Inside-outside cooperative intelligent fire-fighting clothes monitoring system

Abstract

The application discloses an inside-outside cooperative intelligent fire-fighting clothes monitoring system and relates to the technical field of fire-fighting monitoring.The system comprises a data acquisition component, a data processing component, a calculation and analysis component and a monitoring and alarming component.The data acquisition component is used to acquire external environment data, internal physiological data and equipment team correlation data of the fire-fighting clothes when the fire-fighting clothes is used.The calculation and analysis component comprises an environment risk unit, a physiological risk unit and a safety early warning unit.The application realizes dynamic control of the safety of firemen through the three units.The environment risk unit integrates temperature, toxic gas concentration and other parameters to output a comprehensive risk coefficient, thereby providing a reliable basis for subsequent evaluation.The physiological risk unit combines heart rate and other physiological parameters and an environment coefficient to output a physiological stress index, thereby accurately reflecting current physiological bearing capacity.The safety early warning unit integrates the two indexes and multidimensional parameters such as equipment and cooperation to output an early warning level, thereby effectively supporting risk control of inside-outside cooperative intelligent monitoring.

Description

Technical Field

[0001] This invention relates to the field of fire monitoring technology, specifically to an internal and external collaborative intelligent fire suit monitoring system. Background Technology

[0002] In fire rescue scenarios, the fire scene environment has complex characteristics such as high temperature, toxic gas, smoke obscuring vision, and strong heat radiation. At the same time, firefighters' physiological state is prone to drastic changes under high-intensity operations (such as increased heart rate and elevated core body temperature). The interaction between these two factors can significantly increase the rescue risk. Therefore, an intelligent fire suit monitoring system that coordinates internal and external factors is needed to ensure the personal safety of firefighters.

[0003] Current fire suit monitoring systems may only analyze single or partial environmental parameters independently, without comprehensively considering the synergistic effects of multiple environmental parameters such as temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, and oxygen content, resulting in an incomplete and inaccurate assessment of environmental hazard levels.

[0004] Current fire suit monitoring systems may lack the means to assess and analyze the physiological data of firefighters, thus failing to reflect the dynamic impact of environmental factors on the physiological state of firefighters, and the correlation between physiological stress index and actual operational risks is insufficient.

[0005] Furthermore, the existing monitoring system's early warning and alarm assessment effects are not good. Existing technologies may not integrate multi-dimensional data such as environmental risk coefficients, physiological stress indices, equipment usage time, and relative distances among team members, resulting in a lack of accuracy in early warning level classification. Moreover, the internal and external collaborative response mechanism lacks scientific assessment results to support it. Summary of the Invention

[0006] The purpose of this invention is to provide an internal and external collaborative intelligent fire suit monitoring system, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an internal and external collaborative intelligent fire suit monitoring system, comprising:

[0008] Data acquisition component: used to acquire external environmental data, internal physiological data, and equipment team-related data when fire suits are in use;

[0009] Data processing component: used for transmitting and storing external environmental data, internal physiological data, and equipment team-related data locally, as well as data processing and cleaning;

[0010] The computational analysis component includes an environmental risk unit, a physiological risk unit, and a safety early warning unit.

[0011] The environmental risk unit outputs a comprehensive environmental risk coefficient based on environmental data such as ambient temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, smoke diffusion gradient, and oxygen content. This comprehensive environmental risk coefficient provides firefighters with a quantitative basis for environmental risks.

[0012] The physiological risk unit outputs a physiological stress index based on internal physiological data such as heart rate, respiratory rate, core body temperature, skin conductivity, and pupil dilation rate, combined with the comprehensive environmental risk coefficient. This physiological stress index provides firefighters with a basis for physiological endurance.

[0013] The safety early warning unit is based on the team's associated data, including the duration of fire suit use, the maximum designed protection time, the fabric wear coefficient, the humidity of the microclimate inside the fire suit, the rate of visual obstruction, and the duration of the relative distance threshold between teammates. It also combines the comprehensive environmental hazard coefficient and the physiological stress index to output a comprehensive safety early warning level. The safety early warning level guides firefighters to adjust their action strategies and realizes the fire scene monitoring and alarm function.

[0014] Monitoring and alarm component: Used to input the comprehensive environmental risk coefficient, physiological stress index and comprehensive safety warning level output by the calculation and analysis component, and to perform internal and external collaborative intelligent monitoring and warning based on the input data.

[0015] Optionally, the processing procedure for the environmental risk unit is as follows:

[0016] A1. By analyzing the air temperature at the location of firefighters, the degree of environmental heat exposure can be reflected to obtain the ambient temperature;

[0017] A2. By analyzing the mass concentration of toxic gases such as CO, HCN, and NO2 in the environment, the degree of toxicity of flue gas can be reflected, and the toxic threat of flue gas to the respiratory and nervous systems can be considered to obtain the concentration of toxic gases.

[0018] A3. By analyzing the degree to which smoke obstructs light, visibility can be reflected, thus obtaining the optical density of the smoke.

[0019] A4. By analyzing the radiant heat power density of the fire source to firefighters, the risk of direct heat injury can be reflected for immediate hazard assessment to obtain the intensity of heat radiation.

[0020] A5. By continuously collecting the concentration of toxic gases, the rate of change of the concentration of toxic gases per unit time is analyzed to reflect the spread speed of smoke and to calculate the smoke diffusion gradient.

[0021] A6. By summing the weighting coefficients of the four toxic gases with their concentrations and incorporating the synergistic enhancement coefficient between two gases, the toxicity amplification effect of mixing multiple toxic gases is considered to calculate the flue gas toxicity synergistic coefficient, thereby ultimately outputting the comprehensive environmental hazard coefficient.

[0022] Optionally, the processing procedure for the physiological risk unit is as follows:

[0023] B1. By introducing the comprehensive environmental risk factor EA, environmental risks are correlated with physiological stress, which is used to reflect the amplification effect of the environment on physiological load.

[0024] B2. By considering the number of heartbeats per minute of firefighters, the cardiovascular system load can be reflected to obtain heart rate;

[0025] B3. By considering the number of breaths a firefighter takes per minute, the respiratory system load can be reflected to obtain the respiratory rate;

[0026] B4. By combining the firefighter's skin temperature with the ambient temperature, the firefighter's core body temperature is analyzed to reflect the degree of heat stress and to calculate the core body temperature.

