A human body heat damage synchronous prediction method based on a dynamic coupling physiological model

Abstract

This invention relates to a method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model. First, a refined, segmented, multi-layered human physiological thermal model is constructed. Based on this model, a set of coupled biological heat transfer equations is established, embedding an active thermoregulation mechanism mediated by the human central nervous system. This set of equations is solved by deeply and dynamically coupling skin temperature, core temperature, and physiological parameters such as blood perfusion rate, metabolic rate, and sweat evaporation. Subsequently, by setting the initial and boundary conditions of a fire scene operation, the coupled equations are solved in one go using numerical methods. During the same solution process, the system synchronously and in parallel performs segmented skin burn risk assessment and overall human heat stress risk assessment. This invention overcomes the limitations of traditional models in segmented prediction and coupled active thermoregulation, achieving effective synchronous early warning of skin burn risk and overall heat stress risk in various parts of the human body under extreme thermal environments.

Description

Technical Field

[0001] This invention relates to a physiological state prediction technology for workers in complex thermal environments, and more particularly to a numerical simulation method that can deeply couple the heat transfer and active thermal regulation mechanisms of multi-layered human tissues, thereby synchronously, segmentally, and dynamically predicting the risk of skin burns and heat stress for workers in complex thermal environments, including firefighters and metallurgists. The aim is to provide a more accurate and comprehensive scientific basis for the evaluation of individual thermal protective equipment and safety guidance for extreme thermal environment operations. Background Technology

[0002] Firefighters inevitably expose themselves to complex fire environments characterized by high temperatures and intense heat radiation during firefighting and rescue operations, primarily facing two physiological threats: skin burns and heat stress. These two threats are not only interconnected but also pose serious challenges to human health and safety. Skin burns are typically caused by a rapid increase in skin tissue temperature under a short-term, high-intensity heat flow; while heat stress refers to a sustained rise in core body temperature caused by the sum of metabolic heat production and environmental heat absorption exceeding the body's heat dissipation capacity during prolonged work, leading to physiological reactions ranging from functional impairment to life-threatening conditions. Accurate and comprehensive assessment and prediction of these two risks are crucial for improving the occupational safety protection levels of firefighters and metallurgical workers.

[0003] However, current assessment methods for these two major physiological risks exhibit significant separation and limitations in their technical approaches, resulting in risk assessment results that are often one-sided and lack dynamic adaptability.

[0004] First, in the field of skin burn prediction, existing models mainly face the following prominent shortcomings:

[0005] 1. Neglecting Dynamic Physiological Regulation and Segmental Heterogeneity. Classical skin burn prediction methods, such as the Stoll criterion and the Henriques burn integral model, are mainly used for standardized testing of the performance of thermal protective fabrics in laboratory environments. Their core idea is to simplify human skin as a passive heat receiver. This simplified model completely ignores the active thermoregulation function of the human body as a complex living system. For example, under heat stress, an increase in core temperature triggers vasodilation, increasing skin blood flow to promote heat dissipation. However, existing models typically use parameters such as constant blood perfusion rate for simplified analysis, failing to consider dynamic, bidirectional coupled thermoregulation. This means that under prolonged, low-to-medium intensity heat exposure scenarios, the predictive accuracy of existing models will be significantly reduced when human physiological regulation is activated. Furthermore, existing models typically divide skin tissue into three layers: the epidermis, dermis, and subcutaneous tissue, and use constant parameters for burn prediction. However, the number of tissue layers, thermophysical parameters, and physiological parameters vary across different parts of the human body; for example, the skin structure of the head, trunk, and limbs differs significantly. Existing models are unable to achieve refined, segmented burn prediction for different parts of the human body, and lack a holistic consideration of the interconnections between different parts of the human body through the central nervous system and active thermal regulation, which limits the accuracy of burn prediction for different parts.

[0006] 2. Lack of coordinated consideration of skin burns and human heat stress. Although some burn prediction models have emerged in recent years that consider the heterogeneous distribution of skin thickness or couple the heat and moisture transfer process within clothing, they have failed to effectively couple skin burn prediction with human heat stress assessment. Existing burn models and human thermoregulation models differ in the precision of tissue layer segmentation: burn assessment is based on the temperature at the junction of the epidermis, dermis, and subcutaneous tissue, while heat stress assessment is based on indicators such as core temperature or perspiration. This difference makes it impossible for existing models to simultaneously predict skin burns and heat stress within the same framework, to conduct a comprehensive assessment of firefighter thermal injury for the same fire scenario, to determine whether skin burns or human heat stress occur first or have a greater impact on human safety, and to investigate the complex relationship between the two.

