Underwater life support system and method of intelligent underwater wearable equipment

Abstract

The invention relates to an intelligent life support system in underwater wearable equipment. The system comprises a diver physiological parameter monitoring module, an intelligent analysis and judgment module, a life support equipment control module, an emergency rescue system and a data recording and transmission module. The diver physiological parameter monitoring module can monitor the physiological parameters of the diver in real time, such as the heart rate, the respiratory rate and the oxyhemoglobin saturation. And the intelligent analysis and judgment module analyzes the physiological parameter data, identifies possible health risks encountered by the diver, and provides early warning and response measures. The life support equipment control module automatically adjusts the breathing oxygen concentration and the gas flow so as to provide proper oxygen supply and protection. The emergency rescue system can send a distress signal and determine the position of the diver so as to realize timely rescue action. And the data recording and transmitting module records the physiological parameter data and transmits and stores the physiological parameter data in real time. The intelligent life support system improves the safety and survivability of the diver, and has a wide application prospect.

Description

Technical Field

[0001] This invention relates to an underwater life support system and method for intelligent underwater wearable equipment. The system and method combine advanced sensing technology, data processing and communication technology to provide more powerful life support functions and user experience. Background Technology

[0002] Existing underwater wearable equipment typically offers only limited life support capabilities and lacks intelligent features, restricting user operation and safety in underwater environments. Therefore, there is a need to provide an intelligent underwater wearable equipment-based underwater life support system to improve the user experience and safety in underwater environments.

[0003] Current underwater wearable equipment has some limitations and shortcomings in terms of life support. Traditional underwater breathing apparatus and life support equipment lack intelligence and autonomy, and cannot effectively respond to emergencies encountered by divers. Therefore, there is a need for a new type of underwater wearable equipment with an intelligent life support system that can monitor the diver's health status in real time and provide timely support and protection. Summary of the Invention

[0004] This invention relates to an intelligent life support system for underwater wearable equipment, aiming to provide an advanced device capable of monitoring, analyzing, and protecting the life safety of divers.

[0005] Traditional underwater diving activities carry certain risks, including health risks such as hypoxia, heatstroke, and poisoning for divers. Therefore, this invention provides an underwater wearable device integrating an intelligent life support system to improve the safety and survivability of divers.

[0006] The intelligent life support system includes multiple functional modules, including a diver physiological parameter monitoring module, an intelligent analysis and judgment module, a life support equipment control module, an emergency rescue system, and a data recording and transmission module.

[0007] The diver physiological parameter monitoring module is used to monitor divers' physiological parameters such as heart rate, respiratory rate, and blood oxygen saturation in real time. Through sensors and data acquisition devices, accurate physiological parameter data can be obtained to monitor the diver's health status.

[0008] The intelligent analysis and judgment module analyzes and assesses the health status of divers in real time, identifying potential health risks and providing corresponding warnings and emergency response measures. Through its processor and algorithm modules, this module enables automated health status assessment and early warning functions.

[0009] The specific implementation process of the algorithm during the analysis includes the following steps: Data Acquisition and Processing: Collect historical data on underwater diving activities, including divers' physiological parameters, environmental information, and hazard tags. Ensure data quality and integrity.

[0010] Feature extraction and data preprocessing: Meaningful features are extracted from the collected data, such as the diver's heart rate, breathing rate, and water temperature. Data preprocessing, such as normalization and standardization, is performed to ensure the data is within an appropriate range.

[0011] Neural network construction and definition: Determine the structure of the neural network, including the number of nodes, connection methods, and activation functions in the input, hidden, and output layers. Choose an appropriate network structure based on the complexity of the problem and the characteristics of the data.

[0012] Initialize model parameters: Initialize the weights and biases of the neural network. Common initialization methods include random initialization and pre-trained model initialization.

[0013] Forward propagation: Preprocessed data is input into the neural network, and the output of each node is calculated through the forward propagation of the network. Starting from the input layer, the calculation proceeds through the hidden layers until the output layer.

[0014] Calculate the loss function: Compare the output of the neural network with the actual labels to calculate the loss function (cost function). Common loss functions include mean squared error (MSE) and cross-entropy loss function.

[0015] Backpropagation: Based on the loss function, the backpropagation algorithm is used to update the parameters of the neural network, minimizing the value of the loss function. Backpropagation calculates the gradient at each node using the chain rule and propagates the gradient back into the network to update the parameters.

