An underwater pressure intelligent monitoring and feedback system for diving assistance

Abstract

The application discloses a kind of underwater pressure intelligent monitoring and feedback systems for diving assistance, the present application relates to diving technical field, including acquisition module, microprocessor, display module, alarm module and communication module;The acquisition module is used to collect the real-time data under water when diving equipment diving by sensor, wherein, sensor is installed in diving equipment;The underwater pressure intelligent monitoring and feedback system for diving assistance, display module shows the key parameters after processing, alarm module sends out sound and light alarm under abnormal condition, reminds diver to take measures, communication module will state and position information real-time sent to water surface monitoring equipment;The present application is fused with intelligent analysis by multiple source data, provides comprehensive, real-time safety monitoring and feedback for diver, significantly improves the safety and reliability of diving activity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of diving, in particular to an underwater pressure intelligent monitoring and feedback system for diving assistance. BACKGROUND

[0002] Diving is an activity conducted in underwater environments such as oceans and lakes, involving various safety risks. Divers in underwater environments are affected by factors such as pressure, temperature, gas composition, and may face situations such as decompression sickness, oxygen deficiency, heatstroke or hypothermia. Therefore, real-time monitoring and feedback of changes in diving environment and the physical state of divers are crucial to ensure diving safety.

[0003] Traditional diving monitoring equipment usually relies on single or limited sensors, with limited data types and real-time performance. Although diving equipment can provide basic depth and time information, it cannot monitor the changes of multiple parameters in real time, such as underwater pressure, temperature, gas composition and the motion state of divers. In addition, traditional equipment usually lack deep analysis and intelligent feedback of data, and can only provide limited alarm information in emergency situations, and cannot predict potential risks in advance. SUMMARY

[0004] To solve the above technical problems, the present application is implemented by the following technical scheme: an underwater pressure intelligent monitoring and feedback system for diving assistance, comprising a collection module, a microprocessor, a display module, an alarm module and a communication module; the collection module is used to collect real-time underwater data of diving equipment during diving through sensors, wherein the sensors are installed in the diving equipment;

[0005] The microprocessor is used to receive real-time data collected by the collection module, process and analyze the real-time data, predict the physical condition and diving risk of the diver based on a diving comprehensive analysis model, and generate a prediction result, wherein the diving comprehensive analysis model includes a Kalman filter model, a fuzzy logic model and a neural network model;

[0006] The display module is used to display the processed pressure, depth, temperature, gas composition, position and motion parameters in real time;

[0007] The alarm module is used to issue sound and light alarms when detecting abnormal conditions, reminding the diver to take appropriate measures;

[0008] The communication module is used to send the state and position information of the diver to the monitoring equipment on the water surface for rescue in emergency situations.

[0009] Preferably, the collection module comprises: a pressure sensor, a depth sensor, a temperature sensor, a gas sensor, a positioning sensor, a motion sensor; the pressure sensor is used to monitor the change of underwater pressure in real time and transmit pressure data to the microprocessor; the depth sensor is used to determine the diving depth by measuring water pressure and transmit depth data to the microprocessor; the temperature sensor is used to monitor the underwater temperature and transmit temperature data to the microprocessor; the gas sensor is used to detect the underwater gas composition, including oxygen content and carbon dioxide content, and transmit gas composition data to the microprocessor; the positioning sensor is used to determine the position of the diver and transmit position information to the microprocessor; the motion sensor is used to monitor the motion state of the diver, including speed and acceleration, and transmit motion data to the microprocessor; wherein the real-time data transmission of each sensor adopts low-power Bluetooth technology to reduce energy consumption and prolong the endurance time of the device; the data uses encryption technology during transmission to ensure the security and privacy of the data and prevent the data from being intercepted or tampered with during transmission.

[0010] Preferably, the diving comprehensive analysis model is used to predict the physical condition and diving risk of the diver based on comprehensive consideration of the pressure change rate, pressure peak detection and pressure fluctuation frequency, combined with the following algorithm formula:

[0011] The diving comprehensive analysis model is used to predict the physical condition and diving risk of the diver based on comprehensive consideration of the pressure change rate, pressure peak detection and pressure fluctuation frequency, combined with the following algorithm formula:

[0012] The pressure change rate adopts the following formula:

[0013] ;

[0014] P(t) represents the pressure value at time t; t represents the pressure time point;

[0015] The average change rate calculation formula of multiple time points:

[0016] ;

[0017] m represents the number of time points; let the pressure sequence be P= ; find the maximum pressure value in the pressure sequence; wherein the maximum value ; the minimum pressure value ; find the minimum pressure value in the pressure sequence;

[0018] The pressure fluctuation frequency calculation formula is: ;