[0007] Secondly, in the field of heat stress prediction, research also has the following shortcomings: 3. Limited Model Applicability and Lack of Burn Prediction Capabilities. Existing human thermal regulation models (such as the Stolwijk model, Fiala model, Tanabe model, and UCB model) typically divide the human body into four layers: skin, fat, muscle, and core. These models are physiologically comprehensive and can simulate core body temperature changes, primarily used to assess overall thermal comfort. However, these models are not applicable to fire environments, and they differ in terms of human temperature set point and physiological regulatory mechanisms. Their applicability to simulating human thermal regulation in fire environments requires further validation. Furthermore, these models cannot predict human skin burns, highlighting the necessity for optimizing human thermal regulation models for firefighter thermal injury prediction.

[0008] To address the shortcomings of the aforementioned separate assessment, preliminary attempts at simultaneous prediction have been made in the field. For example, measured heat flow is used as input to skin burn models and human thermoregulation models to calculate skin temperature and core temperature separately. The skin temperature is then substituted into the Pennes heat transfer model and the Henriques burn integral model to predict burn time. However, this method has three major drawbacks: First, it is essentially a one-way transfer of experimental data to the model, failing to realize the process of using model feedback to adjust experimental data. Second, the two models are calculated separately and are unrelated, with significant differences in model structure, tissue layer division, physiological parameters, and time scales, raising questions about the comparability of results. Third, this method is not applicable to burn prediction of clothing and various parts of the human body, and the problem of regional heterogeneity remains unresolved. Furthermore, while some real-time monitoring systems can simultaneously output the instantaneous temperature of the body core and surface, their goal is "real-time status monitoring and early warning," outputting temperature values ​​rather than the "critical time to reach the danger threshold" that this invention aims to provide. The two differ fundamentally in their technical objectives and the nature of their output results.

[0009] Skin burn prediction and human heat stress assessment differ fundamentally in model structure, tissue layer division, dynamic mechanism of physiological parameters, and time scale. Therefore, simply combining existing burn models with heat stress models cannot achieve synchronous prediction.

[0010] Specifically, skin burn prediction mainly focuses on the instantaneous thermal damage accumulation process of local tissues in the epidermis and dermis, while heat stress assessment mainly focuses on changes in core body temperature and systemic thermoregulation. The two not only use different tissue stratification methods, but also have different physiological regulatory parameters, boundary conditions and risk assessment mechanisms.

[0011] More importantly, the human body's heat stress state can influence local skin temperature through active physiological feedback mechanisms such as blood perfusion rate, sweat evaporation, and metabolic regulation. Furthermore, the blood flow obstruction caused by local burns can further alter the systemic thermal circulation, thereby affecting core body temperature. Therefore, there is a clear bidirectional dynamic coupling relationship between the two, rather than a one-way data call relationship.

[0012] Existing technologies have not yet solved the following technical challenges: 1. How to accommodate the different levels of burn prediction and thermal stress assessment under a unified organizational structure; 2. How to simultaneously handle the dynamic feedback between local thermal damage and systemic thermoregulation in the same solution process; 3. How to avoid parameter fragmentation, error accumulation, and time step asynchrony caused by the serial calculation of two independent models; 4. How to establish a thermal coupling mechanism between local burns and systemic blood circulation.

[0013] Therefore, this invention is not a simple combination of existing burn models and heat stress models, but rather a unified dynamic coupling physiological thermoregulation framework that realizes the synchronous correlation between local skin thermal damage and systemic heat stress state, thereby providing a more reasonable evaluation method for thermal damage to workers in complex thermal environments. Summary of the Invention

