Optimization design method and device of fire-fighting protective clothing considering protection and comfort

Abstract

The application discloses a fire extinguishing protective clothing optimization design method and device considering protection and comfort, and relates to the technical field of human body protection. The method comprises the following steps: acquiring activity posture parameters of rescue personnel; constructing a heat-mass coupled transfer model according to heat and moisture transfer parameters of multiple layers of fabric in the fire extinguishing protective clothing, disaster environment parameters, human body heat physiological parameters and air layer space distribution parameters under the clothing; coupling the heat-mass coupled transfer model with a human body heat regulation physiological model to obtain a dynamic two-way coupled model; simulating the air layer space distribution parameters under the clothing and the activity posture parameters to obtain an ergonomics comfort evaluation model; coupling the dynamic two-way coupled model with the ergonomics comfort evaluation model, and iteratively solving by using a multi-objective optimization algorithm to obtain fabric parameters and a fire extinguishing protective clothing structure design scheme. The application makes the fire extinguishing protective clothing have high heat protection and high operation comfort during operation, and improves the comprehensive practical applicability of the fire extinguishing protective clothing.

Description

Technical Field

[0001] This invention relates to the field of human protective technology, and in particular to a method and apparatus for optimizing the design of fire-fighting protective clothing that balances protection and comfort. Background Technology

[0002] Firefighting protective clothing is a crucial personal protective equipment for rescuers when carrying out fire fighting and emergency rescue missions. It protects them from high-temperature heat radiation, prevents burns, and blocks the intrusion of toxic fumes and chemical splashes. It is regarded as an indispensable last line of defense to ensure the safety of rescuers' lives.

[0003] Currently, mainstream firefighting protective clothing typically employs a multi-layered composite fabric design. A typical configuration includes an outer layer (high temperature resistant, flame-retardant, and abrasion-resistant), a waterproof and breathable layer (preventing the penetration of liquid water and chemicals while allowing partial water vapor permeation), a heat insulation layer (reducing heat conduction and slowing the temperature rise of the inner surface), and a comfort layer close to the body (moisture-wicking and improving comfort). This multi-layered structure, through the synergistic effect of each functional layer, achieves physical isolation for rescue personnel from extreme thermal environments and chemical hazards during operations.

[0004] However, this multi-layered and heavy structure results in poor overall breathability and moisture permeability of the clothing, and the fabric is stiff, which causes rescuers to face serious heat stress and limited work efficiency during long-term rescue operations, presenting a technical contradiction of prioritizing protection over comfort. Summary of the Invention

[0005] Therefore, it is necessary to provide optimized design methods and devices for fire-fighting protective clothing that balance protection and comfort to address the aforementioned technical problems.

[0006] This invention provides an optimized design method for fire-fighting protective clothing that balances protection and comfort, including: Obtain the activity posture parameters of rescue personnel during the operation; Based on the heat and moisture transfer parameters of the multi-layer fabric in fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing, a heat and mass coupling transfer model is constructed to describe the heat and moisture coupling transfer process between the fabric, the air layer under the clothing, and the human skin. The numerical human body model corresponding to the rescuers is divided into multiple nodes, and the physiological parameters of each node are used to characterize the thermophysiological regulation response of the rescuers during the operation, thus obtaining the human thermophysiological regulation model. The skin temperature, sensible heat flux density, and latent heat evaporation resistance output by the thermo-mass coupling transfer model are used as boundary inputs to the human thermoregulation physiological model. The skin blood flow, core blood flow, and sweating rate of the rescuers at the current moment are corrected through the hypothalamic setpoint mechanism to update the skin temperature and skin surface humidity. The updated skin temperature and updated skin surface humidity are fed back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, resulting in a dynamic two-way coupling model. Simulations were performed based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain the ergonomic comfort evaluation model; the dynamic two-way coupling model was coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of fire extinguishing protective clothing. By iteratively solving the optimization design problem using a multi-objective optimization algorithm, we obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

[0007] Optionally, the heat and moisture transfer parameters of the multilayer fabric include at least: the density, thermal conductivity, specific heat capacity, thermal diffusivity, air permeability, moisture permeability resistance, and surface emissivity of each layer of fabric. Disaster environmental parameters include at least: ambient air temperature, mean radiant temperature, relative humidity, and ambient wind speed; Human thermophysiological parameters include at least: height, weight, body surface area, basal metabolic rate, initial core temperature, and skin temperature; The spatial distribution parameters of the air layer under clothing should include at least: the thickness and volume distribution of the air layer under clothing in different locations; Activity posture parameters should include at least the following parameters: cadence, stride length, joint range of motion, range of motion, and movement speed. Joint range of motion includes: shoulder abduction angle, shoulder adduction angle, elbow flexion angle, elbow extension angle, hip flexion angle, hip extension angle, hip abduction angle, hip adduction angle, knee extension and flexion angle, knee flexion angle, ankle extension and flexion angle, and ankle flexion angle.

[0008] Optionally, based on the heat and moisture transfer parameters of the multiple layers of fabric in the fire-fighting protective clothing, disaster environment parameters, human thermophysiological parameters, and spatial distribution parameters of the air layer beneath the clothing, a heat and mass coupling transfer model is constructed to describe the heat and moisture coupling transfer process between the fabric, the air layer beneath the clothing, and the human skin. Specifically, this includes: Heat-mass coupling transfer models include: sensible heat transfer models and latent heat transfer models; Sensible heat transfer models include: sensible heat transfer coupled with heat conduction, heat convection and heat radiation; latent heat transfer models include: latent heat transfer caused by phase change processes such as evaporation, condensation, adsorption and desorption of sweat and moisture in fabrics between multiple layers of fabric. The following formula represents the heat and mass coupling transfer model for each layer of a multilayer fabric: ; in,( ρc p ) eff The effective volumetric heat capacity of the fabric. k eff Δ is the effective thermal conductivity of the fabric. h vap Δ is the enthalpy of evaporation or condensation. h abs The enthalpy of absorption or desorption. m vs The mass flow rate of water vapor flowing out of or into the fiber. T For temperature, t For time, x These are the coordinates in the thickness direction. This is the portion of the outer fabric that absorbs incident thermal radiation. The following equation represents the heat and mass coupling transfer model in the microenvironment under clothing: ; in,( ρc p ) γ The effective density of the microenvironment under clothing. k γ The effective thermal conductivity of the microenvironment under clothing. This refers to the portion of radiation absorbed by the microenvironment beneath the clothing.

[0009] Optionally, the numerical human body model corresponding to the rescuers is divided into multiple nodes, and the physiological parameters of each node are used to characterize the thermophysiological regulatory response of the rescuers during the operation, thus obtaining a human thermophysiological regulatory model, specifically including: A 3D laser scanner was used to perform a full-body scan of the rescue personnel, obtaining point cloud data of their unclothed body surface. The body surface point cloud data is imported into reverse engineering software for encapsulation and processing. The discrete point cloud is converted into a continuous polygonal mesh surface, and the holes are filled and the boundaries are repaired to obtain a three-dimensional numerical dummy model with a realistic human form. The 3D numerical dummy model was imported into a mesh generation tool and divided into multiple segments according to anatomical locations, including the head, torso, upper arm, forearm, hand, thigh, lower leg, and foot. In the radial direction of each segment, skin layer nodes, fat layer nodes, muscle layer nodes, and core layer nodes were sequentially divided according to the physiological tissue structure of the skin, fat, muscle, and core layers. The thickness ratio and thermophysical property parameters of each layer node were set. A central blood pool node was set in the torso region. A physiological model of human thermoregulation is constructed based on the theory of negative feedback regulation. The physiological model of human thermoregulation includes: a sweating regulation model, a shivering regulation model, a vasodilation regulation model, and a vasoconstriction regulation model. The sweating regulation model is used to characterize the response of sweating rate to changes in body temperature under different physiological regulation modes. The shivering regulation model is used to characterize the response of shivering heat production to changes in body temperature under different physiological regulation modes. The vasodilation regulation model is used to characterize the response of skin blood flow to changes in body temperature under different physiological regulation modes. The vasoconstriction regulation model is used to characterize the response of core blood flow to changes in body temperature under different physiological regulation modes.

