Intelligent head-mounted fire self-rescue breathing apparatus and fire scene rescue information support method thereof

Abstract

This invention discloses an intelligent head-mounted fire self-rescue respirator for GPS-denied environments and its fire rescue information support method. The respirator includes a mask body, a breathing-driven energy capture-sensing linkage mechanism, an edge processing module, a multi-parameter detection module, and an adaptive communication and positioning module. The impeller generator of the linkage mechanism connects to a power management unit and a breathing frequency sensing unit, achieving coupling of energy capture and breathing monitoring. The multi-parameter detection module includes a photoelectric sensor array and a smoke concentration sensor. The communication and positioning module integrates fusion calculation and automatic relay node switching units. The method includes breathing-vitality coupled data acquisition, threat gradient-driven dynamic positioning, topology adaptive networking, fire situation-driven path planning, and multimodal adaptive feedback. This invention achieves self-powered endurance and dynamic power consumption matching, forming a closed-loop rescue support through end-edge-cloud collaboration, improving the efficiency and safety of self-rescue guidance in GPS-denied fire environments.

Description

Technical Field

[0001] This invention relates to the field of fire emergency rescue equipment technology, specifically to an intelligent head-mounted fire self-rescue respirator for GPS-denied environments and its fire rescue information support method. The core technical problem this invention aims to solve is: to achieve self-powered operation, high-precision positioning, reliable communication, and effective self-rescue guidance of the respirator in fire environments with GPS denial, high temperature, dense smoke, and dynamic changes in network topology. Background Technology

[0002] Firefighting breathing apparatus is an essential personal protective equipment for personnel at fire scenes, primarily used to filter toxic fumes and provide clean breathing air. With the development of IoT technology, some improvement solutions attempt to integrate sensors and communication modules into the breathing apparatus to enhance the intelligence level of fire rescue.

[0003] Regarding positioning technology, patent CN203870244U discloses a firefighter positioning device that uses RFID signal transmitters pre-deployed inside buildings and RFID signal receivers worn by firefighters to locate personnel in a fire. However, RFID positioning accuracy is typically low (error greater than 5 meters) and it struggles to adapt to signal attenuation issues in dense smoke environments. Patent CN107462868A discloses an indoor positioning system based on Bluetooth MESH, but it does not consider the signal attenuation effect of smoke in a fire, nor does it disclose techniques for dynamically adjusting transmission power and correcting the ranging model based on smoke concentration, nor does it address the collaborative mechanism between breathing power supply and dynamic power consumption management.

[0004] Regarding energy supply, existing breathing masks mostly rely on battery power. In the high-temperature environment of a fire, battery performance drops sharply, and the operating time is greatly shortened. Although some solutions attempt to convert the mechanical energy of human respiration into electrical energy, they have not designed a coordinated control mechanism for energy capture and power consumption management specifically for the fire environment.

[0005] While existing technologies have attempted to apply wireless positioning and sensor monitoring to firefighting equipment, these are mostly single-function improvements lacking systematic integration. Specifically, the following technical problems exist:

[0006] (1) Energy supply and demand mismatch: The peak power consumption of wireless communication modules is high, and the existing technology has not established a dynamic power consumption adjustment mechanism to match the fluctuating energy supply;

[0007] (2) Signal transmission is affected by smoke and dust: Dense smoke in the fire scene has an attenuating effect on wireless signals. The existing positioning model does not take into account the influence of smoke and dust, which leads to an increase in ranging error;

[0008] (3) Insufficient adaptability to high temperature environment: The temperature in the fire scene can reach more than 100℃. The existing solutions have not solved the problems of high temperature reliability of electronic components and smoke and dust pollution prevention of sensor optical paths;

[0009] (4) Dynamic changes in network topology: Beacon nodes may be damaged in the fire, and existing networking technologies have not optimized relay node election strategies for this scenario.

[0010] While existing technologies address individual issues, they fail to consider the coupling effects of these problems in a fire environment: high temperatures accelerate battery degradation, dense smoke interferes with positioning signals and contaminates optical sensors, and beacon damage leads to changes in network topology. Therefore, simply combining existing technologies cannot form a systematic solution for GPS-based fire denial, and it is difficult to provide wearers with timely and effective self-rescue guidance. Summary of the Invention

[0011] To address the aforementioned shortcomings in the prior art, the first objective of this invention is to provide an intelligent head-mounted fire self-rescue respirator for GPS-denied environments that can provide timely and effective self-rescue guidance. The second objective is to provide a fire rescue information support method that aims to solve problems such as inaccurate positioning, insufficient energy supply, unstable communication links, difficulties in human-computer interaction, and the reliability of electronic components under high-temperature environments.

[0012] To achieve the first objective mentioned above, the respirator of the present invention adopts the following technical solution:

[0013] This invention provides an intelligent head-mounted fire self-rescue respirator for GPS-denied environments.

[0014] It includes a mask body, a breathing-driven energy capture-sensing linkage mechanism, an edge processing module, a multi-parameter detection module, an adaptive MESH communication and positioning module, a battery and energy management unit, and an interaction module.

[0015] The mask body is equipped with a breathing channel; the breathing-driven energy capture-sensing linkage mechanism includes an impeller generator located in the breathing channel, a power management unit and a breathing frequency sensing unit respectively connected to the output end of the impeller generator. The impeller generator is used to convert the kinetic energy of breathing air into electrical energy, and the breathing frequency sensing unit is used to detect the periodic fluctuation of the impeller speed in the impeller generator and extract the breathing frequency characteristic signal; the power management unit is used to monitor the power generation in real time, receive the electrical energy generated by the impeller generator, and rectify and stabilize it before sending it to the battery and the power management unit.

[0016] The multi-parameter detection module includes a forehead-mounted photoelectric sensor array, an environmental threat gradient detection unit for detecting the current environmental gradient smoke concentration, and an environmental noise detection unit for collecting environmental noise characteristic signals. The forehead-mounted photoelectric sensor array includes a heart rate and blood oxygen sensor; the forehead-mounted photoelectric sensor array is attached to the forehead skin via flexible circuitry to collect vital signs parameters such as the wearer's heart rate and blood oxygen saturation; the multi-parameter detection module is bidirectionally connected to the edge processing module for transmitting detection data and receiving control commands.

[0017] The adaptive MESH communication positioning module integrates an RSSI / AoA fusion calculation unit for positioning and an automatic relay node switching unit connected to the RSSI / AoA fusion calculation unit; the automatic relay node switching unit is used to switch a nearby respirator node as a temporary relay node when the signal of a preset beacon node is lost; the adaptive MESH communication positioning module is connected to the edge processing module;

[0018] The interaction module includes a bone conduction speaker and an LED indicator on the outside of the mask body, and a vibration motor and a bone conduction microphone on the inside of the mask body. The interaction module is connected to the edge processing module and is used to convey voice commands or light signal prompts to the wearer.

