Intelligent help-seeking ring system based on low-power-consumption Bluetooth

Abstract

The invention discloses an intelligent help-seeking ring system based on low-power-consumption Bluetooth, and the system comprises a multi-mode sensor combination which comprises a motion sensor, a physiological sensor, and an environment sensor; the multi-level triggering module is configured to judge a user state through a preset multi-level early warning algorithm based on the fusion data acquired by the multi-mode sensor, and trigger corresponding distress signals according to different risk levels; the self-adaptive communication module is configured to carry out distress signal transmission through low-power-consumption Bluetooth; the self-organizing network system comprises a plurality of Bluetooth receiving nodes, and mesh networks are formed among the Bluetooth receiving nodes to relay and forward distress signals; and the monitoring processing system is configured to receive and process the distress signal from the self-organizing network system and execute a corresponding rescue response. By constructing the combination of the multi-level triggering module and the multi-mode sensor, the accuracy and the intelligent level of triggering the distress signal are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent distress systems, and more particularly to an intelligent distress ring system based on Bluetooth Low Energy. Background Technology

[0002] With the accelerating pace of modern life and increased population mobility, the demand for personal safety is becoming increasingly prominent, especially in high-risk scenarios such as elderly people living alone, outdoor workers, and those engaging in extreme sports. Timely and effective rescue mechanisms have become crucial for ensuring life safety. Currently, various rescue devices exist on the market, including GPS-based satellite rescue devices, smartwatches relying on mobile networks, and various emergency button devices. While these devices can play a role in specific scenarios, their limitations are gradually becoming apparent in practical applications as the application environment becomes more complex and user needs diversify.

[0003] Existing distress signaling devices suffer from two main technical defects: First, the triggering mechanism is too simplistic and lacks accuracy. Most devices rely on a single sensor or manual button press, which can easily lead to false alarms due to environmental interference or failure to trigger the distress signal in time due to the user's inability to move, resulting in delays in rescue. Second, the signal transmission path is singular and unreliable. Existing devices mostly use point-to-point direct communication. When the signal receiver is too far away, has obstacles blocking it, or is in a signal blind spot, the distress signal is easily lost or the location of the person in distress cannot be accurately located, seriously affecting rescue efficiency. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to provide a distress device with an intelligent triggering mechanism and high signal transmission reliability, which can perform fusion analysis based on multiple sensor data to achieve accurate multi-level early warning triggering, and at the same time ensure stable transmission and accurate positioning of distress signals in complex environments through a self-organizing network system, thereby improving the success rate of distress calls and rescue efficiency.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a smart distress ring system based on Bluetooth Low Energy, comprising a smart ring, and further comprising:

[0007] Multimodal sensor combinations, including motion sensors, physiological sensors, and environmental sensors;

[0008] The multi-level triggering module is configured to determine the user's status based on the fused data collected by the multi-modal sensor through a preset multi-level early warning algorithm, and trigger corresponding distress signals according to different risk levels.

[0009] An adaptive communication module configured to transmit distress signals via Bluetooth Low Energy;

[0010] A self-organizing network system, comprising multiple Bluetooth receiving nodes, which form a mesh network to relay and forward distress signals;

[0011] The monitoring and processing system is configured to receive and process distress signals from the self-organizing network system and execute corresponding rescue responses.

[0012] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the step of determining the user's status through a preset multi-level early warning algorithm includes: establishing the user's personal baseline data, continuously monitoring the user's physiological and behavioral data for X days as a normal status benchmark; calculating the deviation value between the fused data and the personal baseline data; and classifying the deviation into mild abnormality, moderate abnormality, and severe abnormality.

[0013] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the step of classifying according to the degree of deviation includes: when the deviation value is 1.5-2 times the baseline range, it is determined to be a slight anomaly, and the first-level warning is initiated; when the deviation value is 2-3 times the baseline range, it is determined to be a moderate anomaly, the second-level warning is initiated, and the user's confirmation response is waited for within 5 seconds; when the deviation value exceeds 3 times the baseline range, it is determined to be a severe anomaly, and the third-level emergency distress call is immediately initiated.

[0014] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the steps of transmitting distress signals in the self-organizing network system include: the smart ring sending distress signals containing the device ID and distress level to the surrounding area at 1-second intervals; after the first receiving node within a range of N meters receives the distress signal, it records the distress signal strength and forwards it to other nodes in the network; after other receiving nodes receive the forwarded distress signal, they compare the distress signal strength and select the path with the strongest distress signal for secondary forwarding; the forwarding is repeated until the distress signal reaches the monitoring and processing system.

[0015] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the step of selecting the path with the strongest distress signal for secondary forwarding includes: when the receiving node receives multiple identical signals simultaneously, waiting 2 seconds to collect the paths of all distress signals; calculating the signal strength attenuation of each path; selecting the path with the least attenuation as the forwarding path, and sending a forwarding identifier to other paths.

[0016] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the self-organizing network system further performs a positioning step, specifically including: when at least three receiving nodes simultaneously receive a distress signal, each receiving node records the arrival time and signal strength of the distress signal; each receiving node sends the time difference and strength difference data to the monitoring and processing system; the monitoring and processing system calculates the position coordinates of the smart ring based on a triangulation algorithm.

[0017] As a preferred embodiment of the Bluetooth Low Energy-based smart distress ring system of the present invention, the step of calculating the smart ring's position coordinates based on the triangulation algorithm includes: calculating the distance data from the smart ring to each receiving node based on the known coordinates and signal strength of each receiving node; establishing a coordinate system with the first receiving node as the origin and calculating the relative coordinates of other receiving nodes; establishing a circular equation system using the distance data, with each receiving node as the center and the calculated distance as the radius; solving for the intersection points of the circular equation system to obtain the preliminary position coordinates of the smart ring; if multiple intersection points exist, selecting the intersection point with the smallest error from all receiving nodes as the final position coordinates.

[0018] As a preferred embodiment of the smart emergency ring system based on Bluetooth Low Energy described in this invention, the steps of the monitoring and processing system in executing the rescue response include: when receiving a slightly abnormal emergency signal, displaying a yellow alert icon on the mobile phone screen for 10 seconds; when receiving a moderately abnormal emergency signal, vibrating the phone 3 times and popping up a confirmation dialog box, waiting for the user to click a button; when receiving a severely abnormal emergency signal, dialing the preset first emergency contact number, and simultaneously sending an emergency signal containing location coordinates to all emergency contacts; if the first emergency contact does not answer within 30 seconds, automatically dialing the second emergency contact number.

[0019] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the steps of the adaptive communication module in transmitting distress signals include: detecting the Bluetooth signal interference intensity in the current environment; adjusting the transmission power according to the interference intensity; increasing the power when the interference intensity exceeds a set threshold; decreasing the power when the interference intensity does not exceed the set threshold; selecting the Bluetooth channel with the least interference intensity for signal transmission; and switching to a backup channel to retransmit if three consecutive transmissions fail.

