Ship crew telemedicine system and method based on video call technology

Abstract

The invention discloses a sailor remote medical system and method based on a video call technology, the sailor remote medical system comprises a shipborne terminal and a remote medical platform which are intercommunicated through a public internet, and the shipborne terminal comprises sailor health monitoring equipment, a data acquisition module, a terminal self-adaptive high-definition video call module and an intelligent communication gateway; the remote medical platform comprises a platform video call service module, a medical data intelligent fusion and analysis auxiliary diagnosis module, a remote expert cooperation and consultation module, a treatment feedback and dynamic adjustment module and a health risk prediction and active prevention and control module. According to the invention, high-efficiency, stable and intelligent telemedicine service for marine crew can be realized.

Description

Technical Field

[0001] This invention relates to the field of telemedicine technology, and more specifically to a remote medical system and method for seafarers based on video call technology. Background Technology

[0002] Telemedicine technology has been widely used in rural and urban healthcare, playing a positive role in various medical specialties such as cardiology and neurosurgery. However, in the maritime shipping sector, the medical treatment of crew members still faces many challenges.

[0003] Currently, maritime telemedicine management systems (such as the one disclosed in CN116936069A, based on the BeiDou Navigation Satellite System and 5G technology) typically include a land-based medical command center and a shipboard medical terminal. The land-based command center contains components such as a medical data server and a medical record database; the shipboard medical terminal is equipped with basic communication base stations and simple vital sign detection equipment. This system uses satellite communication to connect the shipboard vessel with the land-based medical center, enabling the collection of crew members' health information and the provision of remote guidance.

[0004] However, existing technologies have significant shortcomings: when maritime medical terminals transmit data via satellite communication, bandwidth limitations result in blurry video call quality and frequent buffering, limiting the transmission of only simple text-based vital sign data; complex medical imaging data cannot be transmitted smoothly. When ships sail to remote waters, satellite signal attenuation and limited communication resources further exacerbate the risk of data transmission interruptions. Furthermore, existing systems lack the ability to automatically link complete medical histories of crew members; onshore systems must rely on manual searches of past medical records, which is inefficient and prone to errors. Treatment plans are primarily formulated by onboard medical personnel based on experience, making it difficult for onshore experts to participate deeply and dynamically in the treatment process to address changes in the patient's condition. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a remote medical system and method for seafarers based on video call technology, so as to realize efficient, stable and intelligent remote medical services for seafarers at sea.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0007] A remote medical system for seafarers based on video call technology includes a shipborne terminal and a remote medical platform interconnected via a public internet. The shipborne terminal includes a seafarer health monitoring device, a data acquisition module, a terminal-adaptive high-definition video call module, and an intelligent communication gateway. The output of the seafarer health monitoring device is connected to the input of the data acquisition module, and the output of the data acquisition module is connected to the input of the intelligent communication gateway. The terminal-adaptive high-definition video call module is bidirectionally connected to the intelligent communication gateway. The telemedicine platform includes a platform video call service module, a medical data intelligent fusion and analysis auxiliary diagnosis module, a remote expert collaboration and consultation module, a treatment feedback and dynamic adjustment module, and a health risk prediction and proactive prevention module. The platform video call service module is connected to the medical data intelligent fusion and analysis auxiliary diagnosis module and the remote expert collaboration and consultation module, respectively. The medical data intelligent fusion and analysis auxiliary diagnosis module is connected to the remote expert collaboration and consultation module and the treatment feedback and dynamic adjustment module, respectively. The remote expert collaboration and consultation module is also connected to the treatment feedback and dynamic adjustment module. The treatment feedback and dynamic adjustment module is connected to the health risk prediction and proactive prevention module.

[0008] Preferably, the crew health monitoring device is used to collect crew physiological parameter data in real time; the crew health monitoring device is a smart wearable device with a built-in low-power microprocessor to issue early warnings based on preset emergency thresholds of physiological parameters; The data acquisition module connects to the crew health monitoring equipment through multiple interfaces to unify the data format, integrate physiological parameter data into the local database, and record measurement time, equipment number, and crew ID metadata.

[0009] Preferably, the terminal adaptive high-definition video call module adopts the H.265 video encoding standard and WebRTC real-time communication framework, and integrates an adaptive bitrate control algorithm to dynamically adjust video parameters according to real-time network bandwidth to establish and maintain video connections. It also has a built-in image processing algorithm to clearly capture the crew's facial color, mental state and injury details, ensuring that the remote medical team obtains intuitive and detailed visual information. The intelligent communication gateway integrates maritime satellite communication, VHF data communication and mobile communication functions. It is used to continuously monitor the real-time network signal strength and available bandwidth of each communication link through a built-in intelligent decision-making algorithm, and dynamically select and switch the optimal communication link for data transmission according to a preset data priority strategy.

