Portable intelligent life jacket capable of automatically triggering a distress signal

Abstract

This invention discloses a portable smart life jacket that automatically triggers a distress signal, belonging to the marine engineering equipment industry. The portable smart life jacket includes a life jacket body with a sealed waterproof compartment fixedly installed on the shoulder of the body. An environmental sensing module, a data processing module, and a wireless communication module are integrated within the life jacket body and the waterproof compartment. This invention solves the problem that existing life jackets rely on manual triggering and cannot promptly send a distress signal when a person is in distress, unconscious, or unable to operate the jacket. This invention uses the environmental sensing module to collect user status data in real time, automatically analyzes and makes decisions through multi-level intelligent triggering logic preset in the data processing module, and finally, autonomously sends distress signals of different levels to the rescue network via the wireless communication module, which has retransmission and optimization mechanisms. This achieves full automation from the awareness of danger to the successful transmission of the distress message.

Description

Technical Field

[0001] This invention relates to the field of marine engineering equipment technology, specifically a portable intelligent life jacket that automatically triggers a distress signal. Background Technology

[0002] In water activities, water rescue, and firefighting water rescue scenarios, timely and effective distress signals are crucial for rescue operations when people face dangers such as drowning or falling into the water. However, most existing life jackets rely on manual triggering of distress signals, which is inconvenient to operate and prone to failure to send distress signals in time due to factors such as unconsciousness. Therefore, to address these needs, we propose a portable smart life jacket that automatically triggers distress signals. Summary of the Invention

[0003] The purpose of this invention is to provide a portable smart life jacket that automatically triggers a distress signal. By integrating environmental perception, intelligent data processing, and wireless communication modules, and based on preset multi-level triggering logic, it automatically determines whether the user meets the distress signal triggering conditions. When the distress signal triggering conditions are met, a distress command is immediately generated, thereby automatically sending a distress signal to an external rescue center and a designated terminal, thus solving the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a portable smart life jacket that automatically triggers a distress signal, comprising a life jacket body, a sealed waterproof chamber fixedly disposed on the shoulder of the life jacket body, and an environmental sensing module, a data processing module and a wireless communication module integrated in the life jacket body and the waterproof chamber;

[0005] The environmental perception module is integrated into the life jacket body and is configured to monitor the status data of the user's environment in real time through various intelligent sensors. The status data includes water depth, water pressure, immersion status, acceleration, attitude, and user position data.

[0006] The data processing module is located inside the waterproof compartment and is electrically connected to the environmental sensing module. It is configured to receive and analyze the status data, determine whether the distress signal triggering conditions are met based on a preset multi-level triggering logic, and immediately generate distress commands of different levels and send them to the wireless communication module when the distress signal triggering conditions are met.

[0007] The wireless communication module is located inside the waterproof compartment and is electrically connected to the data processing module. It is configured to automatically send distress signals to external rescue centers and designated terminals after receiving distress commands of different levels.

[0008] Furthermore, the data processing module includes:

[0009] The data fusion module is configured to receive state data from the environment perception module, perform data fusion using a Kalman filter algorithm and timestamp alignment technology, and generate a multi-dimensional state vector with a unified timestamp.

[0010] The logic judgment module is configured to perform serialization analysis on the fused multi-dimensional state vector based on preset multi-level triggering logic. The multi-level triggering logic includes:

[0011] Water immersion trigger: When the environmental sensing module detects that continuous water immersion exceeds a first preset duration and the water depth exceeds a first preset threshold, a primary alarm is triggered;

[0012] Disruption Trigger: If the environmental perception module detects that the user is in a non-upright posture for a second preset period of time while the immersion trigger condition is met, a medium-level alarm is triggered.

[0013] Impact trigger: If the environmental perception module detects that the acceleration momentarily exceeds the preset impact threshold and subsequently meets the immersion trigger condition, the highest level alarm will be triggered immediately and the distress command will be generated.

[0014] The instruction generation module is configured to receive primary, intermediate, and highest-level alarm flags from the logic judgment module, trigger the light alarm on the light strip on the life jacket body, and simultaneously generate a distress instruction containing corresponding warning information based on the primary, intermediate, and highest-level alarm flags and send it to the wireless communication module.

[0015] Furthermore, the logic judgment module performs correlation analysis when processing different alarm levels. When a primary alarm has been triggered and the user is in a non-upright posture but has not yet reached the duration threshold of the intermediate alarm, the logic judgment module needs to closely monitor the acceleration data. If the acceleration momentarily exceeds the preset impact threshold at this time, it will immediately combine the primary alarm status and directly trigger the highest level alarm.

[0016] Furthermore, the instruction generation module is further configured as follows:

[0017] Different sending priorities are set for distress commands of different alarm levels, with distress commands of the highest alarm level having the highest priority;

[0018] Upon receiving the highest level alarm flag, the emergency transmission mode will be activated immediately. At this time, the instruction generation module will automatically activate a special communication protocol to prioritize the acquisition of wireless communication channel resources.

[0019] Meanwhile, the instruction generation module will also optimize the configuration of the wireless communication module, including adjusting the transmission power to the maximum allowable range, in order to enhance signal strength and coverage.

[0020] In addition, the command generation module will embed a unique emergency identification code in the distress command of the highest level alarm, so that external rescue centers and designated terminals can identify and prioritize this signal.

[0021] Furthermore, the environment perception module includes:

[0022] The data acquisition module is configured to monitor real-time status data, including water depth, water pressure, immersion status, acceleration, attitude, and user position, through multiple smart sensors integrated on the life jacket body.

[0023] The data acquisition module is designed with a clock synchronization circuit to provide a unified time reference for all smart sensors. The data acquisition module adopts a distributed data acquisition architecture, which divides the smart sensors into at least two or more independent acquisition nodes. Each acquisition node independently acquires data and transmits it to the data processing module.

[0024] The intelligent processing module is configured to perform preliminary processing and feature extraction on the collected status data, and automatically adjust the data processing strategy according to the user's current usage scenario, which includes, but is not limited to, swimming, surfing, and falling into the water.

[0025] Furthermore, the intelligent processing module executes the following process:

[0026] The status data undergoes preliminary processing including data filtering, noise reduction, and formatting.

[0027] From the pre-processed state data, feature vectors for judging the user's dangerous state are extracted in real time. The feature vectors include, but are not limited to, sinking rate, attitude change frequency, peak impact acceleration and duration.

[0028] Based on the extracted feature vectors, the user's current usage scenario is dynamically identified, and the data processing strategy is adaptively adjusted based on the identified scenario, including:

[0029] When the scene is identified as a surfing scene, the first strategy is activated to increase the weight of high-frequency signal components in the acceleration data.

[0030] When the scene is identified as a swimming scenario, the second strategy is activated, which focuses on the stability and periodic changes of the posture data.

[0031] When a still water immersion scenario is identified, a third strategy is activated to enhance the monitoring of sudden changes in water depth and continuous immersion.

[0032] Furthermore, the wireless communication module is further configured as follows:

[0033] After each distress signal is sent, a confirmation timer is started, waiting for confirmation receipts from external rescue centers and designated terminals;

[0034] Based on the confirmation duration recorded by the confirmation timer, if no confirmation response is received from the rescue center or designated terminal within the specified time, a retransmission mechanism is triggered. This retransmission mechanism includes:

[0035] Try retransmitting via another backup communication channel and gradually increase the retransmission interval until an acknowledgment is received or the maximum number of retransmissions is reached.

[0036] When the retransmission mechanism is triggered, the wireless communication module intelligently selects a retransmission strategy based on the current communication environment and historical communication records.

[0037] The retransmission interval not only increases gradually according to the alarm level, but also adapts to the current communication status. If the signal quality of the communication channel is gradually improved during the retransmission process, the retransmission interval is appropriately shortened. Conversely, if the signal quality continues to deteriorate, the retransmission interval will be further extended.

[0038] Furthermore, before sending a distress signal, the wireless communication module compresses and redundantly encodes the data packets to be sent. After the communication connection is established, it dynamically schedules the content and frequency of data transmission based on the bandwidth and stability of the connection. In the initial stage of the connection, it prioritizes sending a simplified data packet containing the user ID, user location data, and alarm level. After the connection stabilizes, it then sends an extended data packet containing the user's vital signs and ambient water temperature.

