An augmented reality-based medical equipment maintenance system and method, a terminal and a medium

Abstract

The application discloses a kind of based on augmented reality medical equipment maintenance system, method, terminal and medium, it is related to intelligent medical technology field, system includes: mobile terminal, AR equipment and cloud server.Mobile terminal is used to carry out security verification to login information, maintenance order management, the provision of operating guide and maintenance report local preview and generation.AR equipment is AR glasses, for each operation step is projected in the user field of view with first visual angle in real time, and all key data is uploaded to cloud server.Cloud server is used to generate maintenance report, the health status of medical equipment is dynamically evaluated, and when performance degradation trend or potential failure risk exists, early warning information is generated to mobile terminal and AR equipment push.The application constructs a closed-loop medical equipment maintenance system, significantly improves the standardization, authenticity, traceability and forward-looking management level of medical equipment maintenance.

Description

Technical Field

[0001] This invention relates to the field of smart healthcare technology, and in particular to a medical equipment maintenance system, method, terminal, and medium based on augmented reality. Background Technology

[0002] With the rapid development of modern medical technology, various life support medical devices (such as multi-parameter monitors, defibrillators, ventilators, electrocardiographs, and anesthesia machines) play a crucial role in clinical diagnosis and treatment. To ensure the safe, stable, and reliable operation of these high-value, high-risk devices, regular preventative maintenance and upkeep (hereinafter referred to as "maintenance") has become a core aspect of hospital equipment management. Traditional paper-based or mobile app-based record-keeping methods suffer from problems such as susceptibility to falsification, low efficiency, and difficulty in traceability.

[0003] Currently, hospitals generally use the following methods for the maintenance and upkeep of life support medical equipment:

[0004] 1. Paper-based work order mode: Maintenance engineers carry paper maintenance inspection forms, check each item on-site, manually fill in the results, and finally take photos or scan them for archiving. This method is inefficient, prone to omissions and errors, and paper records are easily lost or damaged, making traceability difficult.

[0005] 2. Traditional Mobile App Model: Maintenance engineers use a dedicated app on their mobile phones or tablets to record maintenance information. While this achieves digitization, it still has significant shortcomings: engineers need to frequently look down at paper manuals, which is inconvenient in complex equipment environments and occupies their hands, affecting work efficiency. More importantly, the photos recorded by the app are separate from the inspection results, posing a risk of retroactive data entry and data falsification, failing to guarantee the authenticity and on-site nature of the maintenance process. Furthermore, maintenance data is scattered across paper documents or different engineers' equipment, making unified management and analysis difficult and hindering the creation of a comprehensive health record for the equipment's entire lifecycle. Moreover, different engineers may have different understandings of the maintenance process, leading to non-standard maintenance operations and affecting maintenance quality.

[0006] 3. Simple remote prompts: Currently, most technologies on the market only offer remote video support, lacking deep integration with maintenance processes. They fail to form a closed loop of "guidance-execution-recording-uploading" and lack strong authentication links between operator identity and operation time, resulting in weak anti-counterfeiting capabilities. Furthermore, existing technologies generally remain in a passive mode of "regular maintenance" or "repair after failure," unable to achieve proactive and forward-looking intelligent equipment management.

[0007] Therefore, existing technologies still have shortcomings. Summary of the Invention

[0008] The technical problem to be solved by this invention is to provide an augmented reality-based medical equipment maintenance system, method, terminal, and medium, addressing the aforementioned deficiencies of the prior art. The technical solution adopted by this invention is as follows:

[0009] In a first aspect, the present invention provides a medical device maintenance system based on augmented reality, wherein the system includes: a mobile terminal, an AR device, and a cloud server, and the mobile terminal, the AR device, and the cloud server establish communication links with each other;

[0010] The mobile terminal is used for security verification of login information, management of maintenance orders, provision of operation guides, and local preview and generation of maintenance reports.

[0011] The AR device is AR glasses, which are used to complete the maintenance items in the maintenance order based on a standardized maintenance operation process. In the maintenance operation process, each operation step is projected into the user's field of vision in real time from a first-person perspective, and after the maintenance operation process is completed, all key data is uploaded to the cloud server.

[0012] The cloud server is used to generate standardized maintenance reports based on all key data, dynamically assess the health status of medical equipment based on a preset predictive maintenance mechanism, and automatically generate early warning information when the medical equipment is found to have a performance degradation trend or potential failure risk, and push the early warning information to the mobile terminal and the AR device.

[0013] In one implementation, the AR glasses have a built-in high-resolution optical waveguide display module for providing operation guidance for an immersive, paperless, and visualized maintenance process.

[0014] The AR glasses are equipped with capacitive touch sensors on the temples, which are used to execute each step of the maintenance process.

[0015] In one implementation, the cloud server includes: a database server layer, an application service layer, and a file storage layer;

[0016] The database server layer adopts a hybrid storage architecture that combines a MySQL relational database and a Redis cache database. The MySQL relational database is used to persistently store core structured data, and the Redis cache database is used to cache frequently accessed static data.

[0017] The application service layer is used to receive various requests sent by the mobile terminal and the AR device. The application service layer integrates a report generation engine and a predictive maintenance mechanism.

[0018] The file storage layer is used to store unstructured data generated by the system.

[0019] Secondly, embodiments of the present invention provide a method for maintaining medical devices based on augmented reality, wherein the method is applied to any of the above-described solutions in an augmented reality-based medical device maintenance system, and the method includes:

[0020] Log in to the mobile terminal, select the maintenance order to be processed from the maintenance order management interface, and submit the maintenance order to be processed to the AR device;

[0021] Based on standardized maintenance operation procedures, the maintenance items in the pending maintenance orders are completed. In the maintenance operation process, each operation step is projected to the user's field of vision in real time from a first-person perspective. After the maintenance operation process is completed, all key data is uploaded to the cloud server.