[0010] Optionally, the skin blood flow, core blood flow, and perspiration rate of the rescuers at the current moment are corrected through the hypothalamic setpoint mechanism to update skin temperature and skin surface humidity; the updated skin temperature and updated skin surface humidity are fed back to the heat-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, resulting in a dynamic two-way coupling model, specifically including: The human thermoregulation physiological model is divided into a core layer, muscle layer, fat layer, skin layer and central blood pool node, and the metabolic heat production of each layer is determined in real time according to the set labor intensity. The human thermoregulation physiological model updates the skin blood flow, core blood flow, and sweating rate in the human thermoregulation physiological model based on the skin surface temperature, sensible heat flux density, and latent heat evaporation resistance related to skin surface humidity output by the received thermo-mass coupling transfer model, through the hypothalamic setpoint mechanism, in order to correct the skin temperature and skin surface humidity; and feeds the corrected skin temperature and skin surface humidity back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, thus obtaining a dynamic two-way coupling model.

[0011] Optionally, a work-life comfort evaluation model is obtained by simulation based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing, specifically including: Point cloud data of rescue personnel in unclothed and fire-fighting protective clothing states were obtained using three-dimensional laser scanning technology, resulting in first body surface point cloud data and second body surface point cloud data. The first body surface point cloud data and the second body surface point cloud data are processed by removing noise, smoothing noise and sampling non-uniform curvature. The processed first body surface point cloud data is encapsulated into a first polygonal mesh surface model and the processed second body surface point cloud data is encapsulated into a second polygonal mesh surface model. The first polygonal mesh surface model and the second polygonal mesh surface model were aligned and error analyzed by chromatographic comparison method to obtain the average thickness data and volume distribution data of the air layer under the clothing in different parts. Data on the joint movement angles and range of motion of rescue personnel in different working postures were obtained through a 3D motion capture system. Average thickness data and volume distribution data are used as parameters to characterize spatial fit, and joint movement angle and range of motion data are used as parameters to characterize mobility. A quantitative evaluation model of the impact of protective clothing on the ergonomics of rescue personnel under different working postures is constructed by multi-source information fusion method, so as to determine the fit index and activity restriction index of fire extinguishing protective clothing and obtain the ergonomic comfort evaluation model.

[0012] Optionally, the dynamic two-way coupling model is coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition for fire-fighting protective clothing, specifically including: Obtain the thermal comfort prediction value output by the dynamic two-way coupling model and the fit index and activity restriction index output by the ergonomic comfort assessment model; A comprehensive experiment on the physiological and ergonomic aspects of human clothing was conducted under different environmental conditions and activity intensities. Based on the thermophysiological parameters of rescuers, subjective thermal comfort scores, motion capture data, and changes in the air layer under the clothing during the experiment, the measured values ​​of thermal comfort, fit, and activity restriction were determined. The error functions between predicted and measured thermal comfort values, the error function between the fit index and the measured fit index, and the error function between the activity restriction index and the measured activity restriction value are compared with their respective preset thresholds: If all error functions do not exceed the preset threshold, then the dynamic two-way coupling model and the ergonomic comfort evaluation model will be used as the simulation design proposition for fire extinguishing protective clothing. If any error function exceeds the preset threshold, the dynamic two-way coupling model and the ergonomic comfort evaluation model are corrected by feedback until each error function does not exceed the corresponding preset threshold. The corrected dynamic two-way coupling model and the corrected ergonomic comfort evaluation model are then used as the simulation design topic for fire extinguishing protective clothing. The error functions include, but are not limited to, root mean square error and average relative error.

[0013] Optionally, a multi-objective optimization algorithm is used to iteratively solve the optimization design problem to obtain fabric parameters and fire-fighting protective clothing structural design schemes that maximize thermal comfort and achieve optimal protection and workability. Specifically, this includes: The preset thermophysiological thresholds and ergonomic thresholds include: upper limit of core temperature, upper limit of skin temperature, and upper limit of perspiration; and ergonomic thresholds include: lower limit of joint range of motion and lower limit of air gap under clothing. The thermal resistance, moisture resistance, air permeability, thickness, and areal density of each layer of fabric in the fire extinguishing protective clothing, as well as the ease, opening size, and undergarment gap distribution parameters of each part of the protective clothing, are set as decision variables. An initial population is randomly generated within the range of values ​​of each decision variable. For each individual in the population, the individual's decision variables are input into a dynamic two-way coupling model to obtain the time history curves of core temperature, skin temperature and perspiration under steady-state or dynamic conditions. The decision variables are input into an ergonomic comfort assessment model to obtain the range of motion of each joint and the value of the air gap under clothing. The maximum core temperature, maximum skin temperature, and total perspiration are compared with the upper limits of core temperature, skin temperature, and perspiration, respectively. The range of motion of each joint is compared with the lower limit of the range of motion of the joint. The calculated value of the air gap under clothing is compared with the lower limit of the air gap under clothing. If any indicator exceeds the corresponding threshold, the individual is determined to be an infeasible solution. The constraint violation degree is calculated based on the degree of violation, and a penalty item proportional to the constraint violation degree is imposed on the individual. Using the thermal comfort comprehensive score as the first objective function, the protective performance index as the second objective function, and the ergonomic comprehensive index (a weighted composite of the fitability index and the activity restriction index) as the third objective function, the population individuals are non-dominated stratified through Pareto dominance relationships, and the density of individuals within the same stratum is assessed. Superior individuals are selected from the current population as parents through selection operations, and crossover and mutation operations are performed on the parent individuals to generate offspring individuals. The parent and offspring populations are merged, and non-dominated stratification and density assessment are performed again. A specified number of individuals are selected to form the next generation population, resulting in the current Pareto front. Repeated iterations are performed, and the hypervolume index of the current Pareto front is determined after each iteration. The iteration is terminated when the relative change in hypervolume is less than the preset convergence threshold or the number of generations reaches the preset maximum number of generations. After the iteration terminates, all non-dominant individuals in the current population and their corresponding decision variable combinations are output to obtain the Pareto optimal solution set. The Pareto optimal solution set includes: the combination of heat and moisture transfer parameters of the multilayer fabric and the structural dimension parameters of the protective clothing.

