Diving lung injury risk assessment system based on equivalent water surface oxygen time (ESOT)

Abstract

The invention relates to a diving lung injury risk assessment system based on equivalent water surface oxygen time (ESOT). The diving lung injury risk assessment system comprises a data collection module, an ESOT calculation module, a risk assessment module, an early warning and suggestion module, a data analysis and optimization module, an immersive risk simulation and training module and a multi-device interconnection module. The data collection module collects individual information, diving plans, actual diving data and environment information of divers; the ESOT calculation module dynamically optimizes ESOT value calculation according to a deep learning algorithm; the risk assessment module performs real-time grading based on a personalized safety threshold and visualizes the lung injury risk; the early warning and suggestion module performs voice reminding and generates personalized safety suggestions, and the remote medical support interface provides medical support in a high-risk state; the data analysis and optimization module provides diving suggestions through a diving database and a site risk assessment unit; according to the system, personalized, real-time and accurate lung injury risk assessment and management are realized, and the diving safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of lung injury risk assessment for divers in a high oxygen partial pressure environment, and in particular to a diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) for quantifying and predicting the lung injury risk of divers. Background Art

[0002] During high oxygen partial pressure diving operations, divers are exposed to an environment with oxygen partial pressure higher than normal for a long time, which may lead to pulmonary oxygen toxicity (POT), which has adverse effects on lung health. Typical manifestations of pulmonary oxygen toxicity include a decrease in vital capacity, which may even affect the life safety of divers in severe cases. For a long time, the unit of pulmonary toxicity dose (UPTD) has been regarded as the "gold standard" for measuring the risk of hyperoxia exposure. It calculates the risk of oxygen toxicity by multiplying the partial pressure of oxygen (PO2) and the exposure time. However, UPTD can only be applied to the exposure dose in a single high-pressure hyperoxia environment, and cannot effectively assess the cumulative risk of lung damage from multiple dives or repeated dives.

[0003] In order to more effectively assess and manage the risk of lung injury in multiple dives, researchers have proposed new assessment methods. Among them, the Arieli K index was introduced as an alternative to UPTD. The K index can not only assess the risk of lung injury, but also predict the recovery process of lung injury after hyperoxia exposure. However, the calculation of the K index is relatively complicated, especially in the case of multiple dives, so it is not convenient for real-time calculation and dynamic application. To simplify the calculation, Risberg and van Ooij proposed the concept of equivalent surface oxygen time (ESOT) as a conversion and improvement method for the K index. ESOT converts the cumulative time of hyperoxia exposure in multiple diving environments into equivalent surface exposure time, that is, the equivalent exposure to 1 minute of 100% oxygen at normal pressure. The accumulation of ESOT can accurately reflect the cumulative risk of lung injury after multiple dives, and is easier to calculate and apply than the K index.

[0004] Although the existing lung injury risk assessment methods have achieved certain results in high-oxygen environments, they lack cumulative risk management and real-time warning mechanisms for multiple diving processes.

[0005] Therefore, there is an urgent need for a diving lung injury risk assessment system based on ESOT, which can not only realize the cumulative risk assessment of multiple dives, but also help divers effectively reduce the risk of lung injury under high oxygen exposure through real-time monitoring, intelligent early warning and personalized suggestions, and improve the safety and reliability of diving activities. Summary of the invention

[0006] In view of the above-mentioned deficiencies of the prior art, the present invention provides a diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT), including a data collection module for collecting individual information, diving plans, actual diving data and environmental information of divers;

[0007] Individual information includes age, gender, health status, smoking history and other factors that may affect lung function;

[0008] The dive plan includes the dive depth, time and breathing gas composition;

[0009] Actual diving data automatically records the changes in diving depth, time and breathing gas composition through the dive computer;

[0010] Environmental information includes data such as water temperature, air pressure, and water flow speed;

[0011] ESOT calculation module, used to calculate the ESOT value of each dive according to the dive plan or actual dive data, and accumulate the total ESOT value;

[0012] The ESOT calculation module further optimizes the calculation of ESOT values ​​through deep learning algorithms and establishes a more accurate ESOT calculation model based on individual information and environmental factors;