[0022] Based on all key data, a standardized maintenance report is generated. Based on a preset predictive maintenance mechanism, the health status of medical equipment is dynamically assessed. When the medical equipment is found to have a performance degradation trend or potential failure risk, an early warning message is automatically generated and pushed to the mobile terminal and the AR device.

[0023] In one implementation, maintenance items in a pending maintenance order are completed based on a standardized maintenance operation process, including:

[0024] Obtain the operation guide for the aforementioned maintenance procedure;

[0025] Based on the aforementioned operation guidelines, the qualification of each maintenance item in the maintenance order to be processed is judged, and the key data and judgment results of each maintenance item are recorded;

[0026] The camera is triggered by the capacitive touch sensor on the AR device to take a picture of the part of the medical device to be tested for archiving.

[0027] After the photo is taken, proceed to the next maintenance item or the next maintenance order.

[0028] In one implementation, taking a photograph of the area to be detected by the medical device includes:

[0029] At the moment of taking a photo, multiple anti-counterfeiting watermarks are automatically embedded into the photo. The watermark content includes the maintenance personnel's name, order number, equipment number, and timestamp.

[0030] In one implementation, a pre-defined predictive maintenance mechanism is used to dynamically assess the health status of medical equipment, including:

[0031] Acquire multi-dimensional data, including: maintenance records, equipment operation logs, usage environment data, and failure statistics and lifespan information of similar equipment. The maintenance records include key data from historical maintenance records and current maintenance records.

[0032] Machine learning algorithms are used to analyze the performance development trend and potential failure risk of the medical device based on the multi-dimensional data, thereby obtaining the health status assessment results of the medical device.

[0033] In one implementation, machine learning algorithms are used to analyze the performance development trend and potential failure risk of the medical device using the multi-dimensional data, resulting in a health status assessment of the medical device, including:

[0034] Key features reflecting changes in the performance of the medical device are extracted from the multi-dimensional data, and these key features are input into a trend analysis model constructed using a time series analysis algorithm to obtain the performance development trend results of the medical device.

[0035] The multi-dimensional data is transformed into feature variables, and the feature variables are input into a classification model built based on the random forest algorithm. The risk level information corresponding to the feature variables is output based on the classification model to obtain the potential failure analysis results of the medical device.

[0036] The performance development trend results and the potential failure analysis results are used as the health status assessment results of the medical device.

[0037] Thirdly, embodiments of the present invention also provide a terminal, wherein the terminal includes a memory, a processor, and an augmented reality-based medical device maintenance program stored in the memory and executable on the processor. When the processor executes the augmented reality-based medical device maintenance program, it implements the steps of any of the above-described augmented reality-based medical device maintenance methods.

[0038] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, wherein an augmented reality-based medical device maintenance program is stored on the computer-readable storage medium, and the augmented reality-based medical device maintenance program implements the steps of the augmented reality-based medical device maintenance method described in any one of the above schemes on the computer-readable storage medium.

[0039] Beneficial Effects: Compared with existing technologies, this invention provides a medical device maintenance system based on augmented reality, including a mobile terminal, an AR device, and a cloud server. The mobile terminal, the AR device, and the cloud server all establish communication links with each other. The mobile terminal is used for secure verification of login information, management of maintenance orders, provision of operation guides, and local previewing and generation of maintenance reports. The AR device, AR glasses, is used to complete maintenance items in the maintenance order based on standardized maintenance operation procedures. During the maintenance operation process, each operation step is projected into the user's field of vision in real time from a first-person perspective. After the maintenance operation process is completed, all key data is uploaded to the cloud server. The cloud server is used to generate standardized maintenance reports based on all key data. Based on a preset predictive maintenance mechanism, it dynamically assesses the health status of the medical device and automatically generates early warning information when it detects a performance degradation trend or potential failure risk in the medical device, pushing the early warning information to the mobile terminal and the AR device.

[0040] This invention constructs a closed-loop medical equipment maintenance system by deeply integrating AR device visualization guidance, automated data collection, and cloud-based predictive analysis. It effectively solves the problems of easy record falsification, difficulty in process traceability, and untimely response in traditional maintenance models, and significantly improves the standardization, authenticity, traceability, and forward-looking management level of medical equipment maintenance. Attached Figure Description

[0041] Figure 1 This is an architecture diagram of an augmented reality-based medical device maintenance system provided in an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the cloud server architecture in the augmented reality-based medical equipment maintenance system provided in an embodiment of the present invention.

[0043] Figure 3 This is a flowchart illustrating a preferred embodiment of the augmented reality-based medical device maintenance method provided in this invention.

[0044] Figure 4 This is a flowchart illustrating the application of an AR device in an augmented reality-based medical device maintenance method, as provided in an embodiment of the present invention.

[0045] Figure 5 A schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0047] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content, operations, or steps, nor does it require execution in the described order. For example, some operations or steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0048] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0049] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. For example, the first control information and the second control information are only used to distinguish different control information and do not limit their order.

[0050] Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or the order of execution, and that the words "first" and "second" do not necessarily imply that they are different.

[0051] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0052] Considering the characteristics of equipment maintenance and upkeep in existing technologies, existing technologies have at least the following drawbacks:

[0053] 1. Inefficiency: Maintenance engineers need to frequently refer to paper manuals and manually enter equipment information and inspection results, which is cumbersome and time-consuming.

[0054] 2. Difficulty in Guaranteeing Authenticity: Existing maintenance records mainly rely on paper work orders with checkmarks or photos taken with mobile phones / cameras, which have significant anti-counterfeiting loopholes. The photos taken are easily replaced or tampered with, and cannot be strongly correlated with the identity of maintenance personnel or the specific operation timestamps, providing opportunities for "proxy signing" or "fake maintenance," resulting in a serious lack of traceability in the maintenance process.

