Medical equipment-oriented digital twin system implementation method and related device
By collecting, verifying, and analyzing multi-source data from medical devices through a digital twin system, the problems of data silos and inefficiency in traditional management have been solved, enabling full-cycle management and predictive maintenance of equipment, and improving equipment utilization and security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Heyou Hospital, Shunde District, Foshan City
- Filing Date
- 2025-12-17
- Publication Date
- 2026-04-28
AI Technical Summary
Traditional medical equipment management suffers from data silos, lacks multi-source data integration, is passive in its management approach, relies on manual experience, has low calibration management efficiency, and cannot achieve predictive maintenance, leading to interruptions in diagnosis and treatment, unreasonable resource allocation, and safety and compliance risks.
The system employs a digital twin system, which collects multi-source fused data through a data acquisition module, verifies consistency and validity through a data verification module, outputs simulated data from the digital twin model, and performs failure rate and remaining life prediction through an analysis and prediction module, generating a health score and displaying it in real time.
It enables comprehensive lifecycle management of medical equipment, solves the problem of data silos, achieves data-driven closed-loop management, reduces downtime, and improves equipment utilization and security.
Smart Images
Figure CN121938583A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of medical device data processing technology, and in particular to a method and related apparatus for implementing a digital twin system for medical devices. Background Technology
[0002] Medical equipment (such as PET-CT, MRI, CT, etc.) is a core asset of hospitals, and its normal operation is directly related to medical quality and safety and hospital operational efficiency. However, traditional medical equipment management faces significant challenges: unplanned downtime leads to interruptions in diagnosis and treatment; uneven equipment utilization and unreasonable resource allocation; manual calibration and planned maintenance are inefficient, prone to errors or delays, and bring safety and compliance risks.
[0003] Existing solutions suffer from data silos and lack the integration of multi-source data; management is passive, relying on manual experience and fixed plans, making predictive maintenance impossible; calibration management is inefficient and fails to form a data-driven closed-loop management system. Summary of the Invention
[0004] To address the aforementioned issues, this application provides a method and related apparatus for implementing a digital twin system for medical devices, which can solve the problem of data silos in the prior art.
[0005] According to one aspect of the embodiments of this application, a method for implementing a digital twin system for medical devices is proposed. The digital twin system includes a data acquisition module, a data verification module, a digital twin model, and an analysis and prediction module. The method includes: The data acquisition module collects multi-source fusion data for the medical device, including the operating data of the medical device and image data of the medical device. The operating data of the medical device and the image data are input into the data verification module. The operating data of the medical device and the image data are verified for consistency and validity through the preset annotation verification set in the data verification module, and the verification result is obtained. If the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, then the operating data of the medical device and the image data are input into the digital twin model so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data; The analysis and prediction module analyzes the simulated data output by the digital twin model to obtain the failure rate analysis results and remaining life prediction results for the medical device.
[0006] In the above scheme, the step of verifying the consistency and validity of the medical device's operating data and the image data through a preset annotation verification set in the data verification module includes: Determine the first verification data corresponding to the operating data of the medical device from the preset annotation verification set; Determine the second verification data corresponding to the image data from the preset annotation verification set; The first verification data is used to perform consistency and validity checks on the operating data of the medical device. The image data is then subjected to consistency and validity checks using the second verification data.
[0007] In the above scheme, the step of analyzing the simulated data output by the digital twin model through the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical device includes: Determine from the simulation data a first simulated sub-data corresponding to the operating data of the medical device and a second simulated sub-data corresponding to the image data; The first analysis result is determined based on the first simulation sub-data and the historical operating data of the medical equipment; The second analysis result is determined based on the second simulated sub-data and historical image data for the medical device; The failure rate analysis result and the remaining lifetime prediction result are determined based on the first analysis result and the second analysis result.
[0008] In the above scheme, determining the failure rate analysis result and the remaining lifetime prediction result based on the first analysis result and the second analysis result includes: The failure rate analysis result is determined based on the first analysis result; The remaining lifetime prediction result is determined based on the failure rate analysis result and the second analysis result.