[0013] A risk assessment module is used to assess the risk of lung injury and perform risk grading based on the calculated ESOT value and the set safety threshold;

[0014] The risk assessment module sets a personalized ESOT safety threshold and dynamically adjusts the threshold based on the physical condition of different divers;

[0015] The risk assessment module divides risks into low risk, medium risk and high risk levels, and visualizes the risk levels through a 3D dynamic risk model;

[0016] The warning and advice module is used to issue warnings when the diver's ESOT value approaches or exceeds the safety threshold and generate personalized safety advice based on the risk level;

[0017] The warning and advice module includes a voice reminder function to remind the diver of the current status in real time when the risk increases;

[0018] The Recommendations module generates personalized recommendations using natural language processing technology, including decompression stop optimization, adjusting dive depth, or pausing dive activities;

[0019] Data analysis and optimization module, used to collect historical data and health information of divers, and conduct statistical analysis and model optimization;

[0020] The data analysis and optimization module further uses algorithm self-learning and automatic update functions to optimize ESOT safety thresholds and risk assessment models based on historical data;

[0021] Immersive risk simulation and training module, which provides immersive risk simulation and personalized stress relief guidance through virtual reality (VR) or augmented reality (AR) technology;

[0022] The VR module simulates lung injury risk in a virtual environment to help divers become familiar with high-risk scenarios;

[0023] The AR module is used to provide real-time diving paths, decompression stops and risk management guidance during diving;

[0024] Multi-device interconnection module, used to support divers to view data and health information on multiple devices simultaneously;

[0025] The multi-device interconnection module supports data synchronization and cross-platform data sharing between devices such as smartphones, dive computers and tablets.

[0026] In the above-mentioned diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT), accurate, real-time and personalized lung injury risk management is achieved through personalized ESOT calculation, deep learning optimization, real-time risk grading and visualization, telemedicine support, VR immersive training, multi-device interconnection and privacy protection, effectively reducing the risk of lung injury for divers in a high oxygen environment. Through diversified data analysis and risk assessment, the system provides divers with scientific diving safety guidance and improves the safety and reliability of diving activities.

[0027] The data collection module further includes:

[0028] Smart health monitoring equipment, connected to the smart wearable device worn by the diver, is used to monitor the diver's heart rate, blood oxygen saturation, body temperature and other biological signals in real time;

[0029] The health data analysis unit analyzes and processes the monitored biological signals to identify the health change trends of divers during diving;

[0030] When the detected biological signal abnormality exceeds the preset safety threshold, the data collection module sends out a health warning signal to remind the diver to take appropriate measures.

[0031] The ESOT calculation module further includes:

[0032] Personalized dynamic decompression calculation unit, which generates a personalized decompression stop plan for each dive based on the diver's historical data and individual health status;

[0033] The dynamic decompression calculation unit dynamically adjusts the decompression time, depth and stop points according to real-time diving data, optimizing the gas removal efficiency during decompression to reduce the risk of lung injury;

[0034] The personalized decompression plan is provided to divers in real time through the natural language processing module so that divers can adjust their diving behavior based on it.

[0035] The risk assessment module further includes:

[0036] A group data comparison and analysis unit, used to generate a risk reference benchmark based on the diver group data;

[0037] The group data comparison and analysis unit compares the diver's data with the group data of similar age, health status and diving habits through data clustering and group analysis, providing divers with a more scientific comparison and reference of health status;

[0038] The risk reference benchmark is further visualized to allow divers to clearly understand the changes in their health risks relative to the group average.

[0039] The data analysis and optimization module further includes:

[0040] Diving database management unit, used to establish and maintain diver's diving data and health data records, including diving location, diving time, environmental conditions and other information;

[0041] The Site Risk Assessment Unit analyses the risk to divers of diving at a specific site based on historical data and environmental factors of the dive site;

[0042] The Site Risk Assessment Unit combines real-time and historical environmental data to generate personalized dive recommendations for divers to use at specific dive sites.

