Radiology department equipment intelligent management system and method based on digital twinborn technology

Abstract

The invention discloses a radiology department equipment intelligent management system and method based on a digital twinborn technology. The system comprises a data acquisition module, a digital twinborn model construction module, a core processing module and a user interaction module. The method comprises the steps of S1, data acquisition, S2, digital twin model construction, S3, real-time monitoring and early warning, S4, remote monitoring and maintenance, S5, data analysis and optimization, and S6, benefit analysis. Through the digital twinborn technology, the system can monitor various key parameters of radiology department equipment in real time, such as liquid nitrogen level, bulb tube thermal capacity and the like, and gives out early warning immediately when the parameters are abnormal; compared with traditional manual inspection, the early warning system can accurately give out early warning 30 minutes before a fault occurs, and the timeliness and accuracy of early warning are greatly improved; and the early warning system reduces the occurrence of sudden faults, so that the emergency maintenance cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin technology, and specifically relates to an intelligent management system and method for radiology equipment based on digital twin technology. Background Art

[0002] In the modern medical system, radiology equipment is an important tool for diagnosis and treatment, especially large-scale imaging equipment such as MRI, CT, and DR. Their operating efficiency and stability have a vital impact on the quality and efficiency of medical services. However, traditional radiology equipment management mainly relies on manual inspections and post-maintenance. This method is not only inefficient and difficult to detect potential problems with the equipment in a timely manner, but also may lead to delays in diagnosis or treatment once the equipment fails. In addition, manual management is prone to problems such as inaccurate records and difficulty in tracing data.

[0003] In recent years, with the development of the Internet of Things and big data technologies, some hospitals have begun to try to introduce intelligent equipment management systems. These systems are usually able to monitor the operating status of the equipment in real time and issue an alarm when the equipment fails. However, most of these systems focus on real-time monitoring and fault warning of equipment, and are weak in data analysis, optimization, and benefit analysis of equipment. From the perspective of timeliness, although these intelligent management systems represent the forefront of current technology, they still fail to fully solve the core problem in radiology equipment management, that is, how to optimize the use and maintenance of equipment through data analysis, thereby improving the overall benefits of the equipment.

[0004] In summary, the intelligent equipment management systems currently on the market have the following main shortcomings: First, the functions are single, mainly focusing on real-time monitoring and fault warning, lacking in-depth analysis of equipment usage data and optimization suggestions; second, the data integration capabilities are insufficient, making it difficult to effectively connect with other hospital information systems (such as HIS, RIS, PACS, etc.), resulting in isolated and repeated data entry. Finally, the benefit analysis function is missing, and it is unable to provide hospitals with decision-making support for equipment procurement, updating and maintenance.

[0005] Therefore, the present invention proposes an intelligent management system and method for radiology equipment based on digital twin technology to solve the above technical problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention designs an intelligent management system and method for radiology equipment based on digital twin technology. The system uses digital twin technology and big data analysis, which not only has real-time monitoring and early warning functions, but also provides equipment optimization suggestions through in-depth data analysis, and conducts a comprehensive benefit analysis to achieve comprehensive intelligent management of radiology equipment; it can improve the management efficiency and use efficiency of radiology equipment and reduce the operating costs of hospitals. Secondly, through data analysis and optimization, it can help hospitals better understand the use of equipment and provide decision support for the purchase, update and maintenance of equipment; the promotion and application of this system will help improve the equipment management level and service quality of the entire medical industry.

[0007] In order to achieve the above technical effects, on the one hand, the present invention discloses an intelligent management system for medical equipment based on digital twin technology, including: a data acquisition module, a digital twin model construction module, a core processing module and a user interaction module; the core processing module includes a real-time monitoring and early warning module, a remote monitoring and maintenance module, a data analysis and optimization module, and a benefit analysis module;

[0008] Data acquisition module: used to collect the operating status data of medical equipment in real time;

[0009] Digital twin model building module: Use digital twin technology to digitally model medical equipment, create a virtual device copy, and synchronize data with the physical device;

[0010] Real-time monitoring and early warning module: monitors various parameters of the equipment in real time and automatically issues early warning notifications when the equipment is abnormal;

[0011] Remote monitoring and maintenance module: remotely monitor the operating status of radiology equipment and quickly locate the problem and perform remote maintenance when equipment fails;

[0012] Data analysis and optimization module: Use machine learning technology to conduct in-depth analysis of the data generated during the operation of the equipment, identify potential problems in the use of the equipment, and put forward optimization suggestions;