[0039] Furthermore, the data processing module also includes a peak synchronization transmission control unit, configured to execute an anti-obstruction transmission timing optimization method based on microelectromechanical inertial sensing, the method comprising the following steps:

[0040] Step 1: Vertical Kinematic Reconstruction

[0041] The control unit receives real-time triaxial acceleration data from the environmental sensing module, and extracts the vertical acceleration component after separating the gravity component using attitude quaternions. ;

[0042] An adaptive bandpass filter with a center frequency of 0.05Hz to 0.5Hz is used for... The data is processed to filter out sensor zero-bias drift and high-frequency impact noise, and the filtered data is then subjected to a second numerical integration to calculate the estimated vertical displacement of the user relative to the local sea level in real time. and vertical velocity estimation ;

[0043] Step 2: Sea state energy parameterization

[0044] Control unit based on The historical time series is used to calculate the statistical variance within the sliding window, thereby estimating the current effective wave height parameter in real time. , used to characterize the energy level of the current sea state;

[0045] Step 3: Transmission Opportunity Score Calculation

[0046] The control unit uses a preset weighting algorithm to calculate the channel smoothness prediction score in real time. The calculation formula is defined as follows:

[0047]

[0048] in: Estimated vertical displacement (unit: meters) for real-time calculation; Estimated vertical velocity for real-time calculation (unit: meters per second); The effective wave height (in meters) is estimated in real time. is a non-zero regularization constant used to avoid operational singularities under static water conditions (unit: meter). Normalized frequency factor based on wave dominant frequency (unit: ); This is the cumulative waiting time since the last successful transmission or alarm trigger; It is a natural exponential function; It is a monotonically increasing urgency compensation function, configured to gradually increase the scoring benchmark over time; , , These are dimensionless weighting coefficients, corresponding to the weights of geometric line-of-sight advantage, phase stability, and transmission urgency, respectively.

[0049] Step 4: Adaptive Triggering

[0050] The control unit will calculate in real time With the preset transmission threshold Compare;

[0051] Only when Only then does the data processing module send a transmit enable command to the wireless communication module, driving the RF power amplifier to release the compressed distress data packet during the wave crest window, thereby maximizing the clearance height of the Fresnel zone and reducing signal fading caused by wave obstruction.

[0052] Furthermore, the wireless communication module is further configured with a dynamic cooperative relay mode for constructing a phase diversity communication link in multi-user water-fall scenarios. The logic control flow of this mode includes:

[0053] Step A: Neighbor Discovery and Status Broadcast

[0054] When the environmental perception module detects beacon signals from sources other than the smart life jacket in the surrounding area, it automatically activates the short-range self-organizing network protocol.

[0055] Each smart life jacket transmits its own ID and a currently calculated transmission opportunity score to neighboring nodes via a short-range communication link at a set broadcast period. Status data packets;

[0056] Step B: Dynamic Master-Slave Role Election

[0057] The data processing module executes a dynamic topology control algorithm based on wave phase, and processes the received neighbor node data. Value and its own Values ​​are compared in real time:

[0058] Relay Master Node Determination: If its own... If the value is at its maximum in the local communication cluster and exceeds the preset line-of-sight threshold, the device is determined to be in a peak position and automatically switches to master relay mode.

[0059] Collaboration is determined by the slave node: if its own... If the value is lower than the maximum value of the neighboring nodes, it is determined that it is currently in a trough occlusion position, and the device automatically switches to cooperative slave mode;

[0060] Step C: Layered data transmission

[0061] Life jackets in cooperative slave mode temporarily suppress the transmission of long-distance distress signals to reduce power consumption, and send their own distress data packets to the device identified as the relay master node via a short-range link;

[0062] The life jacket in master relay mode takes advantage of the peak time window to activate the long-distance wireless communication module, aggregates and encodes the distress data it collects and the distress data received from the slave node, and performs high-power burst transmission to the external rescue center;

[0063] Step D: The character rotates with the wave.

[0064] The dynamic master-slave role election process is continuously executed in a cycle as the waves propagate periodically, so that the role of the relay master node rotates in time and space with the physical movement of the wave crests in the group of users who have fallen into the water. This constructs a distributed virtual antenna array with time-varying characteristics, ensuring that the communication cluster always maintains a link connection with the external rescue center through the node with the highest current geometric height.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] This invention integrates an environmental perception module with a data processing module containing multi-level triggering logic. It can monitor key status data such as water depth, attitude, and acceleration of users in real time. Through multi-condition correlation analysis such as immersion, loss of control, and impact, it can accurately determine whether users are in dangerous situations such as drowning, unconsciousness, or severe impact. Thus, even when users are unable to operate manually, it can automatically and promptly send different levels of distress signals to the rescue center, greatly improving the timeliness and reliability of rescue. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the module of the portable smart life jacket that automatically triggers a distress signal according to the present invention;

[0068] Figure 2 This is a module execution diagram of the portable smart life jacket that automatically triggers a distress signal according to the present invention;

[0069] Figure 3 This is an external view of the portable smart life jacket of the present invention.

[0070] In the picture: 1. Life jacket body; 2. Waterproof compartment; 3. Light strip. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] To address the issue that existing life jackets rely on manual activation, and that distress signals cannot be sent in time when the person in distress is unconscious, panicked, or injured and unable to actively call for help, thus delaying the crucial rescue window, please refer to [link to relevant documentation]. Figures 1-3 This embodiment provides the following technical solution:

[0073] A portable smart life jacket that automatically triggers a distress signal includes a life jacket body 1, a waterproof chamber 2 fixedly mounted on the shoulder of the life jacket body 1, an environmental sensing module, a data processing module and a wireless communication module integrated in the life jacket body 1 and the waterproof chamber 2, wherein a light strip 3 is also provided on the shoulder of the life jacket body 1, the light strip 3 is used to provide a light alarm after the distress signal is automatically triggered, and the waterproof chamber 2 is detachable to allow for the inspection or replacement of the electronic components inside the waterproof chamber 2;

[0074] The environmental perception module is integrated on the life jacket body 1 and is configured to monitor the status data of the user's environment in real time through various intelligent sensors, including water depth, water pressure, immersion status, acceleration, attitude and user position data.

[0075] The data processing module is located inside the waterproof compartment 2 and is electrically connected to the environmental sensing module. It is configured to receive and analyze the status data, determine whether the distress signal triggering conditions are met based on a preset multi-level triggering logic, and immediately generate distress commands of different levels and send them to the wireless communication module when the distress signal triggering conditions are met.

[0076] The wireless communication module is located inside the waterproof compartment 2 and is electrically connected to the central processing unit. It is configured to automatically send distress signals to the external rescue center and designated terminal after receiving distress commands of different levels.

[0077] The life jacket body 1 is made of lightweight, high-buoyancy material, which can provide users with sufficient buoyancy support in the water, while ensuring the portability and comfort of the life jacket. It is suitable for various water activities and water rescue scenarios, especially in the field of underwater emergency disaster reduction and fire-fighting equipment. The life jacket body 1 has a foldable vest design, which is convenient to carry when going out.

[0078] The waterproof compartment 2 is made of high-strength waterproof material and has good sealing performance, which can effectively protect the internal electronic components from water corrosion, ensure the normal operation of the data processing module and wireless communication module in the underwater environment, and improve the reliability and safety of the portable smart life jacket in underwater emergency disaster reduction and fire fighting equipment.