[0043] The warning and advice module further includes:

[0044] A remote medical support interface unit, which is used to automatically connect to a preset medical service platform or expert when a diver reaches a high-risk state;

[0045] The remote medical support interface unit can send the diver's ESOT value, health data and risk level information to the remote medical expert through real-time data transmission, so that the medical expert can provide professional medical advice based on the diver's actual situation;

[0046] When a medical professional issues a recommendation, the system automatically communicates the recommendation to the diver via voice or text alert.

[0047] The immersive risk simulation and training module further includes:

[0048] Virtual reality training unit, which is used to simulate virtual scenarios of lung injury risk through VR technology, helping divers to become familiar with diving risk scenarios in an immersive environment;

[0049] Interactive health report generation unit, which generates a personalized health report after diving, showing the diver's ESOT value accumulation, lung health change trend and risk assessment in the form of charts;

[0050] The health report is displayed to the diver through an interactive interface, and the diver can understand the impact of diving behavior on lung health based on the analysis results provided in the report.

[0051] The multi-device interconnection module further includes:

[0052] Cross-platform data synchronization unit, used to synchronize dive data to multiple devices such as smartphones, dive computers, tablets, etc., ensuring that divers can access their dive and health information on different devices;

[0053] Data export and sharing unit, used to export diver's data into encrypted files or share them with preset medical institutions to support subsequent health examinations and medical evaluations;

[0054] Both the cross-platform data synchronization unit and the data export unit support end-to-end data encryption to ensure the privacy and security of divers' health data.

[0055] In summary, this application has the following beneficial effects:

[0056] 1. Through the data collection module and ESOT calculation module, the system can dynamically optimize the calculation of ESOT values ​​based on the diver's individual information (such as age, health status, smoking history, etc.) and environmental factors. During the ESOT calculation process, the deep learning algorithm is adjusted based on the real-time environment and individual differences, providing a high-precision lung injury assessment.

[0057] The system classifies the diver's lung injury risk level in real time based on the accumulation of ESOT values ​​and personalized safety thresholds. The risk assessment module uses a 3D dynamic model to display the risk level, so that divers can intuitively understand the real-time risk status and make corresponding adjustments during the diving process to improve safety.

[0058] 2. When the diver's ESOT value approaches or exceeds the safety threshold, the system automatically reminds the diver through voice and provides specific personalized suggestions. Through the natural language processing module, the system generates concise and clear operation instructions, such as adjusting the diving depth, extending the decompression stop time, etc., to help divers quickly understand and implement corresponding safety measures.

[0059] In high-risk situations, the system can transmit the diver's real-time health data and risk level to medical experts through a remote medical support interface, ensuring real-time medical support in emergency situations. This feature enhances the safety of the system and enables divers to receive timely professional guidance when facing health risks.

[0060] 3. The data analysis and optimization module collects the diver's historical health information, diving data and environmental information, integrates these data through the diving database management unit, and conducts long-term statistical analysis and model optimization. The system uses the algorithm self-learning function to continuously optimize the ESOT safety threshold and risk assessment model according to data changes and the diver's health status, so that the assessment results are more in line with individual needs.

[0061] Through the location risk assessment unit, the system can use the historical data of the diving site and real-time environmental factors (such as water temperature, water flow speed, etc.) to generate personalized diving recommendations for divers at specific locations, helping divers to reasonably arrange diving activities according to location conditions and reduce the possibility of sudden lung injury.

[0062] 4. The system uses VR technology to simulate high oxygen exposure scenarios that can cause lung damage, allowing divers to experience the potential risks of high oxygen exposure in a safe environment and improve their ability to perceive potential dangers during diving. This immersive training can help divers become familiar with the physiological effects of high oxygen exposure and improve their ability to respond in real situations.

[0063] The system automatically generates an interactive health report after each dive, showing the diver's ESOT cumulative value, lung health change trend and risk assessment in the form of a chart. Divers can learn in detail about the impact of diving behavior on lung health, and gradually optimize their diving habits and strategies, thereby effectively avoiding health risks during diving.

[0064] 5. The multi-device interconnection module supports divers to view diving data and health information synchronously on smartphones, diving computers, tablets and other devices. Divers can understand their health status and diving plans at any time on different devices, ensuring flexibility and convenience of data access.