[0010] Preferably, the medical data intelligent fusion and analysis auxiliary diagnosis module is used to actively retrieve the crew's historical electronic medical record data, and associate and fuse the historical electronic medical record data, the received real-time physiological parameter data, and the crew's appearance feature data extracted from the video stream, and perform in-depth analysis on the fused data based on a preset intelligent diagnosis model to generate auxiliary diagnosis information including disease assessment and etiology inference. The remote expert collaboration and consultation module is used to match experts from the expert database based on the keywords of the crew member's current symptoms and their specific medical history characteristics, and to invite experts to join the video consultation through an encrypted video channel. It also provides a shared whiteboard tool for experts to collaboratively annotate and discuss medical images, plan the optimal rescue route, and dispatch medical resources. The treatment feedback and dynamic adjustment module is used to push encrypted treatment plans and reports to the shipboard terminal, and receive health feedback information from crew members. Based on a preset rule engine or machine learning model, it automatically adjusts the health management plan or triggers new video consultations to revise the treatment plan.

[0011] Preferably, the intelligent diagnostic model in the medical data intelligent fusion and analysis auxiliary diagnostic module is a machine learning-based model, used to analyze the fused multi-source data and output a quantitative score of the severity of the condition and / or a probability ranking of potential causes.

[0012] Preferably, the health risk prediction and proactive prevention module is used to conduct retrospective analysis and correlation mining on massive amounts of long-term health data of crew members, weather conditions of shipping routes and work intensity data based on an artificial intelligence deep learning framework, to construct a health risk prediction model, to assess the health risk trends of different seasons and sea areas, and to generate targeted medical resource pre-configuration plans based on the prediction results.

[0013] A method for providing remote medical care to seafarers based on video call technology includes the following steps: S1. Health monitoring and early warning: Collect crew physiological parameters in real time through crew health monitoring equipment and issue early warnings based on preset emergency thresholds of physiological parameters; S2. Data Acquisition and Integration: Through the data acquisition module, various crew health monitoring devices are connected to unify the data format and integrate it into the local database; S3. Intelligent Link Selection and Data Transmission: Through the intelligent communication gateway module, the optimal communication link is dynamically selected based on real-time network status and data priority to upload the collected data to the remote medical platform; S4. Adaptive Video Diagnosis Establishment: Crew members initiate video call requests through the terminal's adaptive high-definition video call module. The terminal's adaptive high-definition video call module dynamically adjusts video parameters to adapt to real-time network bandwidth. Doctors respond to video call requests through the platform's video call service module and establish video connections. S5. Data Fusion and Assisted Diagnosis: Through the medical data intelligent fusion and analysis assisted diagnosis module, the crew's historical medical records, real-time data and video streams are retrieved and merged. Assisted diagnosis information is generated using an intelligent diagnosis model. At the same time, the doctor combines the video footage with the assisted diagnosis information to make a diagnosis. If the diagnosis result is a common disease, proceed to step S7; otherwise, proceed to step S6. S6. Remote Expert Collaboration: Match experts with the remote expert collaboration and consultation module and invite experts to join video consultations through encrypted video channels, and use a shared whiteboard for collaborative image annotation and discussion. S7. Treatment Management and Dynamic Feedback: Through the treatment feedback and dynamic adjustment module, encrypted treatment plans and reports are pushed to crew members; health feedback from crew members is received, and health management plans are automatically adjusted or new video consultations are triggered accordingly.

[0014] Preferably, in step S2, the data priority strategy stipulates that the priority of early warning data and video call data streams is higher than that of daily health record data; The dynamic selection of the optimal communication link specifically means that when physiological parameter data exceeds a preset emergency threshold, the maritime satellite communication link is selected first for data upload in seconds; for daily health monitoring data that does not exceed the emergency threshold, batch transmission is performed when the mobile or VHF signal quality is better than the set threshold. In step S4, the video parameters are dynamically adjusted as follows: based on the real-time detected available bandwidth, the video resolution is dynamically switched between 360P and 1080P, the frame rate is switched between 15fps and 60fps, and the video call latency is controlled within 300 milliseconds.

[0015] Preferably, in step S6, when it is determined that the crew member's condition is critical, the real-time position of the ship is obtained through satellite positioning, the optimal rescue route is planned in combination with electronic nautical chart data, and surrounding medical resources are dispatched.

