A maritime personnel location tracking system based on satellite communication

Abstract

The present invention relates to the field of marine safety technology, and specifically to a satellite communication-based personnel position tracking system at sea, including a position positioning module, a sinking detection and self-rescue module, a dynamic rescue path planning module, and an emergency communication and early warning module, wherein: the position positioning module tracks the position of personnel in the platform and ship environment in real time; the sinking detection and self-rescue module monitors the water pressure changes around the ship and platform and the hull structure status in real time; the dynamic rescue path planning module dynamically adjusts the rescue path and optimizes the rescue route between platforms or ships; the emergency communication and early warning module automatically sends an early warning signal to the rescue center, including real-time position information, sinking status, and personnel status data. The present invention can not only quickly determine risks, but also automatically guide nearby rescue ships, realizing efficient and safe self-rescue and rescue linkage.

Description

Technical Field

[0001] The present invention relates to the field of marine safety technology, and in particular to a marine personnel position tracking system based on satellite communication. Background Art

[0002] With the increasing number of offshore operations, especially in offshore oil exploration, fishery production and offshore engineering, the safety of marine personnel has become an important task. Under complex sea conditions and severe weather conditions, marine workers face high risks, and emergencies such as accidental falling into the water, hull damage or sinking occur frequently. Therefore, a system that can achieve real-time location tracking, automatic early warning and efficient rescue of marine personnel plays an extremely important role in ensuring personnel safety. In recent years, the development of satellite communication technology and GPS technology has provided new solutions for marine rescue, but the combination and application of these technologies still need to be further optimized.

[0003] The existing maritime rescue system has some shortcomings in technical application. First, the traditional positioning system is easily affected by the complexity of sea conditions and signal weakening, resulting in large errors in real-time tracking of the location of workers. In addition, the existing rescue path planning system cannot be flexibly adjusted according to real-time environmental data and the dynamic state of the ship, resulting in low rescue efficiency and loss of precious rescue time. The triggering of self-rescue equipment also relies on manual operation. When the hull is damaged or the personnel lose consciousness, automated self-rescue cannot be achieved. In addition, the emergency communication and early warning functions are limited, and cannot be effectively combined with real-time data transmission to the rescue center, resulting in untimely rescue response.

[0004] The purpose of the present invention is to provide a maritime personnel position tracking system based on satellite communication, so as to ensure that the rescue mission can be completed efficiently and safely, thereby greatly improving the survival rate of people in distress and the rescue success rate. Summary of the invention

[0005] The invention provides a marine personnel position tracking system based on satellite communication.

[0006] A satellite communication-based marine personnel position tracking system includes a position positioning module, a sinking detection and self-rescue module, a dynamic rescue path planning module, and an emergency communication and early warning module, wherein;

[0007] The position positioning module obtains the real-time geographical location information of offshore workers through GPS and satellite communication technology, and combines the positioning data of ships and platforms to track the positions of personnel in the platform and ship environment in real time;

[0008] The sinking detection and self-rescue module integrates water pressure sensors and acceleration sensors in the equipment worn by ships and offshore workers to monitor the water pressure changes around ships and platforms and the state of the hull structure in real time. When a sinking event is detected, the self-rescue program is automatically triggered, which specifically includes:

[0009] Water pressure and acceleration monitoring: Integrate water pressure sensors and acceleration sensors to monitor the water pressure changes and acceleration status around the equipment worn by ships and offshore workers in real time;

[0010] Sinking detection: Analyze water pressure changes and acceleration status through the sinking detection model to identify sinking or hull damage;

[0011] Self-rescue program triggering: When the ship is identified to be sinking or damaged, a trigger signal is issued to automatically activate life-saving equipment, including automatically inflated life-saving airbags, emergency signal lights, and sound and light alarm devices, and guide nearby rescuers or ships to carry out timely rescue through local signals;

[0012] The dynamic rescue path planning module dynamically adjusts the rescue path based on real-time environmental data, including meteorological data, current speed, wind speed, and the motion status of rescue vessels and platforms, and optimizes the rescue routes between platforms or ships;

[0013] The emergency communication and warning module automatically sends a warning signal to the rescue center when it detects that the position of personnel on the platform or ship is abnormal (such as falling into the water or not moving), the hull structure is damaged or sinking, including real-time position information, sinking status and personnel status data.

[0014] Optionally, the position positioning module includes:

[0015] GPS signal reception: Receive signals from multiple satellites through GPS equipment to obtain the geographic coordinates of offshore workers;

[0016] Combining platform and ship positioning data: Obtaining real-time position data from the positioning devices of ships and platforms and combining it with the geographic coordinates of offshore workers;

[0017] Position data fusion: Through the Kalman filter algorithm, data from GPS, platform and ship positioning devices are integrated in real time to optimize and correct personnel positions.

[0018] Optionally, the water pressure and acceleration monitoring includes:

[0019] Integration and monitoring of water pressure sensors: Install water pressure sensors on ships and personnel wearable equipment to sense the water pressure changes in the surrounding waters in real time, including the magnitude of water pressure changes, water depth changes, and sudden changes in pressure;

[0020] Integration and monitoring of acceleration sensors: Acceleration sensors are integrated into the wearable devices of offshore workers to monitor the movement status of personnel in real time, including acceleration changes (rapid falling, sudden stop or vibration, etc.), motion pattern recognition (such as walking, running), posture changes (such as tilting, flipping), and abnormal stillness (such as losing consciousness or floating without moving after falling into the water).