[0065] The system's multi-device interconnection module provides data export and sharing functions, exporting divers' health and diving data as encrypted files, or securely sharing them with medical institutions when needed. The module uses end-to-end data encryption technology to ensure the privacy and security of data during transmission and storage, meeting the data privacy requirements of modern users. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 System architecture for this application Figure 1 ;

[0067] Figure 2 System architecture for this application Figure 2 ;

[0068] Figure 3 System architecture for this application Figure 3 . DETAILED DESCRIPTION

[0069] Two embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0070] Example 1

[0071] like Figures 1 to 3 As shown, the data collection module collects the following information:

[0072] Individual information: Basic information of divers, such as age, gender, health status, smoking history, etc., is collected and used as personalized input parameters for risk assessment.

[0073] Dive Plan: Records a diver's dive plan, including dive depth, time, and oxygen content of breathing gas, so that potential lung damage risk can be calculated before diving.

[0074] Actual diving data: The diving computer records the diving depth, time and oxygen composition change curve in real time, and the data is automatically transmitted to the system for real-time evaluation.

[0075] Environmental information: Collect environmental data such as water temperature, air pressure and water flow rate. These data are used as correction factors in ESOT calculations to improve the accuracy of the assessment.

[0076] The ESOT calculation module calculates the equivalent surface oxygen time (ESOT) value for each dive based on the dive plan or actual dive data. By taking the oxygen partial pressure and exposure time into account, a formula is used to convert each exposure to a hyperoxic environment into an equivalent time.

[0077] The specific ESOT calculation method is as described in the following formula 6

[0078] The ESOT calculation formula is derived from the following process

[0079] Bardin and Lambertsen first proposed the concept of UPTD in 1970 (see formula 1). One unit of UPTD is defined as lung damage caused by exposure to a PO2 environment of 101 kPa (normal pressure) for 1 minute. This concept provides a basis for quantifying the degree of pulmonary oxygen toxicity and a reference for the formulation of decompression tables after high-pressure diving exposure.

[0080]

[0081] Where: t is the exposure time under the corresponding oxygen partial pressure (min); PO2 is the oxygen partial pressure (atm).

[0082] According to the experiment conducted by Wright et al. on continuous hyperoxia exposure and the definition of UPTD, UPTD can be calculated as follows in multiple high partial pressure oxygen exposures during a single hyperbaric exposure:

[0083] UPTD=UPTD1+UPTD2+…+UPTD n ②

[0084] Applicable to n consecutive exposure time periods under different PO2. The total cumulative UPTD can be obtained by adding up the UPTD calculated for each time period.

[0085] Wright et al. analyzed the correlation between PO2 and VC decrease and found that the UPTD value of high partial pressure oxygen exposure should be limited to 615, at which time VC decreased by 2% on average, and UPTD should not exceed 1425, at which time VC decreased by 10% on average. Currently, the Diving Medical Advisory Committee (DMAC) recommends (DMAC35) that the UPTD of a single dive should not exceed 615, otherwise it will affect the recovery of lung injury. Hamilton et al.'s research showed that the maximum limit of high partial pressure oxygen exposure in 1 day can reach 850 UPTD.

[0086] The K index proposed by Arieli aims to quantitatively describe "a detectable physiological variable of oxygen damage", which is also commonly referred to as "Arieli K". The index is based on the relationship between "a detectable physiological variable of oxygen damage (DMG)" and exposure time and PO2. The calculation formula of Arieli K is as follows (Formula 2):

[0087]

[0088] Where: t is the exposure time under the corresponding oxygen partial pressure (h); PO2 is the oxygen partial pressure (atm).

[0089] In multiple high partial pressure oxygen exposures, the Arieli K is calculated as:

[0090]

[0091] Where: t i is the exposure time under the corresponding oxygen partial pressure (h); PO2 i is the oxygen partial pressure (atm), and the formula is applicable to continuous exposure under n different PO2.

[0092] In addition, Arieli et al. proposed a formula for calculating the decline in vital capacity using the K value, as follows:

[0093] ΔVC=0.0082×K ⑤

[0094] Where: ΔVC is the degree of reduction in vital capacity after high partial pressure oxygen exposure (%).