[0016] Preferably, after step S7, the method further includes: S8. Health risk prediction and proactive prevention and control: Through the health risk prediction and proactive prevention and control module, the long-term health data of the crew, the weather conditions of the route and the intensity of the operation are retrospectively analyzed and correlated. The health risk prediction model is used to assess future health risks and proactively generate a pre-configuration plan for medical resources accordingly.

[0017] Due to the adoption of the above technical solutions, the technical progress achieved by this invention is as follows.

[0018] 1. This invention enables high-quality, high-reliability remote medical communication in harsh maritime communication environments: By combining the dynamic link selection strategy of the shipborne terminal's "intelligent communication gateway" with the bit rate control algorithm of the "adaptive high-definition video call module," a dual communication guarantee mechanism is formed. This mechanism can intelligently utilize satellite, VHF, and mobile networks to prioritize the transmission of critical medical data and dynamically adjust video quality. This ensures the smoothness and stability of high-definition video calls and real-time physiological data transmission in the low-bandwidth, high-latency environment of the open sea, fundamentally solving the communication bottleneck problem of maritime telemedicine.

[0019] 2. This invention improves the accuracy, efficiency, and synergy of medical diagnosis: The "Medical Data Intelligent Fusion and Analysis Assisted Diagnosis Module" deeply integrates real-time physiological parameters, historical electronic medical records, and visual features from video images of crew members. It then utilizes intelligent diagnostic models to provide quantitative auxiliary diagnostic information, offering doctors a comprehensive and objective scientific basis for decision-making, overcoming the limitations of single data sources and the biases of doctors' subjective experience. Furthermore, the "Remote Expert Collaboration and Consultation Module," with its rapid expert matching, encrypted video channels, and shared whiteboards, enables the immediate gathering and efficient collaboration of multiple experts, significantly improving the efficiency and quality of diagnosis and treatment for complex and critical conditions.

[0020] 3. This invention constructs a closed-loop dynamic treatment system of "diagnosis-treatment-feedback-adjustment," realizing personalized medicine: The "treatment feedback and dynamic adjustment module" breaks away from the traditional "one-time diagnosis" model of telemedicine. The system can receive treatment feedback from crew members and automatically adjust subsequent health management plans or trigger new video consultations based on a rule engine or machine learning model. This allows treatment plans to be dynamically optimized according to the crew members' actual recovery, forming a closed-loop management system with continuous monitoring, real-time feedback, and dynamic adjustment, truly achieving personalized and precise full-process health monitoring.

[0021] 4. This invention achieves a revolutionary shift from "passive treatment" to "active prevention and control": Through its unique "Health Risk Prediction and Proactive Prevention Module," the system leverages an artificial intelligence deep learning framework to mine massive amounts of historical data (health data, route weather, operational intensity) and construct predictive models to assess population health risk trends. This enables the system to provide early warnings of diseases that may be prevalent in specific seasons and sea areas, and proactively generate plans for drug reserves, personnel training, and resource allocation. This function moves the focus of maritime medical care forward, reducing disease incidence at its source and significantly improving the overall health management efficiency and safety of the fleet.

[0022] In summary, this invention effectively solves the three core challenges of maritime telemedicine through systematic technological innovation: communication support, intelligent diagnosis, and dynamic management. It also innovatively introduces a proactive prevention and control mechanism, providing crew members with all-weather, full-process, precise, and efficient medical support. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] A remote medical system for seafarers based on video call technology, combined with Figure 1 As shown, it includes a shipborne terminal and a telemedicine platform, which are connected via the public Internet to achieve data exchange.

[0026] The shipborne terminal includes crew health monitoring equipment, a data acquisition module, a terminal adaptive high-definition video call module, and an intelligent communication gateway. The output of the crew health monitoring equipment is connected to the input of the data acquisition module, and the output of the data acquisition module is connected to the input of the intelligent communication gateway. The terminal adaptive high-definition video call module is bidirectionally connected to the intelligent communication gateway.

[0027] The crew health monitoring equipment is a smart wearable device used to collect real-time physiological parameter data from crew members. Specifically, the equipment includes a multi-functional health bracelet and a patch-type physiological monitor. These devices can continuously capture diverse health data 24 hours a day, collecting physiological data in real time, including blood pressure, electrocardiogram waveform, blood oxygen saturation, respiratory rate, heart rate variability, sleep quality, exercise energy consumption, and emotional stress indicators. The equipment also incorporates high-precision sensors to ensure the accuracy of data collection. Furthermore, the equipment has a built-in low-power microprocessor that generates alerts based on preset emergency thresholds for physiological parameters (e.g., heart rate exceeding 120 beats / minute for 2 minutes), and performs local anomaly alerts to notify crew members and onboard medical personnel. It also performs local caching every 5 minutes to prevent data loss.

