Marine medical information transmission method and system based on fusion of 5G network and satellite communication

Abstract

The invention provides a maritime medical information transmission method and system based on fusion of a 5G network and satellite communication. The method comprises the following steps: dynamically monitoring the quality of a real-time communication link between an offshore medical institution and a land medical center to obtain an optimal data segmentation scheme; the optimal data segmentation scheme is utilized, a hybrid error correction coding technology and a priority scheduling mechanism are adopted at a sending end, a deep reinforcement learning algorithm is introduced, and a stably transmitted data packet structure is obtained; based on the stably transmitted data packet structure, selecting an optimal transmission path by using a multi-satellite cooperative transmission network and a 5G network to obtain an optimized transmission path configuration; according to the optimized transmission path configuration, the received data packet is rapidly recombined and verified, possible transmission errors are automatically recognized and repaired, and the marine medical information is obtained. According to the technical scheme provided by the invention, the stability and timeliness of data transmission are remarkably improved, and efficient response of marine medical services is ensured.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of medical information transmission at sea, and in particular, to a method and system for medical information transmission at sea based on the integration of 5G networks and satellite communications. Background Art

[0002] In the marine medical rescue scenario, dynamic monitoring of the real-time communication link quality between medical institutions and land medical centers is crucial. Due to the complexity and unpredictability of the marine environment, it is necessary to combine satellite coverage area prediction, historical communication efficiency analysis and marine meteorological forecasts to intelligently adjust the data segmentation strategy of medical information to adapt to the ever-changing communication conditions and natural environment. This requires the communication system to have a high degree of flexibility and intelligent processing capabilities to ensure that critical medical information can be transmitted efficiently and accurately under any circumstances.

[0003] Currently, the transmission of medical information at sea mainly relies on fixed satellite communication links or limited 5G network coverage. These solutions usually use preset data segmentation and encoding strategies for information transmission. Although they can achieve basic information transmission functions, they lack the ability to respond to communication link quality and natural environmental changes in real time when facing complex marine environments. In addition, the existing error correction coding technology and scheduling mechanism are relatively simple and fail to fully utilize the latest deep reinforcement learning algorithms to intelligently classify and process the importance of medical information.

[0004] Although existing communication solutions can meet the needs of medical information transmission at sea to a certain extent, they often ignore the dynamic changes in the quality of the communication link and the different requirements of different types of medical information for transmission time and integrity. This fixed data processing method is prone to low transmission efficiency, high latency and increased error rate, especially in severe weather conditions or when the communication link is overloaded, which may result in the failure of critical medical information to be delivered in time, affecting the efficiency and effectiveness of rescue. Summary of the invention

[0005] The embodiments of the present application provide a method and system for transmitting medical information at sea based on the integration of 5G networks and satellite communications, so as to solve the problems of low transmission efficiency and delay of key medical information caused by ignoring the dynamic changes of communication link quality in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for transmitting medical information at sea based on the integration of 5G network and satellite communication, including:

[0007] Dynamically monitor the quality of real-time communication links between offshore medical institutions and land medical centers, and intelligently adjust the data segmentation strategy of medical information based on the forecast data of satellite coverage areas, historical communication efficiency analysis, and marine meteorological forecasts to obtain the optimal data segmentation solution, which can adapt to different communication conditions and natural environment changes;

[0008] By using the optimal data segmentation scheme, hybrid error correction coding technology and priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission;

[0009] Based on the stable transmission data packet structure, the multi-satellite cooperative transmission network and the 5G network are used in combination with a graph theory shortest path algorithm to select the optimal transmission path. The graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration;

[0010] According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0011] Optionally, the optimal data segmentation scheme is used, a hybrid error correction coding technology and a priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission, including:

[0012] Using the optimal data segmentation scheme, different types of medical information are analyzed and classified to obtain a preliminary ranking of the importance of the medical information;

[0013] Based on the preliminary ranking of the importance of medical information, a deep reinforcement learning algorithm is introduced to further intelligently classify the importance of medical information and generate optimized medical information classification results;

[0014] According to the optimized medical information classification results, the medical information is encoded and packaged using a hybrid error correction coding technology and a priority scheduling mechanism to generate a draft data packet with adaptive redundancy;

[0015] Apply link quality prediction technology to monitor and predict the status of the current communication link and obtain a future link quality prediction report;

[0016] The structure of the draft data packet is adjusted in real time by using the future link quality prediction report to ensure that it can dynamically change according to the expected communication link status to obtain a data packet structure for stable transmission.

[0017] Optionally, according to the optimized medical information classification result, a hybrid error correction coding technology and a priority scheduling mechanism are used to encode and package the medical information to generate a draft data packet with adaptive redundancy, including:

[0018] According to the optimized medical information classification result, the encoding parameters of different types of medical information are customized to obtain personalized encoding parameter configuration;

[0019] Using the personalized coding parameter configuration and combining it with hybrid error correction coding technology, error correction coding is performed on medical information to obtain a highly reliable coded data stream;

[0020] Based on the high-reliability coded data stream, a priority scheduling mechanism is applied to sort and group the coded medical information to generate an information queue arranged by importance;

[0021] For the information queues arranged by importance, data segmentation and encapsulation processing is performed, necessary transmission control information is added to each information block, and a preliminary data packet framework is generated;

[0022] According to the preliminary data packet framework, the importance of different medical information and the expected communication environment are evaluated, and the proportion of redundant information in the data packet is adjusted to ensure the best transmission effect under various network conditions, thereby obtaining a preliminary draft of the data packet with adaptive redundancy.

[0023] Optionally, using the future link quality estimation report to adjust the structure of the draft data packet in real time to ensure that it can dynamically change according to the expected communication link state to obtain a data packet structure for stable transmission, including:

[0024] Using the future link quality estimation report, the redundant information ratio and the fragment size of the draft data packet are evaluated and processed to obtain preliminary adjustment suggestions;

[0025] According to the preliminary adjustment suggestion and in combination with the actual load of the current link, the redundant information ratio of the data packet is optimized to generate an optimized redundant information configuration;

[0026] Based on the optimized redundant information configuration, the adaptive fragmentation technology is applied to re-plan the fragmentation size of the data packet to obtain a fragmentation strategy suitable for the current link conditions;

[0027] For the sharding strategy suitable for the current link conditions, a path selection algorithm is implemented to select the most suitable transmission path for each shard, thereby forming an optimized path allocation scheme;

[0028] According to the optimized path allocation scheme, combined with real-time link quality feedback, the structure of the data packet is fine-tuned to ensure that it can adapt to the expected changes in the communication link state and obtain a data packet structure for stable transmission.

[0029] Optionally, the data packet structure based on the stable transmission utilizes a multi-satellite collaborative transmission network and a 5G network in combination with a graph theory shortest path algorithm to select an optimal transmission path, wherein the graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration, including:

[0030] Based on the stable transmission data packet structure, the real-time load, expected availability and geographical location of each satellite in the multi-satellite cooperative transmission network are monitored and evaluated to obtain comprehensive satellite status information. At the same time, the status of 5G network nodes, including but not limited to base station load, expected bandwidth and service quality indicators, is monitored to obtain communication environment information;

[0031] Using the comprehensive satellite status information and communication environment information, combined with a graph theory shortest path algorithm, all possible transmission paths from the transmitter to the receiver are calculated and analyzed to obtain a preliminary list of optimal transmission path candidates;

[0032] According to the preliminary optimal transmission path candidate list, spectrum sensing technology is applied to detect and avoid potential signal interference risks, and generate interference-free transmission path options;

[0033] The stability and delay characteristics of the interference-free transmission path option are further evaluated to ensure that the best transmission performance can be provided in different communication environments, so as to obtain the final optimized transmission path configuration.

[0034] Optionally, the applying spectrum sensing technology to detect and avoid potential signal interference risks according to the preliminary optimal transmission path candidate list to generate interference-free transmission path options includes:

[0035] According to the preliminary optimal transmission path candidate list, real-time monitoring and processing are performed on the spectrum usage on each candidate path to obtain detailed spectrum occupancy information;

[0036] Using the detailed spectrum occupancy information and combining spectrum sensing technology, possible signal interference sources on the candidate path are identified and located, and a potential interference source report is generated;

[0037] Based on the potential interference source report, the impact of different interference sources on transmission performance is evaluated, interference avoidance strategy selection processing is implemented, and a targeted interference avoidance solution is obtained;

[0038] For the targeted interference avoidance scheme, adjusting the spectrum usage plan of the candidate path to avoid known high interference areas and generate an optimized spectrum allocation strategy;

[0039] According to the optimized spectrum allocation strategy, a path that is not affected by signal interference or has the least interference is screened out from the preliminary optimal transmission path candidate list to obtain an interference-free transmission path option.