[0020] As a preferred embodiment of the smart distress ring system based on Bluetooth Low Energy described in this invention, the step of collecting and fusing data using the multimodal sensor combination includes: collecting triaxial acceleration and angular velocity data every 0.1 seconds using a motion sensor; collecting heart rate data every 1 second and skin temperature data every 10 seconds using a physiological sensor; collecting ambient light and air pressure data every 30 seconds using an environmental sensor; and aligning the collected data according to timestamps to form a fused data packet.

[0021] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a multi-level triggering module and a multi-modal sensor combination, the accuracy and intelligence level of distress signal triggering are improved. The multi-modal sensor combination can comprehensively monitor the user's physiological state, movement behavior, and environmental changes. By establishing a personalized baseline model and a multi-level early warning algorithm, it achieves a progressive risk assessment from mild anomalies to severe emergencies, effectively solving the technical problems of high false alarm rates and high missed alarm risks of traditional equipment. At the same time, based on the deviation value calculation and level classification mechanism of fused data, the trigger threshold can be adjusted according to the user's actual situation, which not only avoids frequent false alarms caused by environmental interference, but also ensures that distress signals can still be automatically triggered in a timely manner when the user loses the ability to act independently, greatly improving the reliability and practicality of the distress system.

[0022] This invention employs a self-organizing network system and an adaptive communication module, fundamentally solving the problem of unstable signal transmission in complex environments using traditional point-to-point communication methods. Through a mesh network architecture composed of multiple Bluetooth receiving nodes, it achieves multi-path redundant transmission and intelligent routing selection of distress signals. Even if some nodes fail or the signal is blocked, the system can still ensure reliable signal transmission through backup paths. Combined with a triangulation algorithm based on signal strength and time difference, the system can achieve accurate location calculation without the need for external positioning systems such as GPS, making it particularly suitable for indoor, underground, or signal-shielded environments. Furthermore, the adaptive communication module can dynamically adjust the transmission power and channel selection according to the intensity of environmental interference, optimizing device power consumption and extending battery life while ensuring signal transmission quality, providing users with more durable and reliable security. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 The structure of a smart distress ring system based on Bluetooth Low Energy is described in one embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the Bluetooth receiving node of a smart distress ring system based on Bluetooth Low Energy, according to an embodiment of the present invention. Detailed Implementation

[0026] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figures 1-2 As one embodiment of the present invention, a smart distress ring system based on Bluetooth Low Energy is provided, including a smart ring and further comprising:

[0028] Multimodal sensor combinations, including motion sensors, physiological sensors, and environmental sensors.

[0029] Specifically, the steps for acquiring and fusing data using the multimodal sensor combination include:

[0030] Triaxial acceleration and angular velocity data are collected every 0.1 seconds using motion sensors;

[0031] The motion sensor employs a combination of a MEMS triaxial accelerometer and a triaxial gyroscope. The accelerometer's measurement range is set to ±8G with a 16-bit resolution; the gyroscope's measurement range is ±2000 degrees / second, also with a 16-bit resolution, ensuring accurate angular velocity measurement. The motion sensor performs a complete six-axis data acquisition every 0.1 seconds, specifically including acceleration and angular velocity values ​​along the X, Y, and Z axes, acquiring six data points per acquisition. To reduce noise interference, the system incorporates a low-pass filter with a cutoff frequency set to 50Hz, effectively filtering out high-frequency noise while retaining valid motion signals. The acquired raw data is first compensated for by gravity components, then the composite acceleration and angular velocity vectors are calculated, and finally stored as a motion data structure with precise timestamps.

[0032] Heart rate data is collected every 1 second and skin temperature data is collected every 10 seconds using physiological sensors;

[0033] The physiological sensor module integrates a photoplethysmography (PPG) heart rate sensor and a thermistor temperature sensor. The heart rate sensor employs a reflective detection scheme using a green LED and photodiode. The LED emits green light with a wavelength of 525nm, and the heart rate is calculated by detecting changes in light intensity caused by blood flow. The system measures the heart rate every second, using a sliding window algorithm to analyze five consecutive seconds of pulse signals. Accurate heart rate values ​​are calculated through peak detection and spectral analysis, achieving a measurement accuracy of ±2 BPM. The skin temperature sensor uses an NTC thermistor with a temperature measurement range of 20-45℃ and an accuracy of ±0.1℃, acquiring temperature data every 10 seconds. Ambient temperature compensation is applied to the temperature data; the system corrects the skin temperature based on ambient temperature data from environmental sensors to ensure accurate measurement results.

[0034] Ambient light and air pressure data are collected every 30 seconds using environmental sensors;

[0035] The environmental sensor module includes an ambient light sensor and a barometric pressure sensor. The ambient light sensor uses a silicon photodiode, capable of detecting light intensity ranging from 0.01 to 83000 lux, and features automatic gain adjustment to ensure accurate measurement results under varying lighting conditions. The barometric pressure sensor employs a MEMS piezoresistive design, with a measurement range of 300-1100 hPa and an accuracy of ±1 hPa, capable of detecting subtle changes in altitude. The environmental sensors acquire data every 30 seconds; this acquisition frequency captures gradual changes in the environment while effectively conserving battery power.

[0036] The collected data is aligned according to timestamps to form a fused data packet;

[0037] The system uses a unified time base, establishing a global timestamp with the system startup time as the zero point. All sensor data is assigned a 64-bit timestamp. The data alignment process uses the nearest neighbor interpolation algorithm. When a fused data packet needs to be formed, the system uses a 1-second time interval as the base time, averages the motion data within that second, uses the measurement value of the second directly for heart rate data, uses the valid value within the most recent 10 seconds for skin temperature data, and uses the valid value within the most recent 30 seconds for environmental data.

[0038] The fusion data packet uses a JSON format structure and includes a timestamp, device ID, motion data (6 components), heart rate data, skin temperature data, ambient light intensity, and air pressure values, totaling approximately 64 bytes. Each fusion data packet also includes a data quality flag to indicate the validity and reliability level of the data from each sensor.

[0039] The multi-level triggering module is configured to determine the user's status based on the fused data collected by the multi-modal sensors, and trigger corresponding distress signals according to different risk levels through a preset multi-level early warning algorithm.

[0040] Specifically, the steps for determining the user's status using a pre-set multi-level early warning algorithm include A1 to A3:

[0041] A1. Establish user baseline data and continuously monitor the user's physiological and behavioral data for X days as a benchmark for normal status.

[0042] Upon first use, the system enters a 7-14 day baseline learning mode, during which it continuously collects multimodal sensor data from the user but does not perform anomaly detection. The baseline data establishment process employs a sliding time window analysis method, dividing a 24-hour day into 8 time periods (each time period is 3 hours long), and establishing physiological and behavioral baseline models for different time periods.