[0019] F(k) represents the discrete Fourier transform result of the sequence at frequency index k; represents the nth value in the pressure sequence; N represents the number of points in the input sequence, i.e., the length of the sequence; k represents the frequency index, k = 0, 1, 2, …, n; N-1 represents different frequency components; represents the complex exponential function, also the basis function of the discrete Fourier transform, where i is the imaginary unit;

[0020]

[0021] Calculation to obtain the power of each frequency component, which reflects the strength of the frequency component in the pressure signal; is the main frequency of the pressure fluctuation;

[0022] The comprehensive index calculation formula is as follows:

[0023] ;

[0024] S represents the comprehensive index, which is a comprehensive evaluation of the performance of the pressure sensor and the complexity of the pressure state by combining the information of the pressure rate of change, the pressure peak detection, and the pressure fluctuation frequency;

[0025] represents the adjustment of the influence of the pressure rate of change on the comprehensive index;

[0026] wherein is the weight coefficient of the pressure rate of change, is the absolute value of the pressure rate of change, is the maximum value of the pressure rate of change;

[0027] : represents the adjustment of the influence of the pressure peak detection on the comprehensive index;

[0028] wherein, is the weight coefficient of the pressure peak detection; is the pressure range preset by the system;

[0029] represents the adjustment of the influence of the pressure fluctuation frequency on the comprehensive index;

[0030] wherein, is the weight coefficient of the pressure fluctuation frequency, is the maximum value of the pressure fluctuation frequency, is the main frequency of the pressure fluctuation;

[0031] wherein, the three weight coefficients satisfy ; the role is to adjust the importance of different indicators in the comprehensive index according to actual needs;

[0032] The microprocessor comprises a data processing unit and a data analysis unit; the data processing unit receives real-time data from the sensor and pre-processes the real-time data to improve the accuracy and consistency of the real-time data; during the pre-processing of the real-time data by the data processing unit, the real-time data is also quickly analyzed to generate a trigger event;

[0033] The data processing unit receives real-time data from the sensor and pre-processes the real-time data:

[0034] First, the received real-time data is binary decoded to extract digital data therefrom;

[0035] The digital data is then subjected to a cyclic redundancy check to obtain a set of checked data after the check;

[0036] A median filter is then applied to remove burst noise in the set of checked data to obtain a set of quasi-data;

[0037] The data in the set of quasi-data is then calibrated by setting a standard value to obtain calibrated data;

[0038] The calibrated data is converted into the required format data, and the consecutive format data is compressed using differential encoding to obtain compressed data; the compressed data is then organized into a JSON format data packet;

[0039] The data analysis unit uses a Kalman filter model to fuse and estimate the state of the pre-processed data packet to eliminate the interference and noise of the underwater environment on the data, and to generate a state estimate after fusing the data packet for further analysis;

[0040] The data analysis unit uses a Kalman filter model to fuse and estimate the state of the data packet:

[0041] First, the key parameters of the Kalman filter are initialized, including the initial state of the system, such as pressure, depth, temperature, and the initial state estimate and error covariance matrix are set, and the state transition matrix, control matrix, measurement matrix, and covariance matrix of process noise and measurement noise are defined;

[0042] Based on the state estimate of the previous time and the state transition equation of the system, the system state at the current time is predicted, and this step also includes the covariance matrix of the prediction error, which is used to reflect the uncertainty of the state prediction;

[0043] The predicted state is fused with the actual measurement value with a weight. It is determined by calculating the relationship between the prediction error and the measurement error, and this step is the core of the Kalman filter, making the fused state estimate have higher accuracy;

[0044] By combining the actual measurements with the predicted state, the state estimation at the current time is updated; this updating process corrects the prediction errors and generates a new state estimate, while also updating the error covariance matrix to reflect the updated uncertainty; finally, the results output by the Kalman filter include the state estimate at the current time and the corresponding error uncertainty, which can be used for further analysis, control or decision support of the system.

[0045] Preferably, the data analysis unit further comprises a fuzzy logic model for fuzzy reasoning and decision-making on the data packet;

[0046] The fuzzy logic model converts the data packet into fuzzy linguistic variables, including good, general and poor, including high, medium and low fuzzy sets. The specific fuzzy set is set according to the data type corresponding to the data in the data packet, and based on this, operation suggestions and warning information are provided for the diver, which are displayed to the diver through the display module;

[0047] The fuzzy logic model is used for fuzzy reasoning and decision-making on the data packet:

[0048] Fuzzification: For different types of data in the data packet, corresponding high, medium and low fuzzy sets are set up, and corresponding fuzzy values in the corresponding fuzzy set are determined;

[0049] Then the input data packet is converted into membership in the fuzzy set;