[0027] B5. By considering changes in the electrical conductivity of firefighters' skin to reflect the level of sympathetic nerve excitation, the psychological stress and physiological tension can be assessed to obtain skin electrical conductivity.

[0028] B6. By calculating the diameter of adjacent pupils of firefighters, the rate of change of pupil diameter is analyzed to reflect the degree of nerve stress, thereby obtaining the pupil dilation rate and ultimately outputting the physiological stress index.

[0029] Optionally, the processing procedure of the safety warning unit is as follows:

[0030] C1. By introducing the physiological stress index and the comprehensive environmental risk coefficient, the physiological endurance of firefighters and the degree of environmental risk are reflected, so as to demonstrate the internal and external synergy of the safety early warning unit.

[0031] C2. The safety of fire suits is analyzed by combining the usage time of the fire suit, the maximum design protection time, and the fabric wear coefficient.

[0032] C3. By considering the relative humidity between the inner layer of the fire suit and the skin, the degree of heat stress of firefighters can be assessed to obtain the humidity of the microclimate inside the fire suit;

[0033] C4. By considering the degree of obstruction of the helmet visor's field of vision, the correlation between operational field of vision and safety risks is filled in in order to calculate the line of sight obstruction rate;

[0034] C5. By considering the duration during which the distance between a firefighter and their nearest teammate exceeds the safe distance, the degree of danger when a firefighter works alone is analyzed to obtain the duration of the relative distance threshold between teammates, and thus the comprehensive safety warning level is finally output.

[0035] Optionally, the data transmission and local storage specifically include:

[0036] A high-temperature resistant wireless communication module is used to transmit data from each sensor to the main control chip on the back of the fire suit in real time, and simultaneously store the raw data in the high-temperature resistant flash memory in the main control box to prevent data loss due to signal interruption at the fire scene.

[0037] Optionally, the data processing and cleaning specifically includes:

[0038] The 3σ principle is used to filter instantaneous noise, and the average of the first three valid data points is used to replace outliers. Dimensional data parameters are normalized to adapt to the calculations of the computational analysis components.

[0039] Optionally, the monitoring and alarm component specifically comprises:

[0040] When the comprehensive safety warning level WE < 0.5, it indicates the safety level, representing that the external environment, internal physiology, and equipment team are in a safe state;

[0041] When 0.5 ≤ Comprehensive Safety Warning Level WE < 1.2, it indicates a warning level, representing a slight risk in a single dimension;

[0042] When 1.2 ≤ Comprehensive Safety Warning Level WE < 2.0, it indicates a danger alarm level, representing a moderate risk in multiple dimensions;

[0043] When the comprehensive safety warning level WE≥2.0, it indicates an emergency alarm level, representing a severe risk in the core dimension.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] I. This invention outputs a comprehensive environmental hazard coefficient through an environmental risk unit. By integrating parameters such as ambient temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, smoke diffusion gradient, smoke toxicity synergy coefficient, and oxygen content, it comprehensively covers core risk dimensions in the fire environment, including thermal damage, chemical hazards, visual obstruction, radiation risk, dynamic diffusion, multi-gas interaction, and hypoxia. Ambient temperature reflects the direct thermal impact of the thermal environment on firefighters; toxic gas concentration reflects the degree of chemical hazard; smoke optical density characterizes the visual obstruction; thermal radiation intensity reflects the risk of radiative heat injury; smoke diffusion gradient reflects the dynamic diffusion trend of the hazardous environment; smoke toxicity synergy coefficient considers the amplified hazards of multiple toxic gases; and oxygen content reflects the risk of hypoxia. The comprehensive environmental hazard coefficient can scientifically and accurately quantify the overall hazard level of the fire environment, providing a reliable environmental risk basis for subsequent physiological stress assessment and comprehensive early warning.

[0046] II. This invention outputs a physiological stress index through a physiological risk unit. By integrating parameters such as heart rate, respiratory rate, core body temperature, skin conductivity, pupil dilation rate, and comprehensive environmental risk coefficient, it achieves a dynamic and accurate assessment of the physiological stress state of firefighters. Heart rate reflects the load on the cardiovascular system, respiratory rate reflects the stress response of the respiratory system, core body temperature represents the level of heat stress, skin conductivity reflects the stress state of the autonomic nervous system, pupil dilation rate reflects the degree of nervous tension, and the comprehensive environmental risk coefficient incorporates the dynamic impact of environmental factors on the physiological state into the assessment. By linking environmental and physiological parameters, the physiological stress index can truly reflect the physiological tolerance of firefighters in the current environment, providing a scientific physiological risk basis for comprehensive safety early warning.

[0047] Third, this invention outputs a comprehensive safety warning level through a safety warning unit. By integrating parameters such as physiological stress index, comprehensive environmental hazard coefficient, ratio of fire suit usage time to equipment maximum tolerance time, fabric wear coefficient, humidity, vision obstruction rate, and teammate distance threshold exceeding time, it achieves a comprehensive assessment of the firefighter's overall safety status. The physiological stress index and comprehensive environmental hazard coefficient are the core risk dimensions. The ratio of fire suit usage time to equipment maximum tolerance time and fabric wear coefficient reflect the fatigue level of the equipment. Humidity reflects the comfort level of the inner layer of the fire suit. Vision obstruction rate affects the firefighter's operational safety. Teammate distance threshold exceeding time reflects the safety status of team coordination. This unit integrates multi-dimensional data, enabling the comprehensive safety warning level to accurately classify the firefighter's safety status level, providing a scientific basis for internal response of the fire suit and external team coordination, and effectively supporting the dynamic risk management of the internal and external collaborative intelligent monitoring system. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0049] Figure 2 This is a schematic diagram of the structure of the computational analysis component of the present invention;

[0050] Figure 3 This is a schematic diagram of the operation flow of the computational analysis component of the present invention;

[0051] Figure 4 This is a schematic diagram of the monitoring and alarm component of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figures 1 to 4 This implementation provides an internal and external collaborative intelligent fire suit monitoring system, including:

[0054] The data acquisition component is used to acquire external environmental data, internal physiological data, and equipment team-related data when the fire suit is in use;

[0055] The data processing component is used to input external environmental data, internal physiological data, and equipment team-related data obtained by the data acquisition component. The data processing component performs data transmission and local storage, as well as data processing and cleaning. The data processed by the data processing component is then input into the calculation and analysis component.