[0040] Optionally, according to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea, including:

[0041] According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end, and its working parameters are initialized to ensure that it can efficiently process the upcoming data stream, thereby obtaining a ready parsing engine;

[0042] Using the prepared parsing engine, the received data packets are quickly reassembled, the original medical information sequence is restored according to the control information in the data packets, and a preliminary reassembled information stream is generated;

[0043] Based on the preliminary reorganized information flow, a verification algorithm is implemented to verify the integrity and consistency of the data packet, detect and mark the data segment that may have transmission errors, and obtain an information flow with error marks;

[0044] Applying an automatic error repair mechanism to the information stream with error marks, attempting to repair errors generated during transmission by using redundant information and error correction coding technology, and generating a repaired information stream;

[0045] According to the post-repair information flow, combined with context analysis and content consistency check, the accuracy of the information is further confirmed to ensure that all repair operations will not introduce new errors, and the final marine medical information is obtained.

[0046] In a second aspect, the embodiment of the present application provides a marine medical information transmission system based on integrated 5G network and satellite communication, including:

[0047] The monitoring module is used to dynamically monitor the quality of the real-time communication link between the offshore medical institutions and the land medical centers, and intelligently adjust the data segmentation strategy of the medical information by combining the forecast data of the satellite coverage area, historical communication efficiency analysis and marine meteorological forecast to obtain the optimal data segmentation scheme, which can adapt to different communication conditions and natural environment changes;

[0048] A classification module is used to utilize the optimal data segmentation scheme, adopt a hybrid error correction coding technology and a priority scheduling mechanism at the sending end, and introduce a deep reinforcement learning algorithm to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission;

[0049] A planning module, for selecting an optimal transmission path based on the stable transmission data packet structure, using a multi-satellite collaborative transmission network, a 5G network, and a graph theory shortest path algorithm, wherein the graph theory shortest path algorithm considers the real-time load, expected availability, and geographic location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration;

[0050] The parsing module is used to deploy an intelligent parsing engine at the receiving end according to the optimized transmission path configuration, quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0051] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for transmitting medical information at sea based on the integration of 5G networks and satellite communications as described in the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a method for transmitting medical information at sea based on the integration of 5G network and satellite communication as described in the first aspect.

[0053] In the embodiment of the present application, the real-time communication link quality between the offshore medical institution and the land medical center is dynamically monitored, and the data segmentation strategy of the medical information is intelligently adjusted and processed in combination with the predicted data of the satellite coverage area, the historical communication efficiency analysis and the marine meteorological forecast, so as to obtain the optimal data segmentation scheme, which can adapt to different communication conditions and natural environmental changes; using the optimal data segmentation scheme, a hybrid error correction coding technology and a priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure data integrity. The data packet structure can be dynamically adjusted according to the status of the communication link to obtain a data packet structure for stable transmission; based on the data packet structure for stable transmission, the multi-satellite collaborative transmission network and the 5G network are used in combination with the graph theory shortest path algorithm to select the optimal transmission path. The graph theory shortest path algorithm takes into account the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration; according to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0054] The technical solution of this application has the following beneficial effects:

[0055] By dynamically monitoring the quality of real-time communication links and combining a variety of prediction data (such as satellite coverage, historical performance analysis, and marine meteorological forecasts), the data segmentation strategy can be intelligently adjusted to ensure optimal data transmission performance even under changing communication conditions and natural environments. This greatly enhances the adaptability and stability of the system in harsh environments; hybrid error correction coding technology and priority scheduling mechanism are used, and deep reinforcement learning algorithms are introduced to intelligently classify the importance of different types of medical information, generating data packets with adaptive redundancy and real-time structural updates. This not only improves the transmission stability of data packets, but also optimizes resource utilization efficiency, ensuring that critical medical information can be transmitted in a timely and accurate manner; the combination of multi-satellite collaborative transmission networks and 5G networks, as well as the graph theory shortest path algorithm, consider the real-time load, expected availability, and geographic location of each satellite, and plan a stable and low-latency transmission channel for each data packet. Spectrum sensing technology is implemented to avoid signal interference, further optimize the transmission path configuration, reduce transmission delay and error rate, and improve overall transmission efficiency; the intelligent parsing engine deployed at the receiving end can quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and ensure the integrity and accuracy of the final medical information obtained at sea, which is particularly critical for medical rescue in emergency situations; through the application of the above series of technical means, the time interval from data collection to doctors obtaining information is greatly shortened, allowing medical institutions at sea to obtain the necessary diagnosis and support at the first time, thereby improving the quality of medical services, which is of great significance in emergency and telemedicine scenarios.

[0056] Furthermore, this method realizes intelligent classification and processing of the importance of different types of medical information by utilizing the optimal data segmentation scheme, hybrid error correction coding technology, priority scheduling mechanism, and combining with deep reinforcement learning algorithm, and generates data packets with adaptive redundancy and real-time structure update. At the same time, the link quality prediction technology is applied to ensure that the data packet structure can be dynamically adjusted according to the communication link status, thereby obtaining a stable transmission data packet structure. This method not only improves the reliability and stability of data transmission, but also significantly enhances the system's adaptability to changing communication conditions; by intelligently analyzing and classifying different types of medical information, it optimizes resource allocation and ensures the priority transmission of key medical information; the introduction of deep reinforcement learning algorithm further improves the accuracy and efficiency of medical information classification; the use of link quality prediction technology ensures that the data packet can maintain the best structure in the expected communication environment, reduces transmission errors and delays, and finally achieves fast and accurate data reorganization and verification, greatly improving the speed, accuracy and response efficiency of medical information transmission at sea, which is crucial to protecting the life safety and health of offshore workers.

[0057] Furthermore, this method combines the multi-satellite cooperative transmission network with the 5G network based on the stable transmission data packet structure, and selects the optimal transmission path in combination with the graph theory shortest path algorithm, taking into account the real-time load, expected availability and geographical location of each satellite, and plans a stable and low-latency transmission channel for each data packet. This method not only obtains detailed communication environment information through comprehensive monitoring and evaluation of the status of multiple satellites and 5G network nodes, ensuring the accuracy of path selection; but also calculates and analyzes all possible transmission paths through the graph theory shortest path algorithm, generates a preliminary list of optimal transmission path candidates, and further improves the efficiency and accuracy of path selection. Spectrum sensing technology is applied to detect and avoid potential signal interference risks, generate interference-free transmission path options, and ensure the clarity and reliability of data transmission. Finally, by deeply evaluating the stability and delay characteristics of the interference-free transmission path options, the goal of providing the best transmission performance under different communication environments is achieved, thereby significantly reducing transmission delays and error rates, improving the overall efficiency and quality of medical information transmission at sea, ensuring that critical medical information can be delivered in a timely and accurate manner, and greatly enhancing the response speed and treatment effect of medical services at sea.

[0058] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 A flowchart of a method for transmitting medical information at sea based on the integration of 5G network and satellite communication provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of the structure of a marine medical information transmission system based on the integration of 5G network and satellite communication provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0064] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or in parallel. They are only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to different types.

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0066] Figure 1 A flowchart of a method for transmitting medical information at sea based on the integration of 5G network and satellite communication is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0067] 101. Dynamically monitor the quality of real-time communication links between offshore medical institutions and land medical centers, and intelligently adjust the data segmentation strategy of medical information in combination with the forecast data of satellite coverage areas, historical communication efficiency analysis, and marine meteorological forecasts to obtain the optimal data segmentation solution, which can adapt to different communication conditions and natural environment changes;

[0068] In this step, dynamic monitoring involves real-time monitoring of the quality of the communication link between the offshore medical institutions and the land medical centers, combined with satellite coverage area prediction, historical communication performance analysis and marine meteorological forecasts to evaluate and predict changes in the communication environment. This information is used to intelligently adjust the data segmentation strategy of medical information to ensure that the data transmission scheme can adapt to different communication conditions and natural environment changes.

[0069] In the embodiment of the present application, the system monitors the quality of the communication link in real time by integrating multiple data sources (such as satellite coverage prediction, historical communication records, weather forecasts, etc.), and builds a model based on these data to predict the communication status in the future. According to the prediction results, the system intelligently adjusts the data segmentation strategy to optimize the transmission efficiency and reliability of medical information.

[0070] Suppose a hospital ship in the North Pacific needs to send an emergency surgical instruction video to a medical center in China. The system first detects that the current communication link is affected by the storm and predicts that the communication quality will deteriorate in the next 24 hours. Based on this prediction, the system automatically adjusts the data segmentation strategy, increases redundancy and selects a more stable transmission path to ensure timely and complete transmission of the video.

[0071] 102. Utilize the optimal data segmentation scheme, adopt hybrid error correction coding technology and priority scheduling mechanism at the sending end, and introduce deep reinforcement learning algorithm to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission;

[0072] In this step, the optimal data segmentation scheme specifies how to segment and package different types of medical information for subsequent processing. Hybrid error correction coding technology and priority scheduling mechanism are used to improve the reliability and efficiency of data transmission, and deep reinforcement learning algorithms help intelligently classify different types of medical information to ensure that important information is transmitted first.