[0013] Benefit analysis module: collects the usage data of medical equipment from the hospital's HIS system, RIS system and PACS system, and conducts a comprehensive benefit analysis on the processed data;

[0014] User interaction module: provides user interface (UI) and application program interface (API), enabling users to intuitively view device status, receive warning information, and perform remote operations;

[0015] The data acquisition module collects and preprocesses the operating status data of the equipment, and the processed data is used as the basic input of the digital twin model. The digital twin model construction module then constructs the digital twin model of the equipment with the processed data, so that the model can reflect the operating status of the equipment in real time and keep data synchronized with the physical equipment. The real-time monitoring and early warning module continuously detects the model data constructed by the digital twin module. When an abnormal situation occurs, the system will immediately issue an early warning notification and inform the relevant management personnel through the hospital's internal communication system. After receiving the early warning notification, the management personnel can view the operating status and historical data of the equipment in real time through the remote monitoring module. If it is confirmed that the equipment has a fault or potential problem, the management personnel can quickly locate and perform remote maintenance operations. At the same time, the data analysis and optimization module conducts in-depth analysis of the data generated during the operation of the equipment. Through machine learning algorithms, the system can discover potential problems in the use of equipment and put forward corresponding optimization suggestions. Then the benefit analysis module collects the equipment usage data from the hospital's HIS system, RIS system and PACS system to conduct a comprehensive benefit analysis.

[0016] Further, the data collected by the data acquisition module include data generated by magnetic resonance imaging, CT, DR, molybdenum target, gastrointestinal angiography, bone densitometer and film printer, and the specific data include equipment operation status, equipment usage time, number of scans / prints, image quality parameters, radiation dose, mechanical motion parameters, cooling system status, consumables usage, network communication status and error codes and warning information;

[0017] Furthermore, the data detected by the real-time monitoring and early warning module include liquid nitrogen level, coolant temperature, tube heat capacity, and humidity;

[0018] On the other hand, the present invention provides a medical equipment intelligent management method based on digital twin technology, which specifically includes the following steps:

[0019] S1: Data collection: Connect with the equipment through the data collection interface to collect the equipment's operating status data in real time, and the data is transmitted to the system's data center in real time;

[0020] S2: Construction of digital twin model: Based on data collection, the system uses digital twin technology to build a virtual device copy for the device, namely the digital twin model, which provides a basis for subsequent monitoring, early warning and maintenance.

[0021] S3: Real-time monitoring and early warning: The real-time monitoring module continuously monitors the various parameters of the equipment. When the parameters are abnormal, the system will immediately issue an early warning notification and inform the relevant management personnel through the communication system;

[0022] S4: Remote monitoring and maintenance: After receiving the early warning notification, the manager can view the operating status and historical data of the equipment in real time through the remote monitoring module; and confirm whether there are any faults or potential problems with the equipment. If the equipment fails, the manager can quickly locate it and perform remote maintenance operations to improve the maintenance efficiency of the equipment;

[0023] S5: Data analysis and optimization: The system conducts in-depth analysis of the data generated during the operation of the equipment; through machine learning algorithms, it discovers potential problems in the use of equipment and puts forward corresponding optimization suggestions;

[0024] S6: Benefit analysis: The system collects the usage data of MRI equipment from the hospital's HIS system, RIS system and PACS system to conduct a comprehensive benefit analysis.

[0025] The beneficial effects of the present invention are:

[0026] First, through digital twin technology, the system of the present invention can monitor the key parameters of radiology equipment in real time, such as liquid nitrogen level, tube heat capacity, etc., and immediately issue an early warning when the parameters are abnormal; compared with traditional manual inspections, the early warning system of the present invention can accurately issue an early warning 30 minutes before a fault occurs, greatly improving the timeliness and accuracy of the early warning.

[0027] Secondly, the present invention uses the synchronized data of the digital twin model, so that managers can quickly locate the equipment fault point and repair it in time through the remote maintenance function. Statistics show that after using the present invention, the average fault location time is shortened to 14 minutes, and the remote maintenance success rate is increased by 88%.

[0028] In addition, equipment utilization efficiency is optimized: through data analysis and optimization modules, the system can make equipment utilization optimization suggestions based on historical operating data; in actual applications, these suggestions have increased the average utilization efficiency of equipment by 12%, effectively reducing equipment idling and waste.