[0056] Data transfer and local storage are specifically as follows:

[0057] Employing a high-temperature resistant wireless communication module, data from various sensors is transmitted in real time to the main control chip on the back of the fire suit. Simultaneously, the raw data is stored in the high-temperature resistant flash memory within the main control box to prevent data loss due to signal interruption at the fire scene. The main link for external data transmission can be transmitted via a high-temperature resistant 4G or 5G module through a smoke-proof antenna on the back of the fire suit (attached to the outer shoulder layer to avoid obstruction). For backup links, LoRa modules can be used for short distances (relay between teammates; if the 4G signal is weak, it can be forwarded to the command room by teammates), while satellite communication modules can be used for long distances to cope with wildfires without public network coverage.

[0058] Data processing and cleaning specifically includes:

[0059] The 3σ principle is used to filter instantaneous noise, and the average value of the first three valid data is used to replace outliers. The dimensional data parameters are normalized to adapt to the calculation of the computational analysis component.

[0060] Computational analysis components:

[0061] Based on environmental data such as ambient temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, smoke diffusion gradient, and oxygen content, an environmental comprehensive risk coefficient is output. This environmental comprehensive risk coefficient provides firefighters with a quantitative basis for environmental risks.

[0062] Based on internal physiological data such as heart rate, respiratory rate, core body temperature, skin conductivity, and pupil dilation rate, and combined with the comprehensive environmental risk factor, a physiological stress index is output to provide firefighters with a physiological basis for their endurance.

[0063] Based on team-related data such as the duration of fire suit use, maximum design protection time, fabric wear coefficient, internal microclimate humidity of fire suit, visual obstruction rate, and duration of exceeding the relative distance threshold with teammates, combined with the comprehensive environmental hazard coefficient and physiological stress index, a comprehensive safety warning level is output. The safety warning level guides firefighters to adjust their action strategies and realizes the fire scene monitoring and alarm function.

[0064] The computational analysis components include an environmental risk unit, a physiological risk unit, and a safety early warning unit;

[0065] Monitoring and alarm component: used to input the comprehensive environmental hazard coefficient, physiological stress index and comprehensive safety warning level output by the calculation and analysis component, and to perform internal and external collaborative intelligent monitoring and warning based on the input data;

[0066] When the comprehensive safety warning level WE < 0.5, it indicates the safety level, representing that the external environment, internal physiology, and equipment team are in a safe state;

[0067] Internal response: A solid green indicator light is displayed, the ventilation module of the protective suit maintains a low airflow rate to keep the inner layer at a comfortable humidity level, and there are no vibration or audible / visual alarms;

[0068] External collaboration: The background monitoring system marks it as safe, the location is displayed in blue, and teammates' helmets have no special prompts, only showing normal locations;

[0069] When 0.5 ≤ Comprehensive Safety Warning Level WE < 1.2, it indicates a warning level, representing a slight risk in a single dimension;

[0070] Internal response: A yellow flashing indicator light is displayed (1 time / second), the waist vibration module starts low-frequency vibration (2 times / second), continues for 3 seconds and then pauses for 2 seconds, the helmet visor displays the text prompt "Note that there are slight abnormalities in the environment or physiological indicators, please pay attention to changes in the surroundings", and the ventilation module wind speed is increased to 2.0m / s;

[0071] External collaboration: The background monitoring system marks the warning as an alert, the location is displayed in yellow, and teammates' helmets display a yellow marker next to the firefighter's location. The dispatch center does not intervene and only records the risk trend.

[0072] When 1.2 ≤ Comprehensive Safety Warning Level WE < 2.0, it indicates a danger alarm level, representing a moderate risk in multiple dimensions;

[0073] Internal response: The red flashing indicator light is displayed (2 times / second), the helmet's audible and visual alarm is activated, the red light flashes (3 times / second) in conjunction with intermittent buzzing, the back ventilation module's airflow speed is increased to 3.0m / s to reduce the humidity of the inner layer, the helmet visor displays highlighted text and arrows "Danger, please adjust your position and reduce the intensity of your activity", and indicates the current high-risk parameters (such as excessive concentration of toxic gases), and the waist vibration module continuously vibrates at a high frequency (4 times / second).

[0074] External collaboration: The background monitoring system marks the firefighter as dangerous, displays the location in orange, and pushes it to the key attention area of ​​the command screen. The teammate's helmet displays the firefighter's location as a flashing orange mark and automatically pops up a "Danger, please move closer to your teammates" prompt. The dispatch center sends a voice command to the firefighter: "Please note that your current risk level is dangerous. Please prioritize your own safety."

[0075] When the comprehensive safety warning level WE≥2.0, it indicates an emergency alarm level, representing a severe risk in the core dimension;

[0076] Internal response: The display shows a solid red light in conjunction with a flashing indicator light, and the helmet has a continuous audible and visual alarm (red flashing light combined with a sharp buzzer). The inner circulating water cooling module is automatically activated (water temperature is controlled at 15℃) to lower the core body temperature. The helmet visor displays "Emergency evacuation and request for support" in extra-large font and on a red background, and displays the escape direction in real time (based on UWB positioning and the location of the fire source).

[0077] External coordination: The background monitoring system marks the location as emergency, displays it in red, and pops up an emergency support request window. Teammates' helmets show the firefighter's location as a red flashing marker and automatically plan the shortest support route (overlaid in the field of vision). The dispatch center immediately initiates the emergency rescue process, dispatching two additional support firefighters to the area. The background system automatically retrieves environmental data (temperature and gas concentration) for the location and pushes it to the support team members. If no evacuation feedback is received within 1 minute, the dispatch center contacts the on-site commander for mandatory intervention.

[0078] Based on the above, the three units of this system form a complete logical chain from environmental perception and physiological monitoring to comprehensive early warning, and construct an intelligent monitoring mechanism that coordinates internal and external factors. The comprehensive environmental risk coefficient EA and the physiological stress index PB provide quantitative data from the two dimensions of external threat and internal resilience, respectively, to ensure that the system's judgment on the safety status of firefighters takes into account both external environmental risks and their own physical conditions.

[0079] The Comprehensive Safety Warning Level (WE) is based on the collaborative analysis of both factors, outputting comprehensive warning results so that the system's warning decisions are no longer based on a single-dimensional, one-sided judgment, but rather on precise decisions that take into account both the environment and physiological conditions.