[0021] Optionally, the sinking detection model adopts a hidden Markov model (HMM), and the hidden Markov model (HMM) includes:

[0022] State space expansion and definition: Expanding the state space into multiple states , including normal, warning, precursor to sinking, sinking, and hull damage;

[0023] Observation value definition and probability distribution: Each observation value (i.e., water pressure change and acceleration change) is data obtained in real time by sensors. Assuming that the observation value follows a normal distribution, the observation probability distribution is calculated by combining the water pressure observation value and the acceleration observation value. ;

[0024] State transfer matrix adjustment: Adjust the state transfer matrix Adjustments are made, and the transition probability between each state depends on the changes in water pressure and acceleration;

[0025] Initial state probability adjustment: For different offshore environments or initial conditions (such as bad weather, platform tilt, etc.), the probability distribution of the initial state Make dynamic adjustments;

[0026] Application of forward-backward algorithm: introduce dynamic noise filtering, calculate forward probability through forward-backward algorithm And the backward probability ;

[0027] Calculation of sinking risk: Calculate the sinking risk value at each moment through forward and backward probabilities .

[0028] Optionally, the self-rescue program triggering includes:

[0029] Sinking or hull damage detection: Sinking risk value calculated by Hidden Markov Model (HMM) To monitor the status of ships and offshore workers, when the sinking risk value Exceeding the preset safety threshold , it is judged that there is a high risk of sinking or hull damage, at this time, it will enter the preparation stage of the self-rescue procedure;

[0030] Sending a trigger signal: If sinking or hull damage is detected, a trigger signal is immediately sent to activate the self-rescue procedure. The trigger signal is automatically generated by the vessel or the equipment worn by the personnel, and the self-rescue operation is started;

[0031] Automatic activation of life-saving equipment: After the trigger signal is issued, the life-saving equipment will be automatically activated, including automatically inflated life-saving airbags (to ensure that people in distress can float to the surface in time to avoid sinking), emergency signal lights (to provide visual alarms to help rescuers find people in distress at a long distance or in dim environments) and sound and light alarm devices (to emit strong sound and light alarms to attract the attention of people around and ensure timely response);

[0032] Send local signals: The self-rescue program sends location signals, including GPS data and real-time status updates, to nearby rescuers or ships through local wireless communication devices to guide them to locate the people in distress and implement rescue operations;

[0033] Guided rescue: Guided by local signals, rescuers or rescue vessels obtain the distress location and respond quickly based on the real-time data provided.

[0034] Optionally, the dynamic rescue path planning module includes:

[0035] Real-time environmental data collection: responsible for collecting real-time data related to the rescue environment, including meteorological data, ocean current speed and wind speed;

[0036] Vessel and platform motion status monitoring: responsible for monitoring the motion status of rescue vessels and target platforms or ships, including their real-time position, speed, heading and the current power status of the ship;

[0037] Dynamic path optimization: Based on real-time environmental data and the movement status of rescue vessels and platforms, the rescue route is dynamically calculated and adjusted using a path optimization algorithm.

[0038] Optionally, the real-time environmental data collection includes:

[0039] Sensor deployment: Install environmental monitoring sensors, including current sensors and anemometers, on rescue vessels, platforms and surrounding waters;

[0040] Environmental data collection and processing: Real-time data on sea weather, current speed and wind speed are collected through weather stations, current sensors and anemometers, and the collected environmental data are processed, including filtering and abnormal data detection.

[0041] Optionally, the vessel and platform motion status monitoring includes:

[0042] Real-time location monitoring: GPS installed on ships and platforms can continuously obtain the real-time geographic location of rescue vessels and target platforms or ships;

[0043] Speed ​​and heading monitoring: Use the inertial navigation system (INS) to monitor the speed and heading of ships and platforms in real time;

[0044] Power status monitoring: By connecting to the ship's power system sensors, the current power status of the ship can be obtained, including the engine's power output, fuel consumption and speed regulation status.

[0045] Optionally, the dynamic path optimization includes:

[0046] Path weight calculation: Calculate the path weight based on the current position and target position of the rescue vessel, combined with real-time environmental data (such as weather, ocean currents, wind speed, etc.) ;

[0047] Shortest path solution: based on calculated path weights , use Dijkstra algorithm to calculate the shortest path from the current position of the rescue ship to the target position;

[0048] Dynamic path adjustment: During the rescue process, if the real-time environmental data changes (such as increased wind speed or changes in ocean currents), the path weight will be adjusted according to the new environmental data. Dynamic updates are performed and the optimal rescue path is recalculated using the Dijkstra algorithm.

[0049] Optionally, the emergency communication and warning module includes:

[0050] Personnel location and hull status monitoring: Through positioning devices worn by personnel and sensors on the hull, the location information of personnel and the status of the hull structure are monitored in real time, including signs of personnel falling into the water, being stationary, or hull structure damage and sinking;

[0051] Generate warning signals: After detecting abnormal situations, warning signals are automatically generated. The warning signals include the real-time geographic location of the people in distress, the damage status of the hull, the sinking situation, and the vital signs and activity status of the people. Abnormal situations include people not moving for more than 5 minutes, fast movement (vertical movement speed exceeds 2 meters / second and lasts for 3 seconds), and sensor data abnormalities (water pressure sensor detects pressure changes exceeding 0.5 bar / second, acceleration sensor detects acceleration of the hull exceeding 2g, and the tilt angle of the hull exceeds 20 degrees);

[0052] Rescue information tracking: After the early warning signal is issued, the on-site situation is continuously monitored and updated, and the latest location information and personnel status are transmitted to the rescue center.