[0043] For heart rate data, the system calculates the average heart rate, standard deviation of heart rate variability, maximum heart rate, and minimum heart rate for each time period, forming a four-dimensional heart rate baseline vector. For exercise data, the system analyzes the user's gait characteristics, daily activity intensity distribution, and resting duration to establish a baseline model of exercise behavior. Skin temperature data is statistically analyzed to determine the normal fluctuation range, taking into account the influence of ambient temperature. The system also identifies the user's sleep pattern, work pattern, and exercise pattern, establishing an independent set of baseline parameters for each pattern.

[0044] The baseline data employs an adaptive update mechanism. The system fine-tunes the baseline parameters weekly based on the user's long-term data trends, ensuring the baseline model adapts to natural changes in the user's physiological state. Baseline data is stored in an encrypted and compressed format, occupying approximately 2KB of storage space, ensuring complete baseline information is preserved within the limited storage space of the ring.

[0045] A2. Calculate the deviation between the fused data and the individual baseline data;

[0046] The deviation calculation employs a multi-dimensional weighted scoring mechanism. The system first compares the current fused data with the baseline data for the corresponding time period item by item. For heart rate data, the normalized deviation between the current heart rate and the baseline heart rate is calculated using the formula: the current value minus the baseline mean, divided by the absolute value of the baseline standard deviation. Deviation calculation for exercise data is more complex. The system analyzes the similarity between the current exercise pattern and historical exercise patterns, and uses a dynamic time warping algorithm to calculate the degree of deviation in the exercise sequence.

[0047] The system assigns different weighting coefficients to different types of sensor data, with abnormal heart rate receiving the highest weight, followed by abnormal motion, while temperature and environmental data have relatively lower weights. The overall deviation value is calculated using a weighted average, and a time decay factor is introduced to give higher deviation scores to abnormal states with longer durations. To avoid interference from transient noise, the system uses a 3-second sliding window to smooth the deviation values; only when the deviation values ​​at multiple consecutive time points exceed a threshold is a valid abnormal state recognized.

[0048] A3. Based on the degree of deviation, it is divided into mild abnormality, moderate abnormality and severe abnormality;

[0049] The anomaly level classification uses a fuzzy logic algorithm, avoiding the misjudgment problems that may be caused by hard threshold classification. The system sets a membership function for each level. When the deviation value is in the boundary region, it will calculate the membership degree to multiple levels simultaneously and select the level with the highest membership degree as the final judgment result.

[0050] The grading system also considers the duration of the anomaly. Short-term deviations may not immediately trigger high-level alerts, but if the abnormal state persists, the system will gradually raise the alert level. Furthermore, the system incorporates anomaly type recognition, capable of distinguishing between different types such as motion abnormalities, heart rate abnormalities, and temperature abnormalities, with different grading strategies applied to different types of anomalies.

[0051] The steps for classifying based on the degree of deviation include A3.1 to A3.3:

[0052] A3.1 When the deviation value is 1.5-2 times the baseline range, it is judged as a slight anomaly, and the first-level warning is initiated;

[0053] The mild anomaly detection primarily targets slight fluctuations in a user's physiological indicators or minor deviations in behavioral patterns. When the system detects a mild anomaly, the ring will alert the user to their condition through a weak vibration (lasting 0.5 seconds, at an intensity of 30% of the maximum vibration intensity), while simultaneously displaying a yellow alert icon on the paired mobile app.

[0054] The first-level warning will not immediately send a distress signal. Instead, it will enter an enhanced monitoring mode, temporarily doubling the sensor's sampling frequency for 5 minutes. If the abnormal state automatically returns to normal during this period, the system will automatically lift the warning. If the abnormal state persists or worsens, the system will automatically escalate to a higher-level warning. Users can also manually confirm the current status is normal and lift the warning in advance by pressing the ring button for 3 seconds.

[0055] A3.2 When the deviation value is 2-3 times the baseline range, it is judged as a moderate anomaly, the second-level warning is initiated, and the user's confirmation response is waited for 5 seconds.

[0056] A moderate anomaly detection indicates that the user may be facing a potential security risk, requiring the user to actively confirm the current situation. When the system triggers a level 2 alert, the ring will produce a noticeable vibration alert (vibrating three times consecutively, each time for one second, with a 0.5-second interval), while a low-frequency warning sound will be emitted by a buzzer. The paired phone will pop up a full-screen confirmation dialog box, displaying the message "An anomaly detected. Are you safe?"

[0057] Users need to respond within 5 seconds and can choose from three options: "I am safe," "Need attention," or "Emergency assistance." If the user selects "I am safe," the system will record this false alarm event for optimizing subsequent algorithm parameters; if the user selects "Need attention," the system will notify preset personnel but will not initiate emergency rescue; if the user selects "Emergency assistance" or there is no response within 5 seconds, the system will automatically escalate to the third-level emergency assistance state.

[0058] A3.3 When the deviation value exceeds 3 times the baseline range, it is judged as a severe abnormality, and the third-level emergency rescue should be initiated immediately;

[0059] A severe anomaly detection indicates that the user may have lost mobility or is facing a life-threatening situation, and the system will immediately activate the highest level of emergency rescue procedures. The ring will generate the strongest vibration and sound alarm, while simultaneously sending an emergency distress signal via Bluetooth Low Energy.

[0060] An emergency distress signal includes complete user identification information, a precise timestamp, an anomaly type identifier, and location coordinates (if available). The system will simultaneously activate all available communication paths, including direct connection to the mobile phone, transmission via a mesh network to the nearest receiving node, and broadcasting the distress message to other nearby smart devices.

[0061] Upon receiving an emergency signal, the mobile app will immediately dial the preset first emergency contact number and simultaneously send an emergency text message containing location information to all emergency contacts. If the first contact does not answer the phone within 30 seconds, the system will automatically dial the second contact number, up to a maximum of five emergency contacts. The entire emergency rescue process will continue until a manual confirmation of safety is received or rescue personnel arrive at the scene.

[0062] An adaptive communication module is configured to transmit distress signals via Bluetooth Low Energy.

[0063] The steps for the adaptive communication module to transmit distress signals include B1 to B4:

[0064] B1. Detect the Bluetooth signal interference strength in the current environment;

[0065] The adaptive communication module integrates a dedicated RF signal analysis chip, enabling real-time spectrum scanning within the Bluetooth operating frequency band (2.4-2.485GHz). The system employs an energy detection algorithm, rapidly scanning 40 Bluetooth channels every 100 milliseconds to measure the Received Signal Strength Indicator (RSSI) value for each channel. Interference intensity detection uses a background noise baseline method. The system first measures the baseline noise level of each channel under no-signal transmission conditions to establish an environmental noise baseline. When data transmission is required, the system rescans the spectrum, calculating the difference between the current signal strength of each channel and the baseline noise level. A larger difference indicates stronger interference for that channel. The system classifies interference intensity into five levels: no interference (difference less than -80dBm), mild interference (-80dBm to -70dBm), moderate interference (-70dBm to -60dBm), severe interference (-60dBm to -50dBm), and serious interference (greater than -50dBm). The detection results are stored in a 40-byte channel status array, with each channel occupying 1 byte to record its interference level and last update time. To improve detection accuracy, the system also analyzes the temporal characteristics of the interference signal, identifies the characteristic patterns of different interference sources such as WiFi, microwave ovens, and other Bluetooth devices, and adopts corresponding anti-interference strategies. The detection process employs a low-power design; the RF scanning function is only activated within 50 milliseconds before data transmission is required, and remains in sleep mode to conserve battery power.