[0009] In the above scheme, the method further includes: A health score for the medical device is generated based on the failure rate analysis results and the remaining life prediction results. The operational data of the medical data and the health score are sent to the display screen of the terminal device so that the display screen of the terminal device can display the operational data of the medical data and the health score in real time.
[0010] According to one aspect of the embodiments of this application, a digital twin system implementation apparatus for medical devices is proposed. The digital twin system includes a data acquisition module, a data verification module, a digital twin model, and an analysis and prediction module. The apparatus includes: The acquisition unit is used to acquire multi-source fusion data for the medical device through the data acquisition module. The multi-source fusion data includes the operating data of the medical device and the image data of the medical device. The first input unit is used to input the operating data of the medical device and the image data into the data verification module, and to perform consistency and validity verification on the operating data of the medical device and the image data through the preset annotation verification set in the data verification module, and obtain the verification result. The second input unit is used to input the operating data of the medical device and the image data into the digital twin model if the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data. The analysis unit is used to analyze the simulation data output by the digital twin model through the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical device.
[0011] According to one aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the digital twin system implementation method for medical devices as described above. According to one aspect of the embodiments of this application, a computer program product is provided, the computer program product including a computer program that is read and executed by a processor of an electronic device, causing the electronic device to perform the digital twin system implementation method for medical devices as described above.
[0012] The beneficial effects of this application are as follows: This application uses a data verification module to verify the consistency and validity of the operating data and image data of the medical device. The verified operating data and image data are then input into the digital twin model, causing the digital twin model to output simulated data corresponding to the operating data and image data of the medical device. In this way, the digital twin model can effectively achieve accurate physical mapping with the multi-source fusion data collected by sensors. Furthermore, the analysis and prediction module analyzes the simulated data output by the digital twin model to obtain failure rate analysis results and remaining life prediction results for the medical device. This effectively assesses the failure rate and remaining life of the medical device. Based on this, it effectively solves the data silo problem caused by the lack of data correlation in existing solutions and effectively enables comprehensive lifecycle management of medical devices, completing data-driven closed-loop management. Attached Figure Description
[0013] Figure 1 This is a system architecture diagram of the digital twin system implementation method for medical devices provided in the embodiments of this application; Figure 2 A flowchart illustrating the implementation method of a digital twin system for medical devices provided in this application embodiment; Figure 3 A diagram illustrating the interface of a digital twin system for medical devices provided in this application embodiment; Figure 4 A system architecture diagram of a digital twin system for medical devices provided in this application embodiment; Figure 5 A flowchart illustrating the data processing and analysis provided in the embodiments of this application; Figure 6 A block diagram of a digital twin system implementation apparatus for medical devices provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation
[0014] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0015] It should be noted that while some processes described in the specification, claims, and accompanying drawings include multiple steps appearing in a specific order, it should be clearly understood that these steps may not be performed in the order they appear herein, or may be performed in parallel. The step numbers are merely used to distinguish different steps and do not themselves represent any execution order. Furthermore, descriptions such as "first," "second," or "objective" in this document are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. "Multiple" in this document refers to at least two.
[0016] It is worth noting that in the specific embodiments of this application, data such as operating data and image data of medical devices are involved. When the above embodiments of this application are applied to specific products or technologies, permission or consent from the target object is required, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. For example, when an embodiment of this application needs to obtain operating data, image data, and other related data of medical devices, separate permission or consent from the target object can be obtained through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or consent from the target object, the operating data, image data, and other related data of the medical devices used to enable the embodiments of this application to operate normally can then be obtained.
[0017] Please see Figure 1 , Figure 1 This is a system architecture diagram of the digital twin system implementation method for medical devices provided in this application embodiment. It includes a terminal 140, an Internet connection 130, a gateway 120, a server 110, etc.
[0018] Terminal 140 can take various forms, including desktop computers, laptops, PDAs (personal digital assistants), mobile phones, vehicle terminals, and dedicated terminals. Furthermore, it can be a single device or a collection of multiple devices. For example, multiple desktop computers can be interconnected via a local area network, sharing a single monitor to work collaboratively, forming a single terminal 140. Terminal 140 can communicate with the Internet 130 via wired or wireless means to exchange data.