[0073] In the embodiment of the present application, at the sending end, the system uses the optimal data segmentation scheme to preliminarily classify the medical information, and then introduces a deep reinforcement learning algorithm to further evaluate the importance of each piece of information and generate the final classification result. Then, the system uses hybrid error correction coding technology to add appropriate redundant information to the information, and uses a priority scheduling mechanism to arrange the transmission order of the information, ensuring that the stability of the data packet structure can be maintained even under harsh communication conditions.

[0074] Suppose the same hospital ship needs to send both patient vital signs data and non-urgent daily health reports. The system determines based on the deep reinforcement learning algorithm that vital signs data is more important, so it sets a higher transmission priority for it and applies stronger error correction coding to ensure its integrity. Daily health reports are given a lower priority and a simpler encoding method, thus saving precious bandwidth resources.

[0075] 103. Based on the data packet structure of the stable transmission, the multi-satellite cooperative transmission network and the 5G network are used in combination with a graph theory shortest path algorithm to select the optimal transmission path. The graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration;

[0076] In this step, the stable transmission data packet structure refers to the data packet processed in the previous step, which has adaptive redundancy and real-time update capabilities. The multi-satellite cooperative transmission network and 5G network provide a wide range of coverage and high-speed connection. The graph theory shortest path algorithm is used to select the best one from multiple possible paths, and the spectrum sensing technology is used to avoid signal interference to ensure the security and efficiency of data transmission.

[0077] In the embodiment of the present application, the system comprehensively considers factors such as the real-time load, expected availability and geographical location of each satellite, uses the graph theory shortest path algorithm to calculate the optimal transmission path, and uses spectrum sensing technology to detect and avoid potential signal interference risks. The resulting transmission path configuration is not only stable and has low latency, but also can flexibly respond to changes in the communication environment.

[0078] Assume that critical surgical videos on a medical ship need to be transmitted immediately to a land-based medical center. Based on the real-time status of the satellite and 5G network, the system selects a coordinated transmission path consisting of three synchronous orbit satellites and uses spectrum sensing technology to ensure that no other users occupy the same frequency band. In this way, the video can be transmitted quickly and without interference, supporting the successful implementation of remote surgical guidance.

[0079] 104. According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0080] In this step, the intelligent parsing engine refers to a software tool deployed at the receiving end that can quickly reassemble received data packets and verify their integrity, automatically identifying and repairing possible transmission errors. This mechanism ensures that the final medical information obtained is accurate and complete even in unstable network conditions.

[0081] In the embodiment of the present application, when the receiving end receives a data packet, the intelligent parsing engine will quickly reorganize the original medical information sequence based on the control information in the data packet, and check the integrity and consistency of the data packet through a verification algorithm. For data segments that may have errors, the engine will try to repair them using redundant information and error correction coding technology to ensure that all transmission operations will not introduce new errors.

[0082] Assume that due to brief signal interference, some surgical video data packets have slight transmission errors. The intelligent parsing engine at the receiving end detects these problems during the reassembly process and successfully repairs the errors using the redundant information previously set, ensuring the smoothness and accuracy of the video playback, allowing the doctor to seamlessly continue to watch and refer to this important medical information.

[0083] In summary, steps 101 to 104 cover the entire process from dynamically monitoring the quality of the communication link to intelligently adjusting the data segmentation strategy, to selecting the optimal transmission path, and finally data analysis and error repair, aiming to provide an efficient, stable and secure marine medical information transmission solution to meet the complex and changeable communication needs between marine medical institutions and land medical centers, especially in emergency rescue and telemedicine scenarios. It plays a vital role.

[0084] In order to solve the problem of inconsistent priorities and redundancy of different types of medical information during transmission, in some embodiments, the optimal data segmentation scheme described in step 102 is used, a hybrid error correction coding technology and a priority scheduling mechanism are used at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structure updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status, so as to obtain a data packet structure for stable transmission, including:

[0085] Using the optimal data segmentation scheme, different types of medical information are analyzed and classified to obtain a preliminary ranking of the importance of medical information; based on the preliminary ranking of the importance of medical information, a deep reinforcement learning algorithm is introduced to further intelligently classify the importance of medical information to generate an optimized medical information classification result; based on the optimized medical information classification result, a hybrid error correction coding technology and a priority scheduling mechanism are used to encode and package the medical information to generate a draft data packet with adaptive redundancy; link quality prediction technology is applied to monitor and estimate the status of the current communication link to obtain a future link quality estimation report; using the future link quality estimation report, the structure of the draft data packet is adjusted in real time to ensure that it can dynamically change according to the expected communication link status to obtain a stable transmission data packet structure.

[0086] In this embodiment, the optimal data segmentation scheme refers to a strategy that is intelligently adjusted according to communication conditions and changes in the natural environment, and is used to guide the segmentation methods of different types of medical information (such as images, videos, and text); the preliminary medical information importance ranking is an initial assessment result based on the information type and urgency; the optimized medical information classification result refers to the information priority list that is further refined by the deep reinforcement learning algorithm; the draft data packet with adaptive redundancy refers to a data unit that has been encoded and packaged, and the proportion of redundant information has been adjusted according to the importance of the information and the expected communication environment; the future link quality prediction report is based on the link status in the future period based on the monitoring and prediction of the current communication link status.

[0087] In the embodiment of the present application, first, the system uses the optimal data segmentation scheme to analyze and classify different types of medical information to obtain a preliminary ranking of the importance of the medical information; secondly, based on the preliminary ranking results, a deep reinforcement learning algorithm is introduced to further intelligently classify and process to generate optimized medical information classification results; thirdly, according to the optimized classification results, a hybrid error correction coding technology and a priority scheduling mechanism are used to encode and package the medical information to generate a draft data packet with adaptive redundancy; finally, link quality prediction technology is applied to monitor and estimate the current communication link status, and the structure of the draft data packet is adjusted in real time according to the future link quality prediction report to ensure that it can dynamically change according to the expected communication link status, and finally form a stable transmission data packet structure.

[0088] Here is a specific example:

[0089] Suppose a marine research station in the middle of the Atlantic Ocean needs to transmit a batch of information including high-resolution sample images, low-priority daily record texts, and real-time vital signs data to a research center on land. The system first analyzes and classifies this batch of information according to the optimal data segmentation scheme, and obtains a preliminary ranking of importance and urgency; then, the importance of this information is evaluated more carefully through a deep reinforcement learning algorithm, especially the key details in the sample images are specially marked; then, based on the optimized classification results, hybrid error correction coding technology is used to add additional redundant information to the sample images to ensure their integrity, while the daily record texts are given a lower priority and simplified coding; finally, the system uses link quality prediction technology to monitor that the upcoming solar storm may temporarily affect the communication quality, so the data packet structure is adjusted in advance, the redundancy of all information is increased, and the transmission path is optimized to ensure that the integrity and stability of the data can be maintained even under harsh conditions. Through the above steps, this method successfully realizes the effective transmission of different types of medical information in complex and changing environments, and improves the communication efficiency and reliability between offshore medical institutions and land medical centers.

[0090] In order to solve the reliability and priority issues of different types of medical information during transmission and further improve the efficiency and adaptability of data transmission, in one or more of the above embodiments, according to the optimized medical information classification results, hybrid error correction coding technology and priority scheduling mechanism are used to encode and package the medical information to generate a draft data packet with adaptive redundancy, including:

[0091] According to the optimized medical information classification results, the encoding parameters of different types of medical information are customized to obtain personalized encoding parameter configurations; using the personalized encoding parameter configurations, combined with hybrid error correction coding technology, error correction coding is performed on the medical information to obtain a highly reliable encoded data stream; based on the highly reliable encoded data stream, a priority scheduling mechanism is applied to sort and group the encoded medical information to generate an information queue arranged by importance; data segmentation and encapsulation are performed on the information queue arranged by importance, necessary transmission control information is added to each information block, and a preliminary data packet framework is generated; based on the preliminary data packet framework, the importance of different medical information and the expected communication environment are evaluated, and the proportion of redundant information in the data packet is adjusted to ensure that the best transmission effect can be provided under various network conditions, and a preliminary data packet with adaptive redundancy is obtained.

[0092] In order to ensure that the data packet structure can change dynamically according to the expected communication link state, in some embodiments, the structure of the draft data packet is adjusted in real time by using the future link quality estimation report to ensure that it can change dynamically according to the expected communication link state to obtain a data packet structure for stable transmission, including:

[0093] The future link quality prediction report is used to evaluate the redundant information ratio and fragment size of the draft data packet to obtain preliminary adjustment suggestions; based on the preliminary adjustment suggestions and in combination with the actual load of the current link, the redundant information ratio of the data packet is optimized to generate an optimized redundant information configuration; based on the optimized redundant information configuration, the adaptive fragmentation technology is applied to re-plan the fragment size of the data packet to obtain a fragmentation strategy suitable for the current link conditions; for the fragmentation strategy suitable for the current link conditions, a path selection algorithm is implemented to select the most suitable transmission path for each fragment to form an optimized path allocation plan; based on the optimized path allocation plan and in combination with real-time link quality feedback, the structure of the data packet is fine-tuned to ensure that it can adapt to the expected changes in the communication link state and obtain a data packet structure for stable transmission.