[0079] The technical effects of the above solution are as follows: The environmental perception module utilizes multi-sensor fusion technology to collect multi-dimensional state data such as water depth, water pressure, immersion status, acceleration, attitude, and position in real time. This provides a comprehensive and reliable data foundation for accurately judging dangerous situations, thus avoiding the risk of misjudgment or failure of a single sensor. The data processing module performs in-depth analysis of the data based on preset multi-level triggering logic, intelligently distinguishing different levels of danger. In complex scenarios, it can reduce false alarms by dynamically adjusting the data processing strategy. Furthermore, it automatically triggers the highest-level alarm in truly critical situations, thus solving the fatal problem of being unable to manually call for help due to injury, unconsciousness, or panic. Finally, the wireless communication module can automatically send out a message containing priority and emergency identification codes after the alarm is triggered. The distress signal also features an intelligent retransmission mechanism and dynamic data scheduling function. By persisting in retransmission under harsh communication environments, optimizing channels, and sending critical information in layers, it greatly ensures that distress information can be successfully delivered to the rescue center, thus buying valuable time for rescue operations. In addition, the life jacket body 1 is made of lightweight, high-buoyancy material to ensure the core buoyancy function and wearing comfort, while the waterproof compartment 2 located on the shoulder is made of high-strength waterproof material and is detachable. This provides robust protection for the core electronic components to operate stably in harsh underwater environments, and also facilitates daily maintenance and repair, extending the equipment's lifespan. The shoulder light strip 3 can provide light alarm after the alarm is triggered, which not only enhances visibility in poor lighting conditions to guide rescue, but also serves as a warning to people in the surrounding area.

[0080] In summary, the system, through the synergy of environmental perception, intelligent decision-making, and reliable communication, constructs a complete automated chain from hazard identification to successful transmission of distress information, significantly improving users' chances of survival in scenarios such as water activities, water rescue, and firefighting water rescue.

[0081] The data processing module includes:

[0082] The data fusion module is configured to receive state data from the environment perception module, perform data fusion using a Kalman filter algorithm and timestamp alignment technology, and generate a multi-dimensional state vector with a unified timestamp.

[0083] The logic judgment module is configured to perform serialization analysis on the fused multi-dimensional state vector based on preset multi-level triggering logic. The multi-level triggering logic includes:

[0084] Condition A, Water Immersion Trigger: When the environmental sensing module detects that continuous water immersion exceeds a first preset duration and the water depth exceeds a first preset threshold, a primary alarm is triggered;

[0085] Condition B, Disruption Trigger: If the environmental perception module detects that the user is in a non-upright posture for a second preset period of time while the immersion trigger condition is met, a medium-level alarm is triggered.

[0086] Condition C, Impact Trigger: If the environmental perception module detects that the acceleration momentarily exceeds the preset impact threshold and subsequently meets the immersion trigger condition, then the highest level alarm is immediately triggered and the distress command is generated.

[0087] The instruction generation module is configured to receive the primary, intermediate, and highest level alarm flags from the logic judgment module, trigger the light alarm on the light strip 3 on the life jacket body 1, and simultaneously generate a distress instruction containing corresponding warning information based on the primary, intermediate, and highest level alarm flags and send it to the wireless communication module.

[0088] For the primary alarm, light strip 3 uses a low flashing frequency (e.g., flashing 1-2 times per second) and yellow light alarm.

[0089] For intermediate alarms, the flashing frequency of light strip 3 is increased (e.g., flashing 3-4 times per second), and the color changes to orange;

[0090] For the highest level alarm, light strip 3 flashes at a high frequency (e.g., 5-6 times per second) and is red.

[0091] The technical effects of the above solution are as follows: The data fusion module uses a Kalman filter algorithm and timestamp alignment technology, which can effectively filter out noise interference in sensor data and fuse data from different sensors and at different times under a unified time reference, thereby generating accurate and synchronized multi-dimensional state vectors. This lays a reliable data foundation for subsequent accurate logical judgments, thus avoiding misjudgments caused by data asynchrony or noise. The logical judgment module is based on preset multi-level trigger logic such as immersion trigger, loss of control trigger, and impact trigger, which can form a progressively upgraded and interconnected hazard assessment system. The hazard assessment system, through serial analysis of the multi-dimensional state vectors, can intelligently identify various scenarios, from simple falling into water to loss of attitude after falling into water, and then to falling into water after high-speed impact. In scenarios of similar urgency, the refined classification not only significantly reduces the false alarm rate that may be caused by a single condition, but also ensures that when a user encounters the most dangerous situation, the system can skip the intermediate levels and directly trigger the highest level alarm, thereby achieving intelligent and accurate hazard assessment. The instruction generation module transforms the above-mentioned accurate assessment into clear and efficient instruction output. On the one hand, it drives the light strip 3 of the life jacket body 1 to perform visual alarm, so that rescuers or nearby people can quickly and intuitively identify the hazard level of the person in distress within their visual range, thereby prioritizing the handling of the most urgent situation and greatly improving the efficiency of on-site rescue. On the other hand, it can generate distress instructions containing corresponding early warning information and send them to the wireless communication module, thereby ensuring that the rear rescue center can also obtain detailed hazard level information synchronously.

[0092] The logic judgment module performs correlation analysis when processing different alarm levels. When a primary alarm has been triggered and the user is in a non-upright posture but has not yet reached the duration threshold of the intermediate alarm, the logic judgment module needs to closely monitor the acceleration data. If the acceleration momentarily exceeds the preset impact threshold, it will immediately combine the primary alarm status and directly trigger the highest level alarm. Through correlation analysis, the logical relationship between each alarm level can be utilized more rationally, improving the accuracy and timeliness of distress signal triggering.

[0093] The technical effects of the above solution are as follows: By establishing a dynamic correlation analysis mechanism between multi-level alarm states, the logic judgment module can intelligently introduce the key indicator of impact acceleration for comprehensive judgment within the potential danger window period when the primary alarm has been triggered and the user's posture is abnormal but the conditions for the intermediate alarm have not yet been met. Once an instantaneous over-limit impact is detected, the conventional judgment process can be skipped and the primary alarm state can be directly linked to trigger the highest level alarm. The above design effectively avoids response lag caused by delays or omissions due to single-condition judgment criteria. It is especially suitable for scenarios where users encounter sudden and severe events such as secondary impacts, thereby ensuring that rescue signals can be sent out as quickly and reliably as possible in complex and rapidly evolving dangerous situations.

[0094] The instruction generation module is further configured as follows:

[0095] Different sending priorities are set for distress commands of different alarm levels, with distress commands of the highest alarm level having the highest priority;

[0096] Upon receiving the highest level alarm flag, the emergency transmission mode will be activated immediately. At this time, the instruction generation module will automatically activate a special communication protocol to prioritize the wireless communication channel resources and ensure that the distress signal is sent out with the highest priority in the shortest time.

[0097] Meanwhile, the instruction generation module will also optimize the configuration of the wireless communication module, including adjusting the transmission power to the maximum allowable range, in order to enhance signal strength and coverage, and ensure that the distress signal can reach the external rescue center and the designated terminal quickly and accurately;

[0098] In addition, the command generation module will embed a unique emergency identification code in the distress command of the highest level alarm, which is used by external rescue centers and designated terminals to identify and prioritize this signal, further improving the efficiency and reliability of rescue response.

[0099] The technical effects of the above solution are as follows: By implementing differentiated signal transmission strategies for different alarm levels, the instruction generation module constructs a full-link priority guarantee mechanism from signal generation, transmission, and reception processing. Through a series of coordinated measures, such as automatically activating the emergency transmission mode, prioritizing the communication channel, maximizing transmission power, and embedding emergency identification codes when the highest alarm is triggered, it can ensure that distress signals can still be sent out quickly with the highest priority in adverse communication environments and be accurately identified and prioritized by the rescue system. This buys valuable time for rescue response in emergency situations and greatly improves the timeliness and success rate of rescue operations.

[0100] The environmental perception module includes:

[0101] The data acquisition module is configured to monitor in real time status data including water depth, water pressure, immersion status, acceleration, attitude, and user position through multiple smart sensors integrated on the life jacket body 1. The attitude data is used to determine the user's status. When a continuous acceleration value is detected to exceed the gravitational acceleration threshold, accompanied by a violent and irregular change in attitude in a very short time, it is determined to be an accidental fall into the water. When the user is in the water and their attitude remains prone or lateral for a preset period of time, while the acceleration data shows slight fluctuations or tends to zero, it is determined to be a state of being stunned or unconscious or losing the ability to move independently.