[0019] The edge processing module is located inside the mask body and is connected to the breathing-driven energy capture-sensing linkage mechanism, the multi-parameter detection module, the adaptive MESH communication and positioning module, and the interaction module. The edge processing module includes a vital sign fusion computing unit, a smoke and dust interference prevention unit, and a dynamic power consumption adjustment port.

[0020] The vital signs fusion calculation unit is used to receive the respiratory rate characteristic signal emitted by the respiratory rate sensing unit and the heart rate and blood oxygen characteristic signal emitted by the multi-parameter detection module, and calculate the vital signs of the current mask wearer. It adjusts the sampling frequency of the respiratory rate sensing unit, the environmental threat gradient detection unit and the heart rate and blood oxygen sensor, as well as the detection sensitivity of the environmental threat gradient detection unit, according to the vital signs status.

[0021] The anti-smoke and dust interference unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration.

[0022] The dynamic power consumption adjustment port is used to receive the current power generation and adjust the wake-up of the heart rate and blood oxygen sensor and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit according to the smoke concentration characteristic signal and / or heart rate and blood oxygen characteristic signal and / or respiratory rate characteristic signal and / or environmental noise characteristic signal.

[0023] Furthermore, the self-rescue respirator also includes a thermal protection packaging structure, which includes an isolation shell that houses the edge processing module and the battery. The isolation shell is filled with a thermal insulation filling layer formed by phase change thermal insulation material to ensure the reliable operation of the core electronic module in a high-temperature environment.

[0024] Furthermore, the battery and energy management unit are connected to the power management unit and are charged by the impeller power generation device, and the two work together to supply power to the system.

[0025] Furthermore, the dynamic power consumption adjustment port is used to regulate the wake-up of the heart rate and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit based on the smoke concentration characteristic signal and / or heart rate and / or respiratory rate characteristic signal and / or environmental noise characteristic signal. When the smoke concentration sensor detects a value exceeding a first preset threshold, the heart rate and / or blood oxygen sensor is activated, and the power management unit prioritizes allocating the power output from the impeller generator to the heart rate and blood oxygen sensor. When the heart rate and blood oxygen sensor detects a value exceeding a second preset threshold, the bone conduction speaker is activated, and the dynamic power consumption adjustment port reserves power according to the expected broadcast duration. If the remaining battery power is insufficient, the broadcast content of the bone conduction speaker is shortened and the flashing frequency of the LED indicator is increased.

[0026] Furthermore, the environmental threat gradient detection unit includes a first smoke concentration sensor and a second smoke concentration sensor. The first smoke concentration sensor is disposed at the lower part of the mask body to detect near-ground smoke concentration, and the second smoke concentration sensor is disposed at the upper part of the mask body to detect smoke concentration at eye level. The edge processing module adjusts the transmission power of the adaptive MESH communication positioning module according to the difference ΔC between the detection values ​​of the first smoke concentration sensor and the second smoke concentration sensor: when ΔC is greater than a preset gradient threshold, it is determined that there is a layered smoke flow, and the transmission power is increased. The edge processing module also queries a pre-stored smoke concentration-attenuation coefficient mapping table according to the ΔC value to correct the smoke attenuation coefficient of the RSSI ranging model.

[0027] Furthermore, the automatic relay node switching unit for switching a nearby respirator node as a temporary relay node when the preset beacon node signal is lost is as follows: when the preset beacon node signal is detected to be lost, a relay request message is broadcast; a response message returned by a nearby respirator node is received, the response message containing the node's remaining power and signal strength information; nearby respirator nodes with remaining power greater than a preset power threshold and signal strength greater than a preset signal strength threshold are selected as candidate nodes; at least one candidate node is selected as a temporary relay node; the reconstructed network maintains positioning clock consistency through a multi-hop time synchronization protocol.

[0028] Furthermore, the smoke and dust interference prevention unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration. Specifically, it dynamically adjusts the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration sensor detection value, and activates the red light and infrared dual-wavelength differential measurement mode when the smoke concentration is greater than the smoke and dust prevention activation threshold.

[0029] Furthermore, the bone conduction speaker and LED indicator are configured for multimodal adaptive feedback: the feedback mode is automatically selected based on the environmental noise detection value detected by the environmental noise detection unit; when the environmental noise is less than a first preset threshold, the bone conduction speaker is enabled to broadcast voice navigation; when the environmental noise is greater than or equal to the first preset threshold, the LED indicator switches to the light pulse coding prompt mode.

[0030] Furthermore, the impeller power generation device includes a miniature brushless motor, the stator winding of which is made of high-temperature resistant insulating material, and the rotor permanent magnet of which is made of high-temperature resistant permanent magnet material.

[0031] To achieve the second objective mentioned above, the fire rescue information support method of the present invention adopts the following technical solution:

[0032] This invention provides a fire rescue information support method based on the above-mentioned respirator, comprising the following steps:

[0033] Step 1: Respiratory-vital signs coupled data acquisition: The impeller power generation device converts respiratory airflow kinetic energy into electrical energy. At the same time, the edge processing module identifies the respiratory frequency based on the impeller speed fluctuation characteristics. When the respiratory frequency exceeds the preset threshold and the blood oxygen saturation is lower than the preset threshold, the hypoxia compensation mode is triggered.

[0034] Step 2: Threat gradient driven dynamic localization: The transmission power of the MESH communication localization module is adapted based on the smoke concentration difference between near the ground and eye level, and the smoke attenuation coefficient of the RSSI ranging model is corrected based on the smoke concentration difference.

[0035] Step 3: Topology-adaptive MESH networking: When the signal loss of the preset beacon node is detected, a nearby respirator is automatically selected as a temporary relay node;

[0036] Step 4: Fire situation-driven path planning: The fire rescue information support platform receives coordinate data and threat gradient data uploaded by each breathing apparatus, constructs a time-varying potential field model, and plans the path with minimum threat exposure;

[0037] Step 5: Multimodal adaptive feedback: Select the feedback mode based on the ambient noise detection value, and convey the path information planned in Step 4 to the wearer in the form of voice or light signals.

[0038] Furthermore, the automatic selection of a neighboring respirator as a temporary relay node in step 3 specifically includes:

[0039] S3.1: Broadcast a relay request message to collect responses from neighboring nodes. The response message includes the node ID, remaining battery power, and signal strength.

[0040] S3.2: Filter candidate nodes whose remaining power is greater than a preset power threshold and whose signal strength is greater than a preset signal strength threshold;

[0041] S3.3: Select the candidate node with the strongest signal strength as the temporary relay node. If the signal strengths are the same, select the one with the higher remaining power.

[0042] S3.4: Broadcast the relay node update message to the entire network to synchronously update the network topology;

[0043] Furthermore, the construction of the time-varying potential field model described in step 4 includes:

[0044] The obstacle potential field U_obs(x,y,z,t) = k_obs·C_smoke(x,y,z,t)·exp(-d / λ), where k_obs is the potential field strength coefficient, C_smoke is the smoke concentration, d is the distance to the obstacle, and λ is the attenuation coefficient.