[0028] The data acquisition module connects to crew health monitoring equipment via multiple interfaces to standardize data formats and integrate physiological parameter data into a local database. Specifically, the data acquisition module is equipped with various professional interfaces, such as USB, Bluetooth Low Energy (BLE), and RS232, to connect to different types of medical testing equipment. For example, it connects to a blood glucose meter via USB and an electronic blood pressure monitor via BLE. By using device drivers to standardize data formats, the module integrates the collected blood pressure, blood glucose, body temperature, and other data into the shipboard terminal's local database. It also records metadata such as measurement time, device number, and crew ID. The database uses a lightweight relational database, SQLite, for convenient data management and querying.

[0029] The terminal-adaptive high-definition video call module adopts the H.265 video encoding standard and the WebRTC real-time communication framework, and integrates an adaptive bitrate control algorithm to dynamically adjust video parameters based on real-time network bandwidth to establish and maintain video connections. Specifically, the module uses the H.265 video encoding standard, which improves compression efficiency by 50% compared to H.264, reduces bandwidth requirements, and adapts to different network conditions. Combined with the WebRTC real-time communication framework, it optimizes the UDP transmission protocol and utilizes the adaptive bitrate control algorithm to dynamically adjust the video resolution (from 360P to 1080P) and frame rate (15fps - 60fps) based on network bandwidth (real-time detection of available bandwidth), ensuring relatively smooth calls even with low satellite communication bandwidth and keeping latency below 300 milliseconds for guaranteed real-time interaction. Furthermore, the module employs advanced image processing algorithms to clearly capture crew members' facial complexion, mental state, and details of injuries and illnesses, ensuring that remote medical teams receive intuitive and detailed visual information. Simultaneously, physiological parameter data is embedded into the video footage, forming an integrated information display interface. This design enables the telemedicine team to quickly grasp the crew's overall health status, providing a reliable basis for accurate diagnosis.

[0030] The intelligent communication gateway integrates maritime satellite communication, VHF data communication, and mobile communication (4G or 5G) functions. It continuously monitors the real-time network signal strength and available bandwidth of each communication link through a built-in intelligent decision-making algorithm. Based on a preset data priority strategy, it dynamically selects and switches the optimal communication link for data transmission. Specifically, the intelligent decision-making algorithm selects the optimal link based on network signal strength (measured by RSSI), bandwidth (real-time speed measurement), and data priority (early warning data monitored by crew health monitoring equipment has the highest priority). In case of acute illness, data is uploaded in seconds via satellite communication. Routine health records are transmitted in batches when mobile and VHF signals are good, using the HTTP / 3 protocol to improve transmission efficiency and reduce latency.

[0031] The telemedicine platform includes a video call service module, a medical data intelligent fusion and analysis auxiliary diagnosis module, a remote expert collaboration and consultation module, a treatment feedback and dynamic adjustment module, and a health risk prediction and proactive prevention module. Specifically, the video call service module is connected to both the medical data intelligent fusion and analysis auxiliary diagnosis module and the remote expert collaboration and consultation module; the medical data intelligent fusion and analysis auxiliary diagnosis module is also connected to both the remote expert collaboration and consultation module and the treatment feedback and dynamic adjustment module; the remote expert collaboration and consultation module is further connected to the treatment feedback and dynamic adjustment module; and the treatment feedback and dynamic adjustment module is connected to the health risk prediction and proactive prevention module.

[0032] The platform's video call service module enables remote real-time video calls between doctors and crew members.

[0033] The intelligent fusion and analysis module for medical data in assisted diagnosis proactively retrieves historical electronic medical record data from crew members. It then correlates and fuses this historical data with received real-time physiological parameter data and crew member facial feature data extracted from video streams. Based on a pre-defined intelligent diagnostic model, it performs in-depth analysis of the fused data to generate assisted diagnostic information including condition assessment and etiological inference. This intelligent diagnostic model is based on machine learning and analyzes the fused multi-source data, outputting a quantitative score of the severity of the condition and / or a probability ranking of potential causes.