[0019] Server 110 refers to a computer system capable of providing certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a single high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a single high-performance computer (e.g., a virtual machine), or a combination of portions of multiple high-performance computers (e.g., virtual machines). Server 110 can also communicate with the Internet 130 via wired or wireless means to exchange data.
[0020] Gateway 120, also known as an internetwork connector or protocol converter, is a computer system or device that acts as a translator, enabling network interconnection at the transport layer. It bridges the gap between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are forwarded to the corresponding server 110 via gateway 120. Messages sent from server 110 to terminal 140 are also forwarded to the corresponding terminal 140 via gateway 120.
[0021] The following provides a detailed description of the specific implementation methods of the embodiments of this application: Please see Figure 2 , Figure 2 This is a flowchart illustrating the implementation method of a digital twin system for medical devices provided in this application embodiment. The implementation method of the digital twin system for medical devices can be implemented by server 110 and / or terminal 140. Figure 2 The illustrated implementation method of a digital twin system for medical devices includes: Step 210: Collect multi-source fusion data for the medical device through the data acquisition module. The multi-source fusion data includes the operating data of the medical device and the image data of the medical device. Step 220: Input the operating data of the medical device and the image data into the data verification module, and use the preset annotation verification set in the data verification module to verify the consistency and validity of the operating data of the medical device and the image data to obtain the verification result; Step 230: If the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, then the operating data of the medical device and the image data are input into the digital twin model so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data. Step 240: Analyze the simulation data output by the digital twin model through the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical device.
[0022] The complete embodiment of this application will be explained in detail below with reference to steps 210-240: In step 210, the data acquisition module can be specifically a physical device layer, located at the bottom layer, consisting of various medical devices (such as MRI, ventilators, and ultrasound equipment) and camera equipment within the hospital. These medical devices are all equipped with IoT sensor modules to collect real-time operating data such as temperature, vibration, usage time, and power consumption.
[0023] In step 220, the data verification module can specifically be a third-party data verification interface: obtaining third-party calibration data (such as calibration certificates and regulatory audit reports) from equipment manufacturers or calibration service organizations via API or secure file transfer (such as JSON or XML). This third-party data verification interface has preliminary data format and source verification functions, used to verify the consistency and validity of multi-source fusion data. The data verification module compares and analyzes the calibration data provided by the third party with the internal status data read by the device's IoT sensor module (i.e., the multi-source fusion data described in this application) to detect whether there are significant deviations. If a deviation is found, a calibration alarm or recalibration process is automatically triggered.
[0024] In step 230, the validated data is input into the digital twin model, enabling it to effectively simulate the operational and image data of the medical equipment. The digital twin model is initially constructed based on the equipment's CAD or BIM model and continuously updated in real-time with validated data (including the aforementioned calibration data). This allows the virtual model (digital twin model) to accurately map the current state and historical trajectory of the physical equipment and can be used for simulation in a virtual environment (such as simulating equipment performance in an operating room setting).
[0025] In step 240, the simulation data output by the digital twin model is analyzed by the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical equipment. The analysis and prediction module proactively identifies potential failures in the medical equipment, significantly reducing downtime. Simultaneously, it can predict the remaining life, enabling full lifecycle management of the medical equipment.
[0026] In some embodiments, the step of verifying the consistency and validity of the medical device's operational data and the image data using a preset annotation verification set in the data verification module includes: Determine the first verification data corresponding to the operating data of the medical device from the preset annotation verification set; Determine the second verification data corresponding to the image data from the preset annotation verification set; The first verification data is used to perform consistency and validity checks on the operating data of the medical device. The image data is then subjected to consistency and validity checks using the second verification data.
[0027] Here, the first verification data and the second verification data are specifically the aforementioned calibration data. The calibration data can be used to verify the consistency and validity of the medical device's operating data and image data.