[0080] This combination enhances the overall security capabilities of the intelligent fire suit monitoring system, ensuring the scientific validity and applicability of early warnings, and providing more comprehensive and reliable technical support for the safety of firefighters.

[0081] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 The environmental risk unit is specifically as follows:

[0082] A1. By analyzing the air temperature at the location of firefighters, the degree of environmental heat exposure can be reflected to obtain the ambient temperature;

[0083] A2. By analyzing the mass concentration of toxic gases such as CO, HCN, and NO2 in the environment, the degree of toxicity of flue gas can be reflected, and the toxic threat of flue gas to the respiratory and nervous systems can be considered to obtain the concentration of toxic gases.

[0084] A3. By analyzing the degree to which smoke obstructs light, visibility can be reflected, thus obtaining the optical density of the smoke.

[0085] A4. By analyzing the radiant heat power density of the fire source to firefighters, the risk of direct heat injury can be reflected for immediate hazard assessment to obtain the intensity of heat radiation.

[0086] A5. By continuously collecting the concentration of toxic gases, the rate of change of the concentration of toxic gases per unit time is analyzed to reflect the spread speed of smoke and to calculate the smoke diffusion gradient.

[0087] A6. By summing the weighting coefficients of the four toxic gases with the gas concentrations and incorporating the synergistic enhancement coefficient between two gases, the toxicity amplification effect of mixing multiple toxic gases is considered to calculate the flue gas toxicity synergistic coefficient, thereby ultimately outputting the comprehensive environmental hazard coefficient.

[0088] The formula for calculating environmental risk units is as follows:

[0089] ;

[0090] in:

[0091] EA stands for Environmental Risk Entity;

[0092] EAA refers to ambient temperature, which is the air temperature at the firefighter's location. It reflects the degree of environmental heat exposure and can be collected by a high-temperature resistant K-type thermocouple sensor, which can be installed on the shoulder of the outer layer of the fire suit. The introduction of ambient temperature EAA is used to reflect the environmental heat intensity and is a basic parameter for assessing the risk of heat injury. The ambient temperature EAA needs to be normalized. The data collected by the sensor is first subtracted from the comfort temperature (30°C), and the difference is then divided by the difference between the heat stress threshold (60°C) and the comfort temperature (30°C).

[0093] EAB refers to the concentration of toxic gases, specifically the mass concentration of toxic gases such as CO, HCN, and NO2 in the environment. It reflects the degree of toxicity of flue gas and can be acquired using a metal oxide semiconductor gas sensor array. This array can be installed in front of the collar of a fire suit (close to the breathing area). The introduction of the toxic gas concentration EAB is used to assess the toxic threat of flue gas to the respiratory and nervous systems and is a core parameter in environmental hazard assessment. The toxic gas concentration EAB needs to be normalized by subtracting the safe CO concentration (24 mg / m³) from the data collected by the sensor. 3 The difference between the two is then divided by the poisoning threshold (120 mg / m²). 3 ) and the safe concentration of CO (24 mg / m³) 3 The difference between )

[0094] EAC stands for Smoke Optical Density, which is the degree to which smoke obstructs light and reflects visibility. Data can be acquired using an infrared smoke sensor made of high-temperature resistant material, which can be installed inside the helmet visor. The value range is 0-1, where EAC = 0 (clear view) and EAC = 1 (complete obstruction). The introduction of smoke optical density (EAC) is used to assess visibility in a fire scene and avoid affecting the safety of firefighters' operations and direction judgment.

[0095] EAD stands for Thermal Radiation Intensity, which is the radiant heat power density of the fire source to the firefighter. It reflects the risk of direct heat injury and can be obtained through a thermopile sensor. It is installed on the front of the fire suit, facing the direction of the fire source. The introduction of thermal radiation intensity EAD supplements the insufficient temperature parameters and directly reflects the radiant heat injury of the fire source. It is a key parameter for real-time hazard assessment and, unlike conventional temperature monitoring, reflects the direct heat injury of the fire more effectively.

[0096] The thermal radiation intensity EAD needs to be normalized by subtracting the tolerable intensity (100W / m²) from the data collected by the sensor. 2The difference between the two is then divided by the skin burn threshold (1000W / m²). 2 ) and withstand strength (100W / m) 2 ) difference;

[0097] EAE refers to the flue gas diffusion gradient, which is the rate of change of toxic gas concentration per unit time. It reflects the speed of flue gas spread and can be calculated based on continuous sampling data from a gas sensor array. The calculation formula is as follows:

[0098] EAE = (G2 - G1) / (t2 - t1);

[0099] In the above formula, G2 and G1 are the adjacent sampling concentrations;

[0100] In the above formula, t2 and t1 are the corresponding times;

[0101] Furthermore, after the result is calculated in the above formula, it needs to be normalized so that the result of the above formula is first subtracted from the non-diffusion concentration (0 mg / (m3·s)), and the difference between the two is then divided by the difference between the rapid diffusion threshold (10 mg / (m3·s)) and the non-diffusion concentration (0 mg / (m3·s)).

[0102] The introduction of the smoke diffusion gradient (EAE) assesses the dynamic risk of smoke spread, reflects the speed of fire spread, and provides early warning of escalating danger. Existing environmental monitoring focuses mainly on static concentrations, while the diffusion gradient directly reflects the speed of fire spread (the larger the gradient, the faster the fire spreads and the more rapidly the danger escalates), filling the gap in the monitoring of dynamic spread risk.

[0103] EAF refers to the flue gas toxicity synergistic coefficient, which is the toxicity amplification effect coefficient of a mixture of multiple toxic gases. It corrects the limitations of single-gas concentration assessment and can acquire multi-gas concentration data based on a gas sensor array. The calculation formula is as follows:

[0104] ;

[0105] In the above formula, EAFA i The basic toxicity weighting coefficient for a single gas;

[0106] This embodiment provides the weighting coefficients for the four gases:

[0107] EAFA CO =1.0, CO is the most common toxic gas in fire scenes, with moderate basic toxicity;

[0108] EAFA HCN =3.0, HCN is extremely toxic and is the main cause of acute poisoning in fire scenes;

[0109] EAFA NO2=1.5, NO2 is highly irritating to the respiratory mucosa;

[0110] EAFA SO2 =1.2, SO2 causes significant lung damage;

[0111] EAFB in the above formula i Refers to the concentration of a single gas (mg / m³).