[0094] In this embodiment, personalized coding parameter configuration refers to a coding setting customized according to the importance of different types of medical information to ensure that each type of information can be properly protected; the high-reliability coded data stream is a data sequence that can resist transmission errors after processing by combining personalized coding parameter configuration and hybrid error correction coding technology; the information queue arranged by importance is the result of sorting and grouping the encoded medical information according to its importance based on a priority scheduling mechanism; the preliminary data packet framework refers to the initial structure formed after segmenting and encapsulating the information queue arranged by importance and adding necessary transmission control information; the first draft of the data packet with adaptive redundancy refers to a data packet with the redundant information ratio adjusted according to the expected communication environment to ensure the best transmission effect; the future link quality estimation report is an assessment of the link status in the future period based on the current communication link status monitoring and prediction; the preliminary adjustment suggestion is an improvement suggestion proposed after evaluating the redundant information ratio and fragment size of the first draft of the data packet; the optimized redundant information configuration refers to the setting after optimizing the redundant information ratio of the data packet in combination with the actual load of the current link; the fragmentation strategy suitable for the current link condition refers to the re-planned data packet fragment size, which is intended to adapt to the current link condition; the optimized path allocation scheme is the result of selecting the most suitable transmission path for each fragment through the path selection algorithm.

[0095] In the embodiment of the present application, first, the system customizes the encoding parameters of different types of medical information according to the optimized medical information classification results to obtain personalized encoding parameter configurations; secondly, using these personalized encoding parameter configurations, combined with hybrid error correction coding technology, error correction encoding is performed on the medical information to form a coded data stream with high reliability; thirdly, based on the high-reliability coded data stream, a priority scheduling mechanism is applied to sort and group the encoded medical information to generate an information queue arranged by importance; then, data segmentation and encapsulation processing is performed on the information queue arranged by importance, necessary transmission control information is added to each information block, a preliminary data packet framework is generated, and the importance of different medical information and the expected communication environment are evaluated according to the preliminary framework, the proportion of redundant information in the data packet is adjusted, and finally A preliminary draft of the data packet with adaptive redundancy is obtained; finally, the system uses the future link quality prediction report to evaluate the redundant information ratio and fragment size of the preliminary draft of the data packet, and puts forward preliminary adjustment suggestions; based on the preliminary adjustment suggestions, combined with the actual load of the current link, the redundant information ratio of the data packet is optimized to generate an optimized redundant information configuration; based on the optimized redundant information configuration, the adaptive fragmentation technology is applied to re-plan the fragment size of the data packet to obtain a fragmentation strategy suitable for the current link conditions; the path selection algorithm is implemented for the fragmentation strategy suitable for the current link conditions, the most suitable transmission path is selected for each fragment, an optimized path allocation plan is formed, and the data packet structure is fine-tuned in combination with real-time link quality feedback to ensure that it can adapt to the expected changes in the communication link status, and finally a stable transmission data packet structure is obtained.

[0096] Here is a specific example:

[0097] Suppose a research vessel in the waters near Antarctica needs to transmit a batch of data including emergency surgery guidance videos, routine health check reports, and research sample images to a research center on land. The system first customized the encoding parameters of these three types of information based on the optimized medical information classification results, setting high-intensity error correction coding for videos, medium-intensity coding for health check reports, and low-intensity coding for sample images; then, the system used these personalized coding parameter configurations, combined with hybrid error correction coding technology, to perform error correction coding on medical information, forming a highly reliable coded data stream; then, based on the high-reliability coded data stream, the priority scheduling mechanism was applied to sort the encoded information according to importance, and an information queue arranged by importance was generated, with the surgical guidance video ranked first; then, the system implemented data segmentation and encapsulation processing on the information queue arranged by importance, added necessary transmission control information to each information block, generated a preliminary data packet framework, and evaluated the importance of different medical information and the expected communication environment based on the preliminary framework. environment, adjusted the redundant information ratio in the data packet, and obtained the draft data packet with adaptive redundancy; finally, the system used the future link quality estimation report to evaluate the redundant information ratio and fragment size of the draft data packet, and put forward preliminary adjustment suggestions; according to the preliminary adjustment suggestions, combined with the actual load of the current link, the redundant information ratio of the data packet was optimized, and the optimized redundant information configuration was generated; based on the optimized redundant information configuration, the adaptive fragmentation technology was applied to re-plan the fragment size of the data packet, and a fragmentation strategy suitable for the current link conditions was obtained; the path selection algorithm was implemented for the fragmentation strategy suitable for the current link conditions, and the most suitable transmission path was selected for each fragment, forming an optimized path allocation scheme, and the data packet structure was fine-tuned in combination with real-time link quality feedback to ensure that it can adapt to the expected changes in the communication link state, and finally a stable transmission data packet structure was achieved. Through the above steps, this method not only ensures the efficient and stable transmission of different types of medical information in a complex and changeable marine environment, but also improves the transmission priority and accuracy of key medical information, and enhances the communication efficiency and response speed between offshore medical institutions and land medical centers.

[0098] This application takes into account that in the prior art, due to the lack of intelligent classification of medical information and the failure to fully consider dynamic network conditions and system load changes, key medical information may not be transmitted in a timely and accurate manner in a complex and changeable communication environment, affecting the response speed and accuracy of medical services. Therefore, the embodiment of the present invention proposes this optional solution to solve the above technical problems, by introducing a deep reinforcement learning algorithm to further intelligently classify and process the importance of medical information, and re-evaluate the real-time and urgency of each piece of information in combination with the expected transmission delay, information quality and patient health risk factors, to ensure that the comprehensive score can accurately reflect the value of the information, thereby improving the response speed and accuracy of the medical information system.

[0099] Optionally, based on the preliminary ranking of the importance of medical information, a deep reinforcement learning algorithm is introduced to further intelligently classify the importance of medical information to generate an optimized medical information classification result, including:

[0100] When calculating the updated medical information importance score S new (i) Previously, a deep reinforcement learning algorithm was used to evaluate the content complexity, system load, and priority of each piece of information, and the impact of network fluctuations was taken into account, laying the foundation for subsequent adjustments to the importance score;

[0101]

[0102] Among them, S new (i) represents the importance score of the updated i-th medical information; S old (i) is the original importance score of the ith medical information obtained based on the preliminary sorting; α is the learning rate, which determines the degree of influence of new information on the old score; R(i) is the relevance reward of the ith medical information calculated by the deep reinforcement learning algorithm; β is the sensitivity coefficient, which controls the degree of influence of complexity; C(i) is the complexity of the ith medical information; ( is the priority enhancement coefficient, which is used to increase the importance of high-priority information; P(i) is the priority score of the ith medical information; γ is the load factor, which reflects the impact of the current system load on the score; L(i) is the system load when the ith medical information is transmitted; θ is the fluctuation factor, which measures the impact of changes in network conditions on the score; V(i) is the degree of network fluctuation encountered by the ith medical information during transmission;

[0103] Complete S new After calculating F(i), the timeliness and urgency of each piece of information are re-evaluated in combination with the expected transmission delay, information quality, and patient health risk factors to ensure that the comprehensive score F(i) can accurately reflect the value of the information;

[0104]

[0105] Among them, F(i) represents the classification score of the ith medical information after final optimization; ω is the weight coefficient, which balances the relationship between importance and urgency; δ is the delay penalty coefficient, which is used to reduce the score of high-delay information; η is the time decay factor, which affects the degree of influence of delay on the score; D(i) is the expected transmission delay of the ith medical information; λ is the quality enhancement coefficient, which enhances the score of high-quality information; Q(i) is the quality score of the ith medical information; U(i) is the urgency score of the ith medical information, which reflects its urgency in medical decision-making; μ is the health risk factor, which increases the score of medical information closely related to the patient's health status; H(i) is the patient health risk level associated with the ith medical information;

[0106] After calculating the final optimized classification score F(i), all medical information is sorted according to the new score, and a data packet priority scheduling plan is formulated to ensure the timely transmission of critical medical information, which is ultimately applied in actual medical information systems to improve response speed and accuracy.

[0107] This formula is designed to more accurately assess the importance of medical information and adapt to the ever-changing communication environment. This application introduces two core formulas: the updated medical information importance score S new (i) and the final optimized medical information classification score F(i). These two formulas not only take into account static factors such as information content complexity, system load and priority, but also introduce the influence of factors such as network fluctuations, transmission delays, information quality and patient health risks to achieve a more comprehensive and dynamic evaluation of medical information. This enables the system to adjust the priority of information according to actual conditions and ensure that key information is transmitted most efficiently when needed.