[0014] This invention provides an optimized design device for fire-fighting protective clothing that balances protection and comfort, comprising: The data acquisition module is used to acquire the activity posture parameters of rescue personnel during the operation; The thermal-moisture modeling module is used to construct a thermal-mass coupling transfer model to describe the thermal-moisture coupling transfer process between the fabric, the air layer under the clothing and human skin, based on the thermal-moisture transfer parameters of the multi-layer fabric in the fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing. The physiological modeling module is used to divide the numerical human body model corresponding to the rescuers into multiple nodes, and to characterize the thermophysiological regulation response of the rescuers during the operation through the physiological parameters of each node, so as to obtain the human thermophysiological model. The coupling module is used to take the skin temperature, sensible heat flux density, and latent heat evaporation resistance output by the thermo-mass coupling transfer model as boundary inputs to the human thermoregulation physiological model. It corrects the skin blood flow, core blood flow, and sweating rate of the rescuers at the current moment through the hypothalamic setpoint mechanism to update the skin temperature and skin surface humidity. The updated skin temperature and updated skin surface humidity are fed back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, resulting in a dynamic two-way coupling model. The ergonomic modeling module is used to simulate based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain the ergonomic comfort evaluation model; the dynamic two-way coupling model is coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of fire extinguishing protective clothing. The comprehensive module is used to iteratively solve the optimization design problem through a multi-objective optimization algorithm to obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

[0015] The optimized design method and device for fire-fighting protective clothing that balances protection and comfort, provided in this invention embodiment, have the following advantages compared to the prior art: This invention utilizes dynamic bidirectional data interaction between a thermo-mass coupling transfer model and a human thermoregulation physiological model to correlate the thermal-moisture boundary signal with the thermo-physiological regulatory response in real time at every moment. This accurately reproduces the dynamic accumulation process of human thermal stress under multiple layers of thick fabric during operation, while quantifying thermal protection effectiveness and physiological comfort. This allows fire extinguishing protective clothing to have both high protection and high comfort during operation, effectively improving the comprehensive practical applicability of fire extinguishing protective clothing. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an optimized design method for fire-fighting protective clothing that balances protection and comfort, provided in one embodiment. Figure 2 A diagram showing the distribution of the air layer under the clothing in an embodiment of an optimized design method for fire-fighting protective clothing that balances protection and comfort. Figure 3 A diagram of joint motion parameters for an optimized design method of fire-fighting protective clothing that balances protection and comfort, provided in one embodiment; Figure 4 A dynamic bidirectional coupling model diagram of an optimized design method for fire-fighting protective clothing that balances protection and comfort, provided in one embodiment; Figure 5 This is a schematic diagram of the model coupling of an optimized design method for fire-fighting protective clothing that balances protection and comfort, provided in one embodiment. Figure 6This is a structural diagram of an optimized design device for fire-fighting protective clothing that balances protection and comfort, provided in one embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] This invention provides an optimized design method for fire-fighting protective clothing that balances protection and comfort. The method includes: Obtain the activity posture parameters of rescue personnel during the operation.

[0019] Based on the heat and moisture transfer parameters of the multi-layered fabric in fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing, a heat and mass coupling transfer model is constructed to describe the heat and moisture coupling transfer process between the fabric, the air layer under the clothing, and the human skin.

[0020] The numerical human body model corresponding to the rescuers is divided into multiple nodes, and the physiological parameters of each node are used to characterize the thermophysiological regulatory response of the rescuers during the operation, thus obtaining the human thermophysiological model.

[0021] The skin temperature, sensible heat flux, and latent heat evaporation resistance output from the heat-mass coupling transfer model are used as boundary inputs to the human thermoregulation physiological model. The skin blood flow, core blood flow, and perspiration rate of the rescuers at the current moment are corrected through the hypothalamic setpoint mechanism to update skin temperature and skin surface humidity. The updated skin temperature and skin surface humidity are then fed back into the heat-mass coupling transfer model to iteratively update the rescuers' thermophysiological parameters at the next moment, resulting in a dynamic two-way coupling model.

[0022] Simulations were performed based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain an ergonomic comfort evaluation model. The dynamic two-way coupling model was coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of fire extinguishing protective clothing.

[0023] By iteratively solving the optimization design problem using a multi-objective optimization algorithm, we obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

[0024] A specific embodiment of the fire-fighting protective clothing optimization design method of the present invention, which balances protection and comfort, is provided, such as... Figure 1 As shown, it includes: Step 101: Obtain the heat and moisture transfer parameters of the multi-layered fabric of the fire-fighting protective suit, the disaster environment parameters, the human body's thermal physiological parameters, the spatial distribution parameters of the air layer under the suit, and the activity posture parameters of the rescuers.

[0025] Specifically, the heat and moisture transfer parameters of multi-layered fire-fighting protective clothing fabrics include at least the density, thermal conductivity, specific heat capacity, thermal diffusivity, air permeability, moisture permeability resistance, and surface emissivity of each layer. In this embodiment, a typical four-layer fire-fighting protective clothing is used as an example: the outer layer is aramid fabric, the waterproof and breathable layer is a PTFE film composite aramid nonwoven fabric, the insulation layer is aramid needle-punched felt, and the comfort layer is flame-retardant cotton fabric. The heat and moisture parameters of each layer can be obtained through experimental methods such as the transient planar heat source method, water vapor transmission rate tester, and thermal radiation reflectivity tester.

[0026] Disaster environmental parameters include at least ambient air temperature, mean radiant temperature, relative humidity, and ambient wind speed. In this embodiment, taking a common fire rescue scenario as an example, the ambient air temperature is set to 45℃, the mean radiant temperature is set to 150℃ (corresponding to a thermal radiation intensity of approximately 2.5kW / m²), the relative humidity is set to 30%, and the ambient wind speed is set to 0.5m / s. These parameters can be adjusted according to the specific disaster scenario.

[0027] Human thermophysiological parameters include at least height, weight, body surface area, basal metabolic rate, initial core temperature, and skin temperature. This example uses an adult male rescuer who is 175cm tall, weighs 70kg, and has a body surface area of ​​approximately 1.85m². The basal metabolic rate is taken as 45W / m², the initial core temperature as 37℃, and the initial skin temperature as 34℃.

[0028] The spatial distribution parameters of the air layer under clothing include at least the thickness and volume distribution of the air layer under clothing in different locations. In this embodiment, point cloud data of the subject's body surface in both unclothed and wearing a specified size fire-fighting protective suit were acquired using three-dimensional laser scanning technology. After noise optimization and non-uniform curvature sampling processing, the point cloud data was encapsulated into a polygonal human body model. A chromatographic comparison method was used to align and analyze the errors of the two models to obtain the average thickness and volume distribution of the air layer under clothing in key areas such as the chest, back, shoulders, upper arms, waist, buttocks, thighs, knees, and calves. Figure 2 As shown.

[0029] The activity posture parameters of rescuers include at least stride frequency, stride length, joint range of motion, range of motion, and movement speed. Joint range of motion includes shoulder abduction / adduction, elbow flexion / extension, hip flexion / extension / abduction / adduction, and the flexion / extension and extension angles of the knee and ankle joints. In this embodiment, a three-dimensional motion capture system is used to attach reflective markers to the subject's body surface to track their three-dimensional motion trajectory in real time under typical conditions such as normal walking, obstacle crossing, and fast shuttle runs. The system calculates stride frequency, stride length, real-time angle changes of each joint in the coronal, sagittal, and horizontal planes, and the range of motion (ROM). Figure 3 As shown.

[0030] Step 102: Based on the heat and moisture transfer parameters, human thermal physiological parameters, disaster environment parameters, and spatial distribution parameters of the air layer under the clothing, construct a heat and mass coupling transfer model of the multi-layer fabric of the protective clothing, the air layer under the clothing, and the human skin. Through the heat and mass coupling transfer model and the human thermal regulation physiological model, a dynamic two-way coupling model of heat and moisture transfer and thermal physiological regulation is formed to characterize the thermal protection performance of the protective clothing and the human thermal comfort response.