[0050] Rule base: Based on expert knowledge and experience, fuzzy rules are defined and a fuzzy rule base is formed, so that the fuzzy rules cover the input combination and its corresponding output decision, which is used to describe the behavior and decision logic of the system;

[0051] Fuzzy reasoning: Apply the fuzzy rule base to match the fuzzified fuzzy values with the fuzzy rules in the fuzzy rule base, and obtain the output decision of the fuzzy output through the Mamdani reasoning mechanism;

[0052] Defuzzification: The output decision obtained by fuzzy reasoning is converted back to an exact numerical value, based on which operation suggestions and warning information are provided for the diver, so that specific decisions can be executed in the real system. This step de-fuzzes the fuzzy output by the maximum membership degree method to convert it into a specific operation amount;

[0053] Decision execution: The final exact numerical value, i.e. the decision result, is used to control the specific operation in the system or make further data processing;

[0054] Feedback and adjustment: Monitor the matching of the actual output of the system with the expected output decision, and adjust the fuzzy rules or membership functions according to the feedback to optimize the performance of the system;

[0055] Iterative processing: the system continuously receives input data and repeatedly executes the above steps, dynamically updating the decision-making process to adapt to changing environmental conditions;

[0056] Through the above operations, the fuzzy logic model can handle uncertain and fuzzy data, make flexible and accurate decisions, and adapt to complex practical application environments.

[0057] Preferably, the data analysis unit further comprises a neural network model; the neural network model is used to learn the relationship between sensor data and diver state, to fuse pressure, depth, temperature, gas composition, position and motion parameters, to predict the physical condition and diving risk of the diver, and to feed back the prediction results to the diver through the display module and the alarm module.

[0058] Preferably, the microprocessor is further used to trigger the alarm module when receiving abnormal data, the alarm module issues sound and light alarms to remind the diver to pay attention to potential dangers such as high pressure, low oxygen content or rapid diving; the alarm module can also adjust the intensity and frequency of the alarm according to the danger level, so that the diver can quickly respond.

[0059] Preferably, the communication module is used to send the diver's state and position information to the monitoring device on the water surface in real time through wireless communication technology; the monitoring device can display the real-time position and state of the diver after receiving the information, and timely issue rescue instructions when detecting that the diver is in a dangerous state.

[0060] Preferably, the display module includes a screen display unit and a sound prompt unit; the screen display unit is used to display the underwater pressure, depth, temperature, gas composition, position and motion parameters in real time, so that the diver can know his own state and the surrounding environment at any time; the sound prompt unit is used to provide voice prompts or warnings when the diver's vision cannot be concentrated on the screen, to ensure that the diver can respond to potential dangers in time.

[0061] Preferably, the diving comprehensive analysis model is also used for adaptive adjustment according to the historical data of the diver, to improve the prediction accuracy of the diver's state and the reliability of risk assessment by continuously learning and optimizing the diving comprehensive analysis model, and to improve the safety of the diver.

[0062] Preferably, the microprocessor is connected with a cloud platform, the cloud platform is used for storing and analyzing the historical data of the diver; the cloud platform performs big data analysis on the diving data, generates a precise diving risk model, and transmits updated model parameters to the microprocessor, and the microprocessor updates the diving comprehensive analysis model according to the model parameters, thereby improving the overall intelligent level of the system; in this way, the system can learn and optimize by using a large amount of data, thereby maintaining efficient and safe operation in various diving environments.

[0063] The application provides an underwater pressure intelligent monitoring and feedback system for diving assistance.

[0064] I. The underwater pressure intelligent monitoring and feedback system for diving assistance collects real-time data of various sensors and intelligently analyzes multiple models to provide comprehensive and accurate underwater environment monitoring and safety feedback for divers; the Kalman filter, fuzzy logic and neural network used by the system provide technical support in noise reduction, fuzzy processing, deep learning and probability inference, respectively, to ensure the accuracy of the data and the reliability of the risk prediction; in addition, the adaptive adjustment function of the system and the integration with the cloud platform enable the system to continuously learn and optimize, thereby improving the application capability in complex underwater environments; overall, the system significantly improves the safety of diving activities and the comfort of divers.

[0065] II. The underwater pressure intelligent monitoring and feedback system for diving assistance introduces multiple models and algorithms, and the system can provide accurate and real-time monitoring and feedback for divers in complex underwater environments; each model focuses on processing specific types of data and risks to ensure that the system can effectively operate in various diving scenarios, thereby improving the safety and comfort of diving. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The application provides a flowchart of the underwater pressure intelligent monitoring and feedback system for diving assistance. DETAILED DESCRIPTION

[0067] The application will be further described in detail below in combination with the drawings and specific embodiments. The embodiments of the application are given for the purpose of illustration and description, and are not exhaustive or limit the application to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are chosen and described in order to better illustrate the principles and practical application of the application, and to enable those of ordinary skill in the art to understand the application so as to design various embodiments with various modifications for specific purposes.