[0014] To address key technical issues such as the separation of existing skin burn prediction and heat stress assessment systems, the neglect of human physiological dynamic regulation in burn prediction models, the lack of segmented prediction capabilities, insufficient consideration of fire environment applicability, and the inability of existing methods to provide unified, efficient, and practical comprehensive risk prediction, this invention proposes a method for simultaneous prediction of skin burns and heat stress based on a multi-node, multi-layer, dynamically coupled physiological thermoregulation model of the human body. The core of this invention lies in constructing a refined, segmented, and dynamically coupled multi-layer human tissue thermal model. By embedding the active thermoregulation mechanism mediated by the human central nervous system into the biological heat transfer equation, a high degree of integration and collaborative solution of the physiological responses of various parts of the body and the skin thermal damage process under heat stress is achieved. This allows for a one-time, synchronous output of skin burn warning times for different parts of the body and overall heat stress warning times, along with a proposed comprehensive safe operating time limit. Unlike the sequential processing method in existing related technologies, which involves "first performing thermal stress calculations and then inputting the results into the burn model", this invention does not involve data transfer between two independent models. Instead, it integrates the local burn evolution process, blood circulation heat exchange process, and human active thermoregulation process into the same dynamic equation system for synchronous solution within a unified multi-layered human tissue structure.

[0015] Because burns alter local blood perfusion capacity, and blood perfusion directly affects tissue heat dissipation and core body temperature changes, a strong nonlinear feedback relationship forms between the two. Traditional independent models struggle to handle this type of bidirectional feedback problem, easily leading to distorted prediction results. This invention establishes a dynamic blood perfusion mechanism, a central blood pool thermal balance mechanism, and a burn-related adverse effect mechanism, achieving real-time coupling and linkage between local tissue damage and systemic heat stress.

[0016] The technical solution of this invention is as follows: A method for synchronously predicting skin burns and heat stress based on a multi-node, multi-layer, dynamically coupled physiological thermoregulation model of the human body, characterized by the following steps: S1. Construct a refined, multi-node, segmented, and hierarchical human physiological thermal model: A human thermal regulation simulation system was constructed based on numerical simulation methods. The system uses a multi-node geometric model of the human body as its core. The specific construction process and system architecture are as follows: (1) Construction of multi-node geometric model of human body Overcoming the limitations of insufficient precision in existing model partitioning, this study refines the human body into 20 representative independent segments (denoted as segment index i), specifically including the face, head, left upper arm, right upper arm, left lower arm, right lower arm, left hand, right hand, chest, shoulder, abdomen, back, left buttock, right buttock, left thigh, right thigh, left calf, right calf, left foot, and right foot. For each segment, a multi-level physiological thermal structure model is constructed along the radial thickness direction of human tissue (from the surface to the core), including five layers of human tissue: epidermis, dermis, fat layer, muscle layer, and core layer (denoted as tissue layer index j). Each tissue layer is further discretized into N micro-mesh nodes along the radial depth direction, forming a refined multi-node model architecture of "segmentation - layer level - micro-node". This layered structure not only covers the epidermis and dermis layers required for skin burn assessment but also incorporates the fat, muscle, and core layers required for heat stress assessment, achieving structural uniformity in the model. Assign each layer of the model with accurate intrinsic thermophysical properties corrected for fire conditions, including density ( ), specific heat capacity ( ) and thermal conductivity ( This provides an accurate physical basis for subsequent heat transfer calculations and dynamic response analysis.

[0017] (2) Simulation System Architecture The multi-node, segmented, and hierarchical human physiological thermal model is embedded into the simulation platform as a unified physiological thermal calculation kernel to construct a human thermal regulation simulation system for simultaneous prediction of skin burns and heat stress.

[0018] S2. Establish a set of dynamically coupled biological heat transfer equations with an embedded active thermal regulation mechanism.

[0019] This step is one of the core innovations of this invention, aiming to solve the technical difficulties of existing burn models ignoring physiological dynamic regulation and heat stress models being unable to predict burns.

[0020] First, most existing models only consider the change in blood perfusion rate with temperature. However, the burn process damages vascular structure, causing a sharp drop in blood perfusion rate after a brief increase, which is a strongly nonlinear process. This invention innovatively proposes a piecewise vascular function damage function Ψ(Ω) and introduces a dynamic additional perfusion rate driven by temperature difference. By using two independent but interconnected nonlinear operators, the abnormal changes in blood flow during the burn process can be reflected, which is something that static parameter models cannot do.

[0021] Second, this invention constructs the "total enthalpy equation for venous return" and the "heat balance equation for the central blood pool," and dynamically maps each node. The real-time changes were used to establish the relationship between local tissue damage and the thermal state of systemic blood circulation.