[0031] Specifically, the heat-mass coupling transfer model includes calculations of sensible heat transfer through heat convection, heat radiation, heat conduction, and the coupling of these three modes between fabric layers, the microenvironment under clothing, and human skin. It also considers latent heat transfer caused by the evaporation, condensation, adsorption, and desorption of moisture and sweat between multiple fabric layers. The heat transfer control equations for each fabric layer in the model are based on the law of conservation of energy, while the moisture transfer control equations are based on Fick's diffusion law and Darcy's law. Natural convection within the air layer under clothing is described using the Boussinesq approximation.

[0032] (1) Model assumptions.

[0033] It is assumed that convection occurs only on the surface of the clothing during heat exposure, and incident radiation only penetrates the outer shell; the absorption and scattering effects of thermal radiation by the porous material and microenvironment of the fabric are considered; mass transfer phenomena such as adsorption, desorption, evaporation and condensation of moisture in the wet fabric participate in the heat transfer process; heat and moisture transfer occur in the thickness direction of the fabric; the properties of the fabric are isotropic (the physical properties of the material do not change in different directions); each phase maintains local thermal equilibrium; the volume change of the textile material due to changes in moisture content is negligible; and the heat and mass convection effects in the fabric and microenvironment are not considered.

[0034] (2) Establish the energy conservation equation.

[0035] A. Heat and mass transfer in fire suits.

[0036] Under heat radiation conditions, the moisture inside the fire suit will significantly affect the heat transfer process of the system. The energy conservation equation for each layer of fabric is expressed as: ; in,( ρc p ) eff The effective volumetric heat capacity of the fabric. k eff Δ is the effective thermal conductivity of the fabric. h vap Δ is the enthalpy of evaporation or condensation (unit: J / kg). h abs The enthalpy of absorption or desorption (unit: J / kg). mvs The mass flow rate of water vapor flowing out of or into the fiber (unit: kg / (m³) 3 ·s)), T Temperature (unit: °C) t Time (unit: seconds) x These are the coordinates in the thickness direction (unit: m). This refers to the portion of incident thermal radiation absorbed by the outer fabric.

[0037] Firefighting suits are multi-layered fabrics considered to be multiphase mixtures composed of fibers, water, and air. Therefore, the thermophysical properties of the fabric can be obtained using the local volume-averaged theory.

[0038] B. Heat and mass transfer in the microenvironment under clothing.

[0039] Based on the law of conservation of energy transfer, a heat transfer model within the microenvironment under clothing was established by combining heat conduction and radiation: ; in,( ρc p ) γ The effective density of the microenvironment under clothing. k γ The effective thermal conductivity of the microenvironment under clothing. This refers to the portion of radiation absorbed by the microenvironment beneath the clothing. ρ γ 、( c p ) γ and are the effective density, specific heat capacity, and thermal conductivity of the microenvironment under clothing, respectively, as follows: ; The system's temperature and humidity are uniformly distributed at the initial moment, as calculated below: ; (3) Model solution.

[0040] The heat and moisture transfer were coupled, and both the gas-phase heat transfer and mass conservation models are nonlinear partial differential equation systems, which increases computational complexity. To reduce errors, the finite volume method and implicit scheme are used to discretize and solve the aforementioned governing equations, initial conditions, and boundary conditions. By dividing the entire computational domain into several non-overlapping control volumes, the conservation equations are integraled over each control volume, transforming the partial differential equations into a system of algebraic equations for nodal physical quantities. The volume fraction of bound water and gas phase within the wet fabric is solved using the mass conservation equation, and the water vapor concentration field distribution inside the garment is then calculated accordingly. The local volume averaging theory is used to dynamically update the thermophysical parameters of each layer of the fabric and the microenvironment to obtain the temperature distribution within the system. The Henriques burn integral model is then used to predict skin burn information of rescue personnel.

[0041] A multi-node human thermophysiological regulation model is constructed as follows: First, a three-dimensional numerical dummy model with a realistic human morphology is obtained using 3D scanning technology. Then, based on human physiological structure, the numerical model is divided into a multi-node structure using a mesh generation tool (such as ICEM CFD). In this embodiment, the human body is divided into 15 segments: head, torso, upper arm, forearm, hand, thigh, calf, and foot. Each segment is further divided radially into a core layer, muscle layer, fat layer, skin layer, and a central blood pool node. This model quantitatively characterizes the central nervous system's regulation of body temperature based on negative feedback regulation theory. It includes physiological regulation models for sweating, shivering, vasodilation, and vasoconstriction, and introduces the influence factors of high-temperature environment on metabolic heat production, saline balance, and blood circulation to achieve accurate prediction of thermophysiological parameters suitable for full-size three-dimensional models of rescue personnel.

[0042] (1) A three-dimensional laser scanner was used to perform a full-body scan on the subject to obtain high-precision point cloud data of the body surface in an unclothed state. During the scan, the subject stood in a standard anatomical posture, and the scanning resolution was set to no less than 1 mm to ensure accurate capture of the curvature features and local morphological details of the human body surface. The acquired point cloud data was preprocessed, including noise removal, noise smoothing, and data simplification. A non-uniform curvature sampling method was used to retain more sampling points in areas with greater curvature and reduce sampling points in flat areas, thereby reducing data redundancy while maintaining the accuracy of human geometric features.

[0043] (2) The optimized point cloud data is imported into reverse engineering software for encapsulation processing. The discrete point cloud is converted into a continuous polygonal mesh surface, and the holes are filled and the boundaries are repaired to generate a three-dimensional numerical dummy model with a realistic human body shape. The model fully preserves the geometric dimensions, surface curvature and morphological features of each part of the human body. Compared with the simplified model of traditional standard cylinder or ellipsoid, it can more realistically reflect the spatial relationship between the actual human body and the protective clothing.

[0044] (3) The three-dimensional numerical dummy model was imported into a mesh generation tool and segmented and discretized into multiple nodes according to the physiological structure of the human body. First, the human body model was divided into multiple segments such as head, torso, upper arm, forearm, hand, thigh, calf and foot according to anatomical parts. Then, in the radial direction of each segment, skin layer nodes, fat layer nodes, muscle layer nodes and core layer nodes were sequentially divided according to the physiological tissue structure of skin, fat, muscle and core layer, and the thickness ratio and thermophysical property parameters of each layer node were set; at the same time, a central blood pool node was set in the core area such as the torso to simulate the heat transport and exchange of blood circulation between segments. The final generated model is a multi-node numerical dummy model with a real human body geometry and complete physiological layer structure, which provides an accurate geometric basis and node network for subsequent heat and moisture coupling transfer calculation and thermophysiological regulation simulation.

[0045] The heat-mass coupling model is coupled with a multi-node human thermophysiological regulation model to form a dynamic two-way coupled model of heat and moisture transfer-thermophysiological regulation, such as... Figure 4 As shown in the diagram. Specifically, within each coupled simulation time step (Δt = 0.1s in this embodiment), the skin surface temperature, sensible heat flux density, and latent heat evaporation resistance related to skin surface humidity calculated by the thermo-mass coupling transfer model are used as boundary inputs to the human thermophysiological regulation model. Based on the received heat flux signal, the human thermophysiological regulation model corrects skin blood flow, core blood flow, and sweating rate through the hypothalamic setpoint mechanism, and feeds the updated skin temperature and skin surface humidity back to the thermo-mass coupling transfer model, thereby iteratively updating the thermophysiological parameters for the next moment. Through this bidirectional coupling mechanism, the thermal protection performance of the protective clothing and the dynamic thermophysiological response of the human body can be accurately characterized.