[0028] In some embodiments, the step of analyzing the simulated data output by the digital twin model through the analysis and prediction module to obtain failure rate analysis results and remaining life prediction results for the medical device includes: Determine from the simulation data a first simulated sub-data corresponding to the operating data of the medical device and a second simulated sub-data corresponding to the image data; The first analysis result is determined based on the first simulation sub-data and the historical operating data of the medical equipment; The second analysis result is determined based on the second simulated sub-data and historical image data for the medical device; The failure rate analysis result and the remaining lifetime prediction result are determined based on the first analysis result and the second analysis result.
[0029] The first simulated sub-data corresponds to the operational data of the medical device. By combining the first simulated sub-data with the historical operational data of the medical device, a first analytical result characterizing the operational status of the medical device can be determined. Similarly, by combining the second simulated sub-data with the historical image data of the medical device, a second analytical result characterizing the motion trajectory of the medical device can be determined. The failure rate analysis result and the remaining life prediction result are determined by combining the first and second analytical results. For example, if the first analytical result shows that the operational data of the medical device is frequently inaccurate, it indicates that the failure rate analysis result is high; if the second analytical result shows that the motion trajectory of the medical device is not smooth, it indicates that the remaining life of the medical device is not long, and so on.
[0030] In some embodiments, the method further includes: A health score for the medical device is generated based on the failure rate analysis results and the remaining life prediction results. The operational data of the medical data and the health score are sent to the display screen of the terminal device so that the display screen of the terminal device can display the operational data of the medical data and the health score in real time.
[0031] Here, the main focus is on human-computer interface interaction via terminal devices, allowing doctors to visually view the operational status of medical equipment through the device's display screen. Specifically, this could be a graphical dashboard (see...). Figure 3 This allows hospital administrators and equipment engineers to view the real-time status, health scores, calibration alerts, and generated maintenance or relocation recommendations for all equipment (such as "MRI-01 is expected to require maintenance in 14 days" or "Relocate outpatient ultrasound diagnostic equipment No. 3 to the operating room"). Figure 3It showcases a typical web dashboard, displaying a list of devices and their status (normal, warning, fault), the latest calibration alerts, a list of AI-generated predictive maintenance recommendations, a heatmap of device utilization, and digital twin device images.
[0032] See Figure 4 , Figure 4 The following is a flowchart of the entire digital twin system, illustrating the system flow: 1. Data Collection: The system continuously collects data from IoT sensor modules and camera devices on medical devices, as well as from calibration service providers through third-party interfaces.
[0033] 2. Data Verification and Fusion: A third-party data interface verifies the integrity and origin of the calibration certificate. The AI calibration verification module compares the third-party calibration data with sensor readings to verify their consistency and validity.
[0034] 3. Digital Twin Updates: The digital twin model updates the virtual model of the corresponding device using verified sensor and calibration data to ensure it is highly synchronized with the physical entity.
[0035] 4. Analysis and Decision-Making: 4.1 The predictive maintenance module analyzes digital twin data (i.e., analog data) to predict equipment failure probability and remaining service life.
[0036] 4.2 The calibration and verification module continuously monitors data deviations and generates an alarm immediately upon detecting anomalies.
[0037] 4.3 The optimization module analyzes the overall equipment utilization rate of the hospital and proposes optimization solutions.
[0038] 5. Implementation and Feedback: 5.1 The system sends alerts and suggestions to staff through the user interface or other communication methods.
[0039] 5.2 The system can automatically create maintenance work orders and trigger recalibration processes.
[0040] 5.3 All actions will be recorded and fed back to the digital twin model.
[0041] 6. Report Generation: The system automatically generates full lifecycle management reports, equipment usage compliance reports, and sustainability reports (such as environmentally friendly disposal of obsolete medical equipment).
[0042] Figure 4This paper describes the layered system architecture of this application, including a physical device layer, a data acquisition layer (including third-party data interfaces), an artificial intelligence analysis core, a digital twin engine, a hospital integration layer, and a user interface. The diagram uses arrows to indicate data flows such as "real-time sensor data," "real-time usage environment image data," "third-party calibration data," "post-verification data," "control commands," and "standard usage reports."