[0068] As Figure 1As shown, the present application provides a technical solution: an underwater pressure intelligent monitoring and feedback system for diving assistance, comprising a collection module, a microprocessor, a display module, an alarm module and a communication module; the collection module is used to collect real-time data underwater when the diving equipment is diving through the sensor, wherein the sensor is installed in the diving equipment;

[0069] The microprocessor is used to receive the real-time data collected by the collection module, process and analyze the real-time data, predict the physical condition and diving risk of the diver based on the diving comprehensive analysis model, and generate a prediction result, the diving comprehensive analysis model including Kalman filter model, fuzzy logic model and neural network model;

[0070] The display module is used to display the processed pressure, depth, temperature, gas composition, position and motion parameters in real time;

[0071] The alarm module is used to issue sound and light alarm when detecting abnormal situation, reminding the diver to take corresponding measures;

[0072] The communication module is used to send the state and position information of the diver to the monitoring equipment on the water surface, so as to rescue in emergency.

[0073] The collection module includes: pressure sensor, depth sensor, temperature sensor, gas sensor, positioning sensor, motion sensor: the pressure sensor is used to monitor the change of underwater pressure in real time, and transmit the pressure data to the microprocessor; the depth sensor is used to determine the diving depth by measuring water pressure, and transmit the depth data to the microprocessor; the temperature sensor is used to monitor the underwater temperature, and transmit the temperature data to the microprocessor; the gas sensor is used to detect the underwater gas composition, including oxygen content and carbon dioxide content, and transmit the gas composition data to the microprocessor; the positioning sensor is used to determine the position of the diver, and transmit the position information to the microprocessor; the motion sensor is used to monitor the motion state of the diver, including speed and acceleration, and transmit the motion data to the microprocessor; wherein the real-time data transmission of each sensor adopts low-power Bluetooth technology to reduce energy consumption and prolong the endurance time of the equipment; encryption technology is used in the transmission process to ensure the security and privacy of the data, prevent the data from being intercepted or tampered in the transmission process. It can monitor the underwater pressure, depth, temperature, gas composition, position and motion parameters in real time, provide comprehensive environmental information for the diver, and improve the safety of diving.

[0074] After the system is enabled, each sensor module starts to collect the real-time data of the diver, and when the data processing unit receives the data from each sensor, it starts to preliminarily process the data: the real-time data from the sensor is denoised and normalized to eliminate noise and environmental interference, and ensure the accuracy and consistency of the data;

[0075] The processed data is uniformly formatted for subsequent analysis; the pre-processed data is sent to the data analysis unit, where the system fuses the data through a Kalman filter model, which can effectively remove noise in the data, smooth data fluctuations, and generate stable pressure, depth, temperature, gas composition, position, and motion parameters.

[0076] The diving comprehensive analysis model is based on comprehensive consideration of pressure change rate, pressure peak detection, and pressure fluctuation frequency, and is used to predict the physical condition and diving risk of divers by combining the following algorithm formula:

[0077] The diving comprehensive analysis model is based on comprehensive consideration of pressure change rate, pressure peak detection, and pressure fluctuation frequency, and is used to predict the physical condition and diving risk of divers by combining the following algorithm formula:

[0078] The pressure change rate is calculated using the following formula:

[0079] ;

[0080] P(t) represents the pressure value at time t; t represents the pressure time point;

[0081] The average change rate calculation formula for multiple time points is:

[0082] ;

[0083] m represents the number of time points; let the pressure sequence be ; find the maximum pressure value in the pressure sequence; where the maximum value ; the minimum pressure value ; find the minimum pressure value in the pressure sequence;

[0084] The pressure fluctuation frequency calculation formula (discrete Fourier transform converts the time domain pressure signal to the frequency domain, allowing us to analyze the intensity of different frequency components in the pressure signal): ;

[0085] X(k) represents the discrete Fourier transform result of the sequence at frequency index k; X(n) represents the nth value in the pressure sequence; N represents the number of points in the input sequence, i.e., the length of the sequence; k represents the frequency index, k=0,1,2, …, n, N-1 represents different frequency components; e(jk) represents the complex exponential function, also representing the basis function of DFT, where i is the imaginary unit;

[0086]

[0087] Computing to obtain the power of each frequency component, which reflects the intensity of the frequency component in the pressure signal. is the main frequency of pressure fluctuation;

[0088] The formula for calculating the comprehensive index is as follows:

[0089] ;