[0045] The potential field of the personnel target point U_target(x,y,z,t) = -k_target·H_risk(t)·exp(-d_s / μ), where k_target is the target attraction coefficient, H_risk(t) is the risk level function based on heart rate and blood oxygen, d_s is the distance to the safety exit, and μ is the attenuation coefficient;

[0046] The minimum cumulative threat exposure path is obtained by solving the integral curve of the potential field gradient ∇(U_obs+U_target)=0.

[0047] Furthermore, the multimodal adaptive feedback mentioned in step 5 specifically refers to:

[0048] When the noise level is less than a first preset threshold, the bone conduction speaker will broadcast a voice command.

[0049] When the noise level is greater than or equal to the first preset threshold, the LED indicator lights encode directional information using light pulse frequencies: flashing at the first frequency indicates going straight, flashing at the second frequency indicates turning left, and flashing at the third frequency indicates turning right. At the same time, the vibration motor inside the mask emits vibration prompts in the corresponding direction.

[0050] Furthermore, the method also includes step 6: when the wearer issues a voice distress call through the bone conduction microphone, the respirator automatically triggers a rapid flashing red LED and sends an SOS signal and real-time coordinates to the fire rescue information support platform, while simultaneously broadcasting a distress message at maximum transmission power, triggering an audible and visual alarm for nearby respirators.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) Self-powered and dynamic power consumption matching: The breathing-driven energy capture-sensing linkage mechanism described in claim 1 converts breathing kinetic energy into electrical energy to achieve self-powered system; at the same time, the power management unit and dynamic power consumption adjustment port of claim 1, and the cascade wake-up and energy budget collaborative control of claim 2 achieve dynamic matching of energy supply and communication power consumption, effectively extending the battery life in high temperature environments.

[0053] (2) High-precision positioning and adaptive correction: High-precision positioning is achieved through the RSSI / AoA fusion calculation unit of claim 1; the threat gradient difference is detected by the dual-height smoke concentration sensor of claim 3, and the smoke attenuation coefficient is corrected in real time to eliminate the interference of smoke on wireless signals and ensure positioning reliability.

[0054] (3) Network topology adaptation: Through the relay node automatic switching unit of claim 1 and the election process of claim 4, a nearby respirator can be quickly elected as a temporary relay node when the beacon node is damaged, the network topology can be reconstructed, the positioning clock consistency can be maintained, and the communication link can be ensured to be stable.

[0055] (4) Closed-loop intelligent rescue support: Through the edge processing module of claim 1, the adaptive MESH communication and positioning module, and the fire rescue information support method of claims 8-13, a complete closed loop of data acquisition, edge computing, cloud decision-making, and terminal feedback is achieved. The wearer can obtain graded alarms and escape guidance instructions in real time, and the command center can simultaneously grasp the overall situation, improving rescue efficiency.

[0056] (5) Multimodal human-computer interaction: Through the bone conduction speaker and LED indicator of claim 1 and the multimodal adaptive feedback configuration of claim 6, the voice navigation or light pulse code prompt mode is automatically switched according to the ambient noise to ensure that the wearer can still accurately receive instructions in a high noise environment.

[0057] (6) High temperature environment adaptability: Through the thermal protection packaging structure of claim 1 and the high temperature resistant material design of claim 7, the influence of external high temperature on the core electronic module is effectively blocked, ensuring the stable operation of the system in the extreme environment of the fire scene.

[0058] (7) Rapid emergency rescue response: Through the voice-triggered SOS signal, rapid flashing of red LED, real-time coordinate reporting, and multi-node sound and light alarm broadcasting mechanism described in claim 13, the instantaneous reporting of rescue information and neighbor coordination are realized, significantly shortening the rescue response time. Attached Figure Description

[0059] Figure 1 : Schematic diagram of the appearance of the respirator of the present invention.

[0060] Figure 2 : Cross-sectional view of the internal structure of the respirator of the present invention.

[0061] Figure 3 : System architecture block diagram of the present invention.

[0062] Figure 4 : Flowchart of the fire rescue information support method of the present invention.

[0063] Figure 5 This invention provides a schematic diagram of a three-dimensional sand table for fire rescue.

[0064] Reference numerals: 1. Mask body; 2. Smoke concentration sensor; 2a. First smoke concentration sensor; 2b. Second smoke concentration sensor; 3. Heart rate and blood oxygen sensor; 4. LED indicator; 5. Battery; 6. Impeller power generation device; 7. Bone conduction speaker; 8. Edge processing module; 9. Adaptive MESH communication and positioning module; 10. Thermal insulation filling layer; 11. Isolation shell; 12. Data processing and forwarding terminal; 13. Fire rescue information support platform; 14. Mobile rescue information support APP. Detailed Implementation Plan

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the implementation of the present invention is not limited thereto.

[0066] This invention provides an intelligent head-mounted fire self-rescue respirator for GPS-denied environments, comprising a mask body, a breathing-driven energy capture-sensing linkage mechanism, an edge processing module, a multi-parameter detection module, an adaptive MESH communication and positioning module, a battery and energy management unit, and an interaction module. The mask body has a breathing channel. The breathing-driven energy capture-sensing linkage mechanism includes an impeller generator located in the breathing channel, a power management unit and a breathing frequency sensing unit connected to the output end of the impeller generator. The impeller generator converts the kinetic energy of breathing airflow into electrical energy. The breathing frequency sensing unit detects the periodic fluctuations in the impeller rotation speed of the impeller generator and extracts the breathing frequency characteristic signal. The power management unit monitors the power generation in real time, receives the electrical energy generated by the impeller generator, rectifies and stabilizes it before sending it to the battery and energy management unit. The multi-parameter detection module includes a forehead-mounted photoelectric sensor array, an environmental threat gradient detection unit for detecting the current environmental gradient smoke concentration, and an environmental noise detection unit for collecting environmental noise characteristic signals. The forehead-mounted photoelectric sensor array includes a heart rate and blood oxygen sensor; the forehead-mounted photoelectric sensor array is attached to the forehead skin through a flexible circuit to collect vital signs parameters such as the wearer's heart rate and blood oxygen saturation; the multi-parameter detection module is bidirectionally connected to the edge processing module for transmitting detection data and receiving control commands; the adaptive MESH communication and positioning module integrates an RSSI / AoA fusion calculation unit for positioning and an automatic relay node switching unit connected to the RSSI / AoA fusion calculation unit; the automatic relay node switching unit is used to switch a nearby respirator node as a temporary relay node when the preset beacon node signal is lost; the adaptive MESH communication and positioning module is connected to the edge processing module; the interaction module includes a bone conduction speaker and an LED indicator on the outside of the mask body, and a vibration motor and a bone conduction microphone on the inside of the mask body, the interaction module is connected to the edge processing module, and is used to convey voice commands or light signal prompts to the wearer.The edge processing module is located inside the mask body and is connected to the breathing-driven energy capture-sensing linkage mechanism, the multi-parameter detection module, the adaptive MESH communication and positioning module, and the interaction module. The edge processing module includes a vital signs fusion calculation unit, a smoke and dust interference prevention unit, and a dynamic power consumption adjustment port. The vital signs fusion calculation unit receives the respiratory rate characteristic signal from the respiratory rate sensing unit and the heart rate and blood oxygen characteristic signal from the multi-parameter detection module, calculates the vital signs of the current mask wearer, and adjusts the respiratory rate sensing unit and environmental threat gradient detection based on the vital signs status. The sampling frequency of the unit and the heart rate and blood oxygen sensor, and the detection sensitivity of the environmental threat gradient detection unit; the anti-smoke and dust interference unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration; the dynamic power consumption adjustment port is used to receive the current power generation and adjust the wake-up of the heart rate and blood oxygen sensor and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit according to the smoke concentration characteristic signal and / or heart rate and blood oxygen characteristic signal and / or respiratory rate characteristic signal and / or environmental noise characteristic signal.