[0029] Finally, compared with the traditional equipment management method, the present invention reduces the occurrence of sudden failures through the early warning system, thereby reducing the cost of emergency maintenance. Data shows that after using the present invention, the average equipment maintenance cost of the hospital has been reduced by 20%. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for describing the embodiment are briefly introduced below.

[0031] Figure 1 It is a system structure diagram of the present invention;

[0032] Figure 2 It is a diagram of the components and hierarchical structure of the present invention;

[0033] Figure 3 It is a schematic diagram of intelligent management of magnetic resonance (MR) digital twin;

[0034] Figure 4 It is a diagram of the operating status data of the real-time acquisition device of the present invention. DETAILED DESCRIPTION

[0035] Example 1

[0036] like Figure 1 and Figure 2As shown in the figure, the intelligent management system of medical equipment based on digital twin technology includes: data acquisition module, digital twin model construction module and core processing module; the core processing module includes real-time monitoring and early warning module, remote monitoring and maintenance module, data analysis and optimization module and benefit analysis module; it is divided into four major modules during implementation: data acquisition and preprocessing module: responsible for real-time data acquisition from radiology equipment and necessary preprocessing of these data to ensure the accuracy and availability of data. The system collects the operating status data of radiology equipment in real time through the data acquisition module, including key indicators such as temperature, humidity, pressure, current and voltage of the equipment. These data will serve as the basic input of the digital twin model. Digital twin model construction module: the digital twin model of the equipment is constructed using the collected data, which can reflect the operating status of the equipment in real time and keep data synchronization with the physical equipment. Core processing layer module: the core processing module also includes real-time monitoring and early warning module, remote monitoring and maintenance module, data analysis and optimization module and benefit analysis module. Real-time monitoring and early warning: the real-time monitoring and early warning module is used to monitor various parameters of the equipment in real time, such as liquid nitrogen level and tube heat. Once the equipment parameters are detected to be abnormal or exceed the set safety threshold, the system will automatically issue an early warning notification to ensure the safe operation of the equipment. At the same time, the system can also warn of possible failures in advance based on historical data and prediction algorithms so that maintenance can be carried out in time. Remote monitoring and maintenance: Managers can view the operating status and historical data of the equipment in real time through the remote monitoring and maintenance module. When the equipment fails, managers can quickly locate the problem and perform remote maintenance to improve the maintenance efficiency of the equipment. In addition, the system can also provide suggestions for fault diagnosis and troubleshooting to help managers solve problems faster. Data analysis and optimization: The system conducts in-depth analysis of the large amount of data generated during the operation of the equipment through the data analysis and optimization module. Using advanced technologies such as machine learning, potential problems in the use of equipment, such as performance degradation and increased energy consumption, are discovered, and corresponding optimization suggestions are put forward. These suggestions can help medical institutions improve the efficiency of equipment use and reduce operating costs. Benefit analysis: The benefit analysis module collects the use data of medical equipment from the hospital's HIS system, RIS system, and PACS system, and conducts a comprehensive benefit analysis on the processed data. It includes equipment efficiency analysis, such as equipment startup rate, utilization rate, etc.; equipment net benefit analysis, such as the comparison between equipment income and expenditure; and equipment risk assessment, such as equipment failure rate, maintenance cost, etc. Through these analyses, medical institutions can better understand the use of equipment and provide decision support for equipment procurement, update and maintenance. These modules work together to monitor the operating status of equipment in real time, provide fault warnings, support remote monitoring and maintenance, analyze equipment operation data to make optimization suggestions, and conduct comprehensive benefit analysis.User interaction module: Provides user interface (UI) and application program interface (API), enabling users to intuitively view equipment status, receive warning information, perform remote operations, etc. At the same time, this layer also supports integration with other systems to achieve more extensive data sharing and interaction.

[0037] Example 2

[0038] In this embodiment, a medical equipment intelligent management method based on digital twin technology is disclosed, which specifically includes the following steps:

[0039] S1: The data acquisition system is first connected to the MRI device through a specially designed data acquisition interface to collect real-time operating status data of the device, including key indicators such as magnet temperature, liquid helium level, gradient coil temperature, and radio frequency coil status. These data are transmitted to the data center of the intelligent management system in real time, as shown in Figure 4.

[0040] S2: The digital twin model is built on the basis of data collection. The system uses digital twin technology to build a virtual device copy for the MRI device, namely the digital twin model. This model can reflect the operating status of the physical device in real time, providing a basis for subsequent monitoring, early warning and maintenance.