[0066] B2. Adjust the transmission power according to the interference intensity;

[0067] When the interference intensity exceeds a set threshold, increase the power.

[0068] Reduce power when the interference intensity does not exceed the set threshold.

[0069] The Bluetooth chip supports 8 levels of transmit power adjustment, ranging from -20dBm to +4dBm, with 3dBm increments. The power adjustment algorithm employs an adaptive closed-loop control mechanism, dynamically optimizing power output based on interference intensity and communication distance requirements. The system sets an interference threshold of -70dBm. When detected environmental interference exceeds this threshold, the system gradually increases the transmit power to overcome the interference. The power increase uses a progressive strategy, increasing by 3dBm each time, while monitoring the signal transmission success rate. If the success rate of five consecutive transmissions at the current power level is below 80%, the system continues to increase the power until it reaches the maximum value of +4dBm. When the environmental interference intensity is below the threshold, the system adopts a minimum power consumption strategy, reducing the transmit power to the lowest level necessary to ensure reliable communication. The system determines the optimal power level by measuring the signal strength feedback from the receiver, aiming to maintain the receiver's RSSI value within the ideal range of -60dBm to -40dBm. The power adjustment also takes into account battery level. When the battery level is below 20%, the system will prioritize low-power mode, only using maximum power in emergency situations. The system also establishes a power-distance model, which estimates the optimal power configuration for different distances based on the power levels of previous successful communications and receiver feedback, reducing the trial-and-error time for power adjustment.

[0070] B3. Select the Bluetooth channel with the least interference to send the signal;

[0071] The channel selection algorithm is based on real-time spectrum analysis results and historical communication quality statistics. The system maintains a channel quality rating table containing comprehensive ratings for 40 Bluetooth channels. The rating criteria include four dimensions: interference intensity, signal transmission success rate, communication latency, and channel stability.

[0072] The system prioritizes the channel with the highest score for communication, but to avoid channel congestion, a randomization strategy is used to randomly select one channel from the top 5. Channel selection also considers the continuity of the frequency hopping sequence to avoid frequent switching between adjacent channels and reduce the tuning time of the RF front end.

[0073] For emergency distress signals, the system employs a multi-channel concurrent transmission strategy, simultaneously sending the same distress data on three of the highest quality channels to improve signal transmission reliability. Concurrent transmission uses time-division multiplexing, sending a complete data packet on each channel. The receiving end confirms successful reception upon receiving complete data from any one of these channels.

[0074] The system also implements an adaptive frequency hopping algorithm, dynamically adjusting the frequency hopping sequence according to changes in environmental interference. When a sudden increase in interference is detected on certain channels, the system temporarily removes these channels from the frequency hopping sequence, using only clean channels for communication. The frequency hopping sequence is updated every minute to ensure adaptability to dynamically changing radio frequency environments.

[0075] B4. If three consecutive transmissions fail, switch to the backup channel and retransmit.

[0076] The fault detection mechanism is based on a data packet acknowledgment mechanism and a timeout retransmission algorithm. Each sent data packet contains a sequence number and a checksum, and the receiving end needs to return an acknowledgment within 500 milliseconds. If the sending end does not receive an acknowledgment within the timeout period, it considers the transmission to have failed, and the system will automatically retransmit.

[0077] A consecutive failure counter records the number of consecutive transmission failures on the same channel. When the count reaches 3, the system determines that the current channel is unavailable and immediately initiates a channel switching procedure. The backup channel is selected from the currently available channels with the lowest interference intensity. The switching process includes RF front-end retuning, transmit power recalibration, and communication parameter reconfiguration, and the entire process takes approximately 50 milliseconds.

[0078] The backup channel switching employs a fast recovery mechanism. The system pre-calculates and caches the configuration parameters of three backup channels, including optimal transmit power, modulation parameters, and frequency hopping sequences. When switching is required, the pre-configured parameters are directly loaded, eliminating the need for re-scanning the environment and optimizing parameters, thus significantly reducing recovery time.

[0079] For emergency distress signals, the system also implements a broadcast mode backup mechanism. When all regular channels become unavailable, the system switches to Bluetooth broadcast mode, broadcasting the distress message at maximum power on three fixed channels: 2.402GHz, 2.426GHz, and 2.480GHz. Although broadcast mode consumes more power, it has a wider coverage area and can be detected by more receiving devices.

[0080] The system also establishes a fault learning mechanism to record channel failure modes and the effectiveness of recovery strategies under different environmental conditions, and optimizes fault prediction and recovery strategies through machine learning algorithms. All key events in the fault recovery process are recorded in logs for subsequent system performance analysis and algorithm improvement.

[0081] The self-organizing network system includes multiple Bluetooth receiving nodes, which form a mesh network to relay and forward distress signals.

[0082] Specifically, the steps for transmitting distress signals in an ad hoc network system include C1 to C4:

[0083] C1. The smart ring sends a distress signal containing the device ID and distress level to the surrounding area at 1-second intervals.

[0084] The smart ring uses the BLE 5.0 protocol stack and supports extended broadcast functionality, capable of sending broadcast data packets up to 247 bytes. The distress signal uses a custom data frame format, containing a 16-byte header and variable-length payload data. The header includes: a 4-byte unique device identifier (generated based on the MAC address), a 1-byte distress level indicator (0x01 - mild, 0x02 - moderate, 0x03 - severe), a 2-byte serial number (to prevent duplicate processing), a 4-byte timestamp, a 2-byte battery level indicator, a 1-byte signal strength indicator, and a 2-byte checksum.

[0085] The transmission interval employs an intelligent adjustment mechanism: under normal conditions, a heartbeat signal is sent every 10 seconds; during mild anomalies, it is sent every 5 seconds; and during moderate and severe anomalies, it is sent every 1 second. In emergency distress mode, the system will also transmit simultaneously on three Bluetooth broadcast channels (37, 38, and 39), employing a power boosting strategy to increase the transmission power to the maximum value +4dBm, achieving an effective transmission distance of up to 50 meters.

[0086] To conserve battery power, the system employs dynamic duty cycle control, with each transmission lasting 2 milliseconds before entering a low-power sleep mode. The distress signal also utilizes forward error correction coding, adding redundant check bits to the data packet, ensuring correct decoding even with 30% data loss, thus improving communication reliability in harsh environments.