[0067] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. These embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Those skilled in the art can make equivalent substitutions for materials, parameters, and specific structures based on the teachings of these embodiments, and all such substitutions fall within the scope of protection of the present invention.

[0068] Example 1: Respirator Hardware Structure

[0069] like Figures 1 to 3 As shown in the figure, this embodiment provides an intelligent head-mounted fire self-rescue respirator for GPS-denied environments. The overall design is streamlined and includes a mask body 1 and various functional modules integrated thereon.

[0070] I. Main Structure of the Face Mask

[0071] The main body of the mask 1 is made of high-temperature resistant and fireproof material, and its inner edge is equipped with a flexible sealing ring to ensure a close fit to the wearer's face. A breathing channel is located at the front of the main body of the mask 1, and a replaceable filter element is installed at the channel entrance to filter toxic fumes in a fire. The breathing channel contains a mounting cavity for the impeller generator 6.

[0072] II. Breathing-driven energy capture-sensor linkage mechanism

[0073] The breathing-driven energy capture-sensing linkage mechanism is located in the breathing channel of the mask body 1, and includes a turbine generator 6, an energy management unit, and a breathing frequency sensing unit.

[0074] The impeller-driven power generation device 6 includes a miniature brushless motor, whose rotor blades extend into the center of the breathing channel where the airflow is strongest. When the wearer breathes, the airflow drives the blades to rotate, which in turn drives the motor rotor to rotate and generate an induced electromotive force. In this embodiment, the breathing-driven energy capture-sensing linkage mechanism uses the impeller-driven power generation device 6 as its core, and its output terminals are connected to the power management unit and the breathing frequency sensing unit, respectively.

[0075] Power Management Unit: Rectifies and stabilizes the AC power generated by the impeller generator 6 to charge the battery 5, and at the same time monitors the power generation in real time and feeds back the current energy supply status to the dynamic power consumption adjustment port of the edge processing module 8.

[0076] Respiratory rate sensing unit: By detecting the periodic fluctuations in impeller speed, it extracts the respiratory rate feature signal and transmits it to the vital signs fusion calculation unit of edge processing module 8.

[0077] III. Edge Processing Module

[0078] The edge processing module 8 uses an STM32 series microcontroller as its core and is located in a sealed cavity at the bottom of the mask body 1. The edge processing module 8 is connected to the power management unit, the respiratory rate sensing unit, the multi-parameter detection module, the adaptive MESH communication and positioning module 9, and the interaction module, and is responsible for data acquisition, local processing, hierarchical alarm, and command parsing.

[0079] The edge processing module includes a vital signs fusion computing unit, a smoke and dust interference prevention unit, and a dynamic power consumption adjustment port.

[0080] The vital signs fusion calculation unit is used to receive the respiratory rate characteristic signal from the respiratory rate sensing unit and the heart rate and blood oxygen characteristic signal from the multi-parameter detection module, and calculate the vital signs of the current mask wearer. Based on the vital signs status, the sampling frequency of the respiratory rate sensing unit, the environmental threat gradient detection unit, and the heart rate and blood oxygen sensor, as well as the detection sensitivity of the environmental threat gradient detection unit, are adjusted.

[0081] The anti-smoke and dust interference unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration.

[0082] The dynamic power consumption adjustment port is used to receive the current power generation and adjust the wake-up of the heart rate and blood oxygen sensor and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit according to the smoke concentration characteristic signal and / or heart rate and blood oxygen characteristic signal and / or respiratory rate characteristic signal and / or environmental noise characteristic signal.

[0083] Edge processing module 8 incorporates cascaded wake-up and energy budget collaborative control logic, specifically including:

[0084] First-level wake-up: When the smoke concentration sensor 2 detects a value that exceeds the first preset threshold, the heart rate and blood oxygen sensor 3 is activated. At the same time, the power management unit prioritizes the distribution of the power output from the impeller generator 6 to the heart rate and blood oxygen sensor 3.

[0085] Second-level wake-up: When the heart rate and blood oxygen sensor 3 detects a value exceeding the second preset threshold, the bone conduction speaker 7 is activated. At the same time, the dynamic power consumption adjustment port reserves power according to the expected broadcast duration. If the battery 5 has insufficient remaining power, the broadcast content of the bone conduction speaker 7 is shortened and the flashing frequency of the LED indicator 4 is increased.

[0086] IV. Multi-parameter detection module

[0087] The multi-parameter detection module includes a forehead-mounted photoelectric sensor array, an environmental threat gradient detection unit, and an environmental noise detection unit.

[0088] 1. Forehead-mounted photoelectric sensor array

[0089] The forehead-mounted photoelectric sensor array is attached to the skin contact surface of the face mask body 1 on the inner side corresponding to the forehead position via a flexible circuit board, and includes a red LED, an infrared LED, and a photoelectric detector.

[0090] The optical path of the sensor array is corrected using an anti-smoke and dust interference algorithm. Specifically, the edge processing module 8 dynamically adjusts the light source intensity of the sensor array based on the detection value of the smoke concentration sensor 2. When the smoke concentration exceeds a set threshold, a dual-wavelength differential measurement mode of red light and infrared light is activated to eliminate the scattering effect of smoke particles on the photoelectric signal.

[0091] 2. Environmental Threat Gradient Detection Unit

[0092] The environmental threat gradient detection unit includes two smoke concentration sensors positioned at different heights:

[0093] The first smoke concentration sensor 2a is located at the lower part of the mask body 1 near the chin, and is used to detect the smoke concentration near the ground.

[0094] The second smoke concentration sensor 2b is located on the upper part of the mask body 1 near the forehead, and is used to detect the smoke concentration at eye level of the wearer.