[0053] Beneficial effects of the present invention:

[0054] The present invention realizes accurate real-time positioning of offshore workers by integrating GPS and satellite communication technology and combining the positioning data of ships and platforms, ensuring that reliable position information can be provided even in complex sea conditions or offshore environments when the signal is weak or there is noise interference. This function greatly improves the reliability and adaptability of the maritime rescue system, effectively reduces the search and rescue time, and ensures that people in distress can receive help in a timely manner.

[0055] The present invention, through the sinking detection and self-rescue module, uses water pressure and acceleration sensors to monitor the status of offshore workers and the hull in real time, combines the hidden Markov model to dynamically predict the sinking risk, automatically triggers the self-rescue program and starts the life-saving equipment, which greatly improves the self-rescue ability of people in distress. The module can not only quickly judge the risk, but also automatically guide nearby rescue ships, realizing efficient and safe self-rescue and rescue linkage.

[0056] The present invention uses real-time environmental data and the movement status of the ship and adopts a path optimization algorithm to ensure that the rescue ship can avoid complex or severe sea conditions and reach the distress site in the shortest time. At the same time, when the emergency communication and warning module detects an abnormal situation, it can send a warning signal to the rescue center in real time and provide detailed real-time data support to ensure that the rescue center can make decisions quickly based on the latest on-site conditions, thereby greatly improving the efficiency and success rate of the rescue operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of a sinking detection and self-rescue module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0061] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0062] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0063] like Figure 1-Figure 2 As shown, a satellite communication-based marine personnel position tracking system includes a position positioning module, a sinking detection and self-rescue module, a dynamic rescue path planning module, and an emergency communication and warning module, wherein;

[0064] The location positioning module obtains the real-time geographic location information of offshore workers through GPS and satellite communication technology, and combines the positioning data of ships and platforms to track the location of personnel in the platform and ship environment in real time;

[0065] The sinking detection and self-rescue module integrates water pressure sensors and acceleration sensors in the equipment worn by ships and offshore workers to monitor the water pressure changes around ships and platforms and the status of the hull structure in real time. When a sinking event is detected, the self-rescue program is automatically triggered, including:

[0066] Water pressure and acceleration monitoring: Integrate water pressure sensors and acceleration sensors to monitor the water pressure changes and acceleration status around the equipment worn by ships and offshore workers in real time;

[0067] Sinking detection: Analyze water pressure changes and acceleration status through the sinking detection model to identify sinking or hull damage;

[0068] Self-rescue program triggering: When a sinking or hull damage situation is identified, a trigger signal is issued to automatically activate life-saving equipment, including automatically inflated life-saving airbags, emergency signal lights, and sound and light alarm devices to ensure that people in distress can obtain self-rescue support, and guide nearby rescuers or ships to carry out timely rescue through local signals;

[0069] The dynamic rescue path planning module is based on real-time environmental data, including meteorological data, ocean current speed, wind speed, and combined with the movement status of rescue ships and platforms, to dynamically adjust the rescue path and optimize the rescue routes between platforms or ships to ensure that the rescue ships can reach the location of the people in distress in the shortest time;

[0070] Emergency communication and warning module: When it detects that the position of personnel on the platform or ship is abnormal (such as falling into the water or not moving), or the hull structure is damaged or sinking, it automatically sends a warning signal to the rescue center, including real-time position information, sinking status and personnel status data, to ensure that the rescue command center can respond quickly;

[0071] Through the above contents, accurate real-time positioning of offshore workers, automatic sinking detection and self-rescue, dynamic rescue path optimization and emergency warning functions are realized, ensuring that the system can respond quickly in complex sea conditions or emergency situations, thereby improving the efficiency of maritime rescue and the chance of survival of personnel.

[0072] The location positioning module includes:

[0073] GPS signal reception: Receive signals from multiple satellites through GPS equipment to obtain the geographic coordinates of offshore workers;

[0074] Combining platform and ship positioning data: Obtaining real-time position data from the positioning devices of ships and platforms and combining it with the geographic coordinates of offshore workers to ensure that the specific location of offshore workers can be accurately tracked in the working environment of platforms and ships;

[0075] Position data fusion: Through the Kalman filter algorithm, data from GPS, platform and ship positioning devices are integrated in real time to optimize and correct personnel positions, ensuring accurate position information can still be provided when the signal is weak or the sea conditions are complex;

[0076] The Kalman filter algorithm specifically includes:

[0077] Prediction stage: predict the current state (position) and error covariance, expressed as:

[0078] ;

[0079] in, It’s prediction time The estimated value of the position state, is the state transfer matrix, which is used to convert the previous moment The state is transferred to the moment , It's time The estimated value of the position state, is the control input matrix, is the control vector, which represents the input control quantity (such as the ship's motion state);

[0080] ;

[0081] in, It’s prediction time The error covariance matrix of It's time The error covariance matrix of is the process noise covariance matrix;

[0082] Update phase: Update the state estimate (combining GPS and platform positioning data), expressed as:

[0083] ;

[0084] in, is the updated state estimate, is the Kalman gain, which determines the impact of new observations on the updated estimate, It is at the moment The actual position observed (from GPS, platform, etc.), is the observation matrix;

[0085] ;

[0086] in, is the observation noise covariance matrix;

[0087] .