[0046] Experiments were conducted on human physiology and thermal comfort under different environmental conditions and activity intensities to obtain local and systemic thermal physiological parameters and subjective thermal comfort evaluation data. In this embodiment, a combination of low, medium, and high radiation intensities and three metabolic equivalents (corresponding to 2 MET, 4 MET, and 6 MET, respectively) were simulated in an artificial climate chamber. The subjects' core temperature, skin temperature, perspiration, and heart rate were monitored in real time, and a 9-point thermal comfort scale was used to collect real-time subjective thermal comfort scores for each part of the subjects.

[0047] Statistical analysis was performed on the thermophysiological parameters and thermal comfort evaluation data of various body parts. A quantitative response relationship between local and systemic thermophysiological parameters and thermal comfort was established using a regression fitting method. For example, the thermal comfort TSV of the torso can be expressed as a multiple linear function of skin temperature (Tsk) and sweat rate (Sw): TSV = aTsk + bSw + c. Integrating the established quantitative response relationship into a dynamic two-way coupled model enables real-time prediction of the local and overall thermal comfort of rescue personnel.

[0048] Step 103: Based on the spatial distribution parameters of the air layer under the clothing and the joint motion parameters, establish an ergonomic comfort assessment model characterizing the fit and mobility of the clothing. Couple the dynamic two-way coupling model with the ergonomic comfort assessment model, using thermophysiological thresholds and ergonomic thresholds as constraints, and employ a multi-objective optimization algorithm to iteratively solve and output the fabric parameters and protective clothing structural design scheme that maximizes thermal comfort and achieves optimal overall protection and ergonomics.

[0049] The iterative solution process of a multi-objective optimization algorithm includes: (1) Initialization: Set the fabric thermal and moisture parameters (thermal resistance, moisture resistance, etc.) and garment structural parameters (gap under garment, opening size, etc.) as decision variables, and randomly generate an initial parameter population.

[0050] (2) Simulation and constraint evaluation: For each set of parameters, call the coupled model to calculate the thermophysiological index (core temperature, skin temperature, perspiration) and ergonomic index (joint range of motion, air gap under clothing). Determine whether the preset thermophysiological threshold and ergonomic threshold are met.

[0051] (3) Multi-objective fitness calculation: Three objectives are constructed: maximizing thermal comfort, maximizing protective performance, and maximizing ergonomic performance. The Pareto dominance relation is used to hierarchically divide the solution and a penalty term is applied to the solution that violates the constraints.

[0052] (4) Evolutionary update: Select superior individuals based on non-dominated sorting and crowding distance, generate offspring population through crossover and mutation operations, and retain elite individuals.

[0053] (5) Iterative convergence: Repeat steps (2)-(4) until the Pareto front hypervolume no longer changes significantly or the maximum algebra is reached. Finally, output a set of Pareto optimal solutions for decision-makers to select the best parameter combination according to actual needs.

[0054] In the specific implementation, the thickness and volume distribution of the air layer under the clothing obtained in step 101 are used as parameters to characterize spatial fit. The fit index Fiti for each part is calculated, which is defined as the weighted sum of the ratio of the actual thickness of the air layer under the clothing to the minimum gap required for human movement. The joint angle and range of motion are used as parameters to characterize mobility. The activity restriction index Mobj for each joint is calculated, which is defined as the normalized ratio of the range of motion of the joint in the dressed state to the range of motion in the naked state.

[0055] By employing a multi-source information fusion method, a weighted comprehensive model is established to assess the impact of protective clothing on the ergonomics of rescue personnel under different working postures, by weighting and integrating the fit index and the activity restriction index. This model outputs an overall fit index F and an overall activity restriction index M, serving as a comprehensive evaluation basis for work comfort.

[0056] The thermal comfort prediction value output by the dynamic two-way coupling model and the fit index and activity restriction index output by the ergonomic comfort assessment model are used as comprehensive comfort evaluation indicators. A series of comprehensive physiological and ergonomic experiments on human wear are designed to measure the above indicators under different environmental conditions and activity intensities. In this embodiment, subjects wearing standard-sized fire-fighting protective clothing complete a specified sequence of actions in environments with radiation intensities of 1.5 kW / m², 2.5 kW / m², and 3.5 kW / m², while simultaneously recording thermophysiological parameters, subjective thermal comfort scores, motion capture data, and changes in the air layer beneath the clothing.

[0057] The simulated values ​​from the model are compared with the experimentally measured values, and the root mean square error (RMSE) and mean relative error (MRE) of each index are calculated. If the error exceeds the preset threshold, the key parameters in the dynamic two-way coupling model and the ergonomic comfort assessment model (including: the fabric thermal and moisture parameter correction factor in the thermal and moisture coupling transfer model, the set point coefficient in the thermophysiological regulation model, and the weighting factor in the ergonomic assessment model, etc.) are corrected through feedback until the simulation prediction accuracy meets the requirements.

[0058] Based on the modified coupling model, sensitivity analysis of fabric heat and moisture transfer parameters and clothing structural parameters was carried out to quantitatively characterize the influence weight of each parameter on human thermal comfort, fit and mobility, and to identify key parameters affecting overall performance.

[0059] A multi-objective optimization problem is constructed with the following constraints: a core temperature not exceeding 38.5℃, a skin temperature not exceeding 36℃, and a perspiration rate not exceeding 800g / h; a joint range of motion not less than 70% of the naked state; and a minimum air gap under clothing not less than 5mm. The optimization objectives are to maximize the comprehensive thermal comfort index and minimize the comprehensive ergonomic constraint index, such as... Figure 5As shown.

[0060] A multi-objective optimization algorithm is used to iteratively solve the coupled model to generate a Pareto optimal solution set. Under the constraints of thermophysiological index threshold and ergonomic threshold, the combination of heat and moisture transfer parameters of the multilayer fabric and the structural dimension parameters of the protective clothing that optimize the overall comfort performance are selected from the solution set as the target design scheme.

[0061] In this embodiment, firstly, the heat and moisture transfer parameters of the multi-layer fabric, the disaster environment parameters, the human body's thermal physiological parameters, and the spatial distribution parameters of the air layer under the clothing are obtained. A thermal mass coupling transfer model of protective clothing-air layer-human body is constructed and dynamically coupled with a multi-node human body thermal physiological regulation model. This achieves accurate linkage prediction of thermal protection performance and human body thermal comfort response under real clothing conditions, overcoming the shortcomings of existing off-body evaluation methods that cannot reflect the dynamic interaction between human-clothing-environment.

[0062] Meanwhile, by using 3D scanning and motion capture technology to obtain the thickness and volume distribution of the air layer under the clothing and the joint motion posture parameters, a quantitative evaluation model of spatial fit and mobility flexibility is established. This incorporates ergonomic comfort into a unified simulation framework, effectively making up for the shortcomings of traditional methods in quantifying the constraint effect of clothing under rescue action conditions.

[0063] Based on this, the model prediction accuracy is highly consistent with the actual working conditions through joint simulation of the thermo-humidity-physiology coupling model and the ergonomic evaluation model, as well as feedback correction from experimental data.

[0064] Ultimately, by using thermal physiological and ergonomic thresholds as constraints and solving the problem through multi-objective optimization iterations, we can output fabric parameters and protective clothing structural design schemes that maximize thermal comfort and minimize ergonomic limitations while ensuring that thermal protection performance meets the standards. This provides a systematic and quantifiable theoretical basis and optimization tool for the development of fire suits that balance protection and comfort.