[0095] Both sensors employ miniature smoke concentration detection modules based on electrochemical or optical principles. The output signals are converted from digital signals (A / D) and then transmitted to the edge processing module 8. The edge processing module 8 performs the following adaptive adjustments based on the difference ΔC between the two sensor readings:

[0096] When ΔC is greater than the preset gradient threshold, it is determined that there is a layered smoke flow in the fire, that is, the smoke concentration in the lower part is higher than that in the upper part. At this time, the transmission power of the adaptive MESH communication positioning module 9 is increased to penetrate the high-density smoke layer.

[0097] At the same time, the smoke attenuation coefficient of the RSSI ranging model is corrected according to the ΔC value to compensate for the attenuation effect of smoke on wireless signals.

[0098] 3. Environmental noise detection unit

[0099] The environmental noise detection unit uses a MEMS microphone to collect ambient sound at a sampling rate of 1kHz. It is connected to the edge processing module, which receives the ambient sound detected by the environmental noise detection unit and calculates the A-weighted sound pressure level as the environmental noise detection value.

[0100] V. Adaptive MESH Communication and Positioning Module

[0101] The adaptive MESH communication and positioning module 9 uses a Bluetooth MESH chip that supports Bluetooth 5.0 and above protocols, and integrates an RSSI / AoA fusion calculation unit and a relay node automatic switching unit.

[0102] 1. RSSI / AoA fusion positioning

[0103] The module periodically broadcasts a positioning signal containing the device ID, while simultaneously receiving signals from pre-deployed fixed beacon nodes within the fire area. By measuring the received signal strength (RSSI) and angle of arrival (AoA), and combining this with measurement data from at least three beacon nodes, the three-dimensional coordinates (x, y, z) of the respirator are calculated, achieving high-precision positioning.

[0104] 2. Automatic relay node switching

[0105] The relay node automatic switching unit is configured to perform the following process:

[0106] a. When a signal loss is detected at a preset beacon node, a relay request message is broadcast;

[0107] b. Receive response messages from nearby respirator nodes. The response messages include node ID, remaining battery power, and signal strength information.

[0108] c. Select nearby respirator nodes that have a remaining power greater than a preset power threshold and a signal strength greater than a preset signal strength threshold as candidate nodes;

[0109] d. Select the node with the strongest signal strength from the candidate nodes as the temporary relay node. If the signal strengths are the same, select the one with the higher remaining power.

[0110] e. Broadcast the relay node update message to the entire network. The reconstructed network maintains the consistency of the positioning clock through a multi-hop time synchronization protocol.

[0111] In this embodiment, the multi-hop time synchronization protocol adopts the TSCH (TimeSlotted Channel Hopping) mechanism in the IEEE 802.15.4e standard, which synchronizes the clocks of each node in the network through beacon frames, with a synchronization accuracy better than 1ms.

[0112] VI. Thermal Protection Encapsulation Structure

[0113] The thermal protection encapsulation structure includes a metal isolation shell 11 and a thermal insulation filling layer 10. The edge processing module 8 and the battery 5 are enclosed within the metal isolation shell 11 to achieve electromagnetic shielding protection. The isolation shell 11 is immersed in the thermal insulation filling layer 10 formed by phase change thermal insulation material. In this embodiment, the thermal insulation filling layer 10 uses a paraffin-ceramic fiber composite phase change material, wherein the paraffin mass fraction is 60%-70%, the latent heat of phase change is ≥180J / g, and a large amount of heat is absorbed during phase change in the temperature range of 50-60℃; the ceramic fiber is alumina fiber, with a volume fraction of 30%-40% and a thermal conductivity ≤0.05W / (m·K), effectively blocking external heat from being conducted to the internal electronic modules.

[0114] It should be noted that the specific proportions of the paraffin-ceramic fiber composite phase change material described above (60%-70% paraffin, 30%-40% alumina fiber) are only preferred embodiments of this invention. Those skilled in the art, based on the working principle of phase change materials, know that as long as a material capable of undergoing phase change and heat absorption within a temperature range of 50-60℃ is used, and it possesses sufficient thermal conductivity (≤0.05W / (m·K)), the thermal insulation and protection objectives of this invention can be achieved. Therefore, other phase change composite materials that meet the above functional requirements (such as tetradecyl alcohol-carbon fiber composites, fatty acid-aerogel composites, etc.) are equivalent alternatives to this invention.

[0115] VII. Battery and Energy Management

[0116] Battery 5, in the battery and energy management system, is connected to the power management unit and charged by the impeller generator 6. Together, they power the system. Battery 5 is a high-temperature resistant lithium battery, encapsulated within an isolation shell 11. The electrical energy generated by the impeller generator 6 is processed by the power management unit to charge battery 5, and together they power various modules of the system. Under stable conditions, the impeller generator 6 can meet the system's basic power consumption requirements; during periods of rapid breathing or peak communication power consumption, battery 5 provides supplementary power.

[0117] VIII. Interactive Module

[0118] The interaction module includes a bone conduction speaker 7 and a three-color LED indicator 4 located on the outside of the mask body 1, as well as a vibration motor and a bone conduction microphone located on the inside of the mask body 1. All four are connected to the edge processing module 8.

[0119] The bone conduction speaker 7 is placed close to the inner side of the mask body 1, where it contacts the wearer's temporal bone. It transmits voice commands to the auditory nerve through bone conduction, ensuring that the wearer can still hear the commands clearly even in high-noise environments.

[0120] The three-color LED indicator 4 is located in a prominent position on the front of the mask body 1, using red, yellow, and green colors to indicate different states:

[0121] A solid green light indicates that the system is operating normally.

[0122] Flashing green light: In low-power standby mode;

[0123] Flashing yellow light: Triggers a yellow alert (abnormal vital signs);

[0124] Flashing red light: Triggers a red alert (emergency danger);

[0125] Rapidly flashing red light: SOS distress signal.

[0126] The bone conduction speaker 7 and LED indicator 4 are configured for multimodal adaptive feedback: the edge processing module 8 automatically selects the feedback mode based on the detected ambient noise value. The ambient noise detection unit uses a MEMS microphone to collect ambient sound at a sampling rate of 1kHz, and the edge processing module 8 calculates the A-weighted sound pressure level as the ambient noise detection value. When the ambient noise is less than a first preset threshold, the bone conduction speaker 7 is activated to broadcast voice navigation; when the ambient noise is greater than or equal to the first preset threshold, it switches to the light pulse coding prompt mode of the LED indicator 4.

[0127] IX. System Workflow

[0128] After the system is powered on, the edge processing module 8 initializes all modules and enters a low-power standby mode. When the smoke concentration sensor 2 detects that the smoke exceeds the set threshold, it automatically wakes up the heart rate and blood oxygen sensor 3 to start monitoring vital signs. All detected data is analyzed locally by the edge processing module 8 and then uploaded to the fire rescue information support platform 13 and the mobile rescue information support APP via the adaptive MESH communication positioning module 9 and the data processing and forwarding terminal 12. At the same time, it receives instructions from the platform and conveys them to the wearer through the interaction module.

