic data, reflected by the digital twinborn model, of the sorting equipment roller. And the user interaction module is used for displaying the current state, the fault prediction result and the maintenance plan of the roller of the sorting equipment, according to the technical scheme of the application, through the digital twinborn technology and intelligent prediction analysis, precise management and optimal maintenance of the roller and related parts thereof are realized, and remarkable economic and technical benefits are brought.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment maintenance, and in particular to an intelligent roller maintenance system. Background Art

[0002] With the rapid development of the logistics industry, automated sorting equipment has been widely used in various logistics scenarios. The operating status of rollers, which are the core components of sorting equipment, directly affects the sorting efficiency. However, rollers are prone to wear, offset or fatigue failure in long-term high-load and high-frequency work. Traditional maintenance methods mainly rely on fixed-cycle maintenance and manual inspections, which is not only time-consuming and labor-intensive, but also prone to excessive or delayed maintenance, resulting in shortened equipment life or even unexpected downtime. In order to solve these problems, digital twin technology came into being. Digital twin is a cutting-edge technology that combines physical entities with virtual models. It collects data from physical devices in real time through sensors, and uses simulation algorithms and artificial intelligence technology to reproduce the operating status of equipment in virtual space. At present, this technology has been successfully applied in aerospace, intelligent manufacturing, and intelligent buildings, but its application in logistics sorting equipment, especially roller management, is still in its infancy. In recent years, digital twin technology has been widely used in equipment monitoring and maintenance. By establishing a virtual model of the equipment and combining real-time data for dynamic monitoring, digital twins can fully reflect the operating status of the equipment and provide accurate health assessment and early warning information. The key parameters of the equipment (such as temperature, vibration, pressure, etc.) are collected in real time by sensors and mapped to the virtual model, so as to realize the continuous monitoring of the equipment status. In addition, digital twin technology is also widely used in predictive maintenance. By analyzing the historical data of the equipment and combining the simulation results of the virtual model, it is possible to identify potential faults and wear trends in advance, provide timely maintenance suggestions for maintenance personnel, and avoid the risk of downtime caused by equipment failure. It can also optimize equipment management through real-time simulation and data analysis, and improve the operation efficiency and maintenance accuracy of the equipment. Although digital twin technology has performed well in improving the intelligence of equipment management and optimizing maintenance cycles, there are still some technical challenges to overcome. For example, multi-dimensional data fusion and improvement of dynamic simulation accuracy are one of the main problems currently faced. Although the existing digital twin technology provides a relatively comprehensive solution in equipment monitoring and maintenance, it still has some obvious shortcomings. First of all, although digital twins can reflect the virtual model of the equipment, the existing technology still has deficiencies in data fusion and real-time dynamic simulation, and cannot accurately simulate the full range of equipment operation status in complex environments, resulting in blind spots in the assessment of equipment health. Secondly, although digital twins can integrate a large amount of data for analysis, the accuracy and robustness of existing algorithms and models are limited when predicting potential failures and wear trends, and preventive maintenance of equipment has not been fully realized, and still relies on data analysis after the failure occurs. Finally, existing technologies still rely on manual intervention and fixed-cycle maintenance modes. Although real-time data can be obtained, they lack the ability to deeply learn and automatically adjust failure modes, which means that equipment management has not yet reached full intelligence, resulting in increased maintenance efficiency and costs.These shortcomings have, to some extent, affected the overall effectiveness of digital twin technology in equipment management and limited its effectiveness in practical applications. Summary of the invention

[0003] In view of this, the present invention aims at the deficiencies of the prior art and proposes an intelligent roller maintenance system, aiming to solve at least one of the problems raised by the above background technology.

[0004] The present invention provides an intelligent roller maintenance system, comprising: a data acquisition module, the data acquisition module is used to obtain the running status data of the roller of the sorting equipment in real time;

[0005] A model simulation module, wherein the model simulation module acquires the operating status data of the roller of the sorting device and the mechanical structure of the roller of the sorting device in real time based on the data acquisition module to build a digital twin model of the roller;

[0006] An analysis and decision module, which performs in-depth analysis based on the dynamic data of the sorting equipment rollers reflected by the digital twin model;

[0007] A user interaction module is used to display the current status, fault prediction results and maintenance plan of the rollers of the sorting equipment.