[0112] In the above formula, Refers to the cooperative term in a mixed gas;

[0113] In the above formula, EAFC jk The synergistic enhancement coefficient between the two gases is specifically defined as follows:

[0114] EAFC (CO and HCN) = 0.8, because the toxicity may increase by an additional 0.8 times when CO and HCN are mixed.

[0115] EAFC (CO and NO2) = 0.5, because the synergistic effect of CO and NO2 is relatively weak after mixing;

[0116] EAFC (HCN and NO2) = 1.0, because the toxicity may be significantly amplified when HCN and NO2 are mixed;

[0117] EAFC (CO and SO2) = 0.3, because the synergistic effect of CO and SO2 mixing is weak;

[0118] In the above formula, EAFD j and EAFF k These refer to the concentrations (mg / m³) of the two mixed gases in the synergistic term of the mixed gases.

[0119] The initial value of the flue gas toxicity synergy coefficient EAFS calculated by the above formula needs to be normalized:

[0120] First, set:

[0121] The safety threshold EAFSA = 1, which corresponds to a scenario where the concentration of a single gas does not exceed the standard and there is no obvious synergistic toxicity.

[0122] The danger threshold EAFSB = 5, which corresponds to a scenario where the synergistic toxicity of the mixed gases is significant and reaches the critical value for the risk of acute poisoning of firefighters.

[0123] Linear normalization formula:

[0124] ;

[0125] EAG refers to oxygen content, which is the volume fraction of oxygen in the environment. It is used to reflect the degree of hypoxia and can be obtained by a zirconia oxygen sensor (resistant to 700℃), installed in front of the collar of the fire suit (in the same position as the gas sensor array). It is used to assess the risk of hypoxia. Hypoxia will increase physiological burden and the risk of poisoning. It is an important correction parameter for environmental hazards. The volume fraction of oxygen is usually expressed as volume percentage (%VOL). It represents the volume ratio of oxygen in the gas mixture. Since %VOL represents the volume ratio of oxygen in the gas mixture, it does not involve specific physical quantities such as length, mass or time. Therefore, it is regarded as a dimensionless unit. This is the prior art.

[0126] The term refers to the oxygen correction term, where k is a constant. In this embodiment, k can be preset to 5. Thus, when the oxygen content is normal (EAG = 21), the correction term is 0. When the oxygen content decreases (EAG < 21), the correction term increases exponentially with the degree of hypoxia (approaching 1).

[0127] The introduction of the flue gas toxicity synergy factor (EAF) fills the gap in the quantitative analysis of toxicity synergy. Current environmental monitoring methods mostly use the superposition of single gas concentrations, ignoring the synergistic toxicity effect of mixed gases (such as the toxicity of CO and HCN increasing by 2-3 times after mixing). It is closer to the dangerous nature of complex flue gas environments in actual fires, allowing the comprehensive environmental hazard factor (EA) to more accurately reflect the hidden toxic risks of the environment and avoid underestimating the lethality of mixed flue gas because the concentration of a single gas does not exceed the standard.

[0128] A1, A2, A3, A4, A5, and A6 represent the weighting factors for ambient temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, smoke diffusion gradient, and smoke toxicity synergy coefficient, respectively. These factors are used to balance the contribution of each environmental parameter to the hazard coefficient, ensuring that the comprehensive environmental hazard coefficient (EA) aligns with the actual fire scene hazard logic. The EA value can be determined and adjusted by using multiple regression methods through real-person experiments simulating fire scene environments and statistical data (e.g., recording firefighters' hazard perception and actual risk in scenarios with different temperature and gas concentration combinations).

[0129] Based on the above, this environmental risk unit integrates scattered fire scene environmental parameters (such as temperature, toxic gas concentration, and heat radiation intensity) into a unified quantitative risk coefficient, realizing a comprehensive assessment of the overall risk level of the environment in which firefighters are located. This fills the gap that a single environmental parameter cannot reflect the comprehensive threat of a complex fire scene. The calculated comprehensive environmental risk coefficient EA provides the system with a quantitative basis for environmental risk, clearly presents the current threat level of the environment to firefighters, and clarifies the severity of potential hazards in the environment. It is the core environmental input source for the system's subsequent early warning decision-making.

[0130] Please see Figure 1 , Figure 2, Figure 3 and Figure 4 The physiological risk unit is specifically as follows:

[0131] B1. By introducing the comprehensive environmental risk factor EA, environmental risks are correlated with physiological stress, which is used to reflect the amplification effect of the environment on physiological load.

[0132] B2. By considering the number of heartbeats per minute of firefighters, the cardiovascular system load can be reflected to obtain heart rate;

[0133] B3. By considering the number of breaths a firefighter takes per minute, the respiratory system load can be reflected to obtain the respiratory rate;

[0134] B4. By combining the firefighter's skin temperature with the ambient temperature, the firefighter's core body temperature is analyzed to reflect the degree of heat stress and to calculate the core body temperature.

[0135] B5. By considering changes in the electrical conductivity of firefighters' skin to reflect the level of sympathetic nerve excitation, the psychological stress and physiological tension can be assessed to obtain skin electrical conductivity.

[0136] B6. By calculating the diameter of adjacent pupils of firefighters, the rate of change of pupil diameter is analyzed to reflect the degree of nerve stress, in order to obtain the pupil dilation rate, and finally output the physiological stress index.

[0137] The formula for calculating the physiological risk unit is as follows:

[0138] ;

[0139] in:

[0140] PB stands for Physiological Stress Index;

[0141] The introduction of the Environmental Comprehensive Risk Factor (EA) links environmental hazards with physiological stress, reflecting the amplification effect of the environment on physiological load, making the assessment more in line with actual scenarios.

[0142] PBA stands for heart rate, which is the number of times a firefighter's heart beats per minute. It reflects the load on the cardiovascular system and can be obtained through a piezoelectric heart rate sensor, which is installed on the inner layer of the fire suit on the chest (close to the skin) to assess cardiovascular load. An excessively fast heart rate is an early signal of physiological overload and needs to be normalized. The heart rate data obtained by the sensor is first subtracted from the resting heart rate (70 beats / minute), and the difference is then divided by the difference between the cardiovascular overload threshold (180 beats / minute) and the resting heart rate (70 beats / minute).