[0034] Specifically, the intelligent fusion and analysis module for medical data-assisted diagnosis retrieves historical data from the global medical data cloud, including the crew's past medical records, family medical history, and allergy details, and automatically correlates this data with real-time collected vital signs data and video images. Based on an intelligent diagnostic model, the fused data undergoes in-depth analysis to identify potential causes and generate a probability ranking of the condition. This analysis process, combined with machine learning algorithms, comprehensively considers multiple factors, such as medical history, current symptoms, and relevant medical images, providing scientific decision support for the telemedicine team. Simultaneously, upon video connection, the telemedicine platform interface displays the crew's real-time health monitoring data, recent vital sign trend charts, and key information from past medical records. Combined with the video feed, doctors immediately begin diagnosis, using professional medical knowledge and experience to preliminarily determine the type and severity of the condition, such as distinguishing between acute trauma and the onset of chronic internal diseases.

[0035] The remote expert collaboration and consultation module matches experts from the expert database based on keywords of the crew member's current symptoms and specific medical history characteristics. Experts are invited to participate in video consultations via an encrypted video channel. A shared whiteboard tool is provided for experts to collaboratively annotate and discuss medical images, and it is also used to plan the optimal rescue route and allocate medical resources. Specifically, in cases of complex illnesses, the local on-duty doctor can submit a consultation request with one click. The remote expert collaboration and consultation module filters and matches medical experts from the database based on symptoms keywords and the crew member's medical history characteristics. For example, if a heart disease is involved, a cardiovascular expert is summoned; if a maritime occupational disease is involved, a maritime medical authority is consulted. Expert invitations are ensured to be completed within 5 minutes, and consultations are conducted via an encrypted video channel. Once a crew member's condition is determined to be critical and beyond the ship's capacity, the remote expert collaboration and consultation module immediately plans the optimal rescue route based on the distribution of medical resources in the surrounding waters and the dynamic information of rescue vessels, and dispatches helicopters or nearby rescue vessels for rapid assistance.

[0036] Meanwhile, the remote expert collaboration and consultation module utilizes HTML5 WebSocket technology to achieve full-duplex real-time communication, ensuring information synchronization between experts and on-board and remote doctors. The desktop consultation software, developed using the Electron cross-platform framework, is compatible with Windows, Mac, and Linux systems. It leverages screen sharing (implemented through a native Chrome browser API extension) and an electronic whiteboard (using the fabric.js library for drawing, annotation, and writing) to allow doctors to draw diagrams, annotate key points, or add text descriptions during consultations, facilitating expert focus on key images and data for discussion. For example, doctors can directly circle lesions on shared medical images or use arrows to indicate details requiring attention. This intuitive annotation method not only improves communication efficiency but also reduces misunderstandings caused by unclear language descriptions, enhancing diagnostic accuracy. Furthermore, the software supports saving annotations for later review and reference.

[0037] The remote expert collaboration and consultation module supports multi-point video consultations, employing a high-quality multi-party video conferencing framework that allows multiple professional doctors to participate in consultations simultaneously online, ensuring efficient collaboration within the medical team. Video calls are clear and smooth with extremely low latency, meeting the needs of real-time interaction, especially in emergency situations where expert teams can quickly assemble and begin discussions.

[0038] To facilitate information sharing among doctors, the remote expert collaboration and consultation module integrates secure data transmission protocols, creating a unified data sharing environment. During consultations, the expert team can not only view the crew member's video call footage in real time but also simultaneously access their health data and medical records. This data is presented in a structured format, allowing experts to quickly understand the patient's basic condition, disease progression, and past treatment records. Furthermore, the remote expert collaboration and consultation module supports dynamically updated data, such as real-time monitored vital signs, providing the latest diagnostic information.

[0039] To ensure the rigor and transparency of the consultation process, the remote expert collaboration and consultation module is designed with a comprehensive consultation recording function. This function automatically records all content during the consultation, including video calls, voice discussions, shared data, and annotations. These records are encrypted and stored on a secure server, with backup technology ensuring data integrity. Doctors can retrieve the records at any time after the consultation for review, analysis, or to develop subsequent treatment plans. Furthermore, the recorded content serves as a crucial basis for medical decision-making, helping the team continuously optimize the treatment process.

[0040] The treatment feedback and dynamic adjustment module is used to push encrypted treatment plans and reports to the shipboard terminal and receive health feedback information from crew members. Based on a preset rule engine or machine learning model, it automatically adjusts the health management plan or triggers new video consultations to revise the treatment plan.