[0129] Example 2: Information Support Method for Rescue in Confined Spaces During Fire

[0130] Combination Figure 4 and Figure 5This embodiment details a method for supporting fire rescue information based on the aforementioned hardware system. This method utilizes an "edge-cloud" collaborative architecture to achieve a complete closed loop from data acquisition, local processing, cloud-based decision-making, to terminal feedback.

[0131] Once firefighters or trapped individuals correctly don this breathing apparatus and enter the fire scene, the system will automatically activate. Follow these steps:

[0132] Step 1: Acquisition of Respiratory-Vitality Coupled Data

[0133] When the wearer begins to breathe, the airflow drives the impeller generator 6, located within the breathing channel, to rotate. The impeller integrates a miniature brushless motor that converts the mechanical energy of the breathing airflow into electrical energy, which, after rectification and voltage regulation, charges the battery 5, enabling the system to be self-powered. Simultaneously, the breathing frequency sensing unit monitors the periodic fluctuations in the impeller rotation speed, extracts the breathing frequency characteristic signal, and transmits it to the vital sign fusion calculation unit of the edge processing module 8.

[0134] The multi-parameter detection module starts working synchronously:

[0135] The forehead-mounted photoelectric sensor array fits into the forehead skin to collect the wearer's heart rate (HR) and blood oxygen saturation (SpO2) in real time.

[0136] The first smoke concentration sensor 2a and the second smoke concentration sensor 2b detect the smoke concentration at near-ground level and eye level, respectively, and calculate the threat gradient difference ΔC.

[0137] All sensor data is transmitted to the edge processing module 8 in real time in digital signal form. The edge processing module 8 identifies the respiratory rate based on the impeller speed fluctuation characteristics. When the respiratory rate exceeds a preset threshold (e.g., >30 breaths / minute) and the blood oxygen saturation is lower than a preset threshold (e.g., <90%), a hypoxia compensation mode is triggered: the sensor sampling frequency is reduced to 1Hz to save power, while the sensitivity of smoke concentration detection is improved. The edge processing module 8 also dynamically adjusts the LED drive current of the heart rate and blood oxygen sensor 3 according to the real-time output power of the impeller power generation device 6, maintaining the standard sampling frequency when power is sufficient and switching to intermittent sampling mode when power is insufficient.

[0138] Step 2: Threat Gradient-Driven Dynamic Localization

[0139] Edge processing module 8 performs dynamic positioning adjustment based on the difference ΔC between the detection values ​​of the first smoke concentration sensor 2a and the second smoke concentration sensor 2b:

[0140] When ΔC is greater than the preset gradient threshold (e.g., 20%obs / m), it is determined that there is a layered smoke flow. The edge processing module 8 sends a command to the adaptive MESH communication and positioning module 9 to increase the transmission power to penetrate the high-density smoke layer.

[0141] Meanwhile, the edge processing module 8 corrects the smoke attenuation coefficient of the RSSI ranging model based on the ΔC value. The specific correction method is as follows: a smoke concentration-attenuation coefficient mapping table is pre-established to store the signal attenuation coefficients corresponding to different smoke concentrations; based on the currently detected smoke concentration difference ΔC, the mapping table is queried to obtain the current smoke attenuation coefficient k_smoke; the standard RSSI ranging formula is corrected to: d = d0·10^((RSSI0 - RSSI - k_smoke·C) / 10n), where C is the current smoke concentration.

[0142] The revised positioning model, combined with RSSI / AoA fusion calculation, can still maintain high-precision positioning even in dense smoke environments at fire sites.

[0143] Step 3: Topology-adaptive MESH networking

[0144] The adaptive MESH communication positioning module 9 continuously monitors the connection status with preset beacon nodes. When a beacon node signal loss is detected, the relay node automatic switching unit immediately initiates the relay election process.

[0145] S3.1: Broadcast relay request message to collect response messages returned by nearby respirator nodes. The response message includes node ID, remaining battery power, and signal strength information.

[0146] S3.2: Select neighboring respirator nodes with remaining power greater than a preset power threshold (e.g., 30%) and signal strength greater than a preset signal strength threshold (e.g., RSSI-60dBm) as candidate nodes;

[0147] S3.3: Select the node with the strongest signal strength from the candidate nodes as the temporary relay node. If the signal strengths are the same, select the one with the higher remaining power.

[0148] S3.4: Broadcast the relay node update message to the entire network, notifying all nodes to update the network topology information;

[0149] S3.5: The reconstructed network maintains the consistency of positioning clocks through a multi-hop time synchronization protocol, ensuring time base synchronization between nodes.

[0150] Through the aforementioned adaptive networking mechanism, even if some beacon nodes are damaged due to high temperatures, the entire positioning network can still maintain connectivity and avoid positioning blind spots.

[0151] Step 4: Fire Situation-Driven Path Planning

[0152] The edge processing module 8 integrates the collected multi-parameter data with location information to generate a standardized personnel status vector in the following format: [Device ID, Coordinates (x, y, z), Heart Rate (HR), Blood Oxygen (SpO2), Smoke Concentration, Battery Percentage, Timestamp]. This status vector is forwarded by the adaptive MESH communication positioning module 9 via relay nodes, processed by the data processing and forwarding terminal 12, and finally uploaded to the fire rescue information support platform 13 via long-distance communication methods such as 4G / LoRa.

[0153] After receiving data uploaded by each respirator, the fire rescue information support platform 13 constructs a time-varying potential field model and plans the path of least threat exposure. In this embodiment, the construction of the time-varying potential field model includes:

[0154] Obstacle potential field: U_obs(x,y,z,t) = k_obs·C_smoke(x,y,z,t)·exp(-d / λ) where:

[0155] k_obs is the potential field strength coefficient, which can be preset according to the fire scale;

[0156] C_smoke(x,y,z,t) is the smoke concentration at position (x,y,z) at time t, which is obtained by interpolation of the detection data uploaded by each respirator;

[0157] d is the distance from the current position to the nearest obstacle;

[0158] λ is the attenuation coefficient, which controls the range of influence of obstacles.

[0159] Personnel target potential field: U_target(x,y,z,t) = -k_target·H_risk(t)·exp(-d_s / μ) where:

[0160] k_target is the target attraction coefficient;

[0161] H_risk(t) is the risk level function, defined as H_risk(t) = (HR / HR_normal)·(SpO2_normal / SpO2). That is, the greater the deviation of heart rate from normal value and the greater the deviation of blood oxygen from normal value, the higher the H_risk value and the stronger the attraction of the target point.

[0162] d_s is the distance from the current location to the nearest safe exit;

[0163] μ is the attenuation coefficient.

[0164] The path to minimum cumulative threat exposure is obtained by solving the integral curve of the potential field gradient ∇(U_obs+U_target)=0. The platform uses numerical methods (such as gradient descent or fast travel) to solve this integral curve, obtaining an optimized path from each wearer's current position to the nearest safe exit. This path is represented in three-dimensional space as a series of path points, ensuring that the wearer's cumulative exposure to smoke concentration is minimized during movement along the path.