[0090] S represents the comprehensive index, which is a comprehensive evaluation of the performance of the pressure sensor and the complexity of the pressure state by combining the information of the pressure rate of change, the pressure peak detection, and the pressure fluctuation frequency;

[0091] : represents the adjustment of the influence of the pressure rate of change on the comprehensive index;

[0092] wherein is the weight coefficient of the pressure rate of change, is the absolute value of the pressure rate of change, is the maximum value of the pressure rate of change;

[0093] represents the adjustment of the influence of the pressure peak detection on the comprehensive index;

[0094] wherein, is the weight coefficient of the pressure peak detection; is the pressure range preset by the system;

[0095] : represents the adjustment of the influence of the pressure fluctuation frequency on the comprehensive index;

[0096] wherein, is the weight coefficient of the pressure fluctuation frequency, is the maximum value of the pressure fluctuation frequency, is the main frequency of pressure fluctuation;

[0097] wherein, the three weight coefficients satisfy ; the role is to adjust the importance of different indicators in the comprehensive index according to actual needs;

[0098] wherein, the value of can be specifically changed or reset according to specific use requirements, that is, if the pressure rate of change is considered to have the greatest impact on diving safety, the value of can be increased; if the pressure peak detection is more critical, the value of can be increased; if the pressure fluctuation frequency is more important, the value of can be increased;

[0099] Through the continuous input and output of data in the model, and combined with the historical data of divers, the diving comprehensive analysis model is trained and optimized, so that the model learns the relationship between stress and diving depth, potential danger and other factors; for example, when the stress rate of change is large and continuously rising, it indicates that the diver is diving quickly or encountering water flow impact, and the neural network outputs the corresponding danger warning according to these characteristics, reminding the diver to take corresponding measures to reduce diving risk;

[0100] The microprocessor includes a data processing unit and a data analysis unit; the data processing unit receives real-time data from the sensor and pre-processes the real-time data to improve the accuracy and consistency of the real-time data; during the pre-processing of the real-time data by the data processing unit, the data processing unit is also used to quickly analyze the real-time data and generate a trigger event;

[0101] The data processing unit receives real-time data from the sensor and pre-processes the real-time data:

[0102] First, the received real-time data is binary decoded to extract valid information and extract digital data therefrom; then, the digital data is subjected to CRC check to obtain a check data set after the check, verify the integrity of the data, and ensure that the data is not damaged or lost during transmission; subsequently, a median filter is applied to remove burst noise in the check data set to improve the reliability and accuracy of the data, and a quasi-data set is obtained; subsequently, a standard value is set to calibrate the data in the quasi-data set to eliminate system error, and a calibrated data is obtained; the calibrated data is converted into the required format data, and differential encoding is used to compress the continuous format data to reduce the bandwidth requirement for storage and transmission, and compressed data is obtained; subsequently, the compressed data is organized into a JSON format data packet for subsequent processing or transmission;

[0103] During the pre-processing of the real-time data by the data processing unit, the data processing unit is also used to quickly analyze the real-time data and generate a trigger event:

[0104] When the temperature data in the collected real-time data exceeds the set temperature threshold as a whole, the collection module at this time generates a temperature warning data and transmits it to the microprocessor, generates a corresponding temperature short warning signal through the microprocessor, and maps the temperature short warning signal into a temperature short warning flashing reminder box and transmits it to the display module, reminding the diver through the display module, and giving the diver a warning prompt on the current diving temperature; at the same time, the microprocessor also encodes the temperature short warning signal into a temperature short warning ringtone through audio, and transmits it to the alarm module, and issues a short and urgent prompt sound through the alarm module to give a sound warning reminder to the diver; so that the diver can subjectively judge whether to continue diving under the current temperature warning in combination with the actual situation, which can help and remind the diver to roughly assess whether the current state meets the current diving environment, and realize safe diving;

[0105] The data analysis unit uses the Kalman filter model to fuse and estimate the state of the preprocessed data packets to eliminate the interference and noise of the underwater environment on the data and generate state estimates after fusing the data packets for further analysis;

[0106] The data analysis unit uses the Kalman filter model to fuse and estimate the state of the data packets:

[0107] First, initialize the key parameters of the Kalman filter, including the initial state of the system, such as pressure, depth, temperature, etc., and set the initial state estimate and error covariance matrix, and define the state transition matrix, control matrix, measurement matrix, and covariance matrix of process noise and measurement noise;

[0108] Based on the state estimate of the previous moment and the state transition equation of the system, predict the system state at the current moment, which also includes the covariance matrix of the prediction error to reflect the uncertainty of the state prediction;