[0065] In one embodiment, taking an adult male rescuer with a height of 175cm, weight of 70kg, and a body surface area of ​​approximately 1.85m² wearing a four-layer fire-fighting protective suit consisting of an outer aramid fabric, a waterproof and breathable PTFE layer, an aramid needle-punched felt insulation layer, and a flame-retardant cotton comfort layer as an example, the heat and moisture transfer parameters such as density, thermal conductivity, specific heat capacity, air permeability, and moisture permeability resistance of each layer of fabric are first obtained through transient planar heat source method and moisture permeability test. At the same time, the environmental parameters corresponding to the fire rescue working conditions are set as air temperature of 45℃, average radiation temperature of 150℃, relative humidity of 30%, and ambient wind speed of 0.5m / s. Then, three-dimensional laser scanning and three-dimensional motion capture technology are used to obtain the average thickness distribution of the air layer under the clothing in key parts such as chest, back, shoulders, hips, and knees, as well as the step frequency, step length, and range of motion of shoulder, elbow, hip, knee, and ankle joints under normal walking and obstacle crossing conditions.

[0066] Based on this, a one-dimensional thermo-mass coupling model was constructed, incorporating heat conduction, convection, radiation, and latent heat transfer from sweat evaporation and condensation within the interlayer and subsurface microenvironment of fabric. This model was then integrated with a multi-node human thermophysiological regulation model, divided into core, muscle, fat, skin, and central blood pool nodes, to exchange bidirectional data on skin temperature and humidity, heat flux density, blood flow, and perspiration rate every second. Simultaneously, quantitative regression relationships between local skin temperature, perspiration rate, and subjective thermal comfort votes, obtained from experiments with subjects in an artificial climate chamber, were integrated to output real-time core temperature, skin temperature, and local thermal comfort indices. In parallel, the volumetric distribution of the subsurface air layer thickness and joint range of motion were weighted and fused to calculate a fit index and a limitation of movement index, forming an ergonomic comfort assessment result.

[0067] The simulated values ​​were compared with the measured thermophysiological data and ergonomic scores of subjects wearing the protective suit under the same combination of thermal radiation intensity and activity intensity. The fabric thermo-humidity coupling correction factor and physiological set point coefficient in the feedback correction model were then used to control the root mean square error of core temperature and skin temperature to within 0.3℃ and 0.5℃, respectively.

[0068] Finally, based on the modified model, sensitivity analysis was conducted on parameters such as fabric thermal resistance, moisture resistance, breathability, thickness, and clothing relaxation. Constrained by a core temperature not exceeding 38.5℃, skin temperature not exceeding 36℃, perspiration not exceeding 800g / h, and knee joint range of motion not less than 70% of the bare state, a multi-objective optimization algorithm was used to solve and output a recommended scheme with a comprehensive thermal comfort score of 8.2 and a work efficiency limitation index below 0.3. This scheme specifically includes quantitative adjustment suggestions for fabric parameters and structural dimensions, such as reducing the thermal resistance of the outer fabric by 12%, increasing breathability by 18%, and increasing knee relaxation by 2.5cm. This significantly improves the operational comfort and mobility of rescue personnel while ensuring thermal protection safety thresholds.

[0069] As can be seen, the simulation technology for optimizing the protective and comfort performance of firefighting protective clothing for rescue personnel in this invention can achieve the following functions: (1) Realize cross-scale dynamic coupling prediction of thermal protection performance and human thermal comfort response under real clothing conditions. By constructing a multi-layer fabric-under-clothing air layer-human skin thermal mass coupling transfer model and conducting bidirectional data interaction with a multi-node human thermal physiological regulation model, it is possible to simulate the spatiotemporal evolution of human core temperature, skin temperature, perspiration and temperature and humidity of the microenvironment under clothing under fire environment and different activity intensities in real time, accurately reflect the dynamic heat and moisture transfer and physiological regulation process between human, clothing and environment, and make up for the shortcomings of traditional fabric off-body testing in characterizing the actual clothing thermal protection and thermal comfort response.

[0070] (2) Achieve quantitative evaluation of the fit and mobility of clothing under rescue operation conditions. By using 3D scanning technology to obtain the thickness and volumetric spatial distribution of the air layer under the clothing in different postures, and combining the step frequency, step length and range of motion data of joints such as shoulder, elbow, hip, knee and ankle obtained by 3D motion capture, establish a quantitative characterization method for spatial fit index and activity restriction index, effectively solve the problem of the lack of evaluation of the ergonomic constraint effect of fire fighting clothing in existing technologies, and provide objective data support for the ergonomic optimization of protective clothing structural design.

[0071] (3) Achieve coordinated optimization and output of protective performance and comprehensive comfort performance, including thermal comfort and work-related comfort. Based on the coupled model corrected by experimental data feedback, conduct sensitivity analysis of the thermal and moisture parameters of the fabric and the structural parameters of the clothing on the comprehensive performance. With the thermal physiological safety threshold and the joint activity work-related threshold as constraints, use a multi-objective optimization algorithm to iteratively solve the problem. Finally, output the combination of fabric parameters and the structural size scheme of the protective clothing that maximizes thermal comfort and minimizes work-related limitations. This provides a systematic and quantifiable decision-making basis and optimization tool for the development of fire-fighting clothing that takes into account both protection and comfort.

[0072] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0073] Based on the same inventive concept, embodiments of the present invention also provide an optimized design device for fire-fighting protective clothing that balances protection and comfort, the device comprising: The data acquisition module is used to acquire the activity posture parameters of rescue personnel during the operation.

[0074] The thermal-moisture modeling module is used to construct a thermal-mass coupling transfer model to describe the thermal-moisture coupling transfer process between the fabric, the air layer under the clothing and human skin, based on the thermal-moisture transfer parameters of the multi-layer fabric in the fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing.

[0075] The physiological modeling module is used to divide the numerical human body model corresponding to the rescuers into multiple nodes, and to characterize the thermophysiological regulation response of the rescuers during the operation through the physiological parameters of each node, so as to obtain the human thermophysiological model.

[0076] The coupling module uses the skin temperature, sensible heat flux, and latent heat evaporation resistance output from the thermo-mass coupling transfer model as boundary inputs to the human thermoregulation physiological model. It then uses the hypothalamic setpoint mechanism to correct the rescuer's skin blood flow, core blood flow, and perspiration rate at the current moment, updating the skin temperature and skin surface humidity. The updated skin temperature and skin surface humidity are fed back to the thermo-mass coupling transfer model, iteratively updating the rescuer's thermophysiological parameters for the next moment, resulting in a dynamic two-way coupling model.

[0077] The ergonomic modeling module is used to simulate based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain the ergonomic comfort evaluation model; the dynamic two-way coupling model is coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of fire extinguishing protective clothing.

[0078] The comprehensive module is used to iteratively solve the optimization design problem through a multi-objective optimization algorithm to obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

[0079] A specific embodiment of the fire-fighting protective clothing optimization design device of the present invention, which balances protection and comfort, is provided, such as... Figure 6 As shown, it includes: The system includes a data acquisition module 601, a thermo-humidity-physiology coupling modeling module 602, an ergonomic modeling module 603, and a comprehensive simulation and optimization module 604.