[0165] In the aforementioned time-varying potential field model, the obstacle potential field U_obs and the target point potential field U_target jointly determine the final escape path. Specifically, the smoke concentration C_smoke in U_obs is collected in real-time by the environmental threat gradient detection unit, and the risk level function H_risk(t) in U_target is calculated from heart rate and blood oxygen data collected by a forehead-mounted photoelectric sensor array. These algorithmic and technical features (sensor detection) functionally support and interact with each other, together constituting a complete path planning technical solution, rather than an abstract mathematical method.

[0166] Step 5: Multimodal adaptive feedback

[0167] The fire rescue information support platform 13 converts the route information planned in step 4 into navigation instructions and sends them to the corresponding breathing apparatus via the MESH network. After receiving the instructions, the breathing apparatus's edge processing module 8 automatically selects the feedback mode based on the detected environmental noise level.

[0168] When the ambient noise level is below a first preset threshold (e.g., 85dB), the bone conduction speaker 7 is activated to broadcast voice navigation commands. Example command format:

[0169] "Please proceed straight ahead for 10 meters."

[0170] Please turn left at the next intersection.

[0171] "The emergency exit is 5 meters ahead."

[0172] The content broadcast by the bone conduction speaker 7 is dynamically updated based on the wearer's current location to ensure the continuity of navigation guidance.

[0173] When the ambient noise is greater than or equal to a first preset threshold (e.g., 85dB), the system switches to the light pulse encoding indication mode of LED indicator 4. LED indicator 4 encodes directional information using light pulses of different frequencies.

[0174] 1Hz flashing indicates straight line

[0175] A 2Hz flash indicates a left turn.

[0176] A 3Hz flash indicates a right turn.

[0177] Continuous rapid flashing indicates that you have reached the safe exit.

[0178] At the same time, the vibration motors on the inside of the mask in the corresponding direction emit vibration prompts (such as the left vibration motor starting when turning left), enhancing the perceptibility of navigation information.

[0179] Step 6: Emergency SOS and Status Broadcast

[0180] Step 6 is the preferred implementation. When the wearer encounters danger and needs emergency help, they can issue a voice distress signal such as "SOS" or "send location for help" through the bone conduction microphone. After the voice recognition unit built into the edge processing module 8 recognizes the distress signal, it automatically triggers the following emergency response process:

[0181] LED indicator 4 switches to a rapid red flashing mode, forming a conspicuous visual marker in the fire scene;

[0182] The adaptive MESH communication positioning module 9 sends an SOS signal and real-time coordinates to the fire rescue information support platform 13 at maximum transmission power.

[0183] At the same time, a distress message is broadcast to nearby respirators, triggering audible and visual alarms on nearby respirators and alerting nearby personnel to go to the rescue;

[0184] After receiving the SOS signal, the fire rescue information support platform 13 marks the location of the person as a bright red flashing mark on the three-dimensional sand table and pushes an emergency distress notification to all rescue personnel's mobile APP 14.

[0185] Through the closed-loop operation of the above six steps, the fire rescue information support method of the present invention realizes intelligent rescue support throughout the entire process, from energy self-sufficiency, environmental perception, edge early warning, precise positioning, cloud decision-making to terminal guidance, which significantly improves the self-rescue success rate of people trapped in the fire and the operational efficiency of rescuers.

[0186] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart head-mounted fire self-rescue breathing apparatus for GPS-denied environments, characterized in that, It includes the main body of the mask, a breathing-driven energy harvesting-sensing linkage mechanism, an edge processing module, a multi-parameter detection module, an adaptive MESH communication and positioning module, a battery and energy management unit, and an interaction module; The mask body is equipped with a breathing channel; The breathing-driven energy capture-sensing linkage mechanism includes an impeller power generation device located in the breathing channel, a power management unit and a breathing frequency sensing unit respectively connected to the output end of the impeller power generation device; the impeller power generation device is used to convert the kinetic energy of breathing air into electrical energy. The breathing frequency sensing unit is used to detect the periodic fluctuations in the impeller speed in the impeller power generation device and extract the breathing frequency characteristic signal; The power management unit is used to monitor the power generation in real time, receive the power generated by the turbine generator, and rectify and stabilize it before sending it to the battery and power management unit. The multi-parameter detection module includes a forehead-mounted photoelectric sensor array attached to the inside of the mask body, an environmental threat gradient detection unit for detecting the current environmental gradient smoke concentration, and an environmental noise detection unit for collecting environmental noise characteristic signals. The forehead-mounted photoelectric sensor array includes a heart rate and blood oxygen sensor. The adaptive MESH communication positioning module includes an RSSI / AoA fusion calculation unit for positioning, and an automatic relay node switching unit connected to the RSSI / AoA fusion calculation unit. The automatic relay node switching unit is used to switch a nearby respirator node as a temporary relay node when the signal of a preset beacon node is lost. The interaction module includes a bone conduction speaker and an LED indicator on the outside of the mask body, and a vibration motor and a bone conduction microphone on the inside of the mask body. The edge processing module is connected to the breathing-driven energy capture-sensing linkage mechanism, the multi-parameter detection module, the adaptive MESH communication and positioning module, and the interaction module. The edge processing module includes a vital sign fusion computing unit, a smoke and dust interference prevention unit, and a dynamic power consumption adjustment port. The vital signs fusion calculation unit is used to receive the respiratory rate characteristic signal emitted by the respiratory rate sensing unit and the heart rate and blood oxygen characteristic signal emitted by the multi-parameter detection module, and calculate the vital signs of the current mask wearer. It adjusts the sampling frequency of the respiratory rate sensing unit, the environmental threat gradient detection unit and the heart rate and blood oxygen sensor, as well as the detection sensitivity of the environmental threat gradient detection unit, according to the vital signs status. The anti-smoke and dust interference unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration. The dynamic power consumption adjustment port is used to receive the current power generation and adjust the wake-up of the heart rate and blood oxygen sensor and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit according to the smoke concentration characteristic signal and / or heart rate and blood oxygen characteristic signal and / or respiratory rate characteristic signal and / or environmental noise characteristic signal.

2. The respirator as described in claim 1, characterized in that, The dynamic power consumption adjustment port is used to regulate the wake-up of the heart rate and / or bone conduction speaker and / or LED indicator and the power distribution of the power management unit based on the smoke concentration characteristic signal and / or heart rate and / or respiratory rate characteristic signal and / or environmental noise characteristic signal. When the smoke concentration sensor detects a value exceeding a first preset threshold, the heart rate and / or bone conduction speaker is activated, and the power management unit prioritizes the power output from the impeller generator to the heart rate and / or bone conduction speaker. When the heart rate and / or bone conduction speaker detects a value exceeding a second preset threshold, the bone conduction speaker is activated, and the dynamic power consumption adjustment port reserves power according to the expected broadcast duration. If the remaining battery power is insufficient, the broadcast content of the bone conduction speaker is shortened and the flashing frequency of the LED indicator is increased.