[0095] Arieli suggested that the maximum K value of a single day should not exceed 250 to control the risk of VC reduction. According to the calculation formula of VC reduction, it is calculated that when K is 244, VC decreases by 2%, and when K is 1220, VC decreases by 10%.

[0096] As can be seen from Formula 4, if multiple dives are calculated, the calculation of Arieli K is more complicated, and high accuracy is required when tabulating. In order to simplify this complex calculation process, Risberg and van Ooij proposed the concept of ESOT (Formula 6). Through mathematical conversion, the calculation process was simplified. The total high partial pressure oxygen exposure index can be obtained by adding up the ESOT of each segment, thereby improving the efficiency and accuracy of tabulation. They suggested using ESOT as an improved Arieli K index to replace UPTD. ESOT = 1 refers to the high partial pressure oxygen exposure after breathing 100% oxygen on the surface for 1 minute, which is similar to UPTD.

[0097]

[0098] Where: t is the exposure time under the corresponding oxygen partial pressure (min); PO2 is the oxygen partial pressure (atm).

[0099] When calculating multiple high partial pressure oxygen exposures, the total ESOT is calculated as:

[0100] ESOT=ESOT1+ESOT2+…+ESOT n ⑦

[0101] ESOT predicts the decrease in VC caused by high partial pressure oxygen exposure, and its effectiveness is comparable to Arieli K, but it is slightly better than Arieli K index in terms of intuitiveness and calculation. Therefore, Risberg and van Ooij recommend that ESOT be used to monitor the risk of lung injury in surface-supplied diving. The relationship between ESOT and K index is as follows:

[0102]

[0103] In addition, ESOT can also be converted to UPTD.

[0104]

[0105] According to the maximum Arieli K of 244 to 1220 in a single day, using formula 8 for conversion, it can be concluded that the maximum exposure level for a single-day diving is ESOT 937 to 2095, at which time the VC drop is also within 10%.

[0106] ESOT combines the advantages of UPTD and Arieli K. It is not only conceptually similar to UPTD, but can also calculate the total hyperoxia exposure index more quickly. The introduction of this new indicator may have a profound impact on diving operation safety management and diving medical research.

[0107] ESOT is the equivalent oxygen exposure of breathing 100% oxygen for 1 minute at the surface. For multiple dives, ESOT values ​​are added together to give a total oxygen exposure index.

[0108] The deep learning algorithm further optimizes the ESOT calculation model, adjusts parameters based on individual information and environmental factors, and provides more accurate calculation of hyperoxia exposure.

[0109] The risk assessment module sets a personalized safety threshold for ESOT. The specific value is dynamically adjusted based on the diver's individual information (such as age and health status) to adapt to the lung injury tolerance of different divers.

[0110] Based on the comparison between ESOT value and safety threshold, the risk of lung injury is divided into low risk, medium risk and high risk levels. The risk level is visualized using a 3D dynamic risk model to help divers intuitively understand the current risk status of lung injury.

[0111] By comparing with group data, a further reference benchmark for health status is provided, and divers can see how their own risk compares to the group average risk, so as to make more informed diving decisions.

[0112] Warning and advice module: when the diver's ESOT value approaches or exceeds the safety threshold, the system automatically sends a warning signal and reminds the diver of the current lung injury risk status through voice.

[0113] The recommendation module generates personalized safety recommendations based on risk levels through natural language processing, including adjusting diving depth, optimizing decompression stops, and recommending divers to suspend diving activities when necessary.

[0114] The personalized advice provided is continuously updated during the dive, helping divers to take timely protective measures.

[0115] The data analysis and optimization module collects historical data and health information of divers and performs statistical analysis to optimize the system's risk assessment model.

[0116] This module uses the algorithm's self-learning function to automatically update ESOT's safety thresholds and assessment models through long-term accumulated data, allowing risk assessment to gradually adapt to changes in the diver's health status.

[0117] The immersive risk simulation and training module integrates virtual reality (VR) and augmented reality (AR) technologies. Divers can use VR to simulate the risk of lung injury in a high-oxygen environment and experience the potential risks of diving in advance.