[0088] Through the above content, GPS and satellite communication technology are integrated, the positioning data of the platform and the ship are combined, and the Kalman filter algorithm is used for multi-source data fusion to ensure that in complex marine environments, even if the signal is unstable or there is noise interference, the system can still provide accurate personnel location tracking and optimize positioning accuracy in real time, making rescue missions more efficient, improving the reliability and adaptability of the system, and is particularly suitable for operations in the open sea and in harsh sea conditions.

[0089] Water pressure and acceleration monitoring includes:

[0090] Integration and monitoring of water pressure sensors: Install water pressure sensors on ships and personnel wearable equipment to sense the water pressure changes in the surrounding waters in real time, including the magnitude of water pressure changes, water depth changes, and sudden changes in pressure;

[0091] Integration and monitoring of acceleration sensors: Acceleration sensors are integrated into the wearable devices of offshore workers to monitor the movement status of workers in real time, including acceleration changes (rapid falling, sudden stop or vibration, etc.), movement pattern recognition (such as walking, running), posture changes (such as tilting, flipping), and abnormal stillness (such as losing consciousness or floating without moving after falling into the water);

[0092] Through the above content, it is possible to monitor the water pressure changes and movement status around offshore workers in real time, provide comprehensive environmental perception and personnel status monitoring. The water pressure sensor can accurately detect water pressure changes, water depth fluctuations and pressure mutations, and quickly identify sinking risks. The acceleration sensor can monitor personnel's acceleration changes, posture adjustments and movement patterns, and identify dangerous situations such as abnormal stillness. Through the combination of these two sensors, the risk status of personnel can be efficiently judged, ensuring timely self-rescue and rescue response, which greatly improves safety and monitoring accuracy.

[0093] The sinking detection model adopts the hidden Markov model (HMM), which includes:

[0094] State space expansion and definition: Expanding the state space into multiple states , including normal, warning, sinking precursor, sinking, and hull damage, expressed as:

[0095] ;

[0096] in, is the normal state (stable water pressure and acceleration), It is a warning state (slight fluctuation), It is a precursor to sinking (dramatic water pressure or acceleration changes). is sinking (large water pressure or acceleration changes), Indicates the hull damage status;

[0097] Observation value definition and probability distribution: Each observation value (i.e., water pressure change and acceleration change) is data obtained in real time by sensors. Assuming that the observation value obeys normal distribution, in order to better adapt to the complexity of the offshore environment and increase noise processing capabilities, the observation probability distribution is calculated by combining the water pressure observation value and the acceleration observation value. , expressed as:

[0098] ;

[0099] in, is the observation vector, For the The water pressure observation value at time, For the The observed value of acceleration at the time;

[0100] ;

[0101] in, is the observed probability distribution, Status The mean vector under , Status The covariance matrix below represents the noise and correlation of water pressure and acceleration;

[0102] State transfer matrix adjustment: Adjust the state transfer matrix Adjustment is made, and the transition probability between each state depends on the change of water pressure and acceleration, which is expressed as:

[0103] ;

[0104] in, is the state transfer matrix, which represents the transition probability of the system between different states. Indicates that the system is at time In state In the case of Still in state The probability of transitioning from the normal state back to the normal state, Indicates that the system is at time In state (normal state), the next moment Transfer to state The probability of transitioning from the normal state to the warning state, represents the probability of the system transferring from the normal state to the pre-sinking state, represents the probability that the system directly transfers from the normal state to the sinking state, It represents the probability of the system returning to the normal state from the warning state. represents the probability that the system remains in the warning state, represents the probability of the system transferring from the warning state to the sinking precursor state, represents the probability of the system transferring from the warning state to the sinking state, represents the probability of the system returning to the normal state from the pre-sinking state, represents the probability of the system returning from the sinking precursor state to the warning state, represents the probability that the system remains unchanged in the state before sinking, represents the probability of the system transitioning from the pre-sinking state to the sinking state, represents the probability of the system returning to the normal state from the sinking state, represents the probability of the system returning to the warning state from the sinking state, represents the probability of the system returning from the sinking state to the pre-sinking state, represents the probability that the system remains unchanged in the sinking state, From normal state Transfer directly to hull damage state The probability of Indicates that the warning state Transfer to hull damage state The probability of Indicates that from the state of sinking Transfer to hull damage state The probability of Indicates damage status from hull Transfer to sinking state The probability of Indicates that the hull is in a damaged state The probability of remaining unchanged is Indicates damage status from hull Return to normal state The probability of Indicates damage status from hull Restore to warning state The probability of Indicates damage status from hull Restored to the state before sinking The probability of Indicates damage status from hull Return to sunken state probability;

[0105] Initial state probability adjustment: For different offshore environments or initial conditions (such as bad weather, platform tilt, etc.), the probability distribution of the initial state Dynamic adjustment, for example, when the sensor detects abnormal wind speed and sea current, the initial state can be adjusted to the warning state , rather than the normal state , expressed as:

[0106] ;

[0107] in, represents the probability distribution of the initial state, which indicates the probability of the system being in each state at the initial moment. Indicates that the system is in state at the initial moment The probability of Corresponding to different states respectively;

[0108] Application of forward-backward algorithm: introduce dynamic noise filtering, calculate forward probability through forward-backward algorithm And the backward probability , ensuring accurate prediction in the presence of noise, expressed as:

[0109] ;

[0110] in, Indicates time Observed values ​​(probably referring to the observed data of water pressure and acceleration), Indicates that the system is at time In state , is the total number of all hidden states, For at the moment In state When we observe that arrive The probability of the observation sequence of the previous moment The forward probability of From the state Transfer to state The transition probability represents the system from time Status Transfer to the moment Status The probability of Status The observation probability under dynamic noise filtering is achieved by adjusting the observation probability at each moment. , increase noise tolerance, thereby improving the robustness of detection, It is the implicit state of the system, representing different internal states, representing different sea conditions or sinking states (such as normal, warning, sinking precursor, sinking, hull damage);

[0111] ;

[0112] in, is the backward probability, indicating that at time In state In the case of arrive The observation sequence of The probability of occurrence, From the state Transfer to state The transition probability of the system at time In state After transfer to time Status The probability of For the status Next, observe the time Observed value The probability of state For the observed value The ability to explain, is the backward probability, indicating that at time In state Under the condition of To the final moment The observation sequence of Probability of occurrence;

[0113] Calculation of sinking risk: Calculate the sinking risk value at each moment through forward and backward probabilities , expressed as:

[0114] ;

[0115] Transition probability It is expressed as:

[0116] ;

[0117] in, Indicates from the state Transfer to state The probability of For water pressure changes, is the acceleration change, and is the weight coefficient;

[0118] Weight coefficient and The setting basis mainly depends on the actual impact of water pressure changes and acceleration changes on the state transition probability. By analyzing historical data and physical scenarios, the contribution of these two factors to sinking or hull damage is determined. It has a greater impact on the sinking risk, so it is set is 0.6, the acceleration changes The effect on sinking is relatively small, so set is 0.4;

[0119] Through the above content, it is possible to dynamically handle changes in water pressure and acceleration during offshore operations, and combine real-time data to accurately predict the risk of sinking or hull damage. This not only improves the system's adaptability to complex sea conditions, but also enables it to self-adjust according to environmental changes, thereby providing more flexible and reliable monitoring and early warning functions to ensure the safety of personnel at sea and the stability of the ship.

[0120] Self-rescue procedure triggers include:

[0121] Sinking or hull damage detection: Sinking risk value calculated by Hidden Markov Model (HMM) To monitor the status of ships and offshore workers, when the sinking risk value Exceeding the preset safety threshold , it is judged that there is a high risk of sinking or hull damage, at this time, it will enter the preparation stage of the self-rescue procedure;

[0122] Sending a trigger signal: If sinking or hull damage is detected, a trigger signal is immediately sent to activate the self-rescue procedure. The trigger signal is automatically generated by the vessel or the equipment worn by the personnel, and the self-rescue operation is started;

[0123] Automatic activation of life-saving equipment: After the trigger signal is issued, the life-saving equipment will be automatically activated, including automatically inflated life-saving airbags (to ensure that people in distress can float to the surface in time to avoid sinking), emergency signal lights (to provide visual alarms to help rescuers find people in distress at a long distance or in dim environments) and sound and light alarm devices (to emit strong sound and light alarms to attract the attention of people around and ensure timely response);

[0124] Send local signals: The self-rescue program sends location signals, including GPS data and real-time status updates, to nearby rescuers or ships through local wireless communication devices to guide them to locate the people in distress and implement rescue operations;

[0125] Guided rescue: Under the guidance of local signals, rescuers or rescue ships obtain the distress location and respond quickly based on the real-time data provided to ensure timely rescue and relief operations and maximize the safety of personnel;

[0126] Safety threshold Based on historical data, it is set up, including:

[0127] Collect historical data: Collect a large amount of historical data, including actual cases of multiple sinkings or hull damage, especially the changes in water pressure and acceleration under different states (such as normal state, warning state, sinking precursor state, sinking state, hull damage state), and collect the risk values ​​generated by the system and the time when the accident occurred;

[0128] Calculate the risk value distribution: For each historical data point, use the Hidden Markov Model (HMM) to calculate the corresponding risk value , establish a complete risk value distribution, these risk values ​​can be counted according to the frequency of events, and the risk value range corresponding to different states can be obtained;

[0129] Determine threshold interval: By analyzing the risk values ​​before sinking or hull damage in history , set a higher percentile (such as 95%) based on the experience percentile, that is, select the events corresponding to more than 95% of the historical data The value is used as a safety threshold and is expressed as:

[0130] ;

[0131] in, It means that among all historical events, 95% of the risk values ​​are lower than this value;

[0132] Error adjustment: adjust the threshold according to the actual rescue conditions (such as rescue response time, equipment response speed), and appropriately increase or decrease the threshold in combination with the redundant design and safety standards in ship operation. , to reduce the risk of false positives or false negatives;

[0133] Verification and optimization: Finally, the threshold is repeatedly verified based on the real-time data collected. The rationality of the threshold is further optimized through simulation or actual exercises to ensure that it can still accurately identify the risk of sinking or hull damage under different environmental conditions.