[0087] C2. After the first receiving node within a range of N meters receives the distress signal, it records the distress signal strength and forwards it to other nodes in the network;

[0088] The Bluetooth receiver nodes adopt a modular design, with each node containing a BLE 5.0 transceiver module, an STM32 microcontroller, a Wi-Fi communication module, and local memory. Node deployment uses a grid layout, with one node every 30 meters in indoor environments and one node every 100 meters in open environments, ensuring continuous signal coverage.

[0089] The receiving node continuously listens to the Bluetooth broadcast channel, employing an interrupt-driven reception mechanism. Upon detecting a distress signal, the processing procedure is immediately activated. The node first verifies the integrity and validity of the data packets, checks the validity of the checksum and timestamp, and filters out duplicate or corrupted data packets.

[0090] Signal strength is recorded using RSSI measurements with an accuracy of 1 dBm and a measurement range of -100 dBm to 0 dBm. Each node records the signal's time of arrival (accurate to 1 millisecond), signal strength, sender identifier, and its own node number, forming a signal reception record. To improve positioning accuracy, the node also measures the angle of arrival (AOA) of the signal using phased array antenna technology, achieving an angle measurement accuracy of up to 5 degrees.

[0091] The receiving node employs a fast response mechanism, with a delay of less than 10 milliseconds from receiving the signal to initiating forwarding. The forwarded signal adds forwarding path information to the original data packet, including the forwarding node ID, forwarding timestamp, and signal strength attenuation value, increasing the total data packet length by 8 bytes.

[0092] C3. After receiving the forwarded distress signal, other receiving nodes compare the distress signal strength and select the path with the strongest distress signal for secondary forwarding.

[0093] The mesh network employs a signal quality-based routing algorithm. Each node maintains a routing table, recording the optimal path to other nodes and the monitoring and processing system. The routing table is updated every 30 seconds, and network topology and link quality information is collected through periodic probe messages between nodes.

[0094] The steps for selecting the path with the strongest distress signal for secondary relay include C3.1 to C3.3:

[0095] C3.1 When the receiving node receives multiple identical signals at the same time, it waits 2 seconds to collect the paths of all distress signals;

[0096] When a receiving node receives a distress signal, it starts a path collection timer with a set waiting time of 2 seconds. During the waiting period, the node continues to listen for distress signals with the same sequence number forwarded by other nodes. For each forwarded signal received, the node records its source node, the number of forwarding hops, and the cumulative signal attenuation.

[0097] The path collection employs a dynamic caching mechanism, allocating 32KB of temporary buffer space to each node to store path information, and can process up to 16 different distress signals simultaneously. Each path record contains 16 bytes of information: source node ID (4 bytes), relay node list (up to 4 nodes, 2 bytes each), cumulative delay (2 bytes), and signal quality score (2 bytes).

[0098] To prevent path explosion, the system sets a maximum hop limit of 6; paths exceeding this limit are automatically discarded. A loop detection algorithm is also employed; if duplicate nodes are found in a path, it is immediately marked as an invalid path.

[0099] C3.2 Calculate the signal strength attenuation level for each path;

[0100] Signal attenuation calculation is based on a link quality assessment algorithm, which comprehensively considers three factors: signal strength attenuation, transmission delay, and packet loss rate. Signal strength attenuation is calculated cumulatively; the cumulative attenuation value increases by the RSSI difference of that link segment after each relay node.

[0101] Transmission delay is calculated by comparing timestamps and includes signal propagation delay and node processing delay. Packet loss rate is based on historical statistics; each node records the success rate of communication with other nodes in the past hour, serving as an important indicator of link quality.

[0102] The overall signal quality score uses a weighted average algorithm; a higher score indicates better path quality.

[0103] C3.3 Select the path with the least attenuation as the forwarding path, and send forwarding identifiers to other paths;

[0104] The path selection employs a greedy algorithm combined with a load balancing strategy. The system first selects the path with the highest overall score as the primary forwarding path, and simultaneously selects the path with the second highest score as a backup path. To avoid network hotspots, if a node forwards more than a threshold number of packets in the past minute, the path score passing through that node will be lowered.

[0105] The forwarding flag is implemented using a Bloom filter. Each node maintains a 64-byte bit vector for quickly detecting duplicate distress signals. When a node decides to forward a signal, it sends a "forwarded notification" to all neighboring nodes, containing the signal sequence number and the forwarding node ID, to prevent other nodes from repeatedly forwarding the same signal.

[0106] The forwarding strategy also takes into account the urgency level. For distress signals with severe anomalies, the system will activate both the primary and backup paths for parallel forwarding, sacrificing some network resources in exchange for higher transmission reliability.

[0107] C4. Repeatedly forward the distress signal until it reaches the monitoring and processing system;

[0108] The monitoring and processing system acts as the aggregation node of the mesh network, connecting to the cloud server via Wi-Fi or Ethernet. The system employs a multi-receiver mechanism, simultaneously monitoring data from multiple network access points to ensure that distress signals can be received even if some network links fail.

[0109] The arrival confirmation mechanism uses an end-to-end confirmation protocol. Upon receiving a distress signal, the monitoring system immediately sends a confirmation message, which returns to the signal source along the original path. If the smart ring receives the confirmation message within 5 seconds, it will stop retransmitting; otherwise, it will continue trying other paths or increase its transmission power.

[0110] Self-organizing network systems also perform location steps, specifically C5-C7:

[0111] C5. When at least three receiving nodes receive a distress signal simultaneously, each receiving node records the arrival time and signal strength of the distress signal.

[0112] When a distress signal is detected, the system activates the positioning mode, requiring all receiving nodes within the signal coverage area to participate in positioning measurements. The nodes employ a high-precision clock synchronization mechanism, using a GPS clock or Network Time Protocol (NTP) to ensure consistent time references across all nodes, achieving a synchronization accuracy of 1 millisecond.

[0113] Signal arrival time measurement employs hardware timestamp technology to record the exact arrival time of the signal at the RF chip level, avoiding the impact of software processing delays. Each node participating in the positioning measures signal strength (RSSI), time of arrival (TOA), and angle of arrival (AOA), forming a complete positioning measurement dataset.

[0114] To improve measurement accuracy, the system employs a multiple sampling averaging algorithm, performing five consecutive measurements within 500 milliseconds and taking the average as the final measurement result. Simultaneously, the system records the measurement confidence level and assesses the reliability of the measurement based on signal quality and environmental noise levels.

[0115] C6. Each receiving node sends the time difference and intensity difference data to the monitoring and processing system;

[0116] Each node packages its positioning measurement data and sends it to the monitoring and processing system. The data packets are compressed to reduce transmission overhead. The data packets contain node coordinates (6 bytes, accuracy 1 meter), signal arrival time (4 bytes, accuracy 1 millisecond), signal strength (2 bytes, accuracy 1 dBm), angle of arrival (2 bytes, accuracy 1 degree), and measurement confidence level (1 byte).