[0055] 3. Information silos: Maintenance data is scattered across paper documents or different engineers' equipment, making it difficult to manage and analyze in a unified manner, which is not conducive to forming a health record for the entire life cycle of the equipment.

[0056] 4. Low standardization: Different engineers may have different understandings of the maintenance process, resulting in non-standard maintenance operations and affecting the quality of maintenance.

[0057] 5. Lack of real-time guidance: When encountering complex problems on-site, timely and visual operational guidance is not available.

[0058] 6. Lack of predictive maintenance capabilities: Current technologies generally remain in a passive mode of "periodic maintenance" or "repair after failure," unable to perform health trend analysis based on historical equipment data, operating status, and usage environment, making it difficult to identify potential failure risks in advance. This "remedial" maintenance approach not only increases the risk of sudden downtime but also leads to wasted maintenance resources, shortened equipment lifespan, and fails to achieve proactive and forward-looking intelligent equipment management.

[0059] The emergence of Augmented Reality (AR) devices offers a new approach to solving these challenges. By applying AR devices to maintenance scenarios, first-person visual operation guidance can be provided, allowing maintenance personnel to complete inspections step-by-step without holding the device, significantly improving work efficiency and standardization. AR devices integrate cameras and identity authentication systems, automatically embedding anti-counterfeiting watermarks containing information such as personnel, equipment, and time when taking photos on-site. This strongly links image recording with operational behavior, fundamentally eliminating "proxy signing" or "fake maintenance," achieving full traceability and credibility of the maintenance process, and safeguarding the safety of medical equipment.

[0060] Based on this, this embodiment provides an augmented reality-based medical device maintenance system, including: a mobile terminal, an AR device, and a cloud server, combined with... Figure 1As shown, the mobile terminal, the AR device, and the cloud server all establish communication links with each other. In this embodiment, the front-end deploys the AR device, integrating functions such as camera, voice recognition, and touch control; the back-end relies on the cloud server to build a device database, a maintenance task scheduling center, and a data analysis engine; bidirectional communication between the terminal and the cloud server is achieved through Wi-Fi or a mobile network. Specifically, the mobile terminal and the AR device can establish a communication connection using Wi-Fi or Bluetooth technology, and the mobile terminal and the cloud server can establish an encrypted communication link using Wi-Fi or a mobile network. Similarly, the AR device and the cloud server can also establish an encrypted communication link using Wi-Fi or a mobile network. Of course, in other implementations, short-range communication technologies such as Bluetooth or UWB (UltraWideband, a carrier-free communication technology) can also be considered to achieve data transmission between the glasses and the mobile phone.

[0061] In this embodiment, the mobile terminal serves as the front-end entry point for user authentication and configuration management, running on Android and iOS operating systems. Its core functions include securely verifying login information, managing maintenance orders, providing operation guides, and locally previewing and generating maintenance reports. This ensures users can easily access operational support, and the identity information and operation timestamps of maintenance personnel are encrypted before being uploaded to the AR device.

[0062] Specifically, the mobile terminal in this embodiment can be a portable smart terminal such as a mobile phone. The mobile terminal's APP provides an intuitive maintenance order management interface, clearly displaying pending, in-process, and completed maintenance orders in a card-style layout. Users can filter by status such as "Pending," "In-process," and "Completed" through drop-down menus, and can also search by keyword such as order number, equipment name, or maintenance type to quickly locate the maintenance order corresponding to the target medical equipment. In practical application, each maintenance order card displays key information, including: equipment name, model, maintenance items, plan content, order date, and current execution status. The execution status can be indicated using different colors, such as red for pending, green for in-process, and gray for completed, helping users efficiently manage multi-task scenarios. Users can click on the maintenance order card to enter the details page to view the complete maintenance plan content, relevant responsible persons, and historical execution records.

[0063] The mobile app also provides electronic operation guides for maintenance, assisting maintenance personnel in accurately performing maintenance procedures for complex equipment. Furthermore, the app includes a built-in maintenance manual module. This module remotely accesses a cloud server resource library, displaying standardized operation guides and professional maintenance manuals for various medical devices in real time. Maintenance personnel can quickly locate the corresponding maintenance manual through category navigation or by scanning the QR code on the medical device. All maintenance manuals are presented in a richly illustrated format, with some content supporting embedded video demonstrations for an immersive learning experience. This feature can be used not only for on-site operation reference but also as a digital training material for new employees, effectively improving the overall professional level of the maintenance team.

[0064] Furthermore, maintenance items in maintenance orders can be pre-defined by experienced engineers in the backend system, with detailed maintenance procedures and items uploaded in a structured format. In one implementation, a mobile app supports the review and management of completed maintenance orders. In practical applications, standardized electronic maintenance reports with uniform format and complete content can be automatically generated based on the uploaded structured data and image data. Users can preview, export, share, or print the maintenance reports online within the app, facilitating submission for signature confirmation by relevant personnel or archiving. In addition, the maintenance report generation process supports custom templates and digital signatures to ensure its legal validity and audit compliance. Through this mechanism, the entire maintenance process is fully traceable, verifiable, and accountable, completely eliminating risks such as "proxy signing" and "supplementary entry," significantly improving the standardization, transparency, and efficiency of hospital equipment management.

[0065] The AR device in this embodiment can be a portable AR wearable device such as AR glasses. When applied to different AR devices, only the corresponding software development kit and gesture recognition logic need to be adjusted. This embodiment uses AR glasses as the AR device for illustration. When the AR glasses are activated, the mobile terminal can automatically project the maintenance order management interface onto the AR glasses for a first-person perspective. Specific maintenance items can be completed by operating the AR glasses, and each maintenance item needs to follow a standardized maintenance operation process. Specifically, the AR glasses have a built-in high-resolution optical waveguide display module for immersive, paperless, and visualized maintenance operation process guidance. Capacitive touch sensors are installed on the temples of the AR glasses, and these sensors are used to execute each step in the maintenance operation process. For example, in the maintenance order management interface, maintenance personnel can select a maintenance order to be processed by sliding the capacitive touch sensor on the right temple and click to enter the maintenance item of that order. The system displays the specific maintenance content item by item according to the pre-set maintenance plan in the background database. In addition, in other implementations, voice control, electromyography control, head posture recognition, or eye tracking can be added as interaction methods.