[0134] Through the above content, when the system detects sinking or hull damage, it can automatically and quickly start the life-saving equipment. The self-rescue program ensures that people in distress can be rescued in time through automatically inflated life-saving airbags, emergency signal lights and sound and light alarm devices. At the same time, it guides nearby rescue forces to respond quickly through local signals, which significantly improves the efficiency of responding to emergencies, minimizes the risk of casualties, and provides valuable time for rescue.

[0135] The dynamic rescue path planning module includes:

[0136] Real-time environmental data collection: responsible for collecting real-time data related to the rescue environment, including meteorological data, ocean current speed and wind speed;

[0137] Vessel and platform motion status monitoring: responsible for monitoring the motion status of rescue vessels and target platforms or ships, including their real-time position, speed, heading and the current power status of the ship;

[0138] Dynamic path optimization: Based on real-time environmental data and the movement status of rescue vessels and platforms, the rescue route is dynamically calculated using a path optimization algorithm, and the rescue route is adjusted to ensure that the vessel can reach the distress location in the shortest time and avoid bad weather or complex sea conditions;

[0139] Through the above content, it is ensured that rescue ships can reach the distress site safely in the shortest time under complex sea conditions, while avoiding bad weather or dangerous areas, thereby improving rescue efficiency and success rate, minimizing rescue time, and ensuring the safety of people in distress.

[0140] Real-time environmental data collection includes:

[0141] Sensor deployment: Install environmental monitoring sensors, including current sensors and anemometers, on rescue vessels, platforms and surrounding waters;

[0142] Environmental data collection and processing: collect real-time data on sea weather, current speed and wind speed through weather stations, ocean current sensors and anemometers, and process the collected environmental data, including filtering and abnormal data detection;

[0143] The filtering adopts exponential smoothing algorithm, which is expressed as:

[0144] ;

[0145] in, It's time The smoothed value of (filtered data), It's time The observed values ​​(original data), It's time The smoothed value of is the smoothing coefficient, and its value range is ;

[0146] Abnormal data detection uses the triple standard deviation method, which is expressed as:

[0147] ;

[0148] ;

[0149] in, It is data points, is the data mean, is the data standard deviation. If a data point satisfy , it is considered as abnormal data;

[0150] Through the above content, the sensor system deployed on rescue ships, platforms and sea areas can obtain key information such as weather, current speed and wind speed in real time and accurately, and process the data in combination with filtering algorithms to ensure the stability and reliability of the data. It can not only reflect the dynamic changes of the environment in a timely manner, but also provide real-time support for rescue route planning, improve the accuracy and efficiency of rescue operations, and help to make accurate decisions in complex sea conditions.

[0151] Vessel and platform motion status monitoring includes:

[0152] Real-time location monitoring: GPS installed on ships and platforms can continuously obtain the real-time geographic location of rescue vessels and target platforms or ships;

[0153] Speed ​​and heading monitoring: Use the inertial navigation system (INS) to monitor the speed and heading of ships and platforms in real time;

[0154] Power status monitoring: By connecting to the ship's power system sensors, the current power status of the ship can be obtained, including the engine's power output, fuel consumption, and speed regulation status;

[0155] Through the above content, the movement of ships and platforms can be accurately grasped, providing real-time data support for the optimization of rescue routes, improving the efficiency and accuracy of rescue operations, and ensuring that ships can reach the target location quickly and safely.

[0156] Dynamic path optimization includes:

[0157] Path weight calculation: Calculate the path weight based on the current position and target position of the rescue vessel, combined with real-time environmental data (such as weather, ocean currents, wind speed, etc.) , expressed as:

[0158] ;

[0159] in, For slave nodes To Node The path weight, For Node and nodes The distance between is the weight coefficient of environmental data impact, which is used to balance distance and environmental impact. node Environmental factors (such as wind speed and ocean current speed) affect the difficulty of navigation;

[0160] Shortest path solution: based on calculated path weights , use Dijkstra algorithm to calculate the shortest path from the current position of the rescue ship to the target position, expressed as:

[0161] ;

[0162] in, From the starting point to the node The shortest path distance, For Node To Node The path weight takes into account distance and environmental influences. To reach the node from the starting point The shortest path distance;

[0163] Dynamic path adjustment: During the rescue process, if the real-time environmental data changes (such as increased wind speed or changes in ocean currents), the path weight will be adjusted according to the new environmental data. Dynamically update and recalculate the optimal rescue path and weights through the Dijkstra algorithm Dynamic updates are expressed as:

[0164] ;

[0165] in, is the updated path weight, Environmental impact factors updated in real time;

[0166] Environmental factors The calculation includes:

[0167] Identify key environmental factors: The main environmental factors affecting the navigation of rescue vessels include wind speed. (Strong winds may slow down the ship and make navigation more difficult), current speed (Upstream will increase the time it takes for a ship to travel, downstream will increase the speed of the ship), wave height (Higher waves may increase the danger of boating);

[0168] Assign weights to each factor: Different environmental factors have different effects on ship navigation, and each factor needs to be assigned a weight To reflect its importance, the weight can be determined based on historical data, expert experience or statistical analysis;