[0108] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0109]

[0110] S old (i) represents the original importance score of the i-th medical information obtained based on the preliminary sorting; it is used to provide a basic score; α·R(i) represents the learning rate multiplied by the relevance reward, which reflects the influence of the new information on the old score; it enhances or weakens the importance of the new information; 1-e -β·C(i) indicates the sensitivity coefficient, which controls the degree of influence of complexity; the higher the complexity, the greater the influence; 1+ζ·P(i) indicates the priority enhancement coefficient, which increases the importance of high-priority information; the higher the priority, the higher the score; 1+γ·L(i) indicates the load factor, which reflects the influence of the current system load on the score; the higher the system load, the lower the score; 1+θ·V(i) indicates the volatility factor, which measures the influence of changes in network conditions on the score; the greater the network volatility, the lower the score;

[0111] The following is a brief introduction to how to obtain the parameters of the formula:

[0112] S. ld (i) indicates that it is directly obtained from the preliminary ranking of the importance of medical information; α indicates that the appropriate learning rate is determined through experiments; R(i) indicates that it is calculated through a deep reinforcement learning algorithm; β indicates that an appropriate sensitivity coefficient is set according to experiments; C(i) indicates that the complexity is evaluated by analyzing the content structure and data volume of medical information; ζ indicates that an appropriate priority enhancement coefficient is set according to experiments; P(i) indicates that it is predefined according to the type and urgency of information; γ indicates that an appropriate load factor is set according to experiments; L(i) indicates that the current load status of the monitoring system is monitored; θ indicates that an appropriate fluctuation factor is set according to experiments; V(i) indicates that the degree of network fluctuation is obtained by real-time monitoring of the network status;

[0113] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0114]

[0115] ω·S new (i) indicates that the weight coefficient balances the relationship between importance and urgency; the higher the importance score, the higher the score; 1-δ / (1+η·D(i)) indicates that the delay penalty coefficient reduces the score of high-delay information; the higher the delay, the lower the score; 1+λ·Q(i) indicates that the quality enhancement coefficient enhances the score of high-quality information; the higher the quality score, the higher the score; (1-ω)·U(i) indicates that the urgency score reflects its urgency in medical decision-making; the higher the urgency, the higher the score; 1+μ·H(i) indicates that the health risk factor increases the score of medical information that is closely related to the patient's health status; the higher the health risk, the higher the score;

[0116] The following is a brief introduction to how to obtain the parameters of the formula:

[0117] ω represents the weight coefficient set according to actual needs; S new(i) indicates that it is calculated by the above formula; δ indicates that the appropriate delay penalty coefficient is set according to the experiment; η indicates that the appropriate time attenuation factor is set according to the experiment; D(i) indicates that the transmission delay is predicted according to the expected transmission path; λ indicates that the appropriate quality enhancement factor is set according to the experiment; Q(i) indicates that it is obtained by evaluating the data integrity and accuracy of medical information; U(i) indicates that it is predefined according to the type of information and the degree of urgency; μ indicates that the appropriate health risk factor is set according to the experiment; H(i) indicates that it is evaluated according to the patient's health record and the severity of the disease.

[0118] Suppose a medical ship in the middle of the Atlantic needs to transmit a batch of data including emergency surgery instruction videos (recorded as information A), routine health check reports (recorded as information B), and research sample images (recorded as information C) to a research center on land. First, the original importance scores of information A, B, and C obtained by the system based on the preliminary sorting are S old (A) = 0.9, S old (B) = 0.5, S old (C)=0.7. Then, the complexity of the relevant information is calculated by the deep reinforcement learning algorithm. C(A)=0.8, C(B)=0.3, C(C)=0.6, the system load L(A)=0.4, L(B)=0.2, L(C)=0.3, the network fluctuation V(A)=0.5, V(B)=0.1, V(C)=0.4, and the priority score P(A)=0.9, P(B)=0.3, P(C)=0.5. Set the parameters α=0.5, β=0.7, ζ=0.8, γ=0.6, θ=0.9. According to the formula:

[0119]

[0120] Similarly, calculate S new (B) and S new (C), respectively get S new (B) = 0.53 and S new (C) = 0.75. Next, combined with the expected transmission delay D(A) = 10s, D(B) = 5s, D(C) = 8s, the information quality score Q(A) = 0.9, Q(B) = 0.7, Q(C) = 0.8, the urgency score U(A) = 0.9, U(B) = 0.3, U(C) = 0.5, and the patient health risk level H(A) = 0.9, H(B) = 0.3, H(C) = 0.5. Set the parameters ω = 0.7, δ = 0.5, η = 0.4, λ = 0.6, μ = 0.8.

[0121] According to the formula: F(A) = 0.7 1.08 (1-0.5 / (1+0.4 10)) (1+0.6 0.9) + (1-0.7) 0.9 (1+0.8 0.9) = 1.36;

[0122] Similarly, F(B) and F(C) are calculated, and F(B) = 0.64 and F(C) = 0.91 are obtained respectively. Through the above steps, the system not only ensures the efficient and stable transmission of different types of medical information in a complex and changeable marine environment, but also significantly improves the integrity and accuracy of the data, and enhances the communication efficiency and response speed between offshore medical institutions and land medical centers. Since F(A)>F(C)>F(B), it shows that information A (emergency surgery guidance video) has the highest priority and should be transmitted first, which helps to improve the response speed and accuracy of critical medical information.

[0123] Optionally, the data packet structure based on the stable transmission in step 103 uses a multi-satellite cooperative transmission network and a 5G network in combination with a graph theory shortest path algorithm to select an optimal transmission path, wherein the graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration, including:

[0124] Based on the stable transmission data packet structure, the real-time load, expected availability and geographical location of each satellite in the multi-satellite collaborative transmission network are monitored and evaluated to obtain comprehensive satellite status information. At the same time, the status of 5G network nodes is monitored, including but not limited to base station load, expected bandwidth and service quality indicators to obtain communication environment information; using the comprehensive satellite status information and communication environment information, combined with the graph theory shortest path algorithm, all possible transmission paths from the transmitter to the receiver are calculated and analyzed to obtain a preliminary list of optimal transmission path candidates; based on the preliminary list of optimal transmission path candidates, spectrum sensing technology is applied to detect and avoid potential signal interference risks and generate interference-free transmission path options; for the interference-free transmission path options, their stability and delay characteristics are further evaluated to ensure that the best transmission performance can be provided in different communication environments to obtain the final optimized transmission path configuration.

[0125] In this embodiment, comprehensive satellite status information refers to information obtained through monitoring and evaluating the real-time load, expected availability and geographical location of each satellite in a multi-satellite collaborative transmission network, which is used to plan the optimal transmission path; communication environment information refers to data obtained by monitoring the status of 5G network nodes (such as base station load, expected bandwidth and service quality indicators) to evaluate ground communication conditions; the preliminary optimal transmission path candidate list is the result of calculating and analyzing all possible transmission paths using a graph theory shortest path algorithm; the interference-free transmission path option refers to the path selection after applying spectrum sensing technology to detect and avoid potential signal interference risks; the final optimized transmission path configuration is a path arrangement that ensures optimal transmission performance after further evaluating the stability and delay characteristics of the interference-free transmission path option.

[0126] In an embodiment of the present application, firstly, based on a data packet structure of stable transmission, the system monitors and evaluates the real-time load, expected availability and geographical location of each satellite in a multi-satellite collaborative transmission network to obtain comprehensive satellite status information; at the same time, the system monitors the status of 5G network nodes, including but not limited to base station load, expected bandwidth and service quality indicators, to obtain communication environment information; secondly, the system uses these comprehensive satellite status information and communication environment information, combined with a graph theory shortest path algorithm, to calculate and analyze all possible transmission paths from the transmitter to the receiver, and obtains a preliminary list of optimal transmission path candidates; thirdly, based on the preliminary list of optimal transmission path candidates, spectrum sensing technology is applied to detect and avoid potential signal interference risks, and generate interference-free transmission path options; finally, the stability and delay characteristics of the interference-free transmission path options are further evaluated to ensure that the best transmission performance can be provided in different communication environments, thereby obtaining the final optimized transmission path configuration.

[0127] Here is a specific example:

[0128] Suppose an offshore oil platform in the South China Sea needs to transmit emergency medical rescue videos to the emergency command center on land. The system first monitors and evaluates the real-time load, expected availability and geographic location of multiple satellites involved in the transmission based on the stable transmission data packet structure, and obtains comprehensive satellite status information; at the same time, it monitors the status of 5G network nodes, including base station load, expected bandwidth and service quality indicators, to obtain detailed communication environment information; then, the system uses this information, combined with the graph theory shortest path algorithm, to calculate and analyze all possible transmission paths from the oil platform to the command center, and obtains a preliminary list of optimal transmission path candidates; next, based on this candidate list, spectrum sensing technology is applied to detect and avoid potential signal interference risks, and non-interference transmission path options are generated; finally, the system further evaluates the stability and delay characteristics of the non-interference transmission path options, selects the path that best suits the current communication environment, and forms the final optimized transmission path configuration. Through the above steps, this method not only ensures the efficient and stable transmission of emergency medical rescue videos in a complex and changeable marine environment, but also significantly reduces transmission delays, improves rescue efficiency, and enhances the communication efficiency and response speed between offshore medical institutions and land command centers.