[0143] PBB stands for respiratory rate, the number of breaths a firefighter takes per minute. It reflects the respiratory system load and can be obtained through a capacitive breathing belt sensor installed on the waist inside the fire suit (to monitor chest rise and fall). It is used to assess the respiratory system load. Rapid breathing is an early sign of hypoxia or poisoning and needs to be normalized. The respiratory rate data obtained by the sensor is first subtracted from the resting respiratory rate (15 breaths / min), and the difference is then divided by the difference between the respiratory failure threshold (30 breaths / min) and the resting respiratory rate (15 breaths / min).

[0144] PBC refers to core body temperature, which is the core body temperature of firefighters and reflects the degree of heat stress. It is estimated using a high-temperature resistant skin temperature sensor installed under the inner armpit and calculated using the following formula:

[0145] PBC = 0.9 × skin temperature + 0.1 × ambient temperature;

[0146] Furthermore, normalization processing is required. The result calculated by the above formula should first be subtracted from the normal body temperature (37℃), and the difference between the two should be divided by the difference between the heatstroke threshold (40℃) and the normal body temperature (37℃).

[0147] The introduction of core body temperature (PBC) is used to assess the degree of heat stress. Excessively high core body temperature can lead to heatstroke and coma, and it is a core parameter of physiological stress.

[0148] PBD stands for skin conductivity, which is a change in skin conductivity used to reflect the level of sympathetic nerve excitation (stress response). It can be acquired by an electrode-type skin conductivity sensor, which is installed on the palm of the inner layer of a fire suit (where changes in skin conductivity are obvious). It is used to assess the level of psychological stress and physiological tension. Increased skin conductivity is an early signal of stress and reflects the level of sympathetic nerve excitation under stress. It is more sensitive than heart rate alone and needs to be normalized. The data acquired by the sensor is first subtracted from the resting conductivity (20μS), and the difference is then divided by the difference between the stress threshold (100μS) and the resting conductivity (20μS).

[0149] PBE refers to the pupillary dilation rate, which is the rate of change in pupil diameter and reflects the degree of neural stress. It can be calculated by installing a high-temperature resistant miniature infrared camera inside a helmet to capture pupil images. The calculation formula is as follows:

[0150] PD=(PDA2-PDB1) / PDAt2-PDAt1);

[0151] In the above formula, PDA2 and PDB1 are the diameters of adjacent pupils, respectively;

[0152] In the above formula, PDAt2 and PDAt1 are the corresponding times;

[0153] Normalization is required. The calculation result of the above formula should first be subtracted from the resting expansion rate (0.01 mm / s), and the difference between the two should be divided by the difference between the stress expansion threshold (0.1 mm / s) and the resting expansion rate (0.01 mm / s).

[0154] The introduction of pupillary dilation rate (PBE) is used to assess the degree of neurological stress. Accelerated pupillary dilation is an early signal of poisoning or extreme tension, providing an early warning of physiological overload. This parameter brings an early warning of neurological stress to this system. Sympathetic nerve excitation under stress will lead to rapid pupillary dilation. This signal is more sensitive than macroscopic physiological indicators such as heart rate and respiration. Existing physiological monitoring does not consider this parameter.

[0155] Q1, Q2, Q3, Q4, Q5, and Q6 refer to the weighting factors for heart rate, respiratory rate, core body temperature, skin conductivity, pupil dilation rate, and comprehensive environmental risk coefficient, respectively.

[0156] Based on the above, this physiological risk unit transforms firefighters' physiological parameters (heart rate, respiratory rate, and core body temperature, etc.) into a comprehensive physiological stress index, enabling dynamic monitoring and assessment of firefighters' physiological state. This solves the problem that a single physiological indicator cannot fully reflect the body's endurance. The calculated physiological stress index PB provides the system with a quantitative basis for firefighters' physiological endurance, allowing the system to accurately grasp whether the firefighter's current physical state is suitable for the environment and whether there is a risk of physiological overload. It is the core physiological input source for the system's subsequent early warning decisions.

[0157] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 The safety early warning unit is specifically as follows:

[0158] C1. By introducing the physiological stress index and the comprehensive environmental risk coefficient, the physiological endurance of firefighters and the degree of environmental risk are reflected, so as to demonstrate the internal and external synergy of the safety early warning unit.

[0159] C2. The safety of fire suits is analyzed by combining the usage time of the fire suit, the maximum design protection time, and the fabric wear coefficient.

[0160] C3. By considering the relative humidity between the inner layer of the fire suit and the skin, the degree of heat stress of firefighters can be assessed to obtain the humidity of the microclimate inside the fire suit;

[0161] C4. By considering the degree of obstruction of the helmet visor's field of vision, the correlation between operational field of vision and safety risks is filled in in order to calculate the line of sight obstruction rate;

[0162] C5. By considering the duration during which the distance between a firefighter and the nearest teammate exceeds the safe distance, the degree of danger when a firefighter works alone is analyzed to obtain the duration of the relative distance threshold between teammates, and thus the comprehensive safety warning level is finally output.

[0163] The calculation formula for the safety early warning unit is as follows:

[0164] ;

[0165] in:

[0166] WE refers to the comprehensive safety warning level;

[0167] The introduction of the physiological stress index PB reflects the firefighters' physiological endurance and is one of the core parameters of the comprehensive early warning system. The introduction of the comprehensive environmental hazard coefficient EA reflects the degree of environmental hazard and is also one of the core parameters of the comprehensive early warning system.

[0168] WEA refers to the duration of use of the fire suit, which is the cumulative time of use from its current deployment to the present. It can be directly recorded by a high-temperature resistant timing module and is used to assess the degree of attenuation of the fire suit's protective capabilities. The longer it is used, the weaker the protection becomes.

[0169] WEB refers to the maximum design protection duration, which is the maximum effective protection duration specified in the design of fire suits. This parameter serves as a reference threshold for the duration of use and is used to assess the relative degree of protection attenuation.

[0170] WEC refers to the fabric wear factor, which is the degree of wear of the fire suit fabric (0 for brand new, 1 for completely worn). It can be used to monitor wear through high-temperature pressure sensors (installed at the elbows and knees). The calculation formula is as follows:

[0171] WEC = (Cumulative number of wear cycles × Single wear level) / Maximum allowable wear level;

[0172] In the above formula, the cumulative number of wear cycles can be statistically analyzed by pressure changes, while the degree of wear per cycle can be estimated by pressure amplitude.