[0109] The weight of the predicted state and the actual measurement value is fused. It is determined by calculating the relationship between the prediction error and the measurement error, and this step is the core of Kalman filtering, which makes the fused state estimate have higher accuracy;

[0110] During diving, the underwater pressure may have slight fluctuations due to environmental factors, and these fluctuations do not mean that the diver is in danger. Through the Kalman filter model, the system filters out these high-frequency noise and only retains the data that has a practical impact on the safety of the diver. This processing can prevent false alarms and improve the reliability of the system;

[0111] By combining the actual measurement value with the predicted state as described above, the state estimation at the current time is updated; this updating process will correct the prediction error and generate a new state estimation value, at the same time, the error covariance matrix will also be updated to reflect the updated uncertainty; finally, the output of the Kalman filter includes the state estimation value at the current time and the corresponding error uncertainty. These outputs can be used for further analysis, control or decision support of the system;

[0112] Through the above process, the Kalman filter model can effectively fuse multi-source sensor data and continuously and real-time estimate and update the system state. This process is widely used in navigation systems, automatic control, signal processing and other fields, and can help the system maintain high-precision state tracking in such an uncertain environment as diving;

[0113] When the data packet contains data provided by no less than two sensors, the raw data collected by these sensors can be input into the Kalman filter model for fusion after being preprocessed in the data packet. The Kalman filter will process real-time data from different sensors and integrate them to generate more accurate state estimation.

[0114] The data analysis unit also includes a fuzzy logic model for fuzzy reasoning and decision-making on the data packet;

[0115] The fuzzy logic model converts the data packet into fuzzy linguistic variables, including good, general and poor, and provides operation suggestions and warning information for the diver based on this, which are displayed to the diver through the display module;

[0116] The fuzzy logic model is used for fuzzy reasoning and decision-making on the data packet:

[0117] 1. Fuzzification

[0118] Corresponding high, medium and low fuzzy sets are established for different types of data in the data packet, and the corresponding fuzzy values in the corresponding fuzzy sets are also specified;

[0119] Then the input data packet is converted into the membership degree in the fuzzy set; specifically, the precise input quantity is mapped to the corresponding fuzzy set, and the degree to which the input quantity belongs to different fuzzy sets is determined through the membership function;

[0120] Example: Taking temperature data as an example, three fuzzy sets of high temperature, medium temperature and low temperature are established, and the degree to which the input temperature data belongs to different fuzzy sets is determined through the membership function. If the input is temperature, the temperature value 35°C is fuzzified as the corresponding value in the high temperature set, for example: the membership degree of high temperature is 0.8, and the membership degree of medium temperature is 0.2;

[0121] 2. Rule Base

[0122] Fuzzy rules are defined based on expert knowledge and experience and form a fuzzy rule base, which covers input combinations and their corresponding output decisions, used to describe the behavior and decision logic of the system, such as fuzzy rules in the form of "if... then..." ;

[0123] Example: One of the rules: If temperature is high and humidity is high, which is the input combination, then start the cooling system output decision;

[0124] 3. Fuzzy Inference

[0125] Apply the fuzzy rule base to match the fuzzified fuzzy values with the fuzzy rules in the fuzzy rule base, and derive the output decision of the fuzzy output through the Mamdani inference mechanism;

[0126] Example: Based on the input of high temperature and high humidity fuzzy values, inference is made on multiple related fuzzy rules, and the output decision of starting the cooling system is derived by combining the fuzzy rules;

[0127] 4. Defuzzification

[0128] Convert the output decision derived from fuzzy inference back to precise numerical values in order to execute specific decisions in real systems. This step defuzzifies the fuzzy output by the maximum membership degree method to convert it into a specific operation amount;

[0129] Example: The fuzzy output is the membership degree of starting the cooling system 0.7, and the defuzzification process decides to run the cooling system at 70% intensity;

[0130] 5. Decision Execution

[0131] The final precise numerical value, i.e. the decision result, is used to control specific operations in the system or make further data processing; for example, control actuators, issue alarms, adjust system parameters, etc.;

[0132] Example: According to the defuzzified precise numerical value, the system starts the cooling system and sets the intensity of the cooling system to the specific value after defuzzification;

[0133] 6. Feedback and Adjustment

[0134] Monitor the matching of the actual output of the system with the expected output decision, and adjust the fuzzy rules or membership functions according to the feedback to optimize system performance;

[0135] Example: If the system detects that the cooling effect is insufficient, it will adjust the high temperature membership function so that the system is more inclined to enhance the cooling intensity under the same conditions;

[0136] 7. Iterative Processing

[0137] The system continuously receives input data and repeatedly performs the above steps, dynamically updating the decision-making process to adapt to changing environmental conditions; Example: In the case of environmental changes, the system continuously optimizes the cooling strategy through continuous fuzzy reasoning and decision adjustment;