[0043] like Figure 5 As shown, Figure 5 The core steps of the method described in this application are as follows: data collection (sensors + third parties + images) → data verification and fusion → updating the digital twin → AI analysis (predictive maintenance, calibration verification, optimization) → action execution (alarms, maintenance, recalibration, reallocation) → report generation. This is a closed-loop process that achieves full lifecycle management of medical devices.
[0044] In summary, this application has the following beneficial effects: 1. A data acquisition layer architecture that integrates third-party data interfaces, especially the function of these interfaces for acquiring and initially verifying external calibration data.
[0045] 2. The "calibration verification module" and its working method in the core of intelligent analysis, namely the algorithm and process of detecting deviations and triggering alarms by comparing third-party calibration data with data from the internal sensors of the device.
[0046] 3. Methods for using validated data (including calibration data) to update and drive digital twin models of medical devices to ensure high accuracy of the virtual models.
[0047] 4. Integrate digital twins, analytics (including predictive maintenance and calibration verification) with hospital operating systems (EHR, HRP, etc.) to form a closed-loop, full lifecycle management system architecture.
[0048] 5. A comprehensive lifecycle management approach covering procurement simulation, operation monitoring, predictive maintenance, calibration management, asset optimization, and asset disposal.
[0049] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a digital twin system implementation apparatus for medical devices provided in an embodiment of this application. The apparatus is applied to a computer device and may include: The acquisition unit 401 is used to acquire multi-source fusion data for the medical device through the data acquisition module. The multi-source fusion data includes the operating data of the medical device and the image data of the medical device. The first input unit 402 is used to input the operating data of the medical device and the image data into the data verification module, and to perform consistency and validity verification on the operating data of the medical device and the image data through the preset annotation verification set in the data verification module to obtain the verification result; The second input unit 403 is used to input the operating data of the medical device and the image data into the digital twin model if the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data. The analysis unit 404 is used to analyze the simulation data output by the digital twin model through the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical device.
[0050] Reference Figure 7 , Figure 7 To implement the structural block diagram of a portion of the terminal 140 in this application embodiment, the terminal 140 includes: a radio frequency (RF) circuit 710, a memory 715, an input unit 730, a display unit 740, a sensor 750, an audio circuit 760, a wireless fidelity (WiFi) module 770, a processor 780, and a power supply 790, among other components. Those skilled in the art will understand that... Figure 7 The terminal 140 structure shown does not constitute a limitation on a mobile phone or computer, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0051] The RF circuit 710 can be used to receive and transmit signals during information transmission or calls. In particular, it receives downlink information from the base station and processes it with the processor 780; in addition, it transmits uplink data to the base station.
[0052] The memory 715 can be used to store software programs and modules. The processor 780 executes various functional applications of the terminal and performs processing for the digital twin system for medical devices by running the software programs and modules stored in the memory 715.
[0053] The input unit 730 can be used to receive input numeric or character information, and to generate key signal inputs related to the terminal's settings and function control. Specifically, the input unit 730 may include a touch panel 731 and other input devices 732.
[0054] The display unit 740 can be used to display input or provided information, as well as various menus of the terminal. The display unit 740 may include a display panel 741.
[0055] Audio circuitry 760, speaker 761, and microphone 762 provide an audio interface.
[0056] In this embodiment, the processor 780 included in the terminal 140 can execute the digital twin system implementation method for medical devices described in the previous embodiment.
[0057] The terminal 140 in this application embodiment includes, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle terminals, and aircraft. This application embodiment can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving.
[0058] Figure 8 This is a partial structural block diagram of a server 110 implementing an embodiment of this application. The server 110 can vary significantly due to different configurations or performance characteristics, and may include one or more central processing units (CPUs) 822 (e.g., one or more processors) and memory 832, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 842 or data 844. The memory 832 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the server 110. Furthermore, the CPU 822 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media 830 on the server 110.
[0059] Server 110 may also include one or more power supplies 826, one or more wired or wireless network interfaces 850, one or more input / output interfaces 858, and / or one or more operating systems 841, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0060] The central processing unit 822 in server 110 can be used to execute the digital twin system implementation method for medical devices according to the embodiments of this application.