[0173] The introduction of the fabric abrasion coefficient (WEC) is used to assess the degradation of the fabric's protective ability; the greater the abrasion, the weaker the protection.

[0174] WED refers to the internal microclimate humidity of the fire suit, which is the relative humidity between the inner layer of the fire suit and the skin. It can be obtained by a capacitive temperature and humidity sensor installed on the back of the inner layer to assess the degree of heat stress. High humidity hinders sweat evaporation, exacerbates the rise in core body temperature, and corrects the warning results. High humidity will hinder sweat evaporation → rise in core body temperature → exacerbate physiological load. This parameter is connected to the comfort of the equipment and physiological safety.

[0175] WEE refers to the field of vision obstruction rate, which is the degree to which the helmet visor obstructs the view. The value ranges from 0 to 1. It is calculated by capturing images of the field of vision using a high-temperature resistant miniature camera (which can be installed inside the visor). The formula is as follows:

[0176] WEE = Obstructed area / Total field of view;

[0177] The introduction of the visibility obstruction rate (WEE) is used to assess operational feasibility. Visibility obstruction increases the risk of operational errors and corrects warning results. Existing alarm monitoring systems rarely use visibility obstruction as a core risk parameter, but in actual rescue operations, visibility obstruction (such as mask fogging and smoke adhesion) can increase the operational error rate by more than 300% (such as misjudging the location of the fire source, stepping into a hole and falling). This parameter fills the gap in the correlation between operational visibility and safety risk, allowing the comprehensive safety warning level (WE) to more comprehensively assess the operational safety of firefighters. When the visibility obstruction rate (WEE) is ≥ 0.6, the comprehensive warning level is directly increased by 1 level, forcibly triggering a vision clearing prompt or a teammate assistance request. When the visibility obstruction rate (WEE) is ≥ 0.6, the comprehensive safety warning level (WE) is increased by an additional 0.5.

[0178] WEF refers to the time it takes for the relative distance threshold between teammates to be exceeded, which is the duration for which the distance between the nearest teammate exceeds a safe threshold (e.g., 5 meters). The position of oneself and teammates can be obtained through a high-temperature resistant UWB positioning module, the relative distance can be calculated, and the timer is set when the threshold is exceeded. The introduction of the WEF parameter assesses the risk of team collaboration. The risk factor of working alone is significantly increased, and the risk of working alone is given early warning. The risk factor of firefighters is significantly increased when working alone. This parameter binds individual safety to the team's positional relationship. Existing early warning systems rarely consider the impact of team dynamics on safety.

[0179] Normalization is required. Based on the breakthrough time data obtained from the sensing device, the non-breakthrough time (0s) is subtracted first, and the difference between the two is then divided by the difference between the dangerous time (30s) and the non-breakthrough time (0s).

[0180] D1, D2, D3, D4, D5, and D6 respectively refer to the weighting coefficients of physiological stress index, comprehensive environmental risk coefficient, protective condition of fire suit, internal microclimate humidity of fire suit, visual obstruction rate, and time of breaking the relative distance threshold of teammates.

[0181] Based on the above, this safety early warning unit integrates environmental hazard coefficients, physiological stress indexes, and other relevant parameters (such as equipment status and teammate distance) into a comprehensive safety early warning level. This enables a multi-dimensional assessment of the overall safety status of firefighters, breaking the limitations of independent and uncoordinated judgment of environmental and physiological indicators. The comprehensive safety early warning level WE calculated by this unit serves as the core early warning information output by the system, directly guiding firefighters to adjust their action strategies or the command center to take intervention measures. It is the ultimate carrier for the system to realize its safety monitoring and early warning functions.

[0182] It is worth noting that this embodiment presents an iterative approach, which calculates the comprehensive safety warning level WE of the safety warning unit in one step to influence and iterate the weighting factor Q6 of the comprehensive environmental risk coefficient in the physiological risk unit, thereby achieving the purpose of consistency and cyclical optimization. The specific iterative process is as follows:

[0183] ;

[0184] in:

[0185] Q6 k+1 The weighting factor refers to the comprehensive environmental risk coefficient after the (k+1)th iteration;

[0186] Q6 k The weighting factor refers to the overall environmental risk coefficient after the k-th iteration;

[0187] α refers to the step size coefficient, which is set to 0.1 in this embodiment to control the iteration range of the comprehensive environmental risk coefficient;

[0188] WE k Refers to the comprehensive security warning level after the k-th iteration;

[0189] WEEQ refers to the warning trigger threshold, which is set to 1 in this embodiment;

[0190] Q6 max The upper limit of the weighting factor, which refers to the comprehensive environmental risk coefficient, is set to 1 in this embodiment to avoid excessive amplification;

[0191] It is worth noting that an iteration termination condition also needs to be set. This embodiment is based on two termination conditions to achieve iteration convergence. The iteration terminates when either of the following two conditions is met.

[0192] Condition 1: The number of iterations reaches the upper limit. In this embodiment, the maximum number of iterations is set to 5 to avoid the iteration from getting stuck in an infinite loop and to ensure the real-time performance of the system.

[0193] Condition 2: ∣WE k -WE k-1| <0.05 indicates that the weight adjustment has stabilized;

[0194] Based on the above, this iterative system adjusts Q6 iteratively to dynamically change the weight of the impact of environmental hazards on physiological stress with the comprehensive warning level. This avoids the problem of disconnect between environmental and physiological assessments under static weights, making the physiological stress index more closely match real-time dangerous scenarios. The iterative mechanism allows the system to optimize parameters based on real-time warning results, reducing assessment bias caused by static weights. In complex and ever-changing fire scenes, the closed-loop feedback loop of the monitoring system organically links the environment, physiology, and warning, making the comprehensive warning level more accurately reflect the firefighters' current actual safety status and reducing misjudgments or omissions. The iterative process transforms static calculation into dynamic adaptive assessment, allowing the system to focus more on current high-priority risk factors, meeting the actual needs of dynamic risk changes in fire rescue.