[0117] The transmission uses a reliable transmission protocol to ensure that data from all participating nodes arrives completely at the monitoring system. The system is set with a 5-second data collection timeout. Even if data from all nodes has not been received after the timeout, the system will still begin location calculations, but will mark the data integrity index in the results.

[0118] C7. The monitoring and processing system calculates the location coordinates of the smart ring based on a triangulation algorithm, specifically including:

[0119] C7.1 Calculate the distance data from the smart ring to each receiving node based on the known coordinates and signal strength of each receiving node;

[0120] After receiving signal strength data from each receiving node, the monitoring and processing system uses a logarithmic distance path loss model to estimate the distance. The distance calculation formula is as follows: ;

[0121] in, The transmit power of the smart ring is +4dBm. Let A be the signal strength measured by the receiving node, and let A be the path loss constant (-40dBm) at a reference distance of 1 meter. The path loss index is 2.5 for indoor environments and 2.0 for open environments.

[0122] The system maintains an environmental calibration database and adjusts path loss parameters based on different deployment environments (indoor office areas, shopping malls, outdoor open spaces, etc.). For known environments, the system loads preset calibration parameters; for unknown environments, an adaptive calibration algorithm is used to estimate environmental parameters in real time by analyzing the RSSI data distribution characteristics of multiple receiving nodes.

[0123] The distance calculation also considers the impact of signal fading. A sliding window filtering technique is used to smooth the RSSI values, with a window size of 5 sampling points. A weighted average algorithm is used, with the most recent sample having a weight of 0.4, and the remaining samples having weights that decrease over time. The calculated distance data has an accuracy of 0.1 meters and an effective distance range of 2-50 meters.

[0124] The system assigns a confidence score to each distance measurement, based on a comprehensive evaluation of signal quality, multipath effect strength, and environmental noise level. Nodes with high confidence scores receive higher weights in subsequent positioning calculations, while data from nodes with confidence scores below 30% are automatically excluded.

[0125] C7.2 Establish a coordinate system with the first receiving node as the origin, and calculate the relative coordinates of the other receiving nodes;

[0126] The monitoring and processing system selects the node with the strongest signal strength from the receiving nodes participating in the positioning process as the coordinate origin to ensure the numerical stability of the positioning calculation. The coordinate system is established using a right-handed Cartesian coordinate system, with the X-axis pointing east, the Y-axis pointing north, and the Z-axis pointing vertically upward (for three-dimensional positioning).

[0127] The coordinates of the first receiving node are set to (0, 0, 0), and the relative coordinates of other receiving nodes are calculated from pre-stored absolute coordinate information. Each receiving node records its GPS coordinates or precise position relative to a reference point during deployment, with a required coordinate accuracy of 0.5 meters.

[0128] The coordinate transformation employs a planar projection algorithm to convert geographic coordinates (longitude and latitude) into Cartesian coordinates. For small areas (radius less than 1 kilometer), a simplified planar approximation algorithm is used; for large areas, UTM projection or Gauss-Kruger projection is used to ensure the accuracy of the coordinate transformation.

[0129] The system also performs coordinate system rotation optimization, selecting the coordinate system direction that most evenly distributes the participating nodes, thus avoiding a decrease in positioning accuracy caused by excessive concentration of nodes in a certain direction. The rotation angle is determined by minimizing the variance of the node coordinates.

[0130] C7.3. Establish a system of circular equations using the distance data, with each receiving node as the center and the calculated distance as the radius;

[0131] Based on the calculated distance data, the system establishes a circular equation for each receiving node. For the i-th receiving node, its circular equation is: ,in, The coordinates of the receiving node. The distance is calculated, and (x, y) are the coordinates of the smart ring's location to be determined.

[0132] During the process of establishing the equation system, the validity of the distance data will be checked, including distance range check (2-50 meters), logical consistency check (the distance difference between adjacent nodes should meet the geometric constraints), and outlier detection (the 3σ criterion is used to remove measurement values ​​with excessive deviation).

[0133] To address the impact of measurement errors, the system employs a weighted system of equations, with each circular equation assigned a weight wi. The weights are determined based on the confidence level and signal quality of the measurement at that node. The weight calculation formula is as follows: ,in, Score the confidence level. The average distance, This represents the maximum effective distance.

[0134] The system also performs ill-conditioning checks on the system of equations, calculates the condition number and determinant value, and evaluates the numerical stability of the system. If ill-conditioning is detected, regularization techniques or additional constraints are used to improve the numerical properties.

[0135] C7.4 Solve for the intersection of the system of circular equations to obtain the preliminary position coordinates of the smart ring;

[0136] The monitoring and processing system employs a nonlinear least squares method to solve the circular equation system, specifically using the Levenberg-Marquardt algorithm for iterative solution. The objective function of the algorithm is the weighted sum of squares of the residuals of each circular equation: .

[0137] The iterative process begins with an initial estimated point, which is chosen as the weighted centroid of the coordinates of all receiving nodes. The iterative formula is: ,in, For step size parameters, Let be the gradient vector of the objective function.

[0138] The algorithm employs adaptive step size control, with an initial step size of 0.1. The step size is increased (multiplied by 1.2) when the objective function value decreases and decreased (multiplied by 0.5) when the objective function value increases. The iteration terminates when the objective function change is less than 0.001 or the number of iterations exceeds 100.

[0139] To avoid getting trapped in local optima, the system employs a multi-starting-point strategy, solving the problem from five different initial points and ultimately selecting the solution with the minimum objective function value. The multiple starting points are selected using a grid method, evenly distributed within the area enclosed by the receiving nodes.

[0140] C7.5 If there are multiple intersection points, select the intersection point with the smallest error from all receiving nodes as the final position coordinates;

[0141] When the circular equation system has multiple intersection points, the system uses a comprehensive error evaluation algorithm to select the optimal solution. The error evaluation includes three dimensions: geometric error, violation of physical constraints, and statistical consistency.

[0142] Geometric error calculation: Distance deviation from each candidate intersection point to all circles. The smaller the deviation, the closer the point is to the theoretical intersection. Physical constraint checks include location rationality (it cannot be located inside a wall or in an inaccessible area) and motion continuity (the distance change from the previous positioning result should conform to the laws of human movement).

[0143] The statistical consistency assessment uses the chi-square test to examine the degree of consistency between each distance measurement and the candidate locations. The formula for calculating the statistic is: ,in, Let be the standard deviation of the i-th distance measurement. Let be the measured distance of the i-th receiving node; Let be the calculated distance from the candidate location to the i-th receiving node.

[0144] The overall score is calculated using a weighted summation: Score is the overall score of the candidate intersection points. The degree of violation of physical constraints (such as positional rationality, deviation from motion continuity). To calculate the consistency error, the weights are assigned as follows: The system selects the intersection point with the lowest overall score as the final location coordinates.