[0041] Specifically, doctors communicate with crew members in real time via video calls, answering health questions and providing personalized treatment advice. If medication or self-care is required, the treatment feedback and dynamic adjustment module provides detailed instructions on treatment steps, medication usage, and precautions. For example, the module demonstrates via video how to correctly use medication and monitor symptom changes, ensuring crew members can follow instructions. In emergencies, doctors can directly issue emergency treatment instructions to crew members through the treatment feedback and dynamic adjustment module platform, providing detailed guidance on subsequent medical treatment and monitoring measures to ensure crew members receive timely medical support.

[0042] Based on remote diagnostic results, doctors develop personalized treatment plans and push detailed treatment suggestions, prescriptions, and health reports through a treatment feedback and dynamic adjustment module. These reports not only include the doctor's diagnostic conclusions and recommended treatment measures, but also specific follow-up plans, medication instructions, and precautions. All reports are transmitted electronically to the ship's terminal in an encrypted manner, ensuring information security and privacy protection. Crew members can view the report content at any time and share it with other medical personnel or experts as needed for further analysis and discussion to optimize treatment plans and follow-up care plans.

[0043] After receiving the treatment plan and recommendations, the crew members begin treatment under the doctor's guidance. During treatment, crew members can provide real-time feedback on their health status, reporting new symptoms or discomfort. The treatment feedback and dynamic adjustment module automatically adjusts the health management plan based on the crew members' feedback and pushes timely medication reminders, examination suggestions, or follow-up measures according to preset rules or remote instructions from the doctor. If new health problems arise during treatment, crew members can consult with the doctor remotely via video call at any time. The doctor can adjust the treatment plan or provide new guidance based on the crew members' latest condition, ensuring continuous monitoring and optimization of treatment effectiveness. The real-time data update and feedback mechanism makes the treatment process more personalized and dynamic, improving the accuracy and effectiveness of treatment.

[0044] The Health Risk Prediction and Proactive Prevention module utilizes an artificial intelligence deep learning framework to retrospectively analyze and mine correlations in massive amounts of long-term health data of crew members, as well as data on route weather conditions and operational intensity. This allows for the construction of a health risk prediction model to assess health risk trends in different seasons and sea areas, and to generate targeted pre-allocation plans for medical resources based on the prediction results. Specifically, relying on an artificial intelligence deep learning framework, the module retrospectively analyzes years of crew members' health data, uncovers hidden correlations, and integrates multi-dimensional data such as route weather conditions, operational intensity, and the frequency of chronic disease flare-ups among crew members. This data is used to construct a prediction model to assess potential health risks in different seasons and sea areas. Based on the analysis results, it enables the advance stockpiling of effective drugs, the organization of specialist training, and the development of targeted medical resource allocation plans. By shifting from passive treatment to proactive prevention, comprehensive protection of crew members' health throughout the voyage is achieved, significantly improving the efficiency of maritime medical services.

[0045] This invention integrates efficient data acquisition, transmission, analysis, and video communication technologies to construct a complete remote medical service system for seafarers. This system enables real-time monitoring, diagnosis, and treatment recommendations for seafarers' health status, and provides decision support for doctors, ensuring that seafarers receive timely medical care even at sea.

[0046] A remote medical care method for seafarers based on video call technology, such as Figure 2 As shown, it includes the following steps: S1. Health monitoring and early warning: Collect crew members' physiological parameters in real time through crew health monitoring equipment, and issue early warnings based on preset emergency thresholds of physiological parameters.

[0047] S2. Data Acquisition and Integration: Through the data acquisition module, various crew health monitoring devices are connected to unify the data format and integrate it into the local database.

[0048] In this step, the data priority strategy stipulates that early warning data and video call data streams have higher priority than daily health record data. Dynamically selecting the optimal communication link specifically involves: when physiological parameter data exceeds a preset emergency threshold, the maritime satellite communication link is prioritized for second-level data upload; for daily health monitoring data that does not exceed the emergency threshold, batch transmission is performed when the mobile or VHF signal quality is better than the set threshold.

[0049] S3. Intelligent Link Selection and Data Transmission: Through the intelligent communication gateway module, based on real-time network status and data priority, the optimal communication link is dynamically selected to upload the collected data to the remote medical platform.

[0050] S4. Adaptive Video Diagnosis Establishment: Crew members initiate video call requests through the terminal's adaptive high-definition video call module. The terminal's adaptive high-definition video call module dynamically adjusts video parameters to adapt to real-time network bandwidth. Doctors respond to video call requests through the platform's video call service module and establish video connections.

[0051] In this step, the video parameters are dynamically adjusted as follows: based on the available bandwidth detected in real time, the video resolution is dynamically switched between 360P and 1080P, the frame rate is switched between 15fps and 60fps, and the video call latency is controlled within 300 milliseconds.