[0080] The data acquisition module 601 is used to acquire heat and moisture transfer parameters of the multi-layer fabric of fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, spatial distribution parameters of the air layer under the clothing, and the activity posture and joint movement parameters of rescuers; The heat-moisture-physiology coupling modeling module 602 is used to construct a heat-mass coupling transfer model of the multi-layer fabric of protective clothing, the air layer under the clothing, and human skin based on heat-moisture transfer parameters, human thermal physiological parameters, disaster environment parameters, and spatial distribution parameters of the air layer under the clothing. It also forms a dynamic two-way coupling model by coupling with the human thermoregulation physiological model. Ergonomic modeling module 603 is used to establish an ergonomic comfort evaluation model that characterizes clothing fit and mobility based on the spatial distribution parameters of the air layer under clothing and joint motion parameters. The integrated simulation and optimization module 604 is used to couple the dynamic two-way coupling model with the ergonomic comfort evaluation model. With the thermophysiological index threshold and ergonomic threshold as constraints, iteratively solves and outputs the fabric parameters and protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and ergonomics.

[0081] It is not difficult to see that this embodiment is a device embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0082] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0083] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of fire-fighting protective clothing that balances protection and comfort, characterized in that, include: Obtain the activity posture parameters of rescue personnel during the operation; Based on the heat and moisture transfer parameters of the multi-layer fabric in fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing, a heat and mass coupling transfer model is constructed to describe the heat and moisture coupling transfer process between the fabric, the air layer under the clothing, and the human skin. The numerical human body model corresponding to the rescuers is divided into multiple nodes, and the physiological parameters of each node are used to characterize the thermophysiological regulation response of the rescuers during the operation, thus obtaining the human thermophysiological regulation model. The skin temperature, sensible heat flux density, and latent heat evaporation resistance output by the thermo-mass coupling transfer model are used as boundary inputs of the human thermoregulation physiological model. The skin blood flow, core blood flow, and sweating rate of the rescuers at the current moment are corrected through the hypothalamic setpoint mechanism to update the skin temperature and skin surface humidity. The updated skin temperature and updated skin surface humidity are fed back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, resulting in a dynamic two-way coupling model. Simulations were performed based on the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain an ergonomic comfort evaluation model; the dynamic two-way coupling model was coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of the fire extinguishing protective clothing. The optimization design problem is solved iteratively by using a multi-objective optimization algorithm to obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

2. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The heat and moisture transfer parameters of the multilayer fabric include at least: the density, thermal conductivity, specific heat capacity, thermal diffusivity, air permeability, moisture permeability resistance, and surface emissivity of each layer of fabric. The disaster environmental parameters include at least: ambient air temperature, mean radiant temperature, relative humidity, and ambient wind speed; The human thermophysiological parameters include at least: height, weight, body surface area, basal metabolic rate, initial core temperature, and skin temperature; The spatial distribution parameters of the air layer under clothing include at least: the thickness and volume distribution of the air layer under clothing in different locations; The activity posture parameters include at least the parameters of step frequency, step length, joint range of motion, range of motion, and movement speed. The joint range of motion includes: shoulder abduction angle, shoulder adduction angle, elbow flexion angle, elbow extension angle, hip flexion angle, hip extension angle, hip abduction angle, hip adduction angle, knee extension angle, knee flexion angle, ankle extension angle, and ankle flexion angle.

3. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The aforementioned heat-mass coupling transfer model, constructed based on the heat and moisture transfer parameters of the multi-layered fabric in fire-fighting protective clothing, disaster environment parameters, human thermophysiological parameters, and spatial distribution parameters of the air layer beneath the clothing, is used to describe the heat and moisture coupling transfer process between the fabric, the air layer beneath the clothing, and the human skin. Specifically, it includes: The heat-mass coupling transfer model includes: a sensible heat transfer model and a latent heat transfer model; The sensible heat transfer model includes sensible heat transfer coupled with heat conduction, heat convection and heat radiation, and the latent heat transfer model includes latent heat transfer caused by the evaporation, condensation, adsorption and desorption processes of sweat and moisture in fabrics between multiple layers of fabric. The following formula represents the heat and mass coupling transfer model for each layer of a multilayer fabric: ； in,( ρc p ) eff The effective volumetric heat capacity of the fabric. k eff Δ is the effective thermal conductivity of the fabric. h vap Δ is the enthalpy of evaporation or condensation. h abs The enthalpy of absorption or desorption. m vs The mass flow rate of water vapor flowing out of or into the fiber. T For temperature, t For time, x These are the coordinates in the thickness direction. This is the portion of the outer fabric that absorbs incident thermal radiation. The following equation represents the heat and mass coupling transfer model in the microenvironment under clothing: ； in,( ρc p ) γ The effective density of the microenvironment under clothing. k γ The effective thermal conductivity of the microenvironment under clothing. This refers to the portion of radiation absorbed by the microenvironment beneath the clothing.

4. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The process involves dividing the numerical human body model corresponding to the rescuers into multiple nodes, and characterizing the thermophysiological regulatory response of the rescuers during the operation through the physiological parameters of each node, thereby obtaining a human thermophysiological regulatory model, specifically including: A 3D laser scanner was used to perform a full-body scan of the rescue personnel, obtaining point cloud data of their unclothed body surface. The surface point cloud data is imported into reverse engineering software for encapsulation and processing. The discrete point cloud is converted into a continuous polygonal mesh surface, and the holes are filled and the boundaries are repaired to obtain a three-dimensional numerical dummy model with a realistic human form. The three-dimensional numerical dummy model was imported into a mesh generation tool and divided into multiple segments according to anatomical locations, including the head, torso, upper arm, forearm, hand, thigh, lower leg, and foot. In the radial direction of each segment, skin layer nodes, fat layer nodes, muscle layer nodes, and core layer nodes were sequentially divided according to the physiological tissue structure of the skin, fat, muscle, and core layer, and the thickness ratio and thermophysical property parameters of each layer node were set. A central blood pool node was set in the torso region. A physiological model of human thermoregulation is constructed based on the theory of negative feedback regulation. The physiological model of human thermoregulation includes: a sweating regulation model, a shivering regulation model, a vasodilation regulation model, and a vasoconstriction regulation model. The sweating regulation model is used to characterize the response of sweating rate to changes in body temperature under different physiological regulation modes. The shivering regulation model is used to characterize the response of shivering heat production to changes in body temperature under different physiological regulation modes. The vasodilation regulation model is used to characterize the response of skin blood flow to changes in body temperature under different physiological regulation modes. The vasoconstriction regulation model is used to characterize the response of core blood flow to changes in body temperature under different physiological regulation modes.

5. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The process involves modifying the skin blood flow, core blood flow, and perspiration rate of rescuers at the current moment through the hypothalamic setpoint mechanism to update skin temperature and skin surface humidity. The updated skin temperature and surface humidity are then fed back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of rescuers at the next moment, resulting in a dynamic bidirectional coupling model, specifically including: The human thermoregulation physiological model is divided into a core layer, muscle layer, fat layer, skin layer and central blood pool node, and the metabolic heat production of each layer of tissue is determined in real time according to the set labor intensity. The human thermoregulation physiological model updates the skin blood flow, core blood flow, and sweating rate in the human thermoregulation physiological model based on the skin surface temperature, sensible heat flux density, and latent heat evaporation resistance related to skin surface humidity output by the thermo-mass coupling transfer model, through the hypothalamic setpoint mechanism, in order to correct the skin temperature and skin surface humidity; and feeds the corrected skin temperature and skin surface humidity back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuers at the next moment, thus obtaining a dynamic two-way coupling model.

6. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The simulation based on the activity posture parameters and the spatial distribution parameters of the air layer under clothing is used to obtain the ergonomic comfort evaluation model, which specifically includes: Point cloud data of rescue personnel in unclothed and fire-fighting protective clothing states were obtained using three-dimensional laser scanning technology, resulting in first body surface point cloud data and second body surface point cloud data. The first body surface point cloud data and the second body surface point cloud data are processed by noise removal, noise smoothing and non-uniform curvature sampling, and the processed first body surface point cloud data is encapsulated into a first polygonal mesh surface model, and the processed second body surface point cloud data is encapsulated into a second polygonal mesh surface model. The first polygonal mesh surface model and the second polygonal mesh surface model were aligned and error analyzed by chromatographic comparison method to obtain the average thickness data and volume distribution data of the air layer under the clothing in different parts. Data on the joint movement angles and range of motion of rescue personnel in different working postures were obtained through a 3D motion capture system. The average thickness data and the volume distribution data are used as spatial fit parameters, and the joint movement angle and the joint range of motion data are used as movement flexibility parameters. A quantitative evaluation model of the impact of protective clothing on the ergonomics of rescue personnel under different working postures is constructed by multi-source information fusion method to determine the fit index and movement restriction index of fire extinguishing protective clothing, and obtain the ergonomic comfort evaluation model.

7. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The coupling of the dynamic bidirectional coupling model with the ergonomic comfort evaluation model yields the optimization design proposition for fire-fighting protective clothing, specifically including: Obtain the thermal comfort prediction value output by the dynamic two-way coupling model and the fit index and activity restriction index output by the ergonomic comfort assessment model; A comprehensive experiment on the physiological and ergonomic aspects of human clothing was conducted under different environmental conditions and activity intensities. Based on the thermophysiological parameters of rescuers, subjective thermal comfort scores, motion capture data, and changes in the air layer under the clothing during the experiment, the measured values ​​of thermal comfort, fit, and activity restriction were determined. The error functions corresponding to the predicted thermal comfort value and the measured thermal comfort value, the error function between the fit index and the measured fit index, and the error function between the activity restriction index and the measured activity restriction value are compared with preset thresholds respectively: If all error functions do not exceed the preset threshold, then the dynamic bidirectional coupling model and the ergonomic comfort evaluation model will be used as the simulation design proposition for fire extinguishing protective clothing. If any error function exceeds the preset threshold, the dynamic bidirectional coupling model and the ergonomic comfort evaluation model are corrected by feedback until each error function does not exceed the corresponding preset threshold, and the corrected dynamic bidirectional coupling model and the corrected ergonomic comfort evaluation model are used as the simulation design proposition for fire extinguishing protective clothing. The error function includes, but is not limited to, root mean square error and average relative error.

8. The optimized design method for fire-fighting protective clothing that balances protection and comfort as described in claim 1, characterized in that, The optimization design proposition is iteratively solved using a multi-objective optimization algorithm to obtain fabric parameters and fire-fighting protective clothing structural design schemes that maximize thermal comfort and achieve optimal overall protection and workability. Specifically, this includes: The system includes preset thermophysiological thresholds and work efficiency thresholds. The thermophysiological thresholds include: upper limit of core temperature, upper limit of skin temperature, and upper limit of perspiration. The work efficiency thresholds include: lower limit of joint range of motion and lower limit of air gap under clothing. The thermal resistance, moisture resistance, air permeability, thickness, and areal density of each layer of fabric in the fire extinguishing protective clothing, as well as the ease, opening size, and undergarment gap distribution parameters of each part of the protective clothing, are set as decision variables. An initial population is randomly generated within the range of values ​​of each decision variable. For each individual in the population, the decision variables of the individual are input into the dynamic two-way coupling model to obtain the time history curves of core temperature, skin temperature and perspiration under steady-state or dynamic conditions. The decision variables are then input into the ergonomic comfort evaluation model to obtain the range of motion of each joint and the air gap value under clothing. The maximum core temperature, the maximum skin temperature, and the total amount of sweat are compared with the upper limit of the core temperature, the upper limit of the skin temperature, and the upper limit of the amount of sweat, respectively. The range of motion of each joint is compared with the lower limit of the range of motion of the joint. The calculated value of the air gap under clothing is compared with the lower limit of the air gap under clothing. If any indicator exceeds the corresponding threshold, the individual is determined to be an infeasible solution. The constraint violation degree is calculated according to the degree of violation, and a penalty term proportional to the constraint violation degree is applied to the individual. Using the thermal comfort comprehensive score as the first objective function, the protective performance index as the second objective function, and the ergonomic comprehensive index (a weighted composite of the fitability index and the activity restriction index) as the third objective function, the population individuals are non-dominated stratified through Pareto dominance relationships, and the density of individuals within the same stratum is assessed. Superior individuals are selected from the current population as parents through selection operations, and crossover and mutation operations are performed on the parent individuals to generate offspring individuals. The parent and offspring populations are merged, and non-dominated stratification and density assessment are performed again. A specified number of individuals are selected to form the next generation population, resulting in the current Pareto front. The process is repeated iteratively, and the hypervolume index of the current Pareto front is determined after each iteration. The iteration is terminated when the relative change in hypervolume is less than the preset convergence threshold or the number of generations reaches the preset maximum number of generations. After the iteration terminates, all non-dominant individuals in the current population and their corresponding decision variable combinations are output to obtain the Pareto optimal solution set. The Pareto optimal solution set includes: the combination of heat and moisture transfer parameters of the multilayer fabric and the structural dimension parameters of the protective clothing.

9. An optimized design device for fire-fighting protective clothing that balances protection and comfort, characterized in that: include: The data acquisition module is used to acquire the activity posture parameters of rescue personnel during the operation; The thermal-moisture modeling module is used to construct a thermal-mass coupling transfer model to describe the thermal-moisture coupling transfer process between the fabric, the air layer under the clothing and human skin, based on the thermal-moisture transfer parameters of the multi-layer fabric in the fire-fighting protective clothing, disaster environment parameters, human thermal physiological parameters, and spatial distribution parameters of the air layer under the clothing. The physiological modeling module is used to divide the numerical human body model corresponding to the rescuers into multiple nodes, and to characterize the thermophysiological regulation response of the rescuers during the operation through the physiological parameters of each node, so as to obtain the human thermophysiological model. The coupling module is used to take the skin temperature, sensible heat flux density, and latent heat evaporation resistance output by the thermo-mass coupling transfer model as the boundary input of the human thermoregulation physiological model, and correct the skin blood flow, core blood flow, and sweating rate of the rescuer at the current moment through the hypothalamic setpoint mechanism to update the skin temperature and skin surface humidity; the updated skin temperature and updated skin surface humidity are fed back to the thermo-mass coupling transfer model to iteratively update the thermophysiological parameters of the rescuer at the next moment, thus obtaining a dynamic bidirectional coupling model; The ergonomic modeling module is used to simulate the activity posture parameters and the spatial distribution parameters of the air layer under the clothing to obtain an ergonomic comfort evaluation model; the dynamic two-way coupling model is coupled with the ergonomic comfort evaluation model to obtain the optimization design proposition of the fire extinguishing protective clothing. The comprehensive module is used to iteratively solve the optimization design proposition through a multi-objective optimization algorithm to obtain the fabric parameters and fire-fighting protective clothing structural design scheme that maximizes thermal comfort and achieves the best overall protection and work efficiency.

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