[0008] In some embodiments, the data acquisition module acquires the operating status data of the rollers of the sorting equipment in real time through sensors, wherein the sensors include: vibration sensors, temperature sensors and strain sensors.

[0009] In some embodiments, the operating status data includes: vibration, temperature and strain data information.

[0010] In some embodiments, the mechanical structure of the sorting device roller includes: geometric shape, material properties, and connection method.

[0011] In some embodiments, the analysis and decision module performs in-depth analysis on the dynamic data of the rollers of the sorting equipment reflected by the digital twin model, including using a regression analysis method to perform in-depth analysis on the operating data of the rollers to identify key factors affecting equipment performance.

[0012] In some embodiments, the method further includes applying a cluster analysis algorithm to classify the operating status of the roller and identify different operating modes or fault types.

[0013] In some embodiments, the method further includes performing a time series analysis on the roller operation data to predict the roller operation trend and the time when a failure occurs.

[0014] In some embodiments, the user interaction module is used to display the current status of the roller of the sorting device including temperature, vibration and strain values, wherein the temperature, vibration and strain values ​​are displayed via a curve graph.

[0015] In some embodiments, the maintenance plan includes regular inspections, replacement of parts, and adjustment of parameters.

[0016] In some embodiments, a maintenance reminder function is set according to a maintenance plan, and information reminders are provided according to a scheduled maintenance time.

[0017] Compared with the prior art, the beneficial effect of the present invention is that through the combination of digital twin model and AI algorithm, accurate prediction of equipment failure can be achieved. This predictive ability can significantly reduce the occurrence of unplanned downtime, ensure the stable operation of equipment, and thus improve overall production efficiency. Traditional maintenance methods often rely on fixed-cycle maintenance and manual inspections, which is not only time-consuming and labor-intensive, but also prone to excessive or delayed maintenance. However, this technology adjusts the maintenance cycle according to the actual operating status and health status of the equipment through real-time monitoring and predictive analysis to ensure the accuracy of maintenance and avoid the loss of equipment caused by excessive maintenance and delayed maintenance. Traditional periodic maintenance methods are prone to excessive maintenance or delayed maintenance, increasing unnecessary costs and waste of resources. This technology adjusts the maintenance cycle according to the actual operating status and health status of the equipment through real-time monitoring and predictive analysis to ensure the accuracy of maintenance and avoid the loss of equipment caused by excessive maintenance and delayed maintenance. This dynamic adjustment capability makes maintenance work more flexible and efficient, reducing unnecessary downtime and maintenance costs. By identifying equipment wear trends in advance, this technology helps to extend the service life of the equipment. Reduce losses in long-term operation and ensure that the equipment functions more efficiently, thereby reducing the frequency and related costs of replacing equipment. Real-time monitoring of equipment status and rapid response to abnormal signals can greatly improve the operating efficiency of equipment and reduce the impact of interruptions. This not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. Real-time monitoring of equipment status and rapid response to abnormal signals can greatly improve the operating efficiency of equipment. Reduce interruptions caused by equipment failures, thereby reducing production downtime and improving overall productivity. In addition, data-driven decision-making and maintenance methods have improved the scientificity and efficiency of equipment management. It helps to improve the accuracy and effectiveness of equipment life cycle management and bring higher operational value to enterprises. This scientific management method not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. Data-driven decision-making and maintenance methods have improved the scientificity and efficiency of equipment management. It helps to improve the accuracy and effectiveness of equipment life cycle management and bring higher operational value to enterprises. This scientific management method not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. By integrating sensor data with advanced data analysis algorithms, early signs of potential failures can be effectively identified, and equipment wear, failure trends and operational abnormalities can be predicted in advance. Based on fault pattern recognition and machine learning algorithms, the system can automatically generate maintenance recommendations and optimization plans to avoid failures and reduce unexpected downtime. By avoiding unplanned downtime, reducing resource waste caused by excessive and delayed maintenance, and extending the service life of equipment, this technical solution can significantly reduce the overall cost of long-term operation and maintenance, which is crucial for enterprises to maintain profitability in a highly competitive market environment.Combining the above points, this technical solution not only improves the operating efficiency and reliability of the equipment, but also reduces operating costs, and ultimately enhances the market competitiveness of the enterprise.