[0129] In order to solve the potential signal interference problem in the transmission path, ensure the stable transmission of medical information in a complex communication environment, and further improve the reliability and efficiency of data transmission, in the above-mentioned multiple embodiments, the spectrum sensing technology is applied according to the preliminary optimal transmission path candidate list to detect and avoid potential signal interference risks and generate interference-free transmission path options, including:

[0130] Based on the preliminary optimal transmission path candidate list, the spectrum usage on each candidate path is monitored in real time to obtain detailed spectrum occupancy information; using the detailed spectrum occupancy information, combined with spectrum sensing technology, the signal interference sources that may exist on the candidate path are identified and located, and a potential interference source report is generated; based on the potential interference source report, the impact of different interference sources on transmission performance is evaluated, and interference avoidance strategy selection is implemented to obtain a targeted interference avoidance plan; for the targeted interference avoidance plan, the spectrum usage plan of the candidate path is adjusted to avoid known high interference areas, and an optimized spectrum allocation strategy is generated; based on the optimized spectrum allocation strategy, the path that is not affected by signal interference or has the least interference is screened out from the preliminary optimal transmission path candidate list to obtain an interference-free transmission path option.

[0131] In this embodiment, detailed spectrum occupancy information refers to data obtained through real-time monitoring and processing of spectrum usage on each candidate path, which is used to identify possible signal interference sources; the potential interference source report is generated based on the detailed spectrum occupancy information in combination with spectrum sensing technology, and is used to describe the possible interference sources and their locations on the candidate paths; the interference avoidance strategy selection process refers to evaluating the degree of influence of different interference sources on transmission performance, and selecting appropriate avoidance strategies accordingly; the optimized spectrum allocation strategy refers to the result of adjusting the spectrum usage plan of the candidate paths to avoid known high interference areas; the interference-free transmission path option is to screen out the path that is not affected by signal interference or has the least interference from the preliminary optimal transmission path candidate list.

[0132] In an embodiment of the present application, first, the system monitors the spectrum usage on each candidate path in real time based on a preliminary candidate list of optimal transmission paths, and obtains detailed spectrum occupancy information; secondly, using these detailed spectrum occupancy information, combined with spectrum sensing technology, it identifies and locates possible signal interference sources on the candidate paths, and generates a potential interference source report; thirdly, based on the potential interference source report, it evaluates the degree of impact of different interference sources on transmission performance, selects appropriate interference avoidance strategies, and forms a targeted interference avoidance plan; finally, based on the targeted interference avoidance plan, adjusts the spectrum usage plan of the candidate paths to avoid known high interference areas, generates an optimized spectrum allocation strategy, and screens out paths that are not affected by signal interference or have the least interference from the preliminary candidate list of optimal transmission paths to obtain interference-free transmission path options.

[0133] Here is a specific example:

[0134] Suppose a research vessel in the South Pacific performing telemedicine missions needs to transmit a batch of critical medical data to a research center on land. The system first monitors the spectrum usage on each candidate path in real time based on the preliminary list of optimal transmission path candidates, and obtains detailed spectrum occupancy information; then, using this detailed spectrum occupancy information, combined with spectrum sensing technology, it identifies and locates possible signal interference sources on the candidate path, such as other satellite communications, maritime radio navigation, etc., and generates a potential interference source report; then, based on the potential interference source report, the system evaluates the impact of different interference sources on transmission performance, selects appropriate interference avoidance strategies, such as changing frequency or time windows, and forms a targeted interference avoidance plan; finally, based on the targeted interference avoidance plan, the system adjusts the spectrum usage plan of the candidate path, avoids known high-interference areas, generates an optimized spectrum allocation strategy, and selects the path that is not affected by signal interference or has the least interference from the preliminary list of optimal transmission path candidates, and obtains the interference-free transmission path option. Through the above steps, this method not only ensures the efficient and stable transmission of critical medical data in a complex and changeable marine environment, but also significantly reduces the negative impact of signal interference, improves the reliability and accuracy of transmission, and enhances the communication efficiency and response speed between offshore medical institutions and land research centers.

[0135] This application takes into account that in the prior art, because the transmission path selection is not intelligent enough and the dynamic network conditions and changes in the real-time communication environment are not fully considered, key medical information may not be transmitted in a timely and accurate manner in a complex and changeable communication environment, affecting the response speed and accuracy of medical services. Therefore, the embodiment of the present invention proposes this optional solution to solve the above technical problems, by introducing a comprehensive evaluation and graph theory shortest path algorithm to calculate the key performance indicators of each path, and combining the soft maximum function to adjust the path selection probability, so as to more accurately measure the possibility of each path becoming the optimal path, thereby improving the response speed and accuracy of the medical information system.

[0136] Optionally, the comprehensive satellite status information and communication environment information are used in combination with a graph theory shortest path algorithm to calculate and analyze all possible transmission paths from the transmitter to the receiver to obtain a preliminary list of optimal transmission path candidates, including:

[0137] In calculating the transfer path comprehensive score P score (j) Before that, it is necessary to comprehensively evaluate all possible transmission paths, analyze the key performance indicators of each path, and consider the path complexity, node load and spectrum efficiency to provide data support for subsequent scoring;

[0138]

[0139] Where P score (j) represents the comprehensive score of the jth transmission path; W delay , W loss , W bandwidth , W reliability are the weight coefficients of delay, packet loss rate, bandwidth and reliability respectively; D(j) is the average transmission delay; L(j) is the expected packet loss rate; B(j) is the available bandwidth; R(j) is the historical reliability score; α c is the complexity factor; C(j) is the path complexity; β u is the load factor; U(j) is the average node load; δ f is the frequency factor; F(j) is the spectrum utilization efficiency; φ e is the environmental impact factor; E(j) is the degree of influence by the real-time communication environment;

[0140] Complete P score (j) After calculation, the score is converted into the probability of selecting the optimal path by introducing the soft maximum function, and the influence of network fluctuations and the urgency of medical information is considered to adjust the path selection probability, thereby generating P optimal (j) to more accurately measure the likelihood of each path being the optimal path;

[0141]

[0142] Where P optimal (j) represents the probability that the jth path is selected as the optimal path; N is the number of all candidate paths; γ v is the fluctuation adjustment coefficient; V(j) is the degree of network fluctuation; η h is the health risk enhancement coefficient; H(j) is the medical information urgency score;

[0143] After calculating P optimal (j) After that, the candidate paths are sorted according to the probability, and the paths with high selection probability are screened out to form a preliminary list of optimal transmission path candidates; finally, these candidate paths are reviewed to see whether they meet the security and stability requirements in practical applications to ensure that the selected paths can provide the best transmission effect.

[0144] This formula is designed to more accurately evaluate the comprehensive performance of all possible transmission paths and adapt to the ever-changing communication environment. This application introduces two core formulas: Transmission path comprehensive score P score (j) and the optimal path selection probability P optinal(j). These two formulas not only take into account static performance indicators such as delay, packet loss rate, bandwidth and reliability, but also introduce the influence of factors such as path complexity, node load and spectrum efficiency. At the same time, they take into account network fluctuations and the urgency of medical information through the soft maximum function and adjustment coefficient, thus achieving a more comprehensive and dynamic evaluation of the transmission path and ensuring that the selected path can provide the best transmission effect.

[0145] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0146]

[0147] W delay D(j): delay weight multiplied by the average transmission delay; the higher the delay, the lower the score; W loss L(j): packet loss rate weight multiplied by the expected packet loss rate; the higher the packet loss rate, the lower the score; W bandwidth B(j): bandwidth weight multiplied by available bandwidth; the larger the bandwidth, the higher the score; W reliability R(j): reliability weight multiplied by the historical reliability score; the higher the reliability, the higher the score; 1+α c C(j): complexity factor multiplied by the path complexity; the higher the complexity, the lower the score; 1+β u U(j): load factor multiplied by the average node load; the higher the load, the lower the score; 1+δ f F(j): frequency factor multiplied by spectrum efficiency; the higher the spectrum efficiency, the higher the score; 1+φ e E(j): Environmental impact factor multiplied by the degree of impact of the real-time communication environment; the greater the environmental impact, the lower the score;

[0148] The following is a brief introduction to how to obtain the parameters of the formula:

[0149] W delay , W loss , W bandwidth , W reliability : Set weight coefficients according to actual needs; D(j), L(j), B(j), R(j): Obtained through monitoring and historical data statistics; α c , β u , δ f ,φ e : Set appropriate adjustment factors based on experiments; C(j): Analyze path structure and number of nodes to evaluate complexity; U(j): Monitor the current load status of each node; F(j): Obtain spectrum utilization efficiency through spectrum sensing technology; E(j): Predict the impact of communication environment based on real-time weather forecast and communication link quality;

[0150] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0151]

[0152] exp(P score (j)): Convert the comprehensive score into an index form to enhance the score difference; The path selection probability is calculated by the soft maximum function to ensure that the sum of the probabilities is 1; 1-γ v V(j): volatility adjustment factor multiplied by the network volatility; the greater the volatility, the lower the probability of selection; 1+η h H(j): health risk enhancement factor multiplied by the medical information urgency score; the higher the urgency, the higher the probability of selection;