[0102] The data acquisition module is equipped with a clock synchronization circuit, which provides a unified time reference for all smart sensors. This ensures that the data collected by all sensors are strictly synchronized in time, avoiding data association errors caused by time deviations. For example, at the moment a user falls into the water, the data collected by each sensor can accurately correspond to the same time point, which is convenient for subsequent analysis and judgment. In addition, the data acquisition module adopts a distributed data acquisition architecture, which divides the smart sensors into at least two or more independent acquisition nodes. Each acquisition node independently collects data and transmits it to the data processing module.

[0103] The intelligent processing module is configured to perform preliminary processing and feature extraction on the collected status data, and automatically adjust the data processing strategy according to the user's current usage scenario. Applicable scenarios include, but are not limited to, swimming, surfing, and falling into the water. For example, in the surfing scenario, the processing of acceleration data focuses more on the extraction of high-frequency signals to determine whether the user has encountered an impact.

[0104] In this embodiment, the data acquisition module is designed with a sensor fault self-diagnosis function, which performs self-tests on the sensors regularly and monitors the working status of the sensors in real time. Once a sensor fault is detected, the system immediately issues an alarm and notifies the user and rescue center through the wireless communication module. At the same time, it activates the backup sensor or enters a low-power mode to extend the device's battery life.

[0105] The technical effects of the above solution are as follows: The data acquisition module uses a unified time base to ensure strict synchronization and accurate correlation of multi-source heterogeneous data in the time dimension, thus laying a solid foundation for accurately judging the user status. At the same time, the distributed data acquisition architecture adopted by the data acquisition module can improve the robustness of the system. In addition, the fault self-diagnosis function of the data acquisition module can promptly detect and report sensor anomalies, thereby activating backup plans. This ensures the continuous and effective operation of the system in emergency situations at the hardware level, comprehensively ensuring the timeliness, accuracy and reliability of dangerous state perception. The intelligent processing module's adaptive processing strategy based on the usage scenario can significantly enhance the pertinence and accuracy of state judgment.

[0106] The intelligent processing module executes the following process:

[0107] The status data undergoes preliminary processing including data filtering, noise reduction, and formatting.

[0108] From the pre-processed state data, feature vectors for judging the user's dangerous state are extracted in real time. The feature vectors include, but are not limited to, sinking rate, attitude change frequency, peak impact acceleration and duration.

[0109] Based on the extracted feature vectors, the user's current usage scenario is dynamically identified, and the data processing strategy is adaptively adjusted based on the identified scenario, including:

[0110] When the scene is identified as a surfing scene, the first strategy is activated to increase the weight of high-frequency signal components in the acceleration data in order to accurately determine whether the user has been hit by a giant wave.

[0111] When the scene is identified as a swimming scenario, the second strategy is activated, which focuses on the stability and periodic changes of the posture data.

[0112] When a still water immersion scenario is identified, a third strategy is activated to enhance the monitoring of sudden changes in water depth and continuous immersion.

[0113] The technical effects of the above solution are as follows: by extracting key feature vectors from the pre-processed state data in real time, and based on the extracted key feature vectors, dynamically identifying the user's actual activity scenario to adaptively activate differentiated data processing strategies, the smart life jacket can accurately distinguish between normal water activities and real dangerous situations. This not only reduces the false alarm rate in high-risk but normal scenarios such as surfing and swimming, but also ensures a rapid response by strengthening relevant monitoring indicators when real dangers such as falling into still water occur. Thus, it achieves an effective balance between the accuracy of danger judgment and the reliability of alarm in complex and ever-changing aquatic environments.

[0114] The wireless communication module is further configured as follows:

[0115] After each distress signal is sent, a confirmation timer is started, waiting for confirmation receipts from external rescue centers and designated terminals;

[0116] Based on the confirmation duration recorded by the confirmation timer, if no confirmation response is received from the rescue center or designated terminal within the specified time (e.g., 5 minutes for a primary alarm, 3 minutes for a secondary alarm, and 1 minute for a primary alarm), a retransmission mechanism is triggered. This retransmission mechanism includes:

[0117] Try retransmitting via another backup communication channel and gradually increase the retransmission interval until an acknowledgment is received or the maximum number of retransmissions is reached.

[0118] When the retransmission mechanism is triggered, the wireless communication module intelligently selects the retransmission strategy based on the current communication environment and historical communication records. For example, if a certain channel has a high delay in the first few communications but the delay gradually decreases in subsequent attempts, the wireless communication module will prioritize retransmission of that channel and dynamically adjust the retransmission interval.

[0119] The retransmission interval not only increases gradually according to the alarm level, but also adapts to the current communication status. If the signal quality of the communication channel is gradually improved during the retransmission process, the retransmission interval is appropriately shortened to speed up the delivery of the distress signal. Conversely, if the signal quality continues to deteriorate, the retransmission interval will be further extended to avoid frequent retransmissions that could cause channel congestion.

[0120] The technical effects of the above solution are as follows: Differentiated confirmation waiting times are set according to different alarm levels, and cross-channel retransmission is automatically triggered when no confirmation is received. At the same time, the optimal channel is dynamically selected and the retransmission interval is adaptively adjusted based on the real-time communication environment and historical records. Thus, while ensuring the reliability of the distress signal delivery, the transmission efficiency in emergency situations can be improved by shortening the interval when the signal quality improves, and the resource consumption and channel congestion caused by blind retransmission can be avoided by extending the interval when the channel deteriorates. This enhances the signal transmission robustness and overall rescue response efficiency of the life jacket body 1 in complex communication environments.

[0121] Before sending a distress signal, the wireless communication module compresses and redundantly encodes the data packets to be sent to improve the transmission success rate under poor channel conditions. After the communication connection is established, the module dynamically schedules the content and frequency of data transmission based on the bandwidth and stability of the connection. In the initial stage of the connection, a simplified data packet containing the user ID, user location data and alarm level is sent first. After the connection is stable, an extended data packet containing the user's vital signs and ambient water temperature is sent.

[0122] The technical effects of the above solution are as follows: By employing data compression and redundancy coding techniques, combined with a dynamic data scheduling strategy based on connection quality, an efficient and robust emergency communication process is constructed. This process not only improves the data transmission success rate under poor channel conditions through forward error correction capabilities, but also ensures the timely delivery of critical data when communication conditions are unfavorable by prioritizing the transmission of core rescue information in the early stages of connection establishment. Detailed data is then supplemented after the connection stabilizes. Thus, in complex rescue communication scenarios, the optimal balance between transmission efficiency and information integrity is achieved, maximizing the ability of the rescue system to quickly obtain critical information and respond effectively.

[0123] Working principle: The environmental perception module collects multi-dimensional status data such as water depth, attitude, and acceleration of the user in real time. The data processing module performs data fusion and multi-level logical judgment to automatically identify dangerous situations such as drowning and falling into the water and generate graded alarms. Finally, the wireless communication module prioritizes sending distress signals according to the alarm level and uses retransmission mechanism and dynamic data scheduling to ensure reliable signal transmission. This realizes full-link automated distress call from environmental perception and intelligent judgment to reliable communication, effectively solving the response delay problem caused by manual triggering and thus improving rescue efficiency and reliability.

[0124] The data processing module further includes a peak synchronization transmission control unit, configured to execute an anti-obstruction transmission timing optimization method based on microelectromechanical inertial sensing, the method comprising the following steps:

[0125] Step 1: Vertical Kinematic Reconstruction

[0126] The control unit receives real-time triaxial acceleration data from the environmental sensing module, and extracts the vertical acceleration component after separating the gravity component using attitude quaternions. ;

[0127] An adaptive bandpass filter with a center frequency of 0.05Hz to 0.5Hz is used for... The data is processed to filter out sensor zero-bias drift and high-frequency impact noise, and the filtered data is then subjected to a second numerical integration to calculate the estimated vertical displacement of the user relative to the local sea level in real time. and vertical velocity estimation ;

[0128] Step 2: Sea state energy parameterization

[0129] Control unit based on The historical time series is used to calculate the statistical variance within the sliding window, thereby estimating the current effective wave height parameter in real time. , used to characterize the energy level of the current sea state;

[0130] Step 3: Transmission Opportunity Score Calculation

[0131] The control unit uses a preset weighting algorithm to calculate the channel smoothness prediction score in real time. The calculation formula is defined as follows:

[0132]

[0133] in: Estimated vertical displacement (unit: meters) for real-time calculation; Estimated vertical velocity for real-time calculation (unit: meters per second); The effective wave height (in meters) is estimated in real time. is a non-zero regularization constant used to avoid operational singularities under static water conditions (unit: meter). Normalized frequency factor based on wave dominant frequency (unit: ); This is the cumulative waiting time since the last successful transmission or alarm trigger; It is a natural exponential function; It is a monotonically increasing urgency compensation function, configured to gradually increase the scoring benchmark over time; , , These are dimensionless weighting coefficients, corresponding to the weights of geometric line-of-sight advantage, phase stability, and transmission urgency, respectively.