[0118] AR technology is used to provide real-time visual guidance on diving paths and decompression stops, allowing divers to obtain intuitive risk management advice during the diving process and improve safety.

[0119] The multi-device interconnection module allows divers to view data and health information on multiple devices simultaneously, including smartphones, dive computers, tablets, etc. Data is transmitted through the cross-platform sharing function, allowing divers to obtain their own diving and health status at any time.

[0120] Working process

[0121] Data collection and input: Divers' individual information, dive plans, real-time dive data and environmental information are input into the system. Actual dive data and environmental data are collected by sensors and dive computers and automatically transmitted to the system.

[0122] ESOT calculation: The system preliminarily calculates the ESOT value based on the dive plan before diving, and dynamically updates the ESOT through real-time data during the dive. Through deep learning algorithms, the system optimizes the calculation model based on the diver's individual information and environmental factors to improve the accuracy of ESOT.

[0123] Risk assessment and grading: The system compares the calculated ESOT value with the personalized safety threshold, grades the current lung injury risk, and displays the risk level through a 3D model for divers to intuitively understand the diving risk.

[0124] Warning and suggestion generation: When the diver's risk level increases, the system reminds the diver of the potential risk through voice and provides adjustment suggestions based on the risk level. The natural language processing module generates personalized suggestions to ensure that the diver understands and adjusts his behavior in time.

[0125] Historical data analysis and model optimization: The system collects all data after diving, performs statistical analysis using the data analysis and optimization module, and continuously updates the ESOT threshold and assessment model through algorithm self-learning to provide more personalized and accurate risk assessment in the future.

[0126] Immersive training and multi-device interconnection: The system provides simulated training for divers through VR and AR technology to improve risk awareness and response capabilities before diving; the multi-device interconnection module supports divers to view data on multiple devices at any time, improving user experience.

[0127] Through personalized ESOT calculation and dynamic risk assessment, the system can more accurately identify the risk of lung injury and avoid health damage caused by high oxygen exposure.

[0128] The system provides personalized risk assessment and safety recommendations based on the diver's individual information, helping divers better manage diving risks.

[0129] Supports viewing real-time data on multiple devices, enhancing divers' real-time control over diving status and improving diving safety.

[0130] Through VR and AR immersive experiences, divers can gain a true understanding of risks before diving and improve their ability to deal with diving accidents.

[0131] Through this system, divers can more intuitively understand their own lung injury risks and obtain scientific and personalized safety guidance, effectively reducing health risks during diving.

[0132] Example 2

[0133] like Figures 1 to 3 As shown in the figure, the data analysis and optimization module includes a diving database management unit: the system establishes a diver's personal diving database to record and maintain diving data and health information, including the diving location, time, depth, breathing gas composition and environmental conditions of each dive. The database is updated in real time to ensure the integrity of information after each dive and provide data support for subsequent analysis.

[0134] Site Risk Assessment Unit: This unit generates a personalized dive risk assessment for divers by analyzing historical data and environmental factors of the dive site. The assessment generates dive recommendations based on risk data and actual environmental factors (such as water velocity and temperature) under similar environmental conditions to help divers choose appropriate dive options.

[0135] The warning and advice module includes a telemedicine support interface unit: when the diver's risk level reaches a high-risk state, the system automatically connects to the preset medical service platform or expert to support real-time medical assistance. The interface unit sends the diver's ESOT value, health data, environmental conditions and risk level information to the telemedicine expert through real-time data transmission.

[0136] Medical experts provide professional medical advice based on the diver's real-time data. Once the advice is generated, the system communicates it to the diver via voice or text to ensure that the diver can receive appropriate medical guidance in high-risk situations.

[0137] The immersive risk simulation and training module includes a virtual reality training unit: the system uses VR technology to simulate high-oxygen exposure scenarios with lung injury risks. The virtual environment simulates the impact of high-oxygen exposure on the lungs, enhances divers' perception of diving risks through visual and sound effects, and allows divers to become familiar with the risk conditions and response strategies of high-oxygen exposure in the simulation.

[0138] Interactive health report generation unit: The system automatically generates a personalized health report after each dive. The report displays the diver's ESOT cumulative value, lung health change trend and risk assessment in the form of a chart, and is presented to the diver through an interactive interface, so that the diver can understand in detail the impact of diving behavior on lung health.