[0052] S5. Data Fusion and Assisted Diagnosis: Through the medical data intelligent fusion and analysis assisted diagnosis module, the crew's historical medical records, real-time data and video streams are retrieved and merged. Assisted diagnosis information is generated using an intelligent diagnostic model. At the same time, the doctor combines the video footage with the assisted diagnosis information to make a diagnosis. If the diagnosis result is a common disease, proceed to step S7; otherwise, proceed to step S6.

[0053] S6. Remote Expert Collaboration: The remote expert collaboration and consultation module matches experts and invites them to join video consultations via encrypted video channels, using a shared whiteboard for collaborative image annotation and discussion.

[0054] In this step, when a crew member's condition is determined to be critical, the ship's real-time location is obtained through satellite positioning, the optimal rescue route is planned in combination with electronic nautical chart data, and surrounding medical resources are dispatched.

[0055] S7. Treatment Management and Dynamic Feedback: Through the treatment feedback and dynamic adjustment module, encrypted treatment plans and reports are pushed to crew members; health feedback from crew members is received, and health management plans are automatically adjusted or new video consultations are triggered accordingly.

[0056] S8. Health Risk Prediction and Proactive Prevention: Through the health risk prediction and proactive prevention module, the long-term health data of crew members, the weather conditions of the route and the intensity of the operation are retrospectively analyzed and correlated. The health risk prediction model is used to assess future health risks and proactively generate a pre-allocation plan for medical resources.

Claims

1. A remote medical system for crew members based on video call technology, comprising a shipborne terminal interconnected via the public internet and a remote medical platform, characterized in that: The shipborne terminal includes a crew health monitoring device, a data acquisition module, a terminal adaptive high-definition video call module, and an intelligent communication gateway; the output of the crew health monitoring device is connected to the input of the data acquisition module, and the output of the data acquisition module is connected to the input of the intelligent communication gateway; the terminal adaptive high-definition video call module is bidirectionally connected to the intelligent communication gateway. The telemedicine platform includes a platform video call service module, a medical data intelligent fusion and analysis auxiliary diagnosis module, a remote expert collaboration and consultation module, a treatment feedback and dynamic adjustment module, and a health risk prediction and proactive prevention module. The platform video call service module is connected to the medical data intelligent fusion and analysis auxiliary diagnosis module and the remote expert collaboration and consultation module, respectively. The medical data intelligent fusion and analysis auxiliary diagnosis module is connected to the remote expert collaboration and consultation module and the treatment feedback and dynamic adjustment module, respectively. The remote expert collaboration and consultation module is also connected to the treatment feedback and dynamic adjustment module. The treatment feedback and dynamic adjustment module is connected to the health risk prediction and proactive prevention module.

2. The remote medical system for seafarers based on video call technology according to claim 1, characterized in that: The crew health monitoring equipment is used to collect crew physiological parameter data in real time; The crew health monitoring device is a smart wearable device with a built-in low-power microprocessor to issue early warnings based on preset emergency thresholds of physiological parameters; The data acquisition module connects to the crew health monitoring equipment through multiple interfaces to unify the data format, integrate physiological parameter data into the local database, and record measurement time, equipment number, and crew ID metadata.

3. The remote medical system for seafarers based on video call technology according to claim 1, characterized in that: The terminal adaptive high-definition video call module adopts the H.265 video encoding standard and WebRTC real-time communication framework, and integrates an adaptive bit rate control algorithm to dynamically adjust video parameters according to real-time network bandwidth to establish and maintain video connections. It also has a built-in image processing algorithm to clearly capture the crew's facial color, mental state and injury details, ensuring that the remote medical team obtains intuitive and detailed visual information. The intelligent communication gateway integrates maritime satellite communication, VHF data communication and mobile communication functions. It is used to continuously monitor the real-time network signal strength and available bandwidth of each communication link through a built-in intelligent decision-making algorithm, and dynamically select and switch the optimal communication link for data transmission according to a preset data priority strategy.

4. A remote medical system for seafarers based on video call technology according to claim 1, characterized in that: The intelligent fusion and analysis auxiliary diagnosis module for medical data is used to actively retrieve the crew's historical electronic medical record data, and associate and fuse the historical electronic medical record data, the received real-time physiological parameter data, and the crew's appearance feature data extracted from the video stream. Based on the preset intelligent diagnosis model, the fused data is deeply analyzed to generate auxiliary diagnostic information that includes condition assessment and etiology inference. The remote expert collaboration and consultation module is used to match experts from the expert database based on the keywords of the crew member's current symptoms and their specific medical history characteristics, and to invite experts to join the video consultation through an encrypted video channel. It also provides a shared whiteboard tool for experts to collaboratively annotate and discuss medical images, plan the optimal rescue route, and dispatch medical resources. The treatment feedback and dynamic adjustment module is used to push encrypted treatment plans and reports to the shipboard terminal, and receive health feedback information from crew members. Based on a preset rule engine or machine learning model, it automatically adjusts the health management plan or triggers new video consultations to revise the treatment plan.