[0018] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0019] Other features and aspects of the present disclosure will become more apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is a functional block diagram of the intelligent roller maintenance system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0024] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0025] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0026] As mentioned in the background technology, the roller, which is the core component of the sorting equipment, has a direct impact on the sorting efficiency due to its operating state. However, the roller is prone to wear, offset or fatigue failure in long-term high-load and high-frequency work. The traditional maintenance method mainly relies on fixed-cycle maintenance and manual inspection, which is not only time-consuming and labor-intensive, but also prone to excessive or delayed maintenance, resulting in a shortened service life of the equipment or even unexpected downtime. In order to solve these problems, digital twin technology came into being. Digital twin is a cutting-edge technology that combines physical entities with virtual models. It collects data from physical devices in real time through sensors, and uses simulation algorithms and artificial intelligence technology to reproduce the operating status of the equipment in virtual space. At present, this technology has been successfully applied in the fields of aerospace, intelligent manufacturing and intelligent buildings, but its application in logistics sorting equipment, especially in the field of roller management, is still in its infancy. In recent years, digital twin technology has been widely used in equipment monitoring and maintenance. By establishing a virtual model of the equipment and combining real-time data for dynamic monitoring, digital twin can fully reflect the operating status of the equipment and provide accurate health assessment and early warning information. The key parameters of the equipment (such as temperature, vibration, pressure, etc.) are collected in real time by sensors and mapped to the virtual model, so as to realize the continuous monitoring of the equipment status. In addition, digital twin technology is also widely used in predictive maintenance. By analyzing the historical data of the equipment and combining the simulation results of the virtual model, it is possible to identify potential faults and wear trends in advance, provide timely maintenance suggestions for maintenance personnel, and avoid the risk of downtime caused by equipment failure. It can also optimize equipment management through real-time simulation and data analysis, and improve the operation efficiency and maintenance accuracy of the equipment. Although digital twin technology has performed well in improving the intelligence of equipment management and optimizing maintenance cycles, there are still some technical challenges to overcome. For example, multi-dimensional data fusion and improvement of dynamic simulation accuracy are one of the main problems currently faced. Although the existing digital twin technology provides a relatively comprehensive solution in equipment monitoring and maintenance, it still has some obvious shortcomings. First of all, although digital twins can reflect the virtual model of the equipment, the existing technology still has deficiencies in data fusion and real-time dynamic simulation, and cannot accurately simulate the full range of equipment operation status in complex environments, resulting in blind spots in the assessment of equipment health. Secondly, although digital twins can integrate a large amount of data for analysis, the accuracy and robustness of existing algorithms and models are limited when predicting potential failures and wear trends. They fail to fully realize preventive maintenance of equipment and still rely on data analysis after the failure occurs. Finally, existing technologies still rely on manual intervention and fixed-cycle maintenance modes. Although real-time data can be obtained, they lack the ability to deeply learn and automatically adjust failure modes, which makes equipment management still not fully intelligent, resulting in increased maintenance efficiency and costs. These deficiencies have affected the overall effectiveness of digital twin technology in equipment management to a certain extent and limited its effectiveness in practical applications.