[0138] Through the above operations, the fuzzy logic model can handle uncertain and fuzzy data, make flexible and accurate decisions, and adapt to complex practical application environments;

[0139] Fuzzy data is processed by the fuzzy logic model; for example, underwater temperature changes may have different effects on different divers, and the fuzzy logic model converts these temperature data into understandable language variables and provides operational recommendations and warnings for divers based on these variables;

[0140] For example, during diving, if the water temperature gradually decreases while the diver's movement speed increases, the fuzzy logic model will determine that the diver is rapidly descending to a colder water layer, and the system will issue a warning, suggesting that the diver slow down or ascend to avoid discomfort or other potential risks caused by temperature differences.

[0141] The data analysis unit also includes a neural network model; the neural network model is used to learn the relationship between sensor data and diver status, and to fuse pressure, depth, temperature, gas composition, location, and motion parameters to predict the diver's physical condition and diving risk, and the prediction results are fed back to the diver through the display module and alarm module; If the oxygen content gradually decreases or the pressure changes abnormally, the system will generate a potential risk warning;

[0142] Composition of the neural network model:

[0143] Input layer: receives normalized data from multiple sensors, including pressure, depth, temperature, oxygen content, etc.

[0144] Hidden layer: a series of hidden layers, each containing multiple neurons, used to extract high-level features of input data.

[0145] Output layer: predicts the current state or potential risk of the diver, such as safe, needs attention, or dangerous.

[0146] In actual diving, the neural network model can be trained through a large amount of historical data to learn the relationship between the physiological responses of divers under different conditions and environmental changes, for example, when the neural network detects that the oxygen content is gradually decreasing and the pressure is rising sharply within a short period of time, the system can predict that the diver may face the risk of suffocation or decompression sickness, and immediately issue an emergency alert.

[0147] The microprocessor is also used to trigger the alarm module when abnormal data is received. The alarm module issues an audible and visual alarm to alert the diver to potential dangers such as excessive pressure, low oxygen content, or rapid descent. The alarm module can also adjust the intensity and frequency of the alarm according to the level of danger to allow the diver to respond quickly.

[0148] The communication module is used to transmit the diver's status and location information in real time to the monitoring device on the water surface through wireless communication technology. The monitoring device can display the diver's real-time location and status and issue rescue instructions in time when the diver is in a dangerous state.

[0149] The display module includes a screen display unit and a sound prompt unit. The screen display unit is used to display the underwater pressure, depth, temperature, gas composition, location, and motion parameters in real time, allowing the diver to understand their own status and surroundings at any time. The sound prompt unit provides voice prompts or warnings when the diver's vision cannot be focused on the screen, ensuring that the diver can respond to potential dangers in time.

[0150] The diving comprehensive analysis model is also used to adaptively adjust according to the diver's historical data. By continuously learning and optimizing the diving comprehensive analysis model, the prediction accuracy of the diver's state and the reliability of risk assessment are improved, and the safety of the diver is improved. The microprocessor is connected to a cloud platform, which is used to store and analyze the diver's historical data. The cloud platform performs big data analysis on the diving data, generates an accurate diving risk model, and transmits the updated model parameters to the microprocessor, which updates its diving comprehensive analysis model accordingly, improving the overall intelligent level of the system. The entire system realizes comprehensive monitoring of the diving environment and intelligent feedback of the diver's state through seamless data transmission and processing between various modules. The data collected by each sensor is preprocessed, fused and denoised through the Kalman filter model, and then analyzed in depth through the fuzzy logic model and neural network model. Finally, real-time feedback and early warning are generated and informed to the diver through the display and alarm devices. At the same time, the communication module is used to transmit data to the remote monitoring device. With the support of the cloud platform, the system can also adaptively optimize the model parameters according to the diver's historical data, ensuring that the system can provide accurate and safe monitoring and feedback in various diving environments.

[0151] The system provides comprehensive and accurate underwater environment monitoring and safety feedback for divers through real-time data collection by various sensors and intelligent analysis of multiple models; the Kalman filter, fuzzy logic and neural network used by the system provide technical support in noise reduction, fuzzy processing, deep learning and probability inference, ensuring the accuracy of the data and the reliability of the risk prediction; in addition, the adaptive adjustment function of the system and the integration with the cloud platform enable it to continuously learn and optimize, improving its application capability in complex underwater environments; overall, the system significantly improves the safety of diving activities and the comfort of divers, and has wide practical value.

[0152] Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art and related fields without creative labor shall belong to the scope of protection of the present application. The structures, devices and operation methods not specifically described and explained in the present application are implemented according to the conventional means in the art, unless otherwise specified and limited.