5. A remote medical system for seafarers based on video call technology according to claim 4, characterized in that: The intelligent diagnostic model in the medical data intelligent fusion and analysis auxiliary diagnosis module is a machine learning-based model used to analyze the fused multi-source data and output a quantitative score of the severity of the condition and / or a probability ranking of potential causes.

6. A remote medical system for seafarers based on video call technology according to claim 1, characterized in that: The health risk prediction and proactive prevention module is used to conduct retrospective analysis and correlation mining on massive amounts of long-term health data of crew members, weather conditions along routes, and work intensity data based on an artificial intelligence deep learning framework. It constructs a health risk prediction model to assess health risk trends in different seasons and sea areas, and generates targeted pre-configuration plans for medical resources based on the prediction results.

7. A method for remote medical care for seafarers based on video call technology, comprising a remote medical care system for seafarers based on video call technology as described in any one of claims 1 to 6, characterized in that, The method includes the following steps: S1. Health monitoring and early warning: Collect crew physiological parameters in real time through crew health monitoring equipment and issue early warnings based on preset emergency thresholds of physiological parameters; S2. Data Acquisition and Integration: Through the data acquisition module, various crew health monitoring devices are connected to unify the data format and integrate it into the local database; S3. Intelligent Link Selection and Data Transmission: Through the intelligent communication gateway module, the optimal communication link is dynamically selected based on real-time network status and data priority to upload the collected data to the remote medical platform; S4. Adaptive Video Diagnosis Establishment: Crew members initiate video call requests through the terminal's adaptive high-definition video call module. The terminal's adaptive high-definition video call module dynamically adjusts video parameters to adapt to real-time network bandwidth. Doctors respond to video call requests through the platform's video call service module and establish video connections. S5. Data Fusion and Assisted Diagnosis: Through the medical data intelligent fusion and analysis assisted diagnosis module, the crew's historical medical records, real-time data and video streams are retrieved and merged. Assisted diagnosis information is generated using an intelligent diagnosis model. At the same time, the doctor combines the video footage with the assisted diagnosis information to make a diagnosis. If the diagnosis result is a common disease, proceed to step S7; otherwise, proceed to step S6. S6. Remote Expert Collaboration: Match experts with the remote expert collaboration and consultation module and invite experts to join video consultations through encrypted video channels, and use a shared whiteboard for collaborative image annotation and discussion. S7. Treatment Management and Dynamic Feedback: Through the treatment feedback and dynamic adjustment module, encrypted treatment plans and reports are pushed to crew members; health feedback from crew members is received, and health management plans are automatically adjusted or new video consultations are triggered accordingly.

8. A method for remote medical care for crew members based on video call technology according to claim 7, characterized in that: In step S2, the data priority strategy stipulates that the priority of early warning data and video call data streams is higher than that of daily health record data. The dynamic selection of the optimal communication link specifically means that when physiological parameter data exceeds a preset emergency threshold, the maritime satellite communication link is selected first for data upload in seconds; for daily health monitoring data that does not exceed the emergency threshold, batch transmission is performed when the mobile or VHF signal quality is better than the set threshold. In step S4, the video parameters are dynamically adjusted as follows: based on the real-time detected available bandwidth, the video resolution is dynamically switched between 360P and 1080P, the frame rate is switched between 15fps and 60fps, and the video call latency is controlled within 300 milliseconds.

9. A method for remote medical care for crew members based on video call technology according to claim 7, characterized in that: In step S6, when it is determined that the crew member's condition is critical, the real-time position of the ship is obtained through satellite positioning, the optimal rescue route is planned in combination with electronic nautical chart data, and surrounding medical resources are dispatched.

10. A method for remote medical care for seafarers based on video call technology according to claim 7, characterized in that: The step S7 is followed by: S8. Health risk prediction and proactive prevention and control: Through the health risk prediction and proactive prevention and control module, the long-term health data of the crew, the weather conditions of the route and the intensity of the operation are retrospectively analyzed and correlated. The health risk prediction model is used to assess future health risks and proactively generate a pre-configuration plan for medical resources.

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