[0016] Parameter optimization: Optimization algorithms (such as gradient descent, Adam optimizer, etc.) are used to optimize the parameters of the neural network to find the optimal solution for the loss function. The value of the loss function is gradually reduced through multiple iterations.

[0017] Model evaluation and validation: A portion of the data is reserved as a validation set. After each training cycle, the validation set is used to evaluate the model's performance and generalization ability, and hyperparameters are adjusted to improve the model's performance.

[0018] Prediction and Application: Using a trained neural network model to predict real-time collected data, and based on the prediction results, provide early warnings of potential dangers and take corresponding measures to ensure the safety of divers.

[0019] The life support equipment control module is connected to the diver's life support equipment and can automatically adjust parameters such as respiratory oxygen concentration and gas flow rate according to the diver's health status and environmental conditions to ensure that the diver receives appropriate oxygen supply and protection.

[0020] The emergency rescue system integrates an emergency distress signal device and a positioning system, enabling divers to quickly send distress signals when they encounter danger or emergency situations, and to determine their exact location through the positioning device, so that rescuers can respond and rescue them quickly.

[0021] The data recording and transmission module records the diver's physiological parameters and transmits the data to a remote monitoring center or other devices for real-time analysis and storage. This module includes a memory and a communication module, enabling long-term data storage and remote transmission, facilitating subsequent analysis and evaluation.

[0022] By integrating the aforementioned functional modules, this intelligent life support system can provide comprehensive real-time monitoring, analysis, and protection for divers, improving their safety and survivability in underwater environments. Simultaneously, the system possesses self-learning and optimization capabilities, enabling adaptive adjustments and improvements based on individual diver characteristics and environmental changes, thereby enhancing the system's accuracy and reliability.

[0023] This intelligent life support system has the following innovative functions: a. Based on artificial intelligence-based predictive analytics, it can provide early warnings of potential dangers based on historical data and environmental perception.

[0024] b. Seamless integration of wearable devices with intelligent life support systems enables real-time data transmission and feedback.

[0025] c. Automated control function, which can adaptively adjust according to the wearer's movement and needs.

[0026] In summary, the intelligent life support system in the underwater wearable equipment provided by this invention is innovative and practical, and can effectively improve the life safety level of divers, with broad application prospects and economic value.

[0027] Figure 1 Structure diagram of an intelligent life support system Figure 2 The implementation process of specific algorithms in the intelligent analysis and judgment module. Figure 3 The implementation process of specific algorithms in the evaluation, verification, and prediction application modules. Figure 3A brief explanation includes: 1. Calculating the loss function: Comparing the output of the neural network with the actual labels to calculate the loss function (cost function). Common loss functions include mean squared error (MSE) and cross-entropy loss. 2. Backpropagation: Based on the loss function, the backpropagation algorithm is used to update the parameters of the neural network to minimize the loss function. Backpropagation calculates the gradient at each node using the chain rule and propagates the gradient back to the network for parameter updates. 3. Parameter optimization: Optimization algorithms (such as gradient descent, Adam optimizer, etc.) are used to optimize the parameters of the neural network to find the optimal solution for the loss function. The value of the loss function is gradually reduced through multiple iterations. 4. Model evaluation and validation: A portion of the data is reserved as a validation set. After each training cycle, the validation set is used to evaluate the model's performance and generalization ability, and hyperparameters are adjusted to improve model performance. 5. Prediction and application: The trained neural network model is used to predict real-time collected data, and based on the prediction results, early warnings of potential dangers are issued, and corresponding measures are taken to ensure the safety of divers. Detailed Implementation

[0028] Artificial intelligence-based predictive analytics, through machine learning and data mining techniques combined with historical data and environmental awareness, can provide early warnings of potential underwater hazards. Below is a basic workflow: Data Acquisition and Processing: Collect historical data on underwater diving activities, including divers' physiological parameters, environmental information (such as water temperature and current), and activity routes. Clean, organize, and label this data for subsequent model training and analysis.

[0029] Feature extraction: Meaningful features are extracted from the collected data. These features may include divers' heart rate changes, breathing rate, oxygen consumption rate, activity intensity, and environmental factors such as water temperature changes and water flow speed. Additionally, time series analysis of historical data can be considered to capture patterns and trends that change over time.