[0041] S3: Real-time monitoring and early warning: The system continuously monitors various parameters of the MRI equipment through the real-time monitoring module. Once abnormal conditions such as abnormally high magnet temperature and low liquid helium level are found, the system will immediately issue an early warning notification and inform relevant management personnel through the hospital's internal communication system.

[0042] S4: Remote monitoring and maintenance: After receiving an early warning notification, managers can view the operating status and historical data of the MRI equipment in real time through the remote monitoring module. If it is confirmed that the equipment has a fault or potential problem, managers can quickly locate and perform remote maintenance operations, such as restarting the equipment, adjusting equipment parameters, etc. This can greatly shorten the fault response time and improve the maintenance efficiency of the equipment.

[0043] S5: Data analysis and optimization: The system will conduct in-depth analysis of the large amount of data generated during the operation of the MRI equipment. Through machine learning algorithms, the system can discover potential problems in the use of the equipment, such as long scanning time, reduced image quality, etc., and make corresponding optimization suggestions. These suggestions can help hospitals improve the efficiency of MRI equipment use and service quality.

[0044] S6: Benefit Analysis: Finally, the system will collect the usage data of MRI equipment from the hospital's HIS system, RIS system and PACS system to conduct a comprehensive benefit analysis. This includes equipment efficiency analysis (such as startup rate, usage rate), net benefit analysis (such as comparison of income and expenditure) and risk assessment (such as failure rate, maintenance cost). Through these analyses, hospitals can better understand the use of MRI equipment and provide decision support for equipment procurement, updating and maintenance.

[0045] Specifically, through this intelligent management system, through digital twin technology, the system can monitor the key parameters of radiology equipment in real time, such as liquid nitrogen level, tube heat capacity, etc., and issue an early warning immediately when the parameters are abnormal; compared with traditional manual inspections, the early warning system of the present invention can accurately issue an early warning 30 minutes before the failure occurs, greatly improving the timeliness and accuracy of the early warning; the operating status data of the CT equipment is collected in real time through the data acquisition interface, including tube heat capacity, current, voltage, exposure time, etc. The digital twin model of the CT equipment is constructed using the collected data to reflect the operating status and heat capacity changes of the equipment in real time. The real-time monitoring module continuously monitors the heat capacity of the tube. When the heat capacity of the tube approaches the set early warning threshold (85%), the system automatically issues an early warning notification.

[0046] From October to December 2023, the digital twin system detected 59 abnormal events of the heat capacity of the tube, of which 57 warnings were issued within 30 minutes before the heat capacity of the tube actually exceeded the safety threshold, and the remaining 2 warnings were issued slightly later than 30 minutes (28 minutes and 25 minutes), but they still successfully notified the management personnel before the equipment failed, avoiding equipment downtime. The details are shown in Table 1 below. Table 1 Warning time

[0047]

[0048]

[0049] In addition, in this embodiment, the data from January to June 2024 show that there are 53 failures in the radiology equipment, the fault location time is 10-18 minutes, the average fault location time is 14 minutes, and the remote maintenance success rate is 88%, as shown in Table 2 below.

[0050] Table 2 Fault location time

[0051]

[0052] As can be seen from the above table, the average fault location time is 15 minutes and the remote maintenance success rate is 88%. The improved system not only significantly improves the equipment maintenance efficiency, but also effectively reduces the operation interruption time caused by equipment failure.

[0053] Through the above steps, the intelligent management system of radiology equipment based on digital twin technology has significantly improved the management efficiency and use efficiency of MRI equipment. Specifically, the key operation data of MRI equipment is collected in real time through the data acquisition interface, ensuring the timeliness and accuracy of the data, effectively solving the problems of low efficiency and data lag of traditional manual inspection, and realizing real-time monitoring of equipment status. Digital twin technology is used to create a virtual copy for MRI equipment, realize the synchronous operation of equipment entity and virtual model, effectively solve the problems of data isolation and repeated entry, and significantly improve data integration capabilities. The system continuously monitors MRI equipment parameters and automatically issues early warning notifications when abnormalities occur, greatly improving the timeliness and accuracy of early warnings, and effectively reducing delays in diagnosis and treatment caused by equipment failures. Managers can view equipment status and historical data in real time through the remote monitoring module, quickly locate and remotely maintain equipment, significantly shorten fault response time, and improve maintenance efficiency and equipment availability. In addition, the use of machine learning algorithms to conduct in-depth analysis of operation data can discover potential problems in equipment use and put forward optimization suggestions, which can effectively reduce equipment idling and waste and reduce operating costs. Comprehensive benefit analysis provides hospitals with a scientific basis for decision-making, optimizes equipment procurement, updating and maintenance strategies, and further improves the overall operational efficiency of the hospital.