Claims

1. An underwater pressure intelligent monitoring and feedback system for scuba diving assistance, characterized by, The device comprises a collection module, a microprocessor, a display module, an alarm module and a communication module. The collection module is used to collect real-time underwater data of the diving equipment during diving through sensors installed in the diving equipment. The microprocessor is used to receive the real-time data collected by the collection module, process and analyze the real-time data, predict the physical condition and diving risk of the diver based on a diving comprehensive analysis model, and generate a prediction result. The display module is used to display the processed pressure, depth, temperature, gas composition, position and motion parameters in real time. The alarm module is used to issue sound and light alarms when an abnormal situation is detected. The communication module is used to send the state and position information of the diver to the monitoring equipment on the water surface. The collection module comprises a pressure sensor, a depth sensor, a temperature sensor, a gas sensor, a positioning sensor and a motion sensor. The microprocessor comprises a data processing unit and a data analysis unit. The data processing unit receives real-time data from the sensors and pre-processes the real-time data. The data analysis unit uses the Kalman filter model to fuse and estimate the state of the pre-processed data packet. First, the key parameters of the Kalman filter are initialized, and the initial state estimation value and error covariance matrix are set. Based on the state estimation of the previous moment and the state transition equation of the system, the system state at the current moment is predicted. The weight of the predicted state and the actual measurement value is determined by calculating the relationship between the prediction error and the measurement error. By combining the actual measurement value with the predicted state, the state estimation at the current moment is updated. Finally, the output result of the Kalman filter includes the state estimation value at the current moment and the corresponding error uncertainty.

2. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 1 wherein: The data analysis unit further comprises a fuzzy logic model for fuzzy reasoning and decision-making on the data packet; The fuzzy logic model converts the data packet into fuzzy language variables including high, medium and low, and provides operation suggestions and warning information for the diver based on this, which are displayed to the diver through the display module; The fuzzy logic model is used for fuzzy reasoning and decision-making on the data packet: Corresponding high, medium and low fuzzy sets are established for different types of data in the data packet, and the corresponding fuzzy values in the corresponding fuzzy set are clearly defined; Then the input data packet is converted into the membership degree in the fuzzy set; Based on expert knowledge and experience, fuzzy rules are defined and a fuzzy rule base is formed, so that the fuzzy rules cover the input combination and its corresponding output decision; The fuzzy rule base is applied to match the fuzzy values after fuzzification with the fuzzy rules in the fuzzy rule base, and the output decision of the fuzzy output is obtained through the Mamdani reasoning mechanism; The output decision obtained by fuzzy reasoning is converted back to an accurate numerical value; The final accurate numerical value, i.e. the decision result, is used to control the specific operation in the system; The actual output of the monitoring system is matched with the expected output decision, and the fuzzy rules are adjusted according to the feedback; The system continuously receives input data and repeatedly executes the above steps to dynamically update the decision-making process.

3. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 2 wherein: The data analysis unit further comprises a neural network model; the neural network model is used to learn the relationship between sensor data and diver state, fuse pressure, depth, temperature, gas composition, position and motion parameters, predict the physical condition and diving risk of the diver, and feed back the prediction result to the diver through the display module and the alarm module.

4. The system for underwater pressure monitoring and feedback for scuba diving assistance as claimed in claim 3 wherein: The microprocessor is also used to trigger the alarm module when receiving abnormal data, and the alarm module issues an audible and visual alarm to remind the diver to pay attention to potential danger; the alarm module can also adjust the intensity and frequency of the alarm according to the danger level.

5. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 4 wherein: The communication module is used to transmit the state and position information of the diver to the monitoring device on the water surface in real time through wireless communication technology; after receiving the information, the monitoring device can display the real-time position and state of the diver, and timely issue a rescue instruction when detecting that the diver is in a dangerous state.

6. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 5 wherein: The display module includes a screen display unit and a sound prompt unit; the screen display unit is used to display the underwater pressure, depth, temperature, gas composition, position and motion parameters in real time; the sound prompt unit is used to provide voice prompts when the diver's vision cannot be concentrated on the screen.

7. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 6 wherein: The diving comprehensive analysis model is also used for adaptive adjustment according to the historical data of the diver, and continuously learns and optimizes the diving comprehensive analysis model.

8. An underwater pressure smart monitoring and feedback system for scuba diving assistance as claimed in claim 7 wherein: The microprocessor is connected with a cloud platform, and the cloud platform is used to store and analyze the historical data of the diver; the cloud platform performs big data analysis on the diving data, generates a precise diving risk model, and transmits the updated model parameters to the microprocessor, which updates its diving comprehensive analysis model accordingly, improving the overall intelligent level of the system.

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