[0134] Step 4: Adaptive Triggering

[0135] The control unit will calculate in real time With the preset transmission threshold Compare;

[0136] Only when Only then does the data processing module send a transmit enable command to the wireless communication module, driving the RF power amplifier to release the compressed distress data packet during the wave crest window, thereby maximizing the clearance height of the Fresnel zone and reducing signal fading caused by wave obstruction.

[0137] To address the problem of severely reduced reliability of distress signal transmission due to wave obstruction in complex and harsh sea conditions, this invention integrates a wave crest synchronous transmission control unit into the data processing module. This control unit is specifically configured to execute an innovative anti-obstruction transmission timing optimization method. In the vast and dynamic marine environment, especially under high sea states, people in the water are constantly tossed about by the waves. When a person is in a wave trough, the antenna of their wireless communication device is at a low height, and the line-of-sight transmission path is easily obstructed by the wave crest, creating a shadow zone for electromagnetic wave propagation, leading to a sharp attenuation of signal strength and even communication interruption. Traditional wireless communication systems cannot adapt to this rapidly changing channel environment, resulting in energy waste and rescue delays. The core idea of ​​this invention is to accurately sense the user's dynamic movement state with the waves, intelligently predict and capture the brief time window when the user is located at a wave crest, and then transmit the signal at that optimal moment to overcome the physical obstruction of the signal propagation path by the waves.

[0138] The detailed working principle and execution process of this anti-interference transmission timing optimization method can be divided into four tightly coupled core stages: vertical kinematics reconstruction, sea state energy parameterization, transmission opportunity score calculation, and adaptive trigger control.

[0139] Phase 1: Vertical Kinematic Reconstruction

[0140] This is the cornerstone of achieving wave crest synchronous transmission, with the goal of calculating the user's vertical motion relative to the local sea level in real time and with high accuracy, including vertical displacement and vertical velocity.

[0141] The control unit first receives high-frequency sampled real-time triaxial acceleration data from the environmental sensing module. This data is provided by a high-precision microelectromechanical inertial sensor integrated into the life jacket body. However, the raw acceleration data is a coupled signal of the user's motion acceleration and gravitational acceleration, and is measured in the life jacket's carrier coordinate system. To extract the purely wave-driven vertical motion information, precise coordinate system transformation and gravity component separation are necessary.

[0142] In practical operation, the control unit utilizes the attitude quaternion calculated in real time by the environmental perception module using an attitude fusion algorithm (such as extended Kalman filtering or complementary filtering). Attitude quaternion is an efficient and singular-free attitude representation method that accurately describes the instantaneous orientation of the life jacket body relative to a geographic coordinate system (such as a north-south coordinate system). By applying this attitude quaternion, the control unit can construct a rotation matrix to rotate the measured three-axis acceleration vector from the vehicle coordinate system to a stable geographic coordinate system. In the geographic coordinate system, the direction of the gravitational acceleration vector is constant and vertically downward. Therefore, the control unit can accurately identify and remove the gravitational component from the total acceleration vector, thereby extracting the vertical acceleration component reflecting the user's dynamic movement in the vertical direction.

[0143] After obtaining the vertical acceleration component, it must be finely filtered to eliminate the inherent zero-bias drift (low-frequency noise) of the inertial sensor and high-frequency impact noise in the environment (such as user limb movements or external impacts). Directly integrating data containing this noise would cause rapid accumulation of errors in velocity and displacement estimations, distorting the solution. Therefore, the control unit employs a sophisticated adaptive bandpass filter. The filter's center frequency and bandwidth are carefully designed to extract frequency components related to ocean wave motion. Typically, ocean waves with significant energy range from 0.05 Hz to 0.5 Hz (corresponding to wave periods of approximately 2 to 20 seconds).

[0144] This adaptive bandpass filter effectively filters out extremely low-frequency sensor zero-bias drift, which is crucial for suppressing the accumulation of integration errors. Simultaneously, it filters out high-frequency impulse noise, ensuring that these transient disturbances do not affect the judgment of overall wave motion. By adaptively adjusting the filter parameters to match the dominant wave frequency under the current sea state, this filter can preserve the true wave motion signal to the maximum extent while minimizing the phase delay introduced by the filter.

[0145] After filtering, the vertical acceleration signal becomes clean and reliable. The control unit then performs a second numerical integration operation on this filtered data. To further improve the accuracy and stability of the integration, high-order numerical integration algorithms are typically used, such as the Runge-Kutta method or trapezoidal rule-based optimization algorithms. The first integration calculates the vertical acceleration in real time as an estimate of the user's vertical velocity relative to the local mean sea level. This velocity estimate reflects the user's current upward or downward trend and rate. The second integration further calculates the vertical velocity estimate as an estimate of the user's vertical displacement relative to the local mean sea level. This displacement estimate directly reflects the user's current instantaneous altitude. At this point, the control unit has successfully reconstructed the user's vertical kinematic state.

[0146] Phase Two: Sea State Energy Parameterization

[0147] To achieve intelligent transmission timing optimization, the system needs not only to know the user's instantaneous altitude but also the overall severity of the current sea conditions, i.e., the intensity of the waves. This is because the same absolute altitude may represent different relative advantages under different sea conditions. For example, in sea conditions with a wave height of 1 meter, a rise of 0.5 meters may already be close to the wave crest, while in sea conditions with a wave height of 5 meters, it may still be near the wave trough. The control unit achieves real-time assessment of the sea conditions by analyzing the historical time series of the vertical displacement estimates calculated in the first stage.

[0148] Specifically, the control unit maintains a sliding window of fixed duration, continuously recording vertical displacement data over a recent period. Within this window, the control unit calculates the statistical variance of the vertical displacement data. Statistical variance is an indicator of data volatility and is closely related to wave energy. By applying appropriate scaling transformations to the statistical variance and combining it with ocean wave statistical models (such as the Rayleigh distribution model), the control unit can estimate the current significant wave height parameter in real time. Significant wave height is a key indicator characterizing the current sea state energy level. Real-time estimation of significant wave height enables the system to dynamically adapt to constantly changing sea states, providing an important normalized benchmark for subsequent transmission opportunity scoring calculations.

[0149] Phase 3: Transmission Opportunity Score Calculation

[0150] After obtaining the user's real-time movement status and the current sea state and energy level, the optimization process enters the core decision-making stage. The control unit needs to comprehensively consider multiple factors and quantitatively assess the potential success rate of data transmission at the current moment. To this end, the control unit uses a preset weighted algorithm to calculate a channel connectivity prediction score in real time. The calculation logic of this score integrates considerations of three dimensions: geometric line-of-sight advantage, motion phase stability, and transmission urgency.

[0151] The first dimension is geometric line-of-sight advantage. This is the most significant factor affecting the reliability of maritime communications. As antenna height increases, its line-of-sight range expands, and the probability of signal obstruction by waves decreases. In the scoring calculation, the contribution of this dimension is obtained by normalizing the real-time calculated vertical displacement estimate with the real-time estimated significant wave height. Specifically, the vertical displacement estimate is divided by the sum of the significant wave height and a non-zero regularization constant. The introduction of this regularization constant is crucial; it is used to avoid computational singularities (i.e., division by zero errors) that may occur under calm water conditions (where the significant wave height is close to zero), ensuring the robustness of the algorithm. When the user is at a wave crest, the vertical displacement estimate is larger, resulting in a higher score for this dimension, indicating better line-of-sight propagation conditions. By normalizing using significant wave height, this score adaptively reflects the user's relative height advantage under the current sea state.