[0030] Model training: Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are used to train the model on the extracted features. Historical data is used as the training set, and the model builds a predictive model by learning patterns and regularities from the historical data.

[0031] Prediction and Analysis: The trained model is used to predict and analyze real-time collected data. Current physiological parameters and environmental information are input into the model, which then uses historical data patterns and regularities, combined with current data, to predict potential hazards. For example, based on a diver's physiological parameters and environmental factors, the model can predict whether the diver faces risks such as hypoxia or heatstroke.

[0032] Early warning and intervention measures: Once the model detects a potential hazard, the system can issue an early warning signal to alert the diver or relevant rescue personnel. Simultaneously, appropriate intervention measures can be taken, such as automatically adjusting oxygen supply and changing the activity route, to minimize the risks to the diver.

[0033] Through continuous data collection and model optimization, AI-based predictive analytics can be continuously improved and enhanced in accuracy, helping divers make more informed decisions in underwater environments, reducing diving risks, and improving life safety.

Claims

1. An underwater life support system and method for intelligent underwater wearable equipment, characterized in that... It features an intelligent life support system, including: a. a diver physiological parameter monitoring module, used to monitor the diver's heart rate, respiratory rate, blood oxygen saturation, and other physiological parameters in real time; b. an intelligent analysis and judgment module, which, through monitoring and analyzing the diver's physiological parameters, judges their health status and can autonomously issue warnings and respond to emergencies; c. a life support equipment control module, connected to the diver's life support equipment, which can automatically adjust parameters such as respiratory oxygen concentration and gas flow rate according to the diver's health status and environmental conditions; d. an emergency rescue system, integrating an emergency distress signal device and a positioning system, which can quickly send distress signals and determine the diver's location when encountering danger or an emergency; e. a data recording and transmission module, used to record the diver's physiological parameter data and can transmit the data to a remote monitoring center or other equipment for real-time analysis and storage.

2. The underwater wearable equipment according to claim 1, characterized in that... The diver physiological parameter monitoring module further includes sensors and data acquisition devices to acquire physiological parameter data such as the diver's heart rate, respiratory rate, and blood oxygen saturation; the intelligent analysis and judgment module further includes a processor and an algorithm module to perform real-time analysis of the diver's physiological parameter data and health status judgment, and generate corresponding early warnings and emergency response measures based on preset rules and algorithms; the life support equipment control module further includes a controller and an actuator to automatically adjust parameters such as respiratory oxygen concentration and gas flow rate according to the diver's health status and environmental conditions, and control the working status of the life support equipment; the emergency rescue system further includes a distress signal transmitter and a positioning device to send out a distress signal when the diver encounters danger or emergency, and to determine the diver's precise location through the positioning device.

3. The underwater wearable equipment according to claim 1, characterized in that... The data recording and transmission module further includes a memory and a communication module for recording the diver's physiological parameter data and transmitting the data to a remote monitoring center or other equipment for real-time analysis and storage via the communication module. Life support equipment includes a respirator, air cylinders, and waterproof clothing, providing the diver with continuous and reliable breathing gas and protection. The intelligent life support system, by integrating sensors, processors, controllers, and communication modules, enables real-time monitoring, analysis, and protection of the diver, improving the safety and survivability of underwater divers. The diver's physiological parameter monitoring module can monitor the diver's body temperature, blood pressure, breathing depth, and other physiological parameters in real time. The intelligent analysis and judgment module can identify potential health risks to divers based on their physiological parameters and historical health records, and provide corresponding warnings and suggestions.

4. The underwater wearable equipment according to claim 1, characterized in that... The life support equipment control module can automatically adjust environmental parameters such as temperature, humidity, and pressure of underwater wearable equipment to provide a suitable working environment; the emergency rescue system can automatically send distress signals and establish communication links with rescuers or rescue centers to enable timely rescue operations; the data recording and transmission module can record the diver's movement trajectory, breathing patterns, and other information, and provide statistical analysis and playback functions; the intelligent life support system has self-learning and optimization capabilities, and can adaptively adjust and improve according to the diver's individual characteristics and environmental changes; the intelligent life support system provides real-time health status prompts and guidance to the diver through sound, vibration, or light signals.

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