[0066] In practical applications, the standardized maintenance operation procedure in this embodiment specifically includes the following steps:

[0067] Step 1: Operation Guidelines for Maintenance Procedures. Specifically, the AR glasses screen will display the specific requirements for the current maintenance item (e.g., "Check if the monitor power cord is damaged"). This step aims to provide users with clear operational guidance, ensuring that each step is performed according to specifications. After completing the maintenance check, maintenance personnel can continue using the "Next" button on the display interface. Click the right temple to enter the judgment stage, or select "Previous" to return to the order list to check other items.

[0068] Step 2: Based on the aforementioned operation guidelines, assess the compliance of each maintenance item in the maintenance order to be processed, and record the key data and assessment results for each maintenance item. In practical application, maintenance personnel switch between the "Normal" and "Abnormal" options by sliding the capacitive touch sensor on the right temple. After clicking to confirm the selection, the system records the assessment results and some key data in real time, ensuring the timeliness and accuracy of the data.

[0069] Step 3: Trigger the photo-taking function based on the capacitive touch sensor on the AR device to take a picture of the area to be inspected on the medical device for archiving. Specifically, the glasses screen will prompt the user to take a photo for archiving, such as displaying "Please take a photo for archiving". At this time, the maintenance personnel will look at the area to be inspected on the medical device and select the "Take a picture" button on the interface. Then, click the capacitive touch sensor on the right temple to trigger the photo-taking function, and the built-in camera in the AR glasses will automatically capture the image. In addition, at the moment of taking the picture, the system automatically embeds multiple anti-counterfeiting watermarks into the photo. The anti-counterfeiting watermark content includes the maintenance personnel's name, order number, equipment number, and precise timestamp, ensuring a strong correlation and tamper-proof nature between the image data and the actual operation. This embodiment allows 1 to 3 photos to be taken for the same maintenance item. The bottom of the interface will display real-time prompts such as "Photo taken successfully" and "Please click the temple to take a photo for archiving X / 3", making it easy for the user to confirm the photo-taking progress.

[0070] Step 4: After taking photos, proceed to the next maintenance item or the next maintenance order.

[0071] With this courseware, maintenance personnel select maintenance orders to be processed using AR glasses. The system then pushes maintenance tasks item by item according to the preset standardized maintenance operation process. Each task includes a four-step closed-loop operation: "Operation prompts for the maintenance operation process → Qualification judgment → Photo archiving → Proceed to the next step". It also supports scanning and recognizing equipment tags, automatically filling in equipment information to reduce manual input errors, and automatically adding a digital watermark containing name, order number, equipment number, and timestamp when taking photos to ensure image authenticity.

[0072] Once all maintenance tasks are completed, the maintenance personnel click on the right temple of the eyeglass, and the system automatically initiates the upload process, uploading all key data to the cloud server, including the following:

[0073] (1) Data aggregation and encrypted transmission: The AR glasses transmit all key data in this maintenance order (including equipment information, the results of the qualification assessment of each maintenance item, all watermarked photos, and operation timestamps, etc.) to the cloud server via Wi-Fi or mobile network security. High-strength encryption algorithms are used during data transmission to ensure the integrity and security of the data during transmission.

[0074] (2) Cloud Processing and Report Generation: After successful upload, the cloud server parses and structures the received key data. Based on the uploaded key data, the server automatically generates a complete electronic maintenance report (PDF format), which includes detailed maintenance content, the results of the qualification assessment of each maintenance item, watermarked photos, and relevant responsible person information. The generated maintenance report supports multi-channel push, and can be sent directly to relevant responsible persons (such as department heads, equipment administrators, etc.), or archived for future auditing.

[0075] (3) Full traceability and management optimization: The entire maintenance process is fully traceable. From task allocation and operation execution to result uploading, each step is recorded in detail to ensure the authenticity and traceability of the data. Through this mechanism, the hospital's equipment management department can keep abreast of the equipment maintenance status, promptly identify potential problems, optimize the maintenance process, and improve the overall management efficiency and quality.

[0076] In this embodiment, the cloud server serves as the central nervous system and data foundation of the system. Deployed on a cloud infrastructure with high availability and elastic scalability, it employs a microservice architecture for modular design, ensuring high concurrency processing capabilities, business decoupling, and long-term stable operation. The server side consists of three core service layers that collaboratively complete data management, business processing, and resource scheduling, and support predictive maintenance functions, enabling a shift from reactive maintenance to proactive prevention.

[0077] Specifically, in combination Figure 2 As shown, the database server layer adopts a hybrid storage architecture combining a MySQL relational database and a Redis cache database. The MySQL relational database is used to persistently store core structured data, including but not limited to: user account information (such as account number, password, name, employee number, affiliated hospital, etc.), medical equipment records (such as equipment number, equipment serial number, model, manufacturer, service life, risk level, affiliated department, etc.), standardized maintenance plan templates (such as inspection item name, inspection content, execution standards), and the full lifecycle status of maintenance orders (such as order number, equipment type, plan content, executor, inspection results, anomaly summary, completion time, etc.). The Redis cache database is used to cache frequently accessed static data, such as maintenance manuals and equipment information, significantly reducing database query pressure and improving system response speed.

[0078] The application service layer, serving as the system's business logic processing center, is built upon an enterprise-level framework. It is responsible for receiving various requests from mobile terminals and AR glasses, and executing core business logic such as user authentication, permission verification, Bluetooth connection status synchronization, maintenance task distribution, inspection result reception and verification, watermark data association, and order status updates. The application service layer integrates a report generation engine that automatically aggregates order information, inspection results, watermarked photos, and other data upon completion of maintenance tasks, dynamically generating a standardized and complete PDF electronic maintenance report. It also supports report version management and digital signatures to ensure its legal validity and audit compliance.