[0169] Quantify each environmental factor: Based on real-time monitoring data, quantify the impact of each environmental factor into numerical form, and use normalization to convert environmental data of different dimensions into a unified numerical range;

[0170] Calculate the comprehensive environmental impact factor: sum all influencing factors according to their respective weights to obtain the final comprehensive environmental impact factor , expressed as:

[0171] ;

[0172] in, , , is the weight of each environmental factor, , , Quantify the impact of wind speed, current and waves on navigation respectively;

[0173] Weight of environmental factors , , The setting basis mainly depends on the importance of various environmental factors (such as wind speed, current speed, wave height) on the navigation of rescue ships. The specific values ​​include:

[0174] Under normal conditions: wind speed weight is 0.4, the current speed weight is 0.4, and the wave height weight is 0.2;

[0175] When the wind speed increases significantly (such as a rescue ship sailing in an area with strong winds): Wind speed weight is 0.6, the current speed weight is 0.3, and the wave height weight is 0.1;

[0176] When the current speed is against the current: wind speed weight is 0.3, the ocean current speed weight is 0.6, and the wave height weight is 0.1;

[0177] Quantification of the impact of wind speed, current and waves on navigation , , Each environmental factor is normalized to convert them into a uniform numerical range.

[0178] Through the above content, it is ensured that the rescue ships can safely reach the target location in the shortest time. By using a comprehensive path optimization algorithm, the rescue route can be flexibly adjusted according to environmental changes, the negative impact of the environment on navigation can be reduced, and the rescue efficiency and success rate can be improved, especially in complex or severe sea conditions, which significantly improves the reliability and safety of rescue operations.

[0179] Emergency communication and warning module includes:

[0180] Personnel location and hull status monitoring: Through positioning devices worn by personnel and sensors on the hull, the location information of personnel and the status of the hull structure are monitored in real time, including signs of personnel falling into the water, being stationary, or hull structure damage and sinking;

[0181] Generate warning signals: After detecting abnormal situations, warning signals are automatically generated. The warning signals include the real-time geographic location of the people in distress, the damage status of the hull, the sinking situation, and the vital signs and activity status of the people. Abnormal situations include people not moving for more than 5 minutes, fast movement (vertical movement speed exceeds 2 meters / second and lasts for 3 seconds), and sensor data abnormalities (water pressure sensor detects pressure changes exceeding 0.5 bar / second, acceleration sensor detects acceleration of the hull exceeding 2g, and the tilt angle of the hull exceeds 20 degrees);

[0182] Rescue information tracking: After the warning signal is issued, the on-site situation is continuously monitored and updated, and the latest location information and personnel status are transmitted to the rescue center to ensure that rescuers can carry out rescue operations based on the latest situation;

[0183] Through the above content, emergency situations such as falling into the water, being stationary for a long time or hull damage can be quickly identified, and early warning signals can be automatically generated, containing detailed real-time data (such as location, vital signs and hull status), ensuring that the rescue center can respond quickly and take effective rescue actions based on the latest information, greatly improving rescue efficiency and the safety of people in distress.

[0184] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0185] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A satellite communication-based marine personnel location tracking system, characterized in that: It includes a location positioning module, a sinking detection and self-rescue module, a dynamic rescue path planning module, and an emergency communication and warning module, among which; The position positioning module obtains the real-time geographical location information of offshore workers through GPS and satellite communication technology, and combines the positioning data of ships and platforms to track the positions of personnel in the platform and ship environment in real time; The sinking detection and self-rescue module integrates water pressure sensors and acceleration sensors in the equipment worn by ships and offshore workers to monitor the water pressure changes around ships and platforms and the state of the hull structure in real time. When a sinking event is detected, the self-rescue program is automatically triggered, which specifically includes: Water pressure and acceleration monitoring: Integrate water pressure sensors and acceleration sensors to monitor the water pressure changes and acceleration status around the equipment worn by ships and offshore workers in real time; Sinking detection: The water pressure change and acceleration state are analyzed through the sinking detection model to identify the sinking or hull damage. The sinking detection model adopts the hidden Markov model, which includes: State space expansion and definition: Expand the state space into multiple states S, including normal, warning, sinking precursor, sinking, and hull damage; Observation value definition and probability distribution: Each observation value is the data obtained by the sensor in real time. Assuming that the observation value follows the normal distribution, the observation probability distribution P(O t |S i ); State transfer matrix adjustment: adjust the state transfer matrix A. The transition probability between each state depends on the changes in water pressure and acceleration. Initial state probability adjustment: Dynamically adjust the probability distribution π of the initial state for different marine environments or initial conditions; Application of forward-backward algorithm: Introduce dynamic noise filtering, calculate the forward probability α through forward-backward algorithm calculation t (i) and the backward probability β t (i); Calculation of sinking risk: Calculate the sinking risk value R at each moment through forward and backward probabilities t ; Self-rescue program triggering: When the ship is identified to be sinking or damaged, a trigger signal is issued to automatically start the life-saving equipment, including automatically inflated life-saving airbags, emergency signal lights, and sound and light alarm devices, and guide nearby rescuers or ships to carry out timely rescue through local signals. The self-rescue program triggering includes: Sinking or hull damage detection: Sinking risk value R calculated by hidden Markov model t To monitor the status of ships and offshore workers, when the sinking risk value R t If the preset safety threshold θ is exceeded, it is judged that there is a high risk of sinking or hull damage. At this time, the preparation stage of the self-rescue procedure will be entered; Sending a trigger signal: If sinking or hull damage is detected, a trigger signal is immediately sent to activate the self-rescue procedure. The trigger signal is automatically generated by the vessel or the equipment worn by the personnel, and the self-rescue operation is started; Automatic activation of life-saving equipment: After the trigger signal is issued, the life-saving equipment will be automatically activated, including the automatically inflated life-saving airbag, emergency signal light and sound and light alarm device; Send local signals: The self-rescue program sends location signals, including GPS data and real-time status updates, to nearby rescuers or ships through local wireless communication devices to guide them to locate the people in distress and implement rescue operations; Guided rescue: Guided by local signals, rescuers or rescue ships obtain the location of the distressed person and respond quickly based on the real-time data provided; The dynamic rescue path planning module dynamically adjusts the rescue path based on real-time environmental data, including meteorological data, current speed, wind speed, and the motion status of rescue vessels and platforms, and optimizes the rescue routes between platforms or ships; The emergency communication and warning module automatically sends a warning signal to the rescue center when it detects that the personnel position on the platform or ship is abnormal, the hull structure is damaged or sinking, including real-time position information, sinking status and personnel status data.