[0145] The final result also includes confidence interval estimation, using the Monte Carlo method to simulate the impact of measurement errors, and calculating a 95% confidence ellipse for the location estimate. The major axis, minor axis, and orientation angle of the ellipse provide information on the uncertainty of the positioning result, helping rescuers assess the size and direction of the search area.

[0146] The final location coordinates output by the system include X and Y coordinate values ​​(accuracy 0.1 meters), confidence ellipse parameters (major axis, minor axis, tilt angle), positioning quality score (0-100 points), and the number of nodes involved in the positioning, providing complete location information and reliability assessment for subsequent rescue decisions.

[0147] The monitoring and processing system is configured to receive and process distress signals from the self-organizing network system and execute corresponding rescue responses.

[0148] The monitoring and processing system adopts a distributed architecture, comprising three core components: a mobile application, a cloud server, and a local gateway. The mobile application runs on Android / iOS smartphones and is responsible for direct user interaction and emergency response; the cloud server provides data storage, intelligent analysis, and multi-device synchronization; and the local gateway serves as a backup processing node, ensuring the system continues to function normally during network outages.

[0149] The system employs a multi-threaded concurrent processing architecture. The main thread handles the user interface and system control, the communication thread specializes in Bluetooth and network data transmission, the analysis thread performs signal processing and location calculations, and the response thread handles emergency calls and message sending. Threads communicate with each other via message queues to ensure real-time performance and data consistency.

[0150] The database design employs a hybrid approach combining local SQLite storage and cloud-based MySQL storage. Local storage handles emergency contact information, historical distress call records, and user configuration parameters, while the cloud storage provides complete device data, statistical analysis results, and cross-device synchronization information. The system also implements an offline working mode, enabling basic distress response functions to continue even when the network is interrupted.

[0151] Specifically, the steps for the monitoring and processing system to execute a rescue response include D1 to D4:

[0152] D1. When a slightly abnormal distress signal is received, a yellow alert icon will be displayed on the phone screen for 10 seconds.

[0153] The minor anomaly response module employs a low-intrusion design to avoid unnecessary interference with users. When the system receives a minor anomaly distress signal, it first verifies the validity and integrity of the signal, including device ID matching, timestamp validity checks, and data integrity verification.

[0154] The yellow alert icon is implemented using a custom UI component. The icon is 24x24 pixels in size and uses a gradient yellow color with a slight pulsating animation effect, blinking once per second. The icon is displayed in a prominent position in the phone's status bar and the application's main screen, ensuring users notice the alert promptly.

[0155] The display duration is precisely controlled to 10 seconds, using a system timer to implement the countdown function. During the display, users can click on the icon to view detailed anomaly information, including the anomaly detection time, the smart ring's battery status, the last known location, and a description of the anomaly type. If the user actively confirms that the "status is normal," the system will immediately cancel the reminder and record the user's feedback.

[0156] The system also generates minor anomaly logs, recording the time of the anomaly, its duration, user response, and subsequent state changes, which are used to optimize the parameters of the anomaly detection algorithm. The logs are stored in JSON format and include fields such as timestamp, device ID, anomaly type, trigger parameters, user actions, and system responses.

[0157] D2. When a moderately abnormal distress signal is received, the phone vibrates 3 times and a confirmation dialog box pops up, waiting for the user to press a button.

[0158] For moderate anomalies, a more prominent alert is provided to ensure users can notice and respond promptly. The phone vibration uses a custom vibration mode, with each vibration lasting 500 milliseconds, spaced 200 milliseconds apart, vibrating three times consecutively for a total duration of 2.1 seconds. The vibration intensity is set to 80% of the maximum intensity, providing noticeable tactile feedback on various phone types.

[0159] The confirmation dialog box uses a full-screen modal design with a semi-transparent orange overlay (60% transparency) to ensure it cannot be ignored by the user. The dialog box size is 90% of the screen width, and the height adapts to the content, with a minimum height of 200 pixels. The dialog box title is "Security Status Confirmation," and the content displays "An abnormal situation may have been detected. Please confirm your current status."

[0160] The dialog box provides three response buttons: a green "I'm Safe" button (120 pixels wide), a yellow "Need Attention" button (120 pixels wide), and a red "Emergency Help" button (120 pixels wide). The buttons are rounded rectangles with a corner radius of 8 pixels and a font size of 16sp to ensure good readability on various screen sizes.

[0161] The timeout for waiting for a user response is set to 5 seconds, displayed as a countdown timer that updates every second. If the user does not respond within the timeout period, the system will automatically select the "Emergency Assistance" option and escalate the process to a severe exception handling procedure. The user's selection will be immediately recorded in the database and simultaneously synchronized to the cloud server via the network.

[0162] D3. Upon receiving a severe distress signal, dial the preset first emergency contact number and simultaneously send a distress signal containing location coordinates to all emergency contacts.

[0163] Severe anomaly response is the system's highest priority function, employing a multi-path parallel execution strategy to ensure timely delivery of rescue information. The system first reads a pre-set list of emergency contacts from the local database, including information such as name, mobile phone number, relationship type, priority, and special instructions.

[0164] The phone dialing function is implemented through Android's TelecomManager or iOS's CallKit interface, supporting both direct dialing and dialing via third-party applications. Before making a call, the system checks call permissions and network status to ensure a successful call connection. During the dialing process, the system displays an emergency call interface on the screen, including the called number, dialing time, and call status.

[0165] Location coordinates are represented in standard GPS format with a precision of 6 decimal places and converted into an easily understandable address description. The system uses a reverse geocoding API to convert the coordinates into a detailed address, including province, city, district, street name, and house number. If reverse geocoding fails, the system directly sends the latitude and longitude coordinates and an online map link.

[0166] The SOS text message uses a preset template format: "{User Name} may be in an emergency. Location: {Detailed Address}, Coordinates: {Longitude}, {Latitude}, Time: {Time of Occurrence}, Map: {Map Link}. Please check and provide assistance immediately!" The text message length is limited to 140 characters to ensure it can be sent as a single message.

[0167] The bulk messaging function uses a batch sending interface to simultaneously send SOS messages to all emergency contacts. The system records the sending status of each SMS message, including the sending time, recipient number, sending result, and error message. If a message fails to be sent to a number, the system will automatically retry up to 3 times, with a retry interval of 5 seconds.

[0168] D4. If the first emergency contact does not answer within 30 seconds, the second emergency contact will be automatically dialed.

[0169] The failover mechanism employs intelligent monitoring and automatic upgrade strategies to ensure timely switching to a backup contact when the primary contact becomes unresponsive. The system detects call status through a call status monitoring interface, including ringing, busy, unanswered, and call setup status.

[0170] The 30-second timeout starts from the moment the call is dialed, using a high-precision timer to ensure accuracy. During the countdown, the system displays a countdown interface showing the current called number, remaining waiting time, and a "call answered" confirmation button. The countdown can be stopped early if the user manually confirms the call has been answered.