[0079] In another implementation, the application service layer also integrates a predictive maintenance mechanism. By analyzing multi-dimensional data, it dynamically assesses the health status of medical equipment. This multi-dimensional data includes: historical maintenance records (e.g., inspection results, anomalies, repair frequency), equipment operation logs (e.g., power-on duration, alarm count, key parameter fluctuations), environmental data (e.g., temperature and humidity, department using the equipment, operator feedback), and statistics on similar equipment failures and lifespan information. Then, machine learning algorithms (e.g., time series analysis, random forest classification models) are used to dynamically assess the health status of the medical equipment, obtaining the health status assessment result. When a device is detected to exhibit a performance degradation trend or an increased risk of failure (e.g., a periodic increase in the screen flickering frequency of a certain model of monitor), the system automatically generates a risk warning and pushes it to the maintenance engineer via a mobile app or AR glasses interface, prompting them to arrange a special inspection or component replacement in advance. This achieves a shift from passive maintenance to proactive prevention, significantly reducing the rate of sudden failures and improving equipment availability.

[0080] The file storage service layer is specifically designed to store the massive amounts of unstructured data generated by the system. Considering the large number of high-definition images (such as on-site photos with anti-counterfeiting watermarks) and electronic manuals, training videos, and other documents generated during maintenance, the system deeply integrates ObjectStorage Service (OSS) as the core object storage middleware. OSS provides high-throughput, low-latency, and persistent storage services, supporting efficient uploading, secure archiving, and fast retrieval of massive files. All image data captured and uploaded by AR glasses is processed by the application service layer and stored in OSS in encrypted form, with metadata linked to the corresponding maintenance order to ensure data integrity, traceability, and long-term preservation capabilities.

[0081] In addition, the system in this embodiment can also integrate remote expert collaboration function, support real-time video calls with AR glasses, and allow experts in the background to remotely guide on-site maintenance; it can be connected to hospital or equipment asset management system to realize automatic dispatch and status synchronization of maintenance tasks, and can also add modules such as energy consumption monitoring and environmental sensor integration to expand the dimensions of maintenance.

[0082] Through the aforementioned multi-layered, highly reliable technical architecture, the cloud server not only achieves centralized and intelligent management of data throughout the entire maintenance process, but also provides solid technical support and data assurance for the compliant and standardized operation and maintenance of medical equipment. In particular, the introduced predictive maintenance function further enhances the system's foresight and intelligence, helping medical institutions achieve more efficient equipment management and lower operating costs.

[0083] Based on the above embodiments, the present invention provides a method for maintaining medical devices based on augmented reality (AR), which is applied to the AR-based medical device maintenance system described in the above embodiments. This embodiment also uses AR glasses as an example for illustration. Figure 3 As shown in the figure, the augmented reality-based medical device maintenance method of this embodiment includes at least the following steps:

[0084] Step S100: Log in to the mobile terminal, select the maintenance order to be processed from the maintenance order management interface, and send the maintenance order to be processed to the AR device.

[0085] In practical applications, the mobile app provides an intuitive maintenance order management interface, clearly displaying pending, in-process, and completed maintenance orders in a card-style layout. Users can filter by status buttons such as "Pending," "In-Process," and "Completed" through a drop-down menu, and can also search by keyword such as order number, equipment name, or maintenance type to quickly locate the target maintenance order. Each maintenance order card displays key information, including: equipment name, model, maintenance items, plan details, order date, and current execution status. Different colors can be used for identification, such as red for pending maintenance orders, green for in-process maintenance orders, and gray for completed maintenance orders, helping users efficiently manage multi-task scenarios. Clicking on a maintenance order card will take you to the details page, where you can view the complete maintenance plan, relevant responsible persons, and historical execution records. After selecting a pending maintenance order, it can be submitted to the AR device.

[0086] Step S200: Based on the standardized maintenance operation process, complete the maintenance items in the pending maintenance order. In the maintenance operation process, each operation step is projected into the user's field of vision in real time from a first-person perspective. After the maintenance operation process is completed, upload all key data to the cloud server.

[0087] When performing maintenance tasks, the AR glasses, through their high-resolution waveguide display module, project each operation step into the user's field of vision in real time from a first-person perspective, achieving immersive, paperless, and visual guidance. Specifically, this embodiment provides a standardized maintenance operation process, such as... Figure 4 As shown in the image, after selecting the maintenance order to be processed, you can obtain the standardized maintenance operation procedure guide, which corresponds to... Figure 4 The standardized maintenance operation process is described in the instructions. Next, based on these instructions, the pass / fail status of each maintenance item in the maintenance order to be processed is assessed, and key data and the assessment result for each maintenance item are recorded. Figure 4 The process involves determining whether the procedure is normal. Then, based on the capacitive touch sensor on the AR glasses, the camera function is triggered to take a picture of the area to be tested on the medical device for archiving, i.e., corresponding to... Figure 4 The steps for taking and saving photos. After taking the photo, you can then proceed with... Figure 4 The system checks whether the maintenance items in the maintenance order have been completed. If not, it proceeds to the next maintenance item and repeats the process. Figure 4 The standardized maintenance procedure guides you through the steps. If the current maintenance order is already completed, you can choose to proceed to the next maintenance order. Furthermore, you can perform further steps... Figure 4 The steps to upload to the cloud server.