[0027] To improve the above problems, the application proposes an intelligent roller maintenance system, which can accurately predict equipment failures through the combination of digital twin models and AI algorithms. This predictive capability can significantly reduce the occurrence of unplanned downtime, ensure the stable operation of the equipment, and thus improve the overall production efficiency. Traditional maintenance methods often rely on fixed-cycle maintenance and manual inspections, which is not only time-consuming and labor-intensive, but also prone to excessive or delayed maintenance. However, this technology adjusts the maintenance cycle according to the actual operating status and health status of the equipment through real-time monitoring and predictive analysis to ensure the accuracy of maintenance and avoid the loss of equipment caused by excessive maintenance and delayed maintenance. Traditional periodic maintenance methods are prone to excessive maintenance or delayed maintenance, increasing unnecessary costs and waste of resources. This technology adjusts the maintenance cycle according to the actual operating status and health status of the equipment through real-time monitoring and predictive analysis to ensure the accuracy of maintenance and avoid the loss of equipment caused by excessive maintenance and delayed maintenance. This dynamic adjustment capability makes maintenance work more flexible and efficient, reducing unnecessary downtime and maintenance costs. By identifying equipment wear trends in advance, this technology helps to extend the service life of equipment. Reduce losses in long-term operation and ensure that equipment functions more efficiently, thereby reducing the frequency and related costs of replacing equipment. Real-time monitoring of equipment status and rapid response to abnormal signals can greatly improve the operating efficiency of equipment and reduce the impact of interruptions. This not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. Real-time monitoring of equipment status and rapid response to abnormal signals can greatly improve the operating efficiency of equipment. Reduce interruptions caused by equipment failure, thereby reducing production downtime and improving overall productivity. In addition, data-driven decision-making and maintenance methods have improved the scientificity and efficiency of equipment management. It helps to improve the accuracy and effectiveness of equipment life cycle management and bring higher operational value to enterprises. This scientific management method not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. Data-driven decision-making and maintenance methods have improved the scientificity and efficiency of equipment management. It helps to improve the accuracy and effectiveness of equipment life cycle management and bring higher operational value to enterprises. This scientific management method not only improves the reliability of equipment, but also enhances the competitiveness of enterprises. By integrating sensor data with advanced data analysis algorithms, it can effectively identify early signs of potential failures and predict equipment wear, failure trends and operational abnormalities in advance. Based on fault pattern recognition and machine learning algorithms, the system can automatically generate maintenance recommendations and optimization plans to avoid failures and reduce unexpected downtime. By avoiding unplanned downtime, reducing resource waste caused by excessive and delayed maintenance, and extending the service life of equipment, this technical solution can significantly reduce the overall cost of long-term operation and maintenance. This is crucial for companies to maintain profitability in a highly competitive market environment.Combining the above points, this technical solution not only improves the operating efficiency and reliability of the equipment, but also reduces operating costs, and ultimately enhances the market competitiveness of the enterprise.

[0028] See also Figure 1 As shown, an intelligent roller maintenance system according to an embodiment of the present application includes:

[0029] A data acquisition module, which is used to obtain the running status data of the rollers of the sorting equipment in real time;

[0030] A model simulation module, wherein the model simulation module acquires the operating status data of the roller of the sorting device and the mechanical structure of the roller of the sorting device in real time based on the data acquisition module to build a digital twin model of the roller;

[0031] An analysis and decision module, which performs in-depth analysis based on the dynamic data of the sorting equipment rollers reflected by the digital twin model;

[0032] A user interaction module is used to display the current status, fault prediction results and maintenance plan of the rollers of the sorting equipment.