[0152] The second dimension is motion phase stability. To ensure signal stability and coherence during data transmission, the ideal transmission time should not only have a significant altitude but also be in a stable phase with low motion speed. Excessive vertical velocity by the user can degrade signal transmission quality. Rapid motion causes Doppler shift, increasing the difficulty for the receiver to acquire and synchronize the signal. When the user reaches the peak of the wave, their vertical velocity instantaneously approaches zero (the inflection point from rising to falling), at which point the motion is most stable and the Doppler effect is minimal. In the scoring calculation, this dimension's contribution is determined by analyzing the vertical velocity estimate. The control unit evaluates phase stability using a nonlinear decay model (based on a natural exponential function), which penalizes the square of the vertical velocity estimate. This means that when the vertical velocity is high, the score drops rapidly. To adapt this evaluation to different wave frequency characteristics, a normalized frequency factor based on the wave's dominant frequency is introduced into the calculation, combined with scaling adjustments based on the effective wave height (specifically, the square of the sum of the effective wave height and the regularization constant). This design ensures that the system tends to transmit at the moment when the motion is most stable, near the peak of the wave.

[0153] The third dimension is transmission urgency. In emergency rescue scenarios, the timeliness of distress signals is crucial. The system cannot wait indefinitely for the perfect wave crest to appear before sending a signal, especially under extremely rough sea conditions and irregular wave cycles. Therefore, an urgency compensation mechanism is introduced into the scoring calculation. This mechanism considers the cumulative waiting time since the last successful transmission or alarm triggering. The control unit uses a monotonically increasing urgency compensation function to handle this waiting time. As the waiting time progresses, this function gradually increases the baseline value of the score. This means that even if the current geometric altitude and phase stability are not ideal, if the waiting time is too long, the total score may exceed the transmission threshold due to the increase in urgency, thereby forcing the transmission to start and ensuring that the distress signal is not delayed indefinitely.

[0154] Finally, the control unit multiplies the scores of the three dimensions by their corresponding dimensionless weighting coefficients (corresponding to the weights of geometric line-of-sight advantage, phase stability, and transmission urgency, respectively), and then sums them to obtain the final channel throughput prediction score. These weighting coefficients are pre-calibrated and optimized based on extensive simulation analysis and actual sea trial data to achieve optimal overall communication performance.

[0155] Phase 4: Adaptive Trigger Control

[0156] The control unit continuously monitors the real-time calculated channel connectivity prediction score and compares it with a preset transmission threshold. This transmission threshold defines the minimum channel quality required to initiate transmission.

[0157] The control unit determines the current moment as the optimal transmission window, i.e., the peak window period, only when the channel connectivity prediction score exceeds the preset threshold. At this time, the data processing module immediately sends a transmit enable command to the wireless communication module. Upon receiving the command, the wireless communication module quickly drives the RF power amplifier to transmit the highly compressed distress data packets waiting in the transmission queue in the form of high-power bursts.

[0158] By employing this transmission mechanism that precisely synchronizes with wave crests, the smart life vest achieves a physical advantage against wave obstruction. It ensures that the antenna is at the highest possible geometric height when transmitting critical distress signals, thereby maximizing the clearance height in the Fresnel zone. The Fresnel zone is a crucial ellipsoidal region in the propagation of electromagnetic waves, and ensuring its clearance is essential for high-quality line-of-sight transmission. Obstacles (such as waves) within this zone can cause signal reflection, diffraction, and scattering, leading to severe signal fading. Therefore, this method significantly reduces signal depth fading caused by wave obstruction, greatly increasing the probability of distress signals successfully reaching the rescue center in adverse sea conditions. Simultaneously, by avoiding ineffective transmission at unfavorable locations such as wave troughs, this method also significantly conserves battery energy, extending the life vest's operating time.

[0159] The wireless communication module is further configured with a dynamic cooperative relay mode for establishing a phase diversity communication link in multi-user water-fall scenarios. The logic control flow of this mode includes:

[0160] Step A: Neighbor Discovery and Status Broadcast

[0161] When the environmental perception module detects beacon signals from sources other than the smart life jacket in the surrounding area, it automatically activates the short-range self-organizing network protocol.

[0162] Each smart life jacket transmits its own ID and a currently calculated transmission opportunity score to neighboring nodes via a short-range communication link at a set broadcast period. Status data packets;

[0163] Step B: Dynamic Master-Slave Role Election

[0164] The data processing module executes a dynamic topology control algorithm based on wave phase, and processes the received neighbor node data. Value and its own Values ​​are compared in real time:

[0165] Relay Master Node Determination: If its own... If the value is at its maximum in the local communication cluster and exceeds the preset line-of-sight threshold, the device is determined to be in a peak position and automatically switches to master relay mode.

[0166] Collaboration is determined by the slave node: if its own... If the value is lower than the maximum value of the neighboring nodes, it is determined that it is currently in a trough occlusion position, and the device automatically switches to cooperative slave mode;

[0167] Step C: Layered data transmission

[0168] Life jackets in cooperative slave mode temporarily suppress the transmission of long-distance distress signals to reduce power consumption, and send their own distress data packets to the device identified as the relay master node via a short-range link;

[0169] The life jacket in master relay mode takes advantage of the peak time window to activate the long-distance wireless communication module, aggregates and encodes the distress data it collects and the distress data received from the slave node, and performs high-power burst transmission to the external rescue center;

[0170] Step D: The character rotates with the wave.

[0171] The dynamic master-slave role election process is continuously executed in a cycle as the waves propagate periodically, so that the role of the relay master node rotates in time and space with the physical movement of the wave crests in the group of users who have fallen into the water. This constructs a distributed virtual antenna array with time-varying characteristics, ensuring that the communication cluster always maintains a link connection with the external rescue center through the node with the highest current geometric height.

[0172] Furthermore, based on the aforementioned wave crest synchronous transmission technology, this invention also proposes a dynamic collaborative relay mode for complex scenarios involving multiple users falling into the water simultaneously. In actual maritime accidents, multiple people often fall into the water at the same time, typically clustered in nearby areas. In such scenarios, if each person in distress independently attempts to establish communication with an external rescue center, several challenges arise. First, due to the randomness of waves, each user is in a different wave phase at any given time, and some users may be deep in wave troughs, severely hindering communication. Second, multiple users simultaneously sending long-distance distress signals causes channel contention and mutual interference, reducing overall communication efficiency and accelerating power consumption.

[0173] To address these issues, the wireless communication module of this invention is further configured with a dynamic cooperative relay mode, aiming to construct a highly reliable communication link with phase diversity characteristics through intelligent collaboration among users who have fallen into the water. The core idea of ​​this mode is to utilize individuals in advantageous positions at the peak of the wave as temporary relay nodes to proxy long-distance communication for individuals in disadvantageous positions at the trough, and to optimize the allocation of communication resources and maximize the survival probability of the group through dynamic role rotation.

[0174] The logical control flow and detailed working principle of this dynamic collaborative relay mode can be divided into four cyclically executed steps: neighbor discovery and status broadcasting, dynamic master-slave role election, hierarchical data transmission, and role rotation.

[0175] Step A: Neighbor Discovery and Status Broadcast

[0176] This forms the basis for establishing multi-user collaborative communication. When the smart life jacket is activated, its environmental perception module (which can be specifically implemented by the short-range communication unit of the wireless communication module) will begin to continuously listen to specific short-range radio channels to detect whether there are beacon signals emitted by other similar smart life jackets in the vicinity.

[0177] Once a nearby beacon signal is detected, indicating the presence of other people in the water, the smart life jacket automatically activates a short-range self-organizing network protocol. This is a low-power, high-reliability wireless communication protocol (e.g., based on low-power wide-area network technology or mesh network technology) specifically designed for maritime emergency communication. It can quickly establish a local communication network without the need for a central node to coordinate, organizing nearby people in the water into a communication cluster.