[0054] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described.

Claims

1. An intelligent management system and method for radiology equipment based on digital twin technology, characterized in that: include: Data acquisition module, digital twin model construction module, core processing module and user interaction module; the core processing module includes real-time monitoring and early warning module, remote monitoring and maintenance module, data analysis and optimization module, and benefit analysis module; Data acquisition module: used to collect the operating status data of medical equipment in real time; Digital twin model building module: Use digital twin technology to digitally model medical equipment, create a virtual device copy, and synchronize data with the physical device; Real-time monitoring and early warning module: monitors various parameters of the equipment in real time and automatically issues early warning notifications when the equipment is abnormal; Remote monitoring and maintenance module: remotely monitor the operating status of radiology equipment and quickly locate the problem and perform remote maintenance when equipment fails; Data analysis and optimization module: Use machine learning technology to conduct in-depth analysis of the data generated during the operation of the equipment, identify potential problems in the use of the equipment, and put forward optimization suggestions; Benefit analysis module: collects the usage data of medical equipment from the hospital's HIS system, RIS system and PACS system, and conducts a comprehensive benefit analysis on the processed data; User interaction module: provides user interface and application program interface, enabling users to intuitively view device status, receive warning information, and perform remote operations; The data acquisition module collects and pre-processes the operating status data of the equipment, and the processed data is used as the basic input of the digital twin model. The digital twin model construction module then constructs the digital twin model of the equipment with the processed data, so that the model can reflect the operating status of the equipment in real time and keep data synchronized with the physical equipment. The real-time monitoring and early warning module continuously detects the model data constructed by the digital twin module. When an abnormal situation occurs, the system will immediately issue an early warning notification and inform the relevant managers through the hospital's internal communication system. After receiving the early warning notification, the manager can view the operating status and historical data of the equipment in real time through the remote monitoring module. If it is confirmed that the equipment has a fault or potential problem, the manager can quickly locate it and perform remote maintenance operations. At the same time, the data analysis and optimization module conducts in-depth analysis of the data generated during the operation of the equipment; through machine learning algorithms, the system can discover potential problems in the use of equipment and make corresponding optimization suggestions. The benefit analysis module then collects equipment usage data from the hospital's HIS system, RIS system, and PACS system to conduct a comprehensive benefit analysis.

2. According to claim 1, the intelligent management system and method of radiology equipment based on digital twin technology is characterized in that: The data collected by the data acquisition module include data generated by magnetic resonance imaging, CT, DR, molybdenum target, gastrointestinal angiography, bone densitometer and film printer. The specific data include equipment operating status, equipment usage time, number of scans / prints, image quality parameters, radiation dose, mechanical motion parameters, cooling system status, consumables usage, network communication status and error codes and warning messages.

3. According to claim 1, the intelligent management system and method of radiology equipment based on digital twin technology is characterized in that: The data detected by the real-time monitoring and early warning module include liquid nitrogen level, bulb temperature, and humidity.

4. The intelligent management method of medical equipment based on digital twin technology is characterized by: The specific steps include: S1: Data collection: Connect with the equipment through the data collection interface to collect the equipment's operating status data in real time, and the data is transmitted to the system's data center in real time; S2: Digital twin model construction: Based on data collection, the system uses digital twin technology to build a virtual device copy for the device, namely the digital twin model, which provides a basis for subsequent monitoring, early warning and maintenance; S3: Real-time monitoring and early warning: The real-time monitoring module continuously monitors the various parameters of the equipment. When the parameters are abnormal, the system will immediately issue an early warning notification and inform the relevant management personnel through the communication system; S4: Remote monitoring and maintenance: After receiving the early warning notification, the manager can view the operating status and historical data of the equipment in real time through the remote monitoring module; and confirm whether there are any faults or potential problems with the equipment. If the equipment fails, the manager can quickly locate it and perform remote maintenance operations to improve the maintenance efficiency of the equipment; S5: Data analysis and optimization: The system conducts in-depth analysis of the data generated during the operation of the equipment; through machine learning algorithms, it discovers potential problems in the use of equipment and puts forward corresponding optimization suggestions; S6: Benefit analysis: The system collects the usage data of MRI equipment from the hospital's HIS system, RIS system and PACS system to conduct a comprehensive benefit analysis.

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