[0033] It should be understood that the operating status data of the roller is collected in real time through various sensors installed on the roller (such as temperature sensors, vibration sensors, speed sensors, etc.). These data include the temperature, vibration frequency, speed, sound, etc. of the roller, which can fully reflect the working status of the roller. High-precision sensors ensure that the collected data is accurate and reliable, providing a solid foundation for subsequent analysis and decision-making. Through continuous monitoring of the roller operation data, potential signs of failure can be discovered in time, maintenance can be carried out in advance, and unexpected downtime and production interruption can be avoided. Based on the real-time collected roller operation data, a digital twin model of the roller is constructed. This model is a virtual roller that can simulate the operation of the actual roller and help engineers better understand the dynamic behavior of the roller. Machine learning algorithms are used to analyze the data in the digital twin model to predict the future state of the roller and identify potential failures or performance degradation trends. This helps to formulate maintenance plans in advance and reduce unexpected downtime. Through the digital twin model, different maintenance strategies and operating conditions can be tested without affecting actual production to find the best maintenance plan. In-depth analysis is carried out based on the dynamic data of the roller reflected by the digital twin model. This includes identifying abnormal patterns, assessing the health of rollers, and predicting the remaining service life. Based on the analysis results, maintenance recommendations and operating instructions are automatically generated. For example, when abnormal vibration of a roller is detected, the system may recommend immediate inspection and repair. The system is able to automatically adjust maintenance plans and operating strategies based on real-time data and analysis results to adapt to the ever-changing production environment. Information such as the current status of the sorting equipment rollers, fault prediction results, and maintenance plans are presented to users in an intuitive manner. This is usually achieved through a user-friendly interface, such as charts, dashboards, etc. Users can remotely access the system via the Internet to view the operating status and maintenance information of the rollers in real time. This allows maintenance personnel to understand the health of the equipment anytime, anywhere.

[0034] In some specific embodiments, the data acquisition module acquires the operating status data of the rollers of the sorting equipment in real time through sensors, wherein the sensors include: vibration sensors, temperature sensors and strain sensors.

[0035] It should be understood that vibration sensors are able to detect small vibration changes in the roller, which may indicate mechanical failure or wear. Through continuous monitoring and analysis of vibration data, potential problems can be discovered in advance and preventive maintenance can be performed. Real-time monitoring of vibration conditions can help optimize the working condition of the roller, reduce unnecessary downtime and maintenance costs, and thus improve overall operating efficiency. Temperature sensors are used to monitor the temperature changes of the roller to prevent equipment damage or performance degradation due to overheating. When the temperature exceeds the preset threshold, the system can automatically take measures to reduce the temperature or issue an alarm. By accurately controlling the temperature of the roller, energy consumption can be reduced, achieving the goal of energy conservation and emission reduction, while ensuring the normal operation of the equipment. Proper temperature control helps to reduce the thermal stress of the roller, extend its service life, reduce the frequency of replacement and maintenance costs. Strain sensors can detect the deformation of the roller under stress, thereby evaluating its structural integrity and stability. This is essential to prevent failures caused by structural fatigue or overload. By analyzing the strain data, the response characteristics of the roller under different loads can be understood, providing a basis for design and improvement, and further improving the performance and reliability of the roller.

[0036] In some specific embodiments, the operating status data includes: vibration, temperature and strain data information.

[0037] In some specific embodiments, the mechanical structure of the sorting device roller includes: geometric shape, material properties, and connection method.

[0038] In some specific embodiments, the analysis and decision-making module performs in-depth analysis on the dynamic data of the rollers of the sorting equipment reflected by the digital twin model, including using a regression analysis method to perform in-depth analysis on the operating data of the rollers to identify key factors affecting equipment performance.

[0039] It should be understood that through regression analysis, a mathematical relationship model between roller operating parameters (such as speed, temperature, vibration, etc.) and equipment performance (such as efficiency, failure rate, etc.) can be established. This model helps to more accurately predict the future performance and possible failures of rollers. After understanding the key factors affecting roller performance, more scientific and reasonable maintenance strategies can be formulated based on these factors. For example, for factors that are prone to performance degradation, preventive measures can be taken in advance to reduce the probability of failure. Through in-depth analysis of roller operation data, bottlenecks and obstacles that affect operating efficiency can be found. Optimizing and adjusting these problems can significantly improve the operating efficiency of the roller, thereby improving the processing capacity of the entire sorting system. Accurate fault prediction and maintenance strategies can reduce unnecessary maintenance work and avoid excessive or insufficient maintenance. This can not only extend the service life of the roller, but also reduce maintenance costs. Through real-time monitoring and data analysis of the roller's operating status, potential problems can be discovered and handled in a timely manner to prevent small problems from turning into major failures. This helps to enhance the stability and reliability of the system. The results of in-depth analysis can provide strong data support for management to help them make more informed decisions. For example, when upgrading, modifying or replacing equipment, the most suitable solution can be selected based on the analysis results.