[0088] In one implementation, this embodiment automatically embeds multiple anti-counterfeiting watermarks into the captured photo at the moment of taking the picture. The watermark content includes the maintenance personnel's name, order number, equipment number, and timestamp, thus ensuring a strong correlation and tamper-proof nature between the image data and the actual operation. When uploading to the cloud server, the AR glasses transmit all key data of this maintenance (including equipment information, the qualification results of each maintenance item, all watermarked photos, and operation time, etc.) to the cloud server via Wi-Fi or mobile network security. High-strength encryption algorithms are used during data transmission to ensure the integrity and security of the data during transmission.

[0089] Step S300: Generate a standardized maintenance report based on all key data; dynamically assess the health status of the medical equipment based on a preset predictive maintenance mechanism; and automatically generate early warning information when the medical equipment is found to have a performance degradation trend or potential failure risk, and push the early warning information to the mobile terminal and the AR device.

[0090] Specifically, the cloud server parses and structures the received key data, and based on the uploaded key data, automatically generates a complete electronic maintenance report, including detailed maintenance content, inspection results, watermarked photos, and information on relevant responsible persons. The generated maintenance report supports multi-channel delivery; it can be sent directly to relevant responsible persons (such as department heads and equipment administrators) or archived for future auditing. The entire maintenance process is fully traceable, with detailed records at every stage from task allocation and operation execution to result uploading, ensuring data authenticity and traceability. Through this mechanism, the hospital's equipment management department can monitor equipment maintenance status in real time, promptly identify potential problems, optimize maintenance processes, and improve overall management efficiency and quality.

[0091] Furthermore, the cloud server in this embodiment can dynamically assess the health status of medical devices based on a preset predictive maintenance mechanism. In practical applications, this embodiment can acquire multi-dimensional data, including: maintenance records, device operation logs, usage environment data, and statistics on similar device failures and lifespan information. The maintenance records include key data from historical and current maintenance records. Then, machine learning algorithms are used to analyze the performance development trends and potential failure risks of the medical devices based on the multi-dimensional data, yielding a health status assessment result for the medical devices.

[0092] In one implementation, when performing performance trend analysis, this embodiment can extract key features reflecting changes in the performance of the medical device from the multi-dimensional data. For example, maintenance frequency, maintenance interval, and key component replacement cycle can be extracted from maintenance records; these features directly reflect the changing trends of potential equipment failures. Parameter fluctuation amplitude, parameter drift trend, and the number of times abnormal parameters occur can be extracted from equipment operation logs; these features reflect the stability of the equipment's real-time operating status. Environmental factor change trends and the duration of extreme environments can be extracted from usage environment data to analyze the long-term impact of the environment on equipment performance. The average failure time of similar equipment and the lifespan distribution of equipment in the same batch can be extracted from data of similar equipment as a reference benchmark for the current health status of the equipment. Then, the extracted key features are input into a trend analysis model constructed using a time series analysis algorithm. The trend analysis model in this embodiment is essentially a model of equipment performance changes over time; its core is predicting trends and identifying anomalies. Therefore, this trend analysis model can automatically analyze the performance development trend of the medical device based on the input key features, thereby obtaining the performance development trend results.

[0093] Furthermore, this embodiment can also perform graded evaluation of performance development trend results. For example, the performance development trend results can be divided into:

[0094] Health Grade: This indicates that the performance trend of the medical device is stable, suggesting that there is no abnormal risk in the medical device within 6 months, the fluctuation of key parameters is within the normal range, and the maintenance interval is longer than the average level of similar equipment.

[0095] Warning level: This indicates that the performance trend of the medical device has begun to slowly decline (e.g., the parameter drift is close to the threshold), suggesting that the medical device may experience minor abnormalities in 3-6 months, requiring enhanced monitoring.

[0096] Failure Risk Level: This indicates that the performance of the medical device is rapidly declining (e.g., a sudden increase in the frequency of abnormal parameters), suggesting that the medical device is likely to fail within 1-3 months and requires immediate maintenance.

[0097] In another implementation, when performing potential failure risk analysis, this embodiment can transform the multi-dimensional data into feature variables. For example, based on maintenance records, quantitative indicators related to failures can be extracted, including but not limited to the number of repairs in the past 3 months, the number of days since the last repair, the cumulative number of replacements of key components (such as circuit boards and sensors), and the average duration of a single repair. Based on operating logs, the average values ​​of core parameters (such as temperature, pressure, and speed) in the past 7 days, the maximum number of abnormal alarms per day, and the cumulative duration of parameters exceeding the normal range can be extracted. Based on environmental data features, the average values ​​of environmental temperature and humidity in the past 30 days, and the average number of days exceeding the equipment's tolerance temperature and humidity range can be extracted. Based on data from similar equipment, the average number of failures of the same model of equipment under the same operating time and the average lifespan of equipment in the same batch can be extracted. Based on this, feature variables can be extracted. Then, the feature variables are input into a classification model built based on the random forest algorithm. The classification model in this embodiment is built based on random forest through ensemble learning of multiple decision trees. When training the classification model, historical data containing equipment data with known health status labels can be divided into training and test sets in a 7:3 ratio. For example, multidimensional features and corresponding fault records of 1,000 similar devices over the past 5 years are collected to construct training and testing sets. Then, a classification model is trained using ensemble learning of multiple decision trees based on a random forest. This trained classification model can be used to capture the non-linear relationship between feature variables and health status. Therefore, based on this classification model, risk level information corresponding to the feature variables can be output, yielding the potential fault analysis results of the medical equipment.

[0098] Finally, the performance trend results and the potential fault analysis results are used as the health status assessment results of the medical device. Based on this health status assessment results, if a performance degradation trend or potential fault risk is detected in the medical device, an early warning message is automatically generated and pushed to the mobile terminal and the AR device to suggest that relevant personnel intervene in advance. The machine learning algorithm used in this embodiment is only for example. In specific applications, it can also be combined with the original operating data provided by the device manufacturer to build a more refined device health assessment model, thereby achieving more accurate monitoring and assessment of medical devices.