[0178] After the ad hoc network is established, each smart life jacket begins to send its own status data packets to neighboring nodes via short-range communication links at a set broadcast period (e.g., once or several times per second). This status data packet contains two key pieces of information: first, its unique identifier to distinguish different users; and second, a transmission opportunity score calculated by the peak synchronization transmission control unit at the current moment, i.e., the aforementioned channel availability prediction score. Through this status broadcasting mechanism, each node in the communication cluster can understand in real time the physical location advantages and potential communication capabilities of its neighbors.

[0179] Step B: Dynamic Master-Slave Role Election

[0180] This is the core decision-making process for achieving intelligent relay. In this step, each smart life jacket's data processing module independently executes a dynamic topology control algorithm based on wave phase. The goal of this algorithm is to quickly and accurately select the most suitable relay master node for long-distance communication within the communication cluster and guide other nodes into cooperative mode.

[0181] The specific execution process of this algorithm is as follows: the data processing module compares and analyzes the channel connectivity prediction scores received from all neighboring nodes via the short-range link with its own real-time score. Based on the comparison results, the system makes the following judgment:

[0182] The logic for determining the relay master node is as follows: If a smart life jacket detects that its channel connectivity prediction score is the highest in the local communication cluster (i.e., it has a higher score than all its neighboring nodes), and this score exceeds a preset line-of-sight threshold, then the system determines that the device is currently in an absolutely dominant position at the peak. The line-of-sight threshold ensures that the device selected as the master node not only has the highest relative position, but its absolute height is also sufficient to support reliable long-distance communication. Devices that meet both conditions will automatically switch to master relay mode and assume responsibility for external communication.

[0183] The logic for determining a collaborative slave node is as follows: If a smart life jacket detects that its channel connectivity prediction score is lower than the maximum value among its neighboring nodes, the system determines that the device is currently in a low-lying or relatively disadvantaged position. At this time, the device will automatically switch to collaborative slave mode and become a slave node that needs to be relayed.

[0184] Through this distributed election mechanism, the communication cluster can optimize its topology in a very short time, forming a dynamic relay topology centered on the current peak node. This eliminates the need for coordination by a central control node and provides high robustness and scalability.

[0185] Step C: Layered data transmission

[0186] After the master and slave roles are determined, the communication cluster begins to implement differentiated data transmission strategies to achieve dual optimization of energy efficiency and communication reliability.

[0187] In cooperative mode, the primary task of a life vest is to reliably transmit its distress signal to the relay master node while conserving power as much as possible. Therefore, slave nodes temporarily suppress their long-distance distress signal transmission. Long-distance communication typically requires high transmission power and is a major source of power consumption. By suppressing long-distance transmission, slave nodes can significantly reduce power consumption and extend the device's runtime. This also helps reduce unnecessary occupation and potential interference of long-distance communication channels within the communication cluster. Slave nodes then utilize low-power short-range communication links, employing an acknowledgment and retransmission mechanism, to send their distress data packets (containing critical information such as location and alarm level) to the device identified as the relay master node. Short-range links, due to their short transmission distance and low path loss, maintain high reliability even in harsh sea conditions.

[0188] In master relay mode, the life jacket plays a crucial role in aggregating data and executing long-distance burst transmissions. The master node first receives and buffers distress packets from various slave nodes. Then, it aggregates and encodes the distress data it has collected with the received slave data. Aggregation coding technology can integrate distress information from multiple users without significantly increasing packet length, reducing protocol overhead, improving data transmission efficiency and channel utilization, and enhancing data immunity by introducing joint error correction coding. When it confirms it is in the optimal time window at the peak (determined by the peak synchronization transmission algorithm), the master node activates its long-distance wireless communication module (e.g., satellite communication module or maritime radio) and sends the aggregated distress packets to the external rescue center in a high-power burst. Because the master node is in the advantageous position with the highest geometric height, its long-distance transmission success rate is much higher than that of slave nodes in the trough.

[0189] Step D: The character rotates with the wave.

[0190] This is a key mechanism to ensure the system's dynamic adaptability and long-term robustness. Ocean waves are constantly moving and propagating. A node that is currently at a wave crest may fall into a wave trough shortly afterward. Therefore, the role of the relay master node cannot be fixed, but must dynamically rotate within the communication cluster as the waves propagate.

[0191] The dynamic master-slave role election step (step B) is continuously executed in a loop as the wave propagates periodically. Within each broadcast or decision cycle, all nodes reassess their roles based on the latest channel throughput prediction score. When the wave peak moves from the current master node's position to another node's position, the new node wins the election and automatically becomes the new master node because its score reaches the maximum value. The original master node automatically switches back to slave mode as its score decreases.

[0192] This wave-shifting mechanism enables the relay master node to seamlessly switch roles in time and space as it follows the physical movement of the wave crests among the users who have fallen into the water. From a macroscopic perspective, this multi-user collaborative system constructs a distributed virtual antenna array with time-varying characteristics. Although each life jacket is equipped with only a single antenna, through intelligent coordination and timing control, the entire system is functionally equivalent to a large antenna array that always places the active antenna elements at the highest point.

[0193] This distributed virtual antenna array ensures that the communication cluster can always maintain a link with the external rescue center through the node with the highest current geometric height. It utilizes the phase diversity characteristic of wave propagation (i.e., the height difference caused by wave motion) to achieve spatial diversity gain, transforming waves from a source of interference into a favorable factor for improving communication performance. Through group cooperation, this dynamic cooperative relay mode significantly improves the success rate of multi-user collective distress calls and the overall survival probability in extremely harsh sea conditions.

[0194] In summary, this invention constructs a complete solution from individual optimization to group collaboration by organically combining wave crest synchronous transmission timing optimization and dynamic cooperative relay mode. At the individual level, through precise motion sensing and intelligent decision-making, adaptive transmission at wave crest timing is achieved, effectively overcoming wave blockage. At the group level, through self-organizing cooperative networks and dynamic topology control, a distributed virtual antenna array is constructed, achieving optimized utilization of communication resources and significantly improved robustness. These innovative designs greatly enhance the communication reliability of the smart life jacket in complex marine environments, providing strong technical support for the timely rescue of people in distress at sea.