[0171] The process for calling the second emergency contact is exactly the same as for the first contact, including making a phone call, sending an SMS, and monitoring their status. The system supports sequential dialing of up to five emergency contacts, and the waiting time for each contact can be configured individually, with a default of 30 seconds. If all contacts cannot be reached, the system will automatically dial preset emergency rescue numbers (such as 120, 110, etc.).

[0172] The fault logging function records the results of each call in detail, including call time, call duration, reason for hanging up, and user feedback. This data is used to optimize the sorting of the contact list and improve failover strategies. The system also supports a manual testing function, allowing users to periodically test the reachability of emergency contacts to ensure uninterrupted communication in critical moments.

[0173] The monitoring and processing system implements complete status monitoring and logging functions, tracking system operating status and performance indicators in real time. Status monitoring includes key indicators such as Bluetooth connection status, network connection quality, battery level, storage space utilization, and CPU / memory usage.

[0174] The logging system employs a tiered logging mechanism, including five levels: DEBUG, INFO, WARN, ERROR, and FATAL. The system automatically records all distress calls, user actions, system responses, and error messages. Log files are split by day, retaining up to 30 days of historical data. Critical events are also synchronized to the cloud server to ensure data is not lost due to equipment failure.

[0175] The system also features a health check function, automatically detecting the operational status of all system components every 5 minutes, including the Bluetooth module, GPS module, communication module, and storage module. If an anomaly is detected, it will automatically attempt to repair or restart the relevant components to ensure the system remains operational at all times.

[0176] Example 2: This example also provides an electronic device applicable to the case of a smart distress ring based on Bluetooth Low Energy, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for implementing a smart distress ring based on Bluetooth Low Energy as proposed in the above examples.

[0177] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for implementing a smart distress ring based on Bluetooth Low Energy as proposed in the above embodiments.

[0178] The storage medium proposed in this embodiment and the method for implementing a smart distress ring based on Bluetooth Low Energy proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0179] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0180] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low power Bluetooth based smart distress ring system comprising a smart ring, characterized in that, Also comprising: A multi-modal sensor combination, including motion sensors, physiological sensors, and environmental sensors; A multi-level trigger module configured to determine the user's state based on the fusion data collected by the multi-modal sensors through a preset multi-level early warning algorithm, and trigger the corresponding help signal according to different risk levels; An adaptive communication module configured to transmit the help signal through Bluetooth Low Energy; A self-organizing network system including multiple Bluetooth receiving nodes forming a mesh network for help signal relay and forwarding; A monitoring processing system configured to receive and process the help signal from the self-organizing network system and execute the corresponding rescue response.

2. The low power Bluetooth based smart distress ring system as claimed in claim 1 wherein, The step of determining the user's state through a preset multi-level early warning algorithm includes: Establishing user personal baseline data, continuously monitoring user physiological and behavioral data for X days as normal state benchmark; Calculating the deviation value of the fusion data from the personal baseline data; According to the deviation degree, it is divided into mild abnormality, moderate abnormality and severe abnormality.

3. The low power Bluetooth based smart distress ring system as claimed in claim 2, wherein, The step of dividing according to the deviation degree includes: When the deviation value is 1.5-2 times the baseline range, it is determined as mild abnormality, and the first level warning is started; When the deviation value is 2-3 times the baseline range, it is determined as moderate abnormality, the second level warning is started and the user's confirmation response within 5 seconds is waited; When the deviation value exceeds 3 times the baseline range, it is determined as severe abnormality, and the third level emergency help is immediately started. 4.The low power Bluetooth based smart distress ring system as claimed in claim 1 or 3, wherein, The step of transmitting the help signal by the self-organizing network system includes: The smart ring sends the help signal containing the device ID and the help level to the surrounding at 1 second interval; After the first receiving node within N meters receives the help signal, it records the help signal strength and forwards it to other nodes in the network; After receiving the forwarded help signal, other receiving nodes compare the help signal strength and select the strongest path for secondary forwarding; Repeat the forwarding until the help signal reaches the monitoring processing system.

5. The low power Bluetooth based smart distress ring system as claimed in claim 4, wherein, The step of selecting the strongest path for secondary forwarding of the help signal includes: When multiple same signals are received by the receiving node at the same time, wait for 2 seconds to collect all the paths of the help signal; Calculate the signal strength attenuation degree of each path; Select the path with the smallest attenuation as the forwarding path and send the forwarded identifier to other paths.

6. The low power Bluetooth based smart distress ring system as claimed in claim 5, wherein, The self-organizing network system also performs a positioning step, specifically including: When at least 3 receiving nodes receive the help signal at the same time, each receiving node records the help signal arrival time and signal strength; Each receiving node sends the time difference and strength difference data to the monitoring processing system; The monitoring processing system calculates the position coordinates of the smart ring based on the triangulation algorithm.

7. The low power Bluetooth based smart distress ring system as claimed in claim 6, wherein, The step of calculating the position coordinates of the smart ring based on the triangulation algorithm includes: According to the known coordinates and signal strength of each receiving node, calculate the distance data from the smart ring to each receiving node; Establish a coordinate system with the first receiving node as the origin, and calculate the relative coordinates of other receiving nodes; Establish a circular equation set through the distance data, with each receiving node as the center and the calculated distance as the radius; Solve the intersection of the circular equation set to get the preliminary position coordinates of the smart ring; If there are multiple intersection points, the intersection point with the minimum error distance to all receiving nodes is selected as the final position coordinate.

8. The low power Bluetooth based smart distress ring system as claimed in claim 3, wherein, The monitoring processing system performs the steps of rescue response, including: When receiving a mild abnormality distress signal, a yellow reminder icon is displayed on the phone screen for 10 seconds; When receiving a moderate abnormality distress signal, the phone vibrates 3 times and a confirmation dialog box is popped up, waiting for the user to click the button; When receiving a severe abnormality distress signal, the preset first emergency contact phone is dialed, and a distress signal containing the position coordinates is sent to all emergency contacts; If the first emergency contact does not answer within 30 seconds, the second emergency contact phone is automatically dialed.

9. The low power Bluetooth based smart distress ring system as claimed in claim 4, wherein, The adaptive communication module transmits the distress signal, including the steps of: Detecting the Bluetooth signal interference strength of the current environment; Adjusting the transmission power according to the interference strength; If the interference strength exceeds the set threshold, increase the power; If the interference strength does not exceed the set threshold, reduce the power; Select the Bluetooth frequency channel with the minimum interference strength to send the signal; If sending fails for 3 consecutive times, switch to the backup channel to resend.

10. The low power Bluetooth based smart distress ring system as claimed in claim 1, wherein, The multi-modal sensor combination collects and fuses data, including the steps of: Collecting three-axis acceleration and angular velocity data every 0.1 seconds through the motion sensor; Collecting heart rate data every 1 second and skin temperature data every 10 seconds through the physiological sensor; Collecting ambient light and air pressure data every 30 seconds through the environmental sensor; Aligning the collected data by timestamp to form a fusion data packet.

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