[0099] The augmented reality-based medical equipment maintenance method and system of this embodiment can be extended from high-value medical equipment such as monitors, ventilators, and anesthesia machines to low- and medium-value conventional medical equipment such as electrocardiographs, infusion pumps, and electric nursing beds. It is suitable for primary healthcare scenarios such as community health service centers, and can also be extended to cross-industry maintenance scenarios such as CNC machine tools in the industrial manufacturing field and transportation equipment in the public service field. At the same time, it supports collaborative management among hospitals, third-party service agencies, regional medical alliances, and cross-industry maintenance entities, and has good replicability and promotion potential.

[0100] Compared with the prior art, the present invention has at least the following technical effects:

[0101] 1. Ensure the authenticity of maintenance: The AR device captures on-site photos from a first-person perspective and automatically embeds multiple watermarks containing information such as personnel, equipment, orders, and time. This strongly binds the photos to the maintenance operation, fundamentally eliminating fraudulent behaviors such as reshooting after the fact and impersonation, and ensuring the immutability and traceability of maintenance records.

[0102] 2. Improved operational efficiency and convenience: Utilizing WYSIWYG AR visualization guidance, maintenance personnel no longer need to look down at their phones or tablets, freeing their hands and allowing them to focus on equipment inspection. All interactions can be completed through simple temple gestures (swipes, clicks) or the ring, making operation intuitive and smooth, significantly improving on-site work efficiency.

[0103] 3. Standardized Processes: The system enforces pre-defined maintenance procedures, with clear prompts and confirmations at each step, effectively preventing missed or incorrect checks and ensuring the standardization and consistency of the maintenance process. All data is stored electronically and in a structured format, facilitating retrieval, statistics, and auditing. Electronic reports are automatically generated, reducing the management and storage costs of paper documents.

[0104] 4. Introducing a predictive maintenance mechanism breaks through the limitations of traditional periodic maintenance or post-failure repair. The cloud server continuously aggregates historical maintenance records, equipment operation logs, usage environment data, and statistics on similar equipment failures, using machine learning models to dynamically assess the equipment's health status. When the system identifies performance degradation trends or potential failure risks, it automatically generates warning messages and proactively pushes them to maintenance personnel via mobile app or AR glasses, prompting them to arrange specialized inspections or component replacements in advance. This mechanism achieves a shift from passive response to proactive prevention, significantly reducing the rate of sudden failures, extending equipment lifespan, and optimizing the allocation of operation and maintenance resources.

[0105] 5. This invention supports end-to-end digital management of maintenance tasks, covering task distribution, on-site execution, data archiving, report generation, and quality analysis. Combined with predictive maintenance capabilities, it provides scientific decision support for hospital equipment management departments, helping to build a standardized, verifiable, and forward-looking intelligent operation and maintenance system, and comprehensively improving the standardization, transparency, and intelligence of medical equipment management.

[0106] The augmented reality-based medical device maintenance method in this embodiment is based on the same principle as the various terminals and modules in the above system embodiment, and will not be repeated here.

[0107] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 5 As shown. The terminal may include one or more processors 100 ( Figure 5 (Only one is shown in the diagram), memory 101, and computer program 102 stored in memory 101 and executable on one or more processors 100. For example, an augmented reality-based medical device maintenance program. When one or more processors 100 execute computer program 102, they can implement various steps in the augmented reality-based medical device maintenance method embodiment. Alternatively, when one or more processors 100 execute computer program 102, they can implement the functions of various modules / units in the augmented reality-based medical device maintenance system embodiment, without limitation herein.

[0108] In one embodiment, the processor 100 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0109] In one embodiment, memory 101 may be an internal storage unit of an electronic device, such as a hard drive or RAM. Memory 101 may also be an external storage device of the electronic device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital Card (SD), or Flash Card. Furthermore, memory 101 may include both internal and external storage units. Memory 101 is used to store computer programs and other programs and data required by the terminal. Memory 101 can also be used to temporarily store data that has been output or will be output.

[0110] Those skilled in the art will understand that Figure 5 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct memory bus RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An augmented reality-based medical device maintenance system, characterized by, The system comprises a mobile terminal, an AR device and a cloud server, and the mobile terminal, the AR device and the cloud server are all connected with each other through communication links; The mobile terminal is configured to perform security verification on login information, manage maintenance orders, provide operation guides, and locally preview and generate maintenance reports; The AR device is an AR glasses, which is configured to complete maintenance items in the maintenance order based on a standardized maintenance operation process, project each operation step in the maintenance operation process in a first perspective into the field of view of a user in real time, and upload all key data to the cloud server after the completion of the maintenance operation process; The cloud server is configured to generate a standardized maintenance report based on all key data, dynamically evaluate the health status of the medical equipment based on a preset predictive maintenance mechanism, and automatically generate early warning information when identifying that the medical equipment has a performance degradation trend or a potential failure risk, and push the early warning information to the mobile terminal and the AR device; The AR glasses are provided with a high-resolution optical waveguide display module, which is configured to realize immersive and paperless visual maintenance operation process operation guidance; a capacitive touch sensor is arranged on the leg of the AR glasses, and the capacitive touch sensor is configured to execute each operation step in the maintenance operation process; The standardized maintenance operation process comprises: obtaining operation guides of the maintenance operation process; performing eligibility judgment on each maintenance item in the maintenance order to be processed based on the operation guides, and recording key data and judgment results of each maintenance item, wherein the key data of each maintenance item comprises device information, a photo with a watermark and an operation timestamp; triggering a photographing function based on the capacitive touch sensor on the AR device to take a photo of a detection part of the medical equipment for archiving; after the photographing is completed, entering a next maintenance item or a next maintenance order; The cloud server comprises an application service layer, and the application service layer integrates a report generation engine and a predictive maintenance mechanism; The predictive maintenance mechanism comprises the following steps when executed: obtaining multi-dimensional data, wherein the multi-dimensional data comprises maintenance records, device running logs, usage environment data, failure statistics and life information of similar devices, and the maintenance records comprise key data in historical maintenance records and current maintenance records; extracting key features reflecting changes in performance of the medical equipment from the multi-dimensional data, inputting the key features into a trend analysis model constructed by using a time series analysis algorithm, and obtaining performance development trend results of the medical equipment, wherein the key features comprise maintenance frequency, maintenance interval and key component replacement cycle extracted from the maintenance records, parameter fluctuation amplitude, parameter drift trend and number of abnormal parameters extracted from the device running logs, environmental factor change trend and extreme environment duration extracted from the usage environment data, and average failure time of similar devices and life distribution of devices in the same batch extracted from the failure statistics and life information of similar devices. transform the multi-dimensional data into feature variables, and input the feature variables into a classification model constructed based on a random forest algorithm, output risk level information corresponding to the feature variables based on the classification model, and obtain a potential failure analysis result of the medical device, wherein the feature variables include: quantitative indicators related to failure extracted based on maintenance records; mean values of core parameters in the last 7 days, maximum values of single-day abnormal alarm times, and cumulative time lengths of parameters exceeding normal ranges extracted based on operation logs; mean values of environmental temperature and humidity in the last 30 days and average values of days exceeding the temperature and humidity range tolerated by the device extracted based on usage environment data; average failure times of the same type of device under the same operation duration and average service life of the same batch of devices extracted based on failure statistics and service life information of similar devices; use the performance development trend result and the potential failure analysis result as the health status evaluation result of the medical device; if performance degradation trends or potential failure risks of the medical device are identified, automatically generate early warning information, and push the early warning information to the mobile terminal and the AR device. The classification model is constructed based on random forest through ensemble learning of multiple decision trees. In training the classification model, historical data containing device data with known health status labels are divided into a training set and a test set in a 7:3 ratio, and the classification model is trained based on random forest through ensemble learning of multiple decision trees. The trained classification model is used to capture the nonlinear relationship between feature variables and health status.