[0061] This application also provides a computer-readable storage medium for storing program code, which is used to execute the digital twin system implementation method for medical devices in the foregoing embodiments.
[0062] This application also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, causing the computer device to perform the above-described method for implementing a digital twin system for medical devices.
[0063] Furthermore, the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.
[0064] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0065] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.
[0066] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.
[0067] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.
[0068] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0069] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0070] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.
[0071] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.
[0072] The above is a detailed description of the embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.
Claims
1. A method for implementing a digital twin system for medical devices, characterized in that, The digital twin system includes a data acquisition module, a data verification module, a digital twin model, and an analysis and prediction module. The method includes: The data acquisition module collects multi-source fusion data for the medical device, including the operating data of the medical device and image data of the medical device. The operating data of the medical device and the image data are input into the data verification module. The operating data of the medical device and the image data are verified for consistency and validity through the preset annotation verification set in the data verification module, and the verification result is obtained. If the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, then the operating data of the medical device and the image data are input into the digital twin model so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data; The analysis and prediction module analyzes the simulated data output by the digital twin model to obtain the failure rate analysis results and remaining life prediction results for the medical device.
2. The method for implementing a digital twin system for medical devices according to claim 1, characterized in that, The process of verifying the consistency and validity of the medical device's operational data and the image data using a preset annotation verification set in the data verification module includes: Determine the first verification data corresponding to the operating data of the medical device from the preset annotation verification set; Determine the second verification data corresponding to the image data from the preset annotation verification set; The first verification data is used to perform consistency and validity checks on the operating data of the medical device. The image data is then subjected to consistency and validity checks using the second verification data.
3. The method for implementing a digital twin system for medical devices according to claim 2, characterized in that, The step involves analyzing the simulated data output by the digital twin model through the analysis and prediction module to obtain failure rate analysis results and remaining life prediction results for the medical device, including: Determine from the simulation data a first simulated sub-data corresponding to the operating data of the medical device and a second simulated sub-data corresponding to the image data; The first analysis result is determined based on the first simulation sub-data and the historical operating data of the medical equipment; The second analysis result is determined based on the second simulated sub-data and historical image data for the medical device; The failure rate analysis result and the remaining lifetime prediction result are determined based on the first analysis result and the second analysis result.
4. The method for implementing a digital twin system for medical devices according to claim 3, characterized in that, The step of determining the failure rate analysis result and the remaining lifetime prediction result based on the first analysis result and the second analysis result includes: The failure rate analysis result is determined based on the first analysis result; The remaining lifetime prediction result is determined based on the failure rate analysis result and the second analysis result.
5. The method for implementing a digital twin system for medical devices according to claim 1, characterized in that, The method further includes: A health score for the medical device is generated based on the failure rate analysis results and the remaining life prediction results. The operational data of the medical data and the health score are sent to the display screen of the terminal device so that the display screen of the terminal device can display the operational data of the medical data and the health score in real time.
6. A device for implementing a digital twin system for medical devices, characterized in that, The digital twin system includes a data acquisition module, a data verification module, a digital twin model, and an analysis and prediction module. The device includes: The acquisition unit is used to acquire multi-source fusion data for the medical device through the data acquisition module. The multi-source fusion data includes the operating data of the medical device and the image data of the medical device. The first input unit is used to input the operating data of the medical device and the image data into the data verification module, and to perform consistency and validity verification on the operating data of the medical device and the image data through the preset annotation verification set in the data verification module, and obtain the verification result. The second input unit is used to input the operating data of the medical device and the image data into the digital twin model if the verification result is that the operating data of the medical device passes the verification and the image data passes the verification, so that the digital twin model outputs simulated data corresponding to the operating data of the medical device and the image data. The analysis unit is used to analyze the simulation data output by the digital twin model through the analysis and prediction module to obtain the failure rate analysis results and remaining life prediction results for the medical device.
7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the digital twin system implementation method for medical devices as described in any one of claims 1 to 5.
8. A computer program product, the computer program product comprising a computer program, characterized in that, The computer program is read and executed by the processor of the electronic device, causing the electronic device to perform the digital twin system implementation method for medical devices as described in any one of claims 1 to 5.