2. A satellite communication-based marine personnel location tracking system according to claim 1, characterized in that: The position positioning module comprises: GPS signal reception: Receive signals from multiple satellites through GPS equipment to obtain the geographic coordinates of offshore workers; Combining platform and ship positioning data: Obtaining real-time position data from the positioning devices of ships and platforms and combining it with the geographic coordinates of offshore workers; Position data fusion: Through the Kalman filter algorithm, data from GPS, platform and ship positioning devices are integrated in real time to optimize and correct personnel positions.

3. The satellite communication-based marine personnel location tracking system according to claim 1, characterized in that: The water pressure and acceleration monitoring includes: Integration and monitoring of water pressure sensors: Install water pressure sensors on ships and personnel wearable equipment to sense the water pressure changes in the surrounding waters in real time, including the magnitude of water pressure changes, water depth changes, and sudden changes in pressure; Integration and monitoring of acceleration sensors: Acceleration sensors are integrated into the wearable equipment of offshore workers to monitor the movement status of personnel in real time, including acceleration changes, motion pattern recognition, posture changes, and abnormal stillness.

4. The satellite communication-based marine personnel location tracking system according to claim 1, characterized in that: The dynamic rescue path planning module includes: Real-time environmental data collection: responsible for collecting real-time data related to the rescue environment, including meteorological data, ocean current speed and wind speed; Vessel and platform motion status monitoring: responsible for monitoring the motion status of rescue vessels and target platforms or ships, including their real-time position, speed, heading and the current power status of the ship; Dynamic path optimization: Based on real-time environmental data and the movement status of rescue vessels and platforms, the rescue route is dynamically calculated and adjusted using a path optimization algorithm.

5. A satellite communication-based marine personnel location tracking system according to claim 4, characterized in that: The real-time environmental data collection includes: Sensor deployment: Install environmental monitoring sensors, including current sensors and anemometers, on rescue vessels, platforms and surrounding waters; Environmental data collection and processing: Real-time data on sea weather, current speed and wind speed are collected through weather stations, current sensors and anemometers, and the collected environmental data are processed, including filtering and abnormal data detection.

6. A satellite communication-based marine personnel location tracking system according to claim 5, characterized in that: The vessel and platform motion status monitoring includes: Real-time location monitoring: GPS installed on ships and platforms can continuously obtain the real-time geographic location of rescue vessels and target platforms or ships; Speed ​​and heading monitoring: Use inertial navigation systems to monitor the speed and heading of ships and platforms in real time; Power status monitoring: By connecting to the ship's power system sensors, the current power status of the ship can be obtained, including the engine's power output, fuel consumption and speed regulation status.

7. A satellite communication-based marine personnel location tracking system according to claim 6, characterized in that: The dynamic path optimization includes: Path weight calculation: Calculate the path weight w(i,j) based on the current position and target position of the rescue vessel and real-time environmental data; Shortest path solution: Based on the calculated path weight w(i,j), the Dijkstra algorithm is used to calculate the shortest path from the current location of the rescue ship to the target location; Dynamic path adjustment: During the rescue process, if the real-time environmental data changes, the path weight w(i, j) is dynamically updated according to the new environmental data, and the optimal rescue path is recalculated using the Dijkstra algorithm.

8. The satellite communication-based marine personnel location tracking system according to claim 1, characterized in that: The emergency communication and warning module includes: Personnel location and hull status monitoring: Through positioning devices worn by personnel and sensors on the hull, the location information of personnel and the status of the hull structure are monitored in real time, including signs of personnel falling into the water, being stationary, or hull structure damage and sinking; Generate warning signals: After detecting abnormal situations, warning signals are automatically generated. The warning signals include the real-time geographic location of the people in distress, the damage status of the hull, the sinking situation, and the vital signs and activity status of the people. Abnormal situations include people not moving for more than 5 minutes, moving quickly, and abnormal sensor data; Rescue information tracking: After the early warning signal is issued, the on-site situation is continuously monitored and updated, and the latest location information and personnel status are transmitted to the rescue center.

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