3. The respirator as described in claim 1, characterized in that, The environmental threat gradient detection unit includes a first smoke concentration sensor and a second smoke concentration sensor b. The first smoke concentration sensor is located at the lower part of the mask body to detect near-ground smoke concentration, and the second smoke concentration sensor is located at the upper part of the mask body to detect smoke concentration at eye level. The edge processing module adjusts the transmission power of the adaptive MESH communication positioning module according to the difference ΔC between the detection values ​​of the first smoke concentration sensor and the second smoke concentration sensor: when ΔC is greater than a preset gradient threshold, it is determined that there is a layered smoke flow, and the transmission power is increased. The edge processing module also queries a pre-stored smoke concentration-attenuation coefficient mapping table based on the ΔC value to correct the smoke attenuation coefficient of the RSSI ranging model.

4. The respirator as described in claim 1, characterized in that, The method for the automatic relay node switching unit that switches a nearby respirator node as a temporary relay node when the signal of a preset beacon node is lost is as follows: when the signal of the preset beacon node is detected to be lost, a relay request message is broadcast; a response message returned by a nearby respirator node is received, the response message containing the node's remaining power and signal strength information; nearby respirator nodes with remaining power greater than a preset power threshold and signal strength greater than a preset signal strength threshold are selected as candidate nodes; at least one candidate node is selected as a temporary relay node. The reconstructed network maintains positioning clock consistency through a multi-hop time synchronization protocol.

5. The respirator as claimed in claim 1, characterized in that, The anti-smoke and dust interference unit is used to receive the smoke concentration characteristic signal emitted by the environmental threat gradient detection unit and dynamically adjust the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration. Specifically, it dynamically adjusts the light source intensity of the heart rate and blood oxygen sensor according to the smoke concentration sensor detection value. When the smoke concentration is greater than the anti-smoke and dust activation threshold, it activates the red light and infrared dual-wavelength differential measurement mode.

6. The respirator as claimed in claim 1, characterized in that, The bone conduction speaker and LED indicator are configured for multimodal adaptive feedback: the feedback mode is automatically selected based on the environmental noise detection value detected by the environmental noise detection unit. When the environmental noise is less than a first preset threshold, the bone conduction speaker is enabled to broadcast voice navigation. When the environmental noise is greater than or equal to the first preset threshold, the LED indicator switches to the light pulse coding prompt mode.

7. The respirator as claimed in claim 1, characterized in that, It also includes a thermal protection packaging structure, which includes an isolation shell that houses the edge processing module and the battery and energy management unit, and the isolation shell is filled with a thermal insulation filling layer formed by phase change thermal insulation material. The impeller power generation device includes a miniature brushless motor. The stator winding of the brushless motor is made of high-temperature resistant insulating material, and the rotor permanent magnet of the brushless motor is made of high-temperature resistant permanent magnet material.

8. A method for providing fire rescue information based on a respirator according to any one of claims 1-7, characterized in that, Includes the following steps: Step 1: The impeller power generation device converts the kinetic energy of breathing air into electrical energy. At the same time, the edge processing module identifies the breathing frequency based on the impeller speed fluctuation characteristics. When the breathing frequency exceeds a preset threshold and the blood oxygen saturation is lower than a preset threshold, the hypoxia compensation mode is triggered. The hypoxia compensation mode includes: reducing the sensor sampling frequency to save power, while improving the sensitivity of smoke concentration detection. Step 2: Using the smoke concentration difference ΔC between near-ground and eye-height levels as threat gradient data, the transmission power of the MESH communication positioning module is adaptively adjusted, and the smoke attenuation coefficient of the RSSI ranging model is corrected based on the smoke concentration difference. Step 3: When the signal of the preset beacon node is lost, a nearby respirator is automatically selected as a temporary relay node; Step 4: The fire rescue information support platform receives the coordinate data and threat gradient data uploaded by each respirator, constructs a time-varying potential field model, and plans the path of minimum threat exposure; the obstacle potential field of the time-varying potential field model is determined according to the threat gradient data, and the potential field of the personnel target point is determined according to the vital signs data. Step 5: Select the feedback mode based on the ambient noise detection value, and convey the planned path information to the wearer in the form of voice or light signals.

9. The method as described in claim 8, characterized in that, The hypoxia compensation mode described in step 1 also includes: the edge processing module dynamically adjusts the LED drive current of the heart rate and blood oxygen sensor according to the real-time output power of the impeller power generation device, maintains the standard sampling frequency when the power is sufficient, and switches to intermittent sampling mode when the power is insufficient.

10. The method as described in claim 8, characterized in that, Step 3, which involves automatically selecting a neighboring respirator as a temporary relay node, specifically includes: S3.1: Broadcast a relay request message to collect responses from neighboring nodes. The response message includes the node ID, remaining battery power, and signal strength. S3.2: Filter candidate nodes whose remaining power is greater than a preset power threshold and whose signal strength is greater than a preset signal strength threshold; S3.3: Select the candidate node with the strongest signal strength as the temporary relay node. If the signal strengths are the same, select the one with the higher remaining power. S3.4: Broadcast the relay node update message to the entire network to synchronously update the network topology; Alternatively, the construction of the time-varying potential field model described in step 4 includes: The obstacle potential field U_obs(x,y,z,t) = k_obs·C_smoke(x,y,z,t)·exp(-d / λ), where k_obs is the potential field strength coefficient, C_smoke is the smoke concentration, d is the distance to the obstacle, and λ is the attenuation coefficient. The potential field of the personnel target point U_target(x,y,z,t) = -k_target·H_risk(t)·exp(-d_s / μ), where k_target is the target attraction coefficient, H_risk(t) is the risk level function based on heart rate and blood oxygen, d_s is the distance to the safety exit, and μ is the attenuation coefficient; The minimum cumulative threat exposure path is obtained by solving the integral curve of the potential field gradient ∇(U_obs+U_target)=0; Alternatively, the multimodal adaptive feedback mentioned in step 5 specifically refers to: When the noise level is less than a first preset threshold, the bone conduction speaker will broadcast a voice command. When the noise is greater than or equal to the first preset threshold, the LED indicator encodes the direction information with light pulse frequency: the first frequency flashing indicates going straight, the second frequency flashing indicates turning left, and the third frequency flashing indicates turning right. At the same time, the vibration motor inside the mask emits vibration prompts in the corresponding direction. Alternatively, it may include step 6: when the wearer issues a voice distress command through the bone conduction microphone, the respirator automatically triggers a rapid flashing red LED and sends an SOS signal and real-time coordinates to the fire rescue information support platform, while simultaneously broadcasting a distress message at maximum transmission power, triggering an audible and visual alarm for nearby respirators.

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