[0195] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0196] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A portable smart life jacket that automatically triggers a distress signal, comprising a life jacket body (1), characterized in that, The life jacket body (1) is fixedly provided with a sealed waterproof chamber (2) on the shoulder. The life jacket body (1) and the waterproof chamber (2) integrate an environmental sensing module, a data processing module and a wireless communication module. The environmental perception module is integrated on the life jacket body (1) and is configured to monitor the status data of the user's environment in real time through various intelligent sensors. The status data includes water depth, water pressure, immersion status, acceleration, attitude and user position data. The data processing module is located inside the waterproof compartment (2) and is electrically connected to the environmental sensing module. It is configured to receive and analyze the status data, and based on the preset multi-level triggering logic, determine whether the distress signal triggering conditions are met. When the distress signal triggering conditions are met, it immediately generates distress instructions of different levels and sends them to the wireless communication module. The wireless communication module is located inside the waterproof compartment (2) and is electrically connected to the data processing module. It is configured to automatically send distress signals to the external rescue center and the designated terminal after receiving distress commands of different levels. The data processing module includes: The data fusion module is configured to receive state data from the environment perception module, perform data fusion using a Kalman filter algorithm and timestamp alignment technology, and generate a multi-dimensional state vector with a unified timestamp. The logic judgment module is configured to perform serialization analysis on the fused multi-dimensional state vector based on preset multi-level triggering logic. The multi-level triggering logic includes: Water immersion trigger: When the environmental sensing module detects that continuous water immersion exceeds a first preset duration and the water depth exceeds a first preset threshold, a primary alarm is triggered; Disruption trigger: If the environmental perception module detects that the user is in a non-upright posture for a second preset period of time while the water immersion trigger condition is met, a medium-level alarm is triggered. Impact trigger: If the environmental perception module detects that the acceleration momentarily exceeds the preset impact threshold and subsequently meets the immersion trigger condition, the highest level alarm will be triggered immediately and the distress command will be generated. The instruction generation module is configured to receive the primary, intermediate and highest level alarm signs from the logic judgment module, trigger the light alarm on the light strip (3) on the life jacket body (1), and generate a distress instruction containing corresponding warning information based on the primary, intermediate and highest level alarm signs and send it to the wireless communication module. The data processing module further includes a peak synchronization transmission control unit, configured to execute an anti-obstruction transmission timing optimization method based on microelectromechanical inertial sensing, which includes the following steps: The control unit receives real-time three-axis acceleration data from the environment perception module, extracts the vertical acceleration component after separating the gravity component by using the attitude quaternion ; An adaptive bandpass filter with a center frequency of 0.05Hz to 0.5Hz is used for... The data is processed to filter out sensor zero-bias drift and high-frequency impact noise, and the filtered data is then subjected to a second numerical integration to calculate the estimated vertical displacement of the user relative to the local sea level in real time. and vertical velocity estimation ; The control unit calculates a statistical variance within a sliding window based on a historical time series of the current significant wave height parameter for characterizing the energy level of the current sea state in real time. The control unit uses a preset weighting algorithm to calculate the channel smoothness prediction score in real time. The calculation formula is defined as follows: in: Estimated vertical displacement for real-time calculation; Estimating the vertical velocity for real-time calculation; For real-time estimation of effective wave height; It is a non-zero regularization constant used to avoid operational singularities under still water conditions; This is a normalized frequency factor based on the dominant wave frequency; This is the cumulative waiting time since the last successful transmission or alarm trigger; It is a natural exponential function; It is a monotonically increasing urgency compensation function, configured to gradually increase the scoring benchmark over time; , , These are dimensionless weighting coefficients, corresponding to the weights of geometric line-of-sight advantage, phase stability, and transmission urgency, respectively. The control unit will calculate in real time With the preset transmission threshold Compare; Only when Only then does the data processing module send a transmit enable command to the wireless communication module, driving the RF power amplifier to release the compressed distress data packet during the wave crest window, thereby maximizing the clearance height of the Fresnel zone and reducing signal fading caused by wave obstruction.

2. The portable intelligent life jacket with automatic distress signal triggering according to claim 1, characterized in that, The logic judgment module performs correlation analysis when processing different alarm levels. When a primary alarm has been triggered and the user is in a non-upright posture but has not yet reached the duration threshold of the intermediate alarm, the logic judgment module needs to closely monitor the acceleration data. If the acceleration momentarily exceeds the preset impact threshold at this time, it will immediately combine the primary alarm status and directly trigger the highest level alarm.

3. A portable intelligent life jacket that automatically triggers a distress signal according to claim 1, characterized in that, The instruction generation module is further configured as follows: Different sending priorities are set for distress commands of different alarm levels, with distress commands of the highest alarm level having the highest priority; Upon receiving the highest level alarm flag, the emergency transmission mode will be activated immediately. At this time, the instruction generation module will automatically activate a special communication protocol to prioritize the acquisition of wireless communication channel resources. Meanwhile, the instruction generation module will also optimize the configuration of the wireless communication module, including adjusting the transmission power to the maximum allowable range, in order to enhance signal strength and coverage. In addition, the command generation module will embed a unique emergency identification code in the distress command of the highest level alarm, so that external rescue centers and designated terminals can identify and prioritize this signal.

4. A portable intelligent life jacket that automatically triggers a distress signal according to claim 1, characterized in that, The environment perception module includes: The data acquisition module is configured to monitor in real time status data including water depth, water pressure, immersion status, acceleration, attitude and user position through multiple smart sensors integrated on the life jacket body (1); The data acquisition module is designed with a clock synchronization circuit to provide a unified time reference for all smart sensors. The data acquisition module adopts a distributed data acquisition architecture, which divides the smart sensors into at least two or more independent acquisition nodes. Each acquisition node independently acquires data and transmits it to the data processing module. The intelligent processing module is configured to perform preliminary processing and feature extraction on the collected status data, and automatically adjust the data processing strategy according to the user's current usage scenario, which includes, but is not limited to, swimming, surfing, and falling into the water.

5. A portable intelligent life jacket that automatically triggers a distress signal according to claim 4, characterized in that, The intelligent processing module executes the following process: The status data undergoes preliminary processing including data filtering, noise reduction, and formatting. From the pre-processed state data, feature vectors for judging the user's dangerous state are extracted in real time. The feature vectors include, but are not limited to, sinking rate, attitude change frequency, peak impact acceleration and duration. Based on the extracted feature vectors, the user's current usage scenario is dynamically identified, and the data processing strategy is adaptively adjusted based on the identified scenario, including: When the scene is identified as a surfing scene, the first strategy is activated to increase the weight of high-frequency signal components in the acceleration data. When the scene is identified as a swimming scenario, the second strategy is activated, which focuses on the stability and periodic changes of the posture data. When a still water immersion scenario is identified, a third strategy is activated to enhance the monitoring of sudden changes in water depth and continuous immersion.

6. A portable intelligent life jacket that automatically triggers a distress signal according to claim 1, characterized in that, The wireless communication module is further configured as follows: After each distress signal is sent, a confirmation timer is started, waiting for confirmation receipts from external rescue centers and designated terminals; If no confirmation response is received from the rescue center or designated terminal within the specified time according to the confirmation timer, a retransmission mechanism is triggered. The retransmission mechanism includes: Try retransmitting via another backup communication channel and gradually increase the retransmission interval until an acknowledgment is received or the maximum number of retransmissions is reached. When the retransmission mechanism is triggered, the wireless communication module intelligently selects a retransmission strategy based on the current communication environment and historical communication records. The retransmission interval not only increases gradually according to the alarm level, but also adapts to the current communication status. If the signal quality of the communication channel is gradually improved during the retransmission process, the retransmission interval is appropriately shortened. Conversely, if the signal quality continues to deteriorate, the retransmission interval will be further extended.

7. A portable intelligent life jacket that automatically triggers a distress signal according to claim 1, characterized in that, Before sending a distress signal, the wireless communication module compresses and redundantly encodes the data packets to be sent. After the communication connection is established, it dynamically schedules the content and frequency of data transmission based on the bandwidth and stability of the connection. In the initial stage of the connection, it prioritizes sending a simplified data packet containing the user ID, user location data, and alarm level. After the connection is stable, it sends an extended data packet containing the user's vital signs and ambient water temperature.

8. A portable intelligent life jacket that automatically triggers a distress signal according to claim 1, characterized in that, The wireless communication module is further configured with a dynamic cooperative relay mode for establishing a phase diversity communication link in multi-user water-fall scenarios. The logic control flow of this mode includes: When the environmental perception module detects beacon signals from sources other than the smart life jacket in the surrounding area, it automatically activates the short-range self-organizing network protocol. Each smart life jacket transmits its own ID and a currently calculated transmission opportunity score to neighboring nodes via a short-range communication link at a set broadcast period. Status data packets; The data processing module executes a dynamic topology control algorithm based on wave phase, and processes the received neighbor node data. Value and its own Values ​​are compared in real time: Relay master node determination: If its own If the value is at its maximum in the local communication cluster and exceeds the preset line-of-sight threshold, the device is determined to be in a peak position and automatically switches to master relay mode. Collaboration is determined from the node: if its own If the value is lower than the maximum value of the neighboring nodes, it is determined that it is currently in a trough occlusion position, and the device automatically switches to cooperative slave mode; Life jackets in cooperative slave mode temporarily suppress the transmission of long-distance distress signals to reduce power consumption, and send their own distress data packets to the device identified as the relay master node via a short-range link; The life jacket in master relay mode takes advantage of the peak time window to activate the long-distance wireless communication module, aggregates and encodes the distress data it collects and the distress data received from the slave node, and performs high-power burst transmission to the external rescue center; The dynamic master-slave role election process is continuously executed in a cycle as the waves propagate periodically. This allows the role of the relay master node to rotate in time and space with the physical movement of the wave crests among the users who have fallen into the water. This constructs a distributed virtual antenna array with time-varying characteristics, ensuring that the communication cluster always maintains a link connection with the external rescue center through the node with the highest current geometric height.

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