[0195] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An internal and external collaborative intelligent fire suit monitoring system, characterized in that, include: The data acquisition component is used to acquire external environmental data, internal physiological data, and equipment team-related data when the fire suit is in use; The data processing component is used for transmitting and storing external environmental data, internal physiological data, and equipment team-related data locally, as well as for data processing and cleaning. The computational analysis component includes an environmental risk unit, a physiological risk unit, and a safety early warning unit. The environmental risk unit outputs a comprehensive environmental risk coefficient based on environmental data such as ambient temperature, toxic gas concentration, smoke optical density, thermal radiation intensity, smoke diffusion gradient, and oxygen content. This comprehensive environmental risk coefficient provides firefighters with a quantitative basis for environmental risks. The physiological risk unit outputs a physiological stress index based on internal physiological data such as heart rate, respiratory rate, core body temperature, skin conductivity, and pupil dilation rate, combined with the comprehensive environmental risk coefficient. This physiological stress index provides firefighters with a basis for physiological endurance. The safety early warning unit outputs a comprehensive safety early warning level based on the team's associated data, including the duration of use of fire suits, the maximum designed protection time, the fabric wear coefficient, the humidity of the microclimate inside the fire suit, the rate of obstruction of vision, and the duration of exceeding the relative distance threshold between teammates. It also combines the comprehensive environmental hazard coefficient and the physiological stress index to guide firefighters to adjust their action strategies. The monitoring and alarm component is used to input the comprehensive environmental risk coefficient, physiological stress index, and comprehensive safety warning level output by the calculation and analysis component, and to perform internal and external collaborative intelligent monitoring and warning based on the input data.

2. The internal and external collaborative intelligent fire suit monitoring system according to claim 1, characterized in that: The process for handling the environmental risk unit is as follows: A1. By analyzing the air temperature at the location of firefighters, the degree of environmental heat exposure can be reflected to obtain the ambient temperature; A2. By analyzing the mass concentration of toxic gases such as CO, HCN, and NO2 in the environment, the degree of toxicity of flue gas can be reflected, and the toxic threat of flue gas to the respiratory and nervous systems can be considered to obtain the concentration of toxic gases. A3. By analyzing the degree to which smoke obstructs light, visibility can be reflected, thus obtaining the optical density of the smoke. A4. By analyzing the radiant heat power density of the fire source to firefighters, the risk of direct heat injury can be reflected for immediate hazard assessment to obtain the intensity of heat radiation. A5. By continuously collecting the concentration of toxic gases, the rate of change of the concentration of toxic gases per unit time is analyzed to reflect the spread speed of smoke and to calculate the smoke diffusion gradient. A6. By summing the weighting coefficients of the four toxic gases with their concentrations and incorporating the synergistic enhancement coefficient between two gases, the toxicity amplification effect of mixing multiple toxic gases is considered to calculate the flue gas toxicity synergistic coefficient, thereby ultimately outputting the comprehensive environmental hazard coefficient.

3. The internal and external collaborative intelligent fire suit monitoring system according to claim 2, characterized in that: The processing procedure for the physiological risk unit is as follows: B1. By introducing the comprehensive environmental risk factor EA, environmental risks are correlated with physiological stress, which is used to reflect the amplification effect of the environment on physiological load. B2. By considering the number of heartbeats per minute of firefighters, the cardiovascular system load can be reflected to obtain heart rate; B3. By considering the number of breaths a firefighter takes per minute, the respiratory system load can be reflected to obtain the respiratory rate; B4. By combining the firefighter's skin temperature with the ambient temperature, the firefighter's core body temperature is analyzed to reflect the degree of heat stress and to calculate the core body temperature. B5. By considering changes in the electrical conductivity of firefighters' skin to reflect the level of sympathetic nerve excitation, the psychological stress and physiological tension can be assessed to obtain skin electrical conductivity. B6. By calculating the diameter of adjacent pupils of firefighters, the rate of change of pupil diameter is analyzed to reflect the degree of nerve stress, thereby obtaining the pupil dilation rate and ultimately outputting the physiological stress index.

4. The internal and external collaborative intelligent fire suit monitoring system according to claim 3, characterized in that: The processing procedure of the security early warning unit is as follows: C1. By introducing the physiological stress index and the comprehensive environmental risk coefficient, the physiological endurance of firefighters and the degree of environmental risk are reflected, so as to demonstrate the internal and external synergy of the safety early warning unit. C2. The safety of fire suits is analyzed by combining the usage time of the fire suit, the maximum design protection time, and the fabric wear coefficient. C3. By considering the relative humidity between the inner layer of the fire suit and the skin, the degree of heat stress of firefighters can be assessed to obtain the humidity of the microclimate inside the fire suit; C4. By considering the degree of obstruction of the helmet visor's field of vision, the correlation between operational field of vision and safety risks is filled in in order to calculate the line of sight obstruction rate; C5. By considering the duration during which the distance between a firefighter and their nearest teammate exceeds the safe distance, the degree of danger when a firefighter works alone is analyzed to obtain the duration of the relative distance threshold between teammates, and thus the comprehensive safety warning level is finally output.

5. The internal and external collaborative intelligent fire suit monitoring system according to any one of claims 1-4, characterized in that: The data transmission and local storage specifically refer to: A high-temperature resistant wireless communication module is used to transmit data from each sensor to the main control chip on the back of the fire suit in real time, and simultaneously store the raw data in the high-temperature resistant flash memory in the main control box to prevent data loss due to signal interruption at the fire scene.

6. The internal and external collaborative intelligent fire suit monitoring system according to claim 5, characterized in that: The data processing and cleaning specifically includes: The 3σ principle is used to filter instantaneous noise, and the average of the first three valid data points is used to replace outliers. Dimensional data parameters are normalized to adapt to the calculations of the computational analysis components.

7. The internal and external collaborative intelligent fire suit monitoring system according to claim 1, characterized in that: The monitoring and alarm component is specifically: When the comprehensive safety warning level WE < 0.5, it indicates the safety level, representing that the external environment, internal physiology, and equipment team are in a safe state; When 0.5 ≤ Comprehensive Safety Warning Level WE < 1.2, it indicates a warning level, representing a slight risk in a single dimension; When 1.2 ≤ Comprehensive Safety Warning Level WE < 2.0, it indicates a danger alarm level, representing a moderate risk in multiple dimensions; When the comprehensive safety warning level WE≥2.0, it indicates an emergency alarm level, representing a severe risk in the core dimension.

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