2. The augmented reality-based medical device maintenance system of claim 1, wherein, The cloud server further comprises a database server layer and a file storage layer. The database server layer adopts a hybrid storage architecture combining a MySQL relational database and a Redis cache database. The MySQL relational database is used for persistent storage of core structured data, and the Redis cache database is used for caching high-frequency access static data. The application service layer is used to receive various requests sent by the mobile terminal and the AR device. The file storage layer is used to store unstructured data generated by the system.

3. An augmented reality-based medical device maintenance method characterized by, The method is applied to the augmented reality-based medical device maintenance system of any one of claims 1-2, and the method comprises: logging into a mobile terminal, selecting a maintenance order to be processed from a maintenance order management interface, and sending the maintenance order to be processed to an AR device; completing maintenance items in the maintenance order to be processed based on a standardized maintenance operation process, projecting each operation step in the maintenance operation process in a first perspective in the user's field of view in real time, and uploading all key data to a cloud server after the completion of the maintenance operation process; generating a standardized maintenance report based on all key data, dynamically evaluating the health status of the medical device based on a preset predictive maintenance mechanism, and automatically generating early warning information when performance degradation trends or potential failure risks of the medical device are identified, and pushing the early warning information to the mobile terminal and the AR device.

4. The augmented reality-based medical device maintenance method of claim 3, wherein, The maintenance operation process based on standardization is used to complete the maintenance items in the maintenance order to be processed, including: obtaining the operation guide of the maintenance operation process; judging the eligibility of each maintenance item in the maintenance order to be processed based on the operation guide, and recording the key data and judgment result of each maintenance item; triggering the photographing function based on the capacitive touch sensor on the AR device to take a photo of the detection part of the medical equipment for archiving; after the photographing is completed, entering the next maintenance item or the next maintenance order.

5. The augmented reality-based medical device maintenance method of claim 3, wherein, Taking a photo of the detection part of the medical equipment, including: At the moment of taking a photo, automatically embedding multiple anti-fake watermarks in the photo, including the name of the maintenance personnel, the order number, the equipment number and the timestamp.

6. The augmented reality-based medical device maintenance method of claim 3, wherein, Based on the preset predictive maintenance mechanism, the health status of the medical equipment is dynamically evaluated, including: obtaining multi-dimensional data, including: maintenance records, equipment operation logs, usage environment data, similar equipment failure statistics and life information, wherein the maintenance records include key data in historical maintenance records and current maintenance records; using machine learning algorithm to analyze the performance development trend and potential failure risk of the medical equipment based on the multi-dimensional data, and obtaining the health status evaluation result of the medical equipment.

7. The augmented reality-based medical device maintenance method of claim 6, wherein, Using machine learning algorithm to analyze the performance development trend and potential failure risk of the medical equipment based on the multi-dimensional data, and obtaining the health status evaluation result of the medical equipment, including: extracting the key features reflecting the performance changes of the medical equipment from the multi-dimensional data, inputting the key features into the trend analysis model constructed by using time series analysis algorithm, and obtaining the performance development trend result of the medical equipment; transforming the multi-dimensional data into feature variables, inputting the feature variables into the classification model constructed based on random forest algorithm, outputting the risk level information corresponding to the feature variables based on the classification model, and obtaining the potential failure analysis result of the medical equipment; the performance development trend result and the potential failure analysis result are used as the health status evaluation result of the medical equipment.

8. A terminal, characterized by comprising: The terminal includes a memory, a processor, and an augmented reality-based medical equipment maintenance program stored in the memory and executable on the processor, and when the processor executes the augmented reality-based medical equipment maintenance program, the steps of the augmented reality-based medical equipment maintenance method according to any one of claims 3-7 are implemented.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores an augmented reality-based medical equipment maintenance program, and the augmented reality-based medical equipment maintenance program implements the steps of the augmented reality-based medical equipment maintenance method according to any one of claims 3-7 on the computer readable storage medium.

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