[0153] The following is a brief introduction to how to obtain the parameters of the formula:

[0154] N: total number of candidate paths; γ v , η h : Set appropriate adjustment coefficients based on experiments; V(j): Obtain network fluctuations by real-time monitoring of network status; H(j): Predefine urgency scores based on medical information type and patient health status;

[0155] Assume that a medical ship in the North Pacific needs to transmit a batch of data including emergency surgery guidance videos (represented as information A), routine health examination reports (represented as information B), and research sample images (represented as information C) to a research center on land. The system first comprehensively evaluates all possible transmission paths, including path 1 (represented as path 1), path 2 (represented as path 2), and path 3 (represented as path 3). Through monitoring and historical data statistics, the following key performance indicators are obtained: average transmission delay D(1) = 10s, expected packet loss rate L(1) = 0.05, available bandwidth B(1) = 10Mbps, historical reliability score R(1) = 0.9 for path 1; D(2) = 8s, L(2) = 0.04, B(2) = 8Mbps, R(2) = 0.8 for path 2; D(3) = 12s, L(3) = 0.06, B(3) = 9Mbps, R(3) = 0.7 for path 3. Set the weight coefficient W delay =0.4, W loss =0.3, W bandwidth =0.2, W reliability=0.1. Other parameters include path complexity C(1)=0.5, C(2)=0.4, C(3)=0.6, node load U(1)=0.4, U(2)=0.3, U(3)=0.5, spectrum efficiency F(1)=0.8, F(2)=0.7, F(3)=0.6, and real-time communication environment impact E(1)=0.2, E(2)=0.1, E(3)=0.3. Set the adjustment factor α c =0.6, β u =0.5,δ f =0.4,φ e =0.3. According to the formula:

[0156]

[0157] Similarly, calculate P score (2) and P score (3), we get P score (2) = 0.92 and P score (3) = 0.78. Next, by introducing the soft maximum function and the adjustment coefficient, we consider the network fluctuations V(1) = 0.2, V(2) = 0.1, V(3) = 0.3 and the medical information urgency scores H(1) = 0.9, H(2) = 0.3, H(3) = 0.5. Set the adjustment coefficient γ v =0.5, η h =0.7. According to the formula:

[0158]

[0159] Similarly, calculate P optimal (2) and P optimal (3), we get P optimal (2) = 0.48 and P optimal (3) = 0.17. Through the above steps, the system not only ensures the efficient and stable transmission of different types of medical information in the complex and changeable marine environment, but also significantly improves the integrity and accuracy of the data, and enhances the communication efficiency and response speed between marine medical institutions and land medical centers. optimal (2)>P optimal (1)>P optimal (3), indicating that path 2 has the highest selection probability and should be selected first for transmission, which helps improve the response speed and accuracy of key medical information. Assuming that the threshold is set to 0.4, since the selection probability of path 2 is greater than the set threshold, it indicates that path 2 is the best transmission path.

[0160] In order to solve the possible errors and inconsistencies when the receiving end processes the data packets and further improve the accuracy and integrity of the medical information transmission, in one or more of the above embodiments, according to the optimized transmission path configuration described in step 104, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea, including:

[0161] According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end, and its working parameters are initialized to ensure that it can efficiently process the upcoming data stream, so as to obtain a ready parsing engine; using the ready parsing engine, the received data packets are quickly reassembled, and the original medical information sequence is restored according to the control information in the data packets to generate a preliminary reassembled information stream; based on the preliminary reassembled information stream, a verification algorithm is implemented to verify the integrity and consistency of the data packets, detect and mark the data segments that may have transmission errors, and obtain an information stream with error marks; for the information stream with error marks, an automatic error repair mechanism is applied to try to repair the errors generated during the transmission process through redundant information and error correction coding technology to generate a repaired information stream; based on the repaired information stream, combined with context analysis and content consistency check, the accuracy of the information is further confirmed to ensure that all repair operations will not introduce new errors, so as to obtain the final medical information at sea.

[0162] In this embodiment, a ready parsing engine refers to an intelligent parsing engine with initialized working parameters to ensure that it can efficiently process the upcoming data stream; the initially reorganized information stream is the original medical information sequence restored by the parsing engine based on the control information in the data packet; the error-marked information stream refers to the result of detecting and marking data segments that may have transmission errors after verification by the verification algorithm; the repaired information stream is the result of applying an automatic error repair mechanism to try to repair errors through redundant information and error correction coding technology; the final medical information at sea is the final output after the accuracy is confirmed and all repair operations are completed after combining context analysis and content consistency check.

[0163] In an embodiment of the present application, first, the system deploys an intelligent parsing engine at the receiving end according to the optimized transmission path configuration, and initializes its working parameters to ensure that it can efficiently process the upcoming data stream and obtain a ready parsing engine; secondly, the ready parsing engine is used to quickly reassemble the received data packets, restore the original medical information sequence according to the control information in the data packets, and generate a preliminary reassembled information stream; thirdly, based on the preliminary reassembled information stream, a verification algorithm is implemented to verify the integrity and consistency of the data packets, detect and mark data segments that may have transmission errors, and obtain an information stream with error marks; finally, an automatic error repair mechanism is applied to the information stream with error marks, and redundant information and error correction coding technology are used to try to repair the errors generated during the transmission process to generate a repaired information stream; based on the repaired information stream, combined with context analysis and content consistency check, the accuracy of the information is further confirmed to ensure that all repair operations will not introduce new errors, and the final medical information at sea is obtained.

[0164] Here is a specific example:

[0165] Suppose a research vessel located in the Arctic Circle needs to receive emergency surgical guidance videos from a land-based research center. The system first deploys an intelligent parsing engine at the receiving end according to the optimized transmission path configuration, and initializes its working parameters to ensure that it can efficiently process the upcoming data stream and obtain a ready parsing engine; then, the ready parsing engine is used to quickly reassemble the received data packets, restore the original medical information sequence based on the control information in the data packets, and generate a preliminary reassembled information stream; then, based on the preliminary reassembled information stream, a verification algorithm is implemented to verify the integrity and consistency of the data packets, detect and mark the data segments that may have transmission errors, and obtain an information stream with error marks; then, for the information stream with error marks, an automatic error repair mechanism is applied, and redundant information and error correction coding technology are used to try to repair the errors generated during the transmission process to generate a repaired information stream; finally, based on the repaired information stream, combined with context analysis and content consistency check, the accuracy of the information is further confirmed to ensure that all repair operations will not introduce new errors, and the final marine medical information is obtained. Through the above steps, this method not only ensures the efficient and stable transmission of surgical guidance videos in the complex and changeable polar environment, but also significantly improves the integrity and accuracy of the data, and enhances the communication efficiency and response speed between offshore medical institutions and land research centers.

[0166] Figure 2 A structural diagram of a marine medical information transmission system based on integrated 5G network and satellite communication is provided for the embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0167] The monitoring module 21 is used to dynamically monitor the quality of the real-time communication link between the offshore medical institution and the land medical center, and intelligently adjust the data segmentation strategy of the medical information in combination with the forecast data of the satellite coverage area, the historical communication efficiency analysis and the marine meteorological forecast to obtain the optimal data segmentation scheme, which can adapt to different communication conditions and natural environment changes;

[0168] The classification module 22 is used to utilize the optimal data segmentation scheme, adopt a hybrid error correction coding technology and a priority scheduling mechanism at the transmitting end, and introduce a deep reinforcement learning algorithm to intelligently classify the importance of different types of medical information, generate a data packet with adaptive redundancy and real-time structural update, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission;

[0169] A planning module 23 is used to select an optimal transmission path based on the data packet structure of the stable transmission, using a multi-satellite cooperative transmission network and a 5G network in combination with a graph theory shortest path algorithm. The graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration;

[0170] The parsing module 24 is used to deploy an intelligent parsing engine at the receiving end according to the optimized transmission path configuration, quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0171] Figure 2 The marine medical information transmission system based on the integration of 5G network and satellite communication can perform Figure 1 The implementation principle and technical effect of the method for transmitting medical information at sea based on the integration of 5G network and satellite communication described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the above-mentioned embodiment of a medical information transmission system at sea based on the integration of 5G network and satellite communication has been described in detail in the embodiment of the method, and will not be elaborated here.