[0040] In some specific embodiments, a cluster analysis algorithm is also applied to classify the operating status of the roller and identify different operating modes or fault types.

[0041] In some specific embodiments, the method further includes performing time series analysis on the operation data of the roller to predict the roller operation trend and the time when a failure occurs.

[0042] In some specific embodiments, the user interaction module is used to display the current state of the roller of the sorting device including temperature, vibration and strain values, wherein the temperature, vibration and strain values ​​are displayed via a curve graph.

[0043] In some specific embodiments, the maintenance plan includes regular inspection, replacement of parts, and adjustment of parameters.

[0044] In some specific embodiments, a maintenance reminder function is set according to a maintenance plan, and information reminders are issued according to a scheduled maintenance time.

[0045] It should be understood that through cluster analysis, the operation data of the rollers can be divided into different categories, so that potential failure modes can be more easily identified. This helps to improve the accuracy and efficiency of fault diagnosis. Based on the results of cluster analysis, more accurate maintenance plans can be formulated for different operation modes or failure types. This helps to reduce unnecessary maintenance work, reduce maintenance costs, and extend the service life of the equipment. By real-time monitoring and analysis of the operation status of the rollers, potential problems can be discovered in time and appropriate measures can be taken to repair them. This helps to improve the reliability and stability of the equipment, reduce downtime, and improve production efficiency. The user interaction module can display information such as the current status of the rollers of the sorting equipment, fault prediction results, and maintenance plans, so that users can understand the operation of the equipment in a timely manner and make corresponding decisions.

[0046] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent roller maintenance system, characterized in that: include: A data acquisition module, which is used to obtain the running status data of the rollers of the sorting equipment in real time; A model simulation module, wherein the model simulation module acquires the operating status data of the roller of the sorting device and the mechanical structure of the roller of the sorting device in real time based on the data acquisition module to build a digital twin model of the roller; An analysis and decision module, which performs in-depth analysis based on the dynamic data of the sorting equipment rollers reflected by the digital twin model; A user interaction module is used to display the current status, fault prediction results and maintenance plan of the rollers of the sorting equipment.

2. The intelligent roller maintenance system according to claim 1, characterized in that: The data acquisition module acquires the running status data of the rollers of the sorting equipment in real time through sensors, wherein the sensors include: a vibration sensor, a temperature sensor and a strain sensor.

3. The intelligent roller maintenance system according to claim 2, characterized in that: The operating status data includes: vibration, temperature and strain data information.

4. The intelligent roller maintenance system according to claim 1, characterized in that: The mechanical structure of the roller of the sorting device includes: geometric shape, material properties, and connection method.

5. The intelligent roller maintenance system according to claim 1, characterized in that: The analysis and decision-making module performs in-depth analysis on the dynamic data of the sorting equipment roller reflected by the digital twin model, including using regression analysis methods to perform in-depth analysis on the roller operation data to identify key factors affecting equipment performance.

6. An intelligent roller maintenance system according to claim 5, characterized in that: It also includes applying cluster analysis algorithms to classify the operating status of the roller and identify different operating modes or fault types.

7. An intelligent roller maintenance system according to claim 6, characterized in that: It also includes time series analysis of roller operation data to predict roller operation trends and failure occurrence times.

8. The intelligent roller maintenance system according to claim 1, characterized in that: The user interaction module is used to display the current state of the roller of the sorting device including temperature, vibration and strain values, wherein the temperature, vibration and strain values ​​are displayed through a curve graph.

9. The intelligent roller maintenance system according to claim 1, characterized in that: The maintenance plan includes regular inspections, replacement of parts, and adjustment of parameters.

10. An intelligent roller maintenance system according to claim 9, characterized in that: The maintenance reminder function is set according to the maintenance plan, and information reminders are given according to the scheduled maintenance time.

Images