[0139] The multi-device interconnection module includes a cross-platform data synchronization unit: the system supports synchronization of dive data and health data on multiple devices such as smartphones, dive computers, tablets, etc., ensuring that divers can access their health status information on different devices and improving the convenience of data acquisition.

[0140] Data export and sharing unit: Divers’ data can be exported as encrypted files or shared with medical institutions through preset protocols for subsequent health checks or professional evaluations. The unit implements end-to-end data encryption to ensure privacy and security during data transmission.

[0141] Working process

[0142] Data analysis and personalized recommendation generation: The diving database management unit records all dive data and health information of divers, while the location risk assessment unit combines historical dive data and real-time environmental conditions to provide divers with location-specific risk assessment and personalized diving recommendations.

[0143] Remote medical support and risk warning: When a diver's risk assessment reaches a high-risk state, the system connects to the medical service platform through the remote medical support interface and automatically transmits the diver's real-time ESOT value, health data and risk level information to medical experts. Experts analyze and provide guidance based on real-time data, and the system automatically conveys expert advice to the diver.

[0144] Immersive training and interactive report generation: Before diving, the system simulates diving risk scenarios through VR virtual reality training units to help divers familiarize themselves with the potential risks of hyperoxia exposure. After diving, the system generates an interactive health report that displays lung health changes and risk assessments through charts to help divers fully understand the health impact of diving.

[0145] Multi-device data synchronization and sharing: The cross-platform data synchronization unit synchronizes dive data and health information between multiple devices, and the data export and sharing unit supports exporting data as encrypted files or securely transmitting data to medical institutions for further evaluation through preset protocols.

[0146] The Dive Database Management and Site Risk Assessment Unit provides a personalized risk assessment for each dive, allowing divers to adjust their dive plans based on the assessment results to reduce the potential risk of lung injury.

[0147] The system uses a remote medical support interface to ensure that divers can get immediate advice from medical experts in high-risk situations, improving safety in emergency situations.

[0148] VR training helps divers improve their risk awareness and coping capabilities, and interactive health reports allow divers to clearly understand the impact of diving on lung health and help them develop good diving habits.

[0149] Through multi-device interconnection and data export functions, divers can check their diving and health information at any time. End-to-end encryption is used during data transmission and sharing to protect divers' privacy and improve the security of the system.

[0150] This embodiment further expands the personalized health management and data synchronization functions on the basic functions of the system, provides diversified risk management support for divers, and enhances user experience and data security.

[0151] In view of current practical needs, the above-mentioned implementation mode adopted in this application is not limited to the scope of protection. Various changes made within the knowledge scope of technical personnel in this field without departing from the concept of this application still fall within the scope of protection of the present invention.

Claims

1. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT), characterized by: It includes a data collection module for collecting individual information of divers, diving plans, actual diving data and environmental information; The individual information includes age, gender, health status, smoking history and other factors that may affect lung function; The dive plan includes dive depth, time, and breathing gas composition; The actual diving data is automatically recorded by the diving computer in terms of diving depth, time and changes in breathing gas composition; The environmental information includes data such as water temperature, air pressure and water flow speed; ESOT calculation module, used to calculate the ESOT value of each dive according to the dive plan or actual dive data, and accumulate the total ESOT value; The ESOT calculation module further optimizes the calculation of ESOT values ​​through a deep learning algorithm and establishes a more accurate ESOT calculation model based on individual information and environmental factors; A risk assessment module is used to assess the risk of lung injury and perform risk grading based on the calculated ESOT value and the set safety threshold; The risk assessment module sets a personalized ESOT safety threshold and dynamically adjusts the threshold according to the physical condition of different divers; The risk assessment module divides risks into low risk, medium risk and high risk levels, and visualizes the risk levels through a 3D dynamic risk model; The warning and advice module is used to issue warnings when the diver's ESOT value approaches or exceeds the safety threshold and generate personalized safety advice based on the risk level; The warning and advice module includes a voice reminder function to remind the diver of the current status in real time when the risk increases; The suggestion module generates personalized suggestions through natural language processing technology, including decompression stop optimization, adjustment of diving depth or suspension of diving activities; Data analysis and optimization module, used to collect divers' historical data and health information, and conduct statistical analysis and model optimization; The data analysis and optimization module further uses algorithm self-learning and automatic update functions to optimize ESOT safety thresholds and risk assessment models based on historical data; Immersive risk simulation and training module, which provides immersive risk simulation and personalized stress relief guidance through virtual reality (VR) or augmented reality (AR) technology; The VR module simulates the risk of lung injury in a virtual environment to help divers become familiar with high-risk scenarios; The AR module is used to provide real-time diving path, decompression stop points and risk management guidance during diving; Multi-device interconnection module, used to support divers to view data and health information on multiple devices simultaneously; The multi-device interconnection module supports data synchronization and cross-platform data sharing between devices such as smartphones, diving computers and tablets.

2. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The data collection module further comprises: Smart health monitoring equipment, connected to the smart wearable device worn by the diver, is used to monitor the diver's heart rate, blood oxygen saturation, body temperature and other biological signals in real time; The health data analysis unit analyzes and processes the monitored biological signals to identify the health change trends of divers during diving; When the monitored biological signal abnormality exceeds the preset safety threshold, the data collection module sends a health warning signal to remind the diver to take appropriate measures.

3. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The ESOT calculation module further comprises: Personalized dynamic decompression calculation unit, which generates a personalized decompression stop plan for each dive based on the diver's historical data and individual health status; The dynamic decompression calculation unit dynamically adjusts the decompression time, depth and stop points according to the real-time diving data, optimizes the gas removal efficiency during the decompression process, and reduces the risk of lung damage; The personalized decompression plan is fed back to the diver in real time through the natural language processing module so that the diver can refer to it to adjust his diving behavior.

4. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The risk assessment module further comprises: A group data comparison and analysis unit, used to generate a risk reference benchmark based on the diver group data; The group data comparison and analysis unit compares the diver with group data of similar age, health status and diving habits through data clustering and group analysis, so as to provide the diver with a more scientific health status comparison and reference; The risk reference benchmark is further displayed through visualization, allowing divers to clearly understand the changes in their health risks relative to the group average.

5. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The data analysis and optimization module further includes: Diving database management unit, used to establish and maintain diver's diving data and health data records, including diving location, diving time, environmental conditions and other information; The Site Risk Assessment Unit analyses the risk to divers of diving at a specific site based on historical data and environmental factors of the dive site; The site risk assessment unit combines real-time and historical environmental data to generate personalized dive recommendations for divers to use at specific dive sites.

6. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The warning and suggestion module further includes: A remote medical support interface unit, which is used to automatically connect to a preset medical service platform or expert when a diver reaches a high-risk state; The remote medical support interface unit can send the diver's ESOT value, health data and risk level information to the remote medical expert through real-time data transmission, so that the medical expert can provide professional medical advice according to the actual situation of the diver; When a medical professional issues a recommendation, the system automatically communicates the recommendation to the diver via voice or text alert.

7. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The immersive risk simulation and training module further includes: Virtual reality training unit, which is used to simulate virtual scenarios of lung injury risk through VR technology, helping divers to become familiar with diving risk scenarios in an immersive environment; Interactive health report generation unit, which generates a personalized health report after diving, showing the diver's ESOT value accumulation, lung health change trend and risk assessment in the form of charts; The health report is displayed to the diver through an interactive interface, and the diver can understand the impact of diving behavior on lung health based on the analysis results provided in the report.

8. A diving lung injury risk assessment system based on equivalent surface oxygen time (ESOT) according to claim 1, characterized in that: The multi-device interconnection module further includes: Cross-platform data synchronization unit, used to synchronize dive data to multiple devices such as smartphones, dive computers, tablets, etc., ensuring that divers can access their dive and health information on different devices; Data export and sharing unit, used to export diver's data into encrypted files or share them with preset medical institutions to support subsequent health examinations and medical evaluations; The cross-platform data synchronization unit and data export unit both support end-to-end data encryption to ensure the privacy and security of divers' health data.

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