[0172] In one possible design, Figure 2 The marine medical information transmission system based on the integrated 5G network and satellite communication of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0173] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0174] The processing component 32 is used to: dynamically monitor the quality of the real-time communication link between the offshore medical institution and the land medical center, and intelligently adjust the data segmentation strategy of the medical information in combination with the predicted data of the satellite coverage area, the historical communication efficiency analysis and the marine meteorological forecast to obtain the optimal data segmentation scheme, which can adapt to different communication conditions and natural environment changes; using the optimal data segmentation scheme, a hybrid error correction coding technology and a priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure The data packet structure can be dynamically adjusted according to the status of the communication link to obtain a data packet structure for stable transmission; based on the data packet structure for stable transmission, the multi-satellite collaborative transmission network and the 5G network are used in combination with the graph theory shortest path algorithm to select the optimal transmission path. The graph theory shortest path algorithm takes into account the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration; according to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

[0175] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0176] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0177] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0178] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0179] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0180] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0181] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for transmitting medical information at sea based on the integration of 5G network and satellite communication.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0183] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0184] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for transmitting medical information at sea based on the integration of 5G network and satellite communication, characterized in that: include: Dynamically monitor the quality of real-time communication links between offshore medical institutions and land medical centers, and intelligently adjust the data segmentation strategy of medical information based on the forecast data of satellite coverage areas, historical communication efficiency analysis, and marine meteorological forecasts to obtain the optimal data segmentation solution, which can adapt to different communication conditions and natural environment changes; By using the optimal data segmentation scheme, hybrid error correction coding technology and priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission; Based on the stable transmission data packet structure, the multi-satellite cooperative transmission network and the 5G network are used in combination with a graph theory shortest path algorithm to select the optimal transmission path. The graph theory shortest path algorithm considers the real-time load, expected availability and geographical location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration; According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

2. The method according to claim 1, characterized in that The optimal data segmentation scheme is used, hybrid error correction coding technology and priority scheduling mechanism are adopted at the sending end, and a deep reinforcement learning algorithm is introduced to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structure update, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status, so as to obtain a stable transmission data packet structure, including: Using the optimal data segmentation scheme, different types of medical information are analyzed and classified to obtain a preliminary ranking of the importance of the medical information; Based on the preliminary ranking of the importance of medical information, a deep reinforcement learning algorithm is introduced to further intelligently classify the importance of medical information and generate optimized medical information classification results; According to the optimized medical information classification results, the medical information is encoded and packaged using a hybrid error correction coding technology and a priority scheduling mechanism to generate a draft data packet with adaptive redundancy; Apply link quality prediction technology to monitor and predict the status of the current communication link and obtain a future link quality prediction report; The structure of the draft data packet is adjusted in real time by using the future link quality prediction report to ensure that it can dynamically change according to the expected communication link status to obtain a data packet structure for stable transmission.

3. The method according to claim 2, characterized in that According to the optimized medical information classification result, the medical information is encoded and packaged using a hybrid error correction coding technology and a priority scheduling mechanism to generate a draft data packet with adaptive redundancy, including: According to the optimized medical information classification result, the encoding parameters of different types of medical information are customized to obtain personalized encoding parameter configuration; Using the personalized coding parameter configuration and combining the hybrid error correction coding technology, the medical information is subjected to error correction coding processing to obtain a highly reliable coded data stream; Based on the high-reliability coded data stream, a priority scheduling mechanism is applied to sort and group the coded medical information to generate an information queue arranged by importance; For the information queues arranged by importance, data segmentation and encapsulation processing is performed, necessary transmission control information is added to each information block, and a preliminary data packet framework is generated; According to the preliminary data packet framework, the importance of different medical information and the expected communication environment are evaluated, and the proportion of redundant information in the data packet is adjusted to ensure the best transmission effect under various network conditions, thereby obtaining a preliminary draft of the data packet with adaptive redundancy.

4. The method according to claim 2, characterized in that: The method of using the future link quality estimation report to adjust the structure of the draft data packet in real time to ensure that it can dynamically change according to the expected communication link state to obtain a data packet structure for stable transmission includes: Using the future link quality estimation report, the redundant information ratio and the fragment size of the draft data packet are evaluated and processed to obtain preliminary adjustment suggestions; According to the preliminary adjustment suggestion and in combination with the actual load of the current link, the redundant information ratio of the data packet is optimized to generate an optimized redundant information configuration; Based on the optimized redundant information configuration, the adaptive fragmentation technology is applied to re-plan the fragmentation size of the data packet to obtain a fragmentation strategy suitable for the current link conditions; For the sharding strategy suitable for the current link conditions, a path selection algorithm is implemented to select the most suitable transmission path for each shard, thereby forming an optimized path allocation scheme; According to the optimized path allocation scheme, combined with real-time link quality feedback, the structure of the data packet is fine-tuned to ensure that it can adapt to the expected changes in the communication link state and obtain a data packet structure for stable transmission.

5. The method according to claim 1, characterized in that The data packet structure based on the stable transmission utilizes a multi-satellite cooperative transmission network and a 5G network, and combines a graph theory shortest path algorithm to select an optimal transmission path. The graph theory shortest path algorithm considers the real-time load, expected availability, and geographic location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration, including: Based on the stable transmission data packet structure, the real-time load, expected availability and geographical location of each satellite in the multi-satellite cooperative transmission network are monitored and evaluated to obtain comprehensive satellite status information. At the same time, the status of 5G network nodes, including but not limited to base station load, expected bandwidth and service quality indicators, is monitored to obtain communication environment information; Using the comprehensive satellite status information and communication environment information, combined with a graph theory shortest path algorithm, all possible transmission paths from the transmitter to the receiver are calculated and analyzed to obtain a preliminary list of optimal transmission path candidates; According to the preliminary optimal transmission path candidate list, spectrum sensing technology is applied to detect and avoid potential signal interference risks, and generate interference-free transmission path options; The stability and delay characteristics of the interference-free transmission path option are further evaluated to ensure that the best transmission performance can be provided in different communication environments, so as to obtain the final optimized transmission path configuration.

6. The method according to claim 5, characterized in that The applying spectrum sensing technology to detect and avoid potential signal interference risks according to the preliminary optimal transmission path candidate list to generate interference-free transmission path options includes: According to the preliminary optimal transmission path candidate list, real-time monitoring and processing are performed on the spectrum usage on each candidate path to obtain detailed spectrum occupancy information; Using the detailed spectrum occupancy information and combining spectrum sensing technology, possible signal interference sources on the candidate path are identified and located, and a potential interference source report is generated; Based on the potential interference source report, the impact of different interference sources on transmission performance is evaluated, interference avoidance strategy selection processing is implemented, and a targeted interference avoidance solution is obtained; For the targeted interference avoidance scheme, adjusting the spectrum usage plan of the candidate path to avoid known high interference areas and generate an optimized spectrum allocation strategy; According to the optimized spectrum allocation strategy, a path that is not affected by signal interference or has the least interference is screened out from the preliminary optimal transmission path candidate list to obtain an interference-free transmission path option.

7. The method according to claim 1, characterized in that According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end to quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea, including: According to the optimized transmission path configuration, an intelligent parsing engine is deployed at the receiving end, and its working parameters are initialized to ensure that it can efficiently process the upcoming data stream, thereby obtaining a ready parsing engine; Using the prepared parsing engine, the received data packets are quickly reassembled, the original medical information sequence is restored according to the control information in the data packets, and a preliminary reassembled information stream is generated; Based on the preliminary reorganized information flow, a verification algorithm is implemented to verify the integrity and consistency of the data packet, detect and mark the data segment that may have transmission errors, and obtain an information flow with error marks; Applying an automatic error repair mechanism to the information stream with error marks, attempting to repair errors generated during transmission by using redundant information and error correction coding technology, and generating a repaired information stream; According to the post-repair information flow, combined with context analysis and content consistency check, the accuracy of the information is further confirmed to ensure that all repair operations will not introduce new errors, and the final marine medical information is obtained.

8. A marine medical information transmission system based on the integration of 5G network and satellite communication, characterized in that: include: The monitoring module is used to dynamically monitor the quality of the real-time communication link between the offshore medical institutions and the land medical centers, and intelligently adjust the data segmentation strategy of the medical information by combining the forecast data of the satellite coverage area, historical communication efficiency analysis and marine meteorological forecast to obtain the optimal data segmentation scheme, which can adapt to different communication conditions and natural environment changes; A classification module is used to utilize the optimal data segmentation scheme, adopt a hybrid error correction coding technology and a priority scheduling mechanism at the sending end, and introduce a deep reinforcement learning algorithm to intelligently classify the importance of different types of medical information, generate data packets with adaptive redundancy and real-time structural updates, and apply link quality prediction technology to ensure that the data packet structure can be dynamically adjusted according to the communication link status to obtain a data packet structure for stable transmission; A planning module, for selecting an optimal transmission path based on the stable transmission data packet structure, using a multi-satellite collaborative transmission network, a 5G network, and a graph theory shortest path algorithm, wherein the graph theory shortest path algorithm considers the real-time load, expected availability, and geographic location of each satellite, plans a stable and low-latency transmission channel for each data packet, implements spectrum sensing technology to avoid signal interference, and obtains an optimized transmission path configuration; The parsing module is used to deploy an intelligent parsing engine at the receiving end according to the optimized transmission path configuration, quickly reorganize and verify the received data packets, automatically identify and repair possible transmission errors, and obtain medical information at sea.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for transmitting medical information at sea based on the integration of 5G network and satellite communication as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for transmitting medical information at sea based on the integration of 5G network and satellite communication as described in any one of claims 1 to 7 is implemented.

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