[0022] For each partition i and each tissue layer j constructed in S1, an unsteady-state heat transfer equation based on the Pennes biological heat transfer equation is established. The key to this invention lies in the blood perfusion term in the unsteady-state heat transfer equation (…). ) and sweat evaporation heat dissipation item ( It is no longer a static constant, but rather an approximation of a dynamic physiological regulatory function mediated by the human nervous system, thus achieving bidirectional coupling of the body's active thermoregulation. Specifically: 2.1 The unsteady heat transfer equation of the epidermis is:

[0023] in, , , These represent the density of the epidermis (kg / m³). 3 ), specific heat capacity (J / kg / K) and thermal conductivity (W / (m·K)), T is tissue temperature, t is time, x is depth, Basal metabolic rate, It is evaporative heat transfer in the epidermis.

[0024] The unsteady biological heat transfer equation of the muscle layer is:

[0025] in, and These are the heat generated by external work and tremors (W / m²). 3 ), It's blood temperature.

[0026] The unsteady biological heat transfer equation of the core layer is:

[0027] Where C is the heat capacity of the core layer (J / K), and T is the heat capacity of the core layer. c It is the temperature (°C) of the core layer. It is a dynamically changing blood flow rate (m 3 / s / m 3 ), It is the thermal conductivity (W / k) of the core layer. and These are basal metabolic rate and additional metabolic rate (W). It is the heat lost by the core layer of the chest through respiration.

[0028] In the dermis and fat layer, the unsteady biological heat transfer equation is expressed as:

[0029] in, , , These represent the density, specific heat capacity, and thermal conductivity of the corresponding layer of tissue, respectively. The heat capacity of blood is determined by blood density. Specific heat capacity of blood Multiply them to get the result. Basal metabolic rate (W / m 3 ), Additional metabolic rate (W / m 3 ), where a is the countercurrent heat exchange ratio. Related to the temperature of the dermis, specifically as follows:

[0030] in These are the setpoint temperatures for various points on the human body.

[0031] 2.2 Dynamic physiological regulatory mechanisms: In this invention, dynamic blood perfusion rate As a function of the human body's thermoregulation control signal, the dynamic blood perfusion rate of each tissue layer is designed differently based on a general formula: 1. Dermis: Introducing a burn damage inhibition term ( The formula is:

[0032] in, The baseline blood perfusion rate of the dermis. Let Ω be the vasodilation coefficient, Ω be the thermal damage value of the skin tissue in this segment, and Ψ(Ω) be the vascular function impairment function. When Ω < 0.5 (no burn), Ψ ≈ 1 (normal adjustment); When 0.5≤Ω<1.0 (inflammation occurs), Ψ>1 (vasodilation and congestion, accelerating heat transfer). When Ω≥1.0 (second-degree burn necrosis), Ψ→0 (vascular embolism, blood flow cessation).

[0033] This equation simulates the instant a skin is burned, when blood flow is blocked, preventing local heat from being dissipated and causing a rapid temperature spike, thus nonlinearly accelerating the process of deep tissue damage. It achieves bidirectional coupling between burns and blood perfusion.

[0034] 2. Muscle layer: Including contributions from external work and heat generation from shivering, the formula is:

[0035] in, Based on blood flow rate, To increase blood flow rate, It performs work and vibrates externally, conforming to the heat-generating properties of the muscle layer.

[0036] 3. Fat layer / core layer: A general formula is used, the formula is as follows:

[0037] Only the regulation of temperature and central nervous system signals is considered, without introducing other factors, to align with its physiological functions. Among these, and These are the vasodilation coefficient and the vasoconstriction coefficient, respectively. and Err represents the vasodilation and vasoconstriction signals emitted by the central nervous system, and Err represents the local temperature deviation term.

[0038] The difference between the baseline blood flow rate under high temperature conditions and the baseline blood flow rate under neutral temperature conditions can be written as:

[0039] and The expression is closely related to the real-time temperature deviation between the core layer and the skin layer:

[0040]

[0041] in, and These are the control coefficients for vasodilation (l / h / ℃) and vasoconstriction (l / ℃) in the core layer of the head, respectively. and These are the control coefficients for vasodilation (l / h / ℃) and vasoconstriction (l / ℃) in the skin layers of each location; and These are vasodilations of the core layer of the head and other skin layers (l / h / ℃). 2 ) and vasoconstriction (l / ℃) 2 The control coefficients are: (1) represents the core layer; Wrm(1) and Cld(1) are the warm and cold signals (°C) of the core layer, respectively; Wrms and Clds are the weighting coefficients (°C) of the warm and cold signals, respectively. Wrm and Cld are the temperature differences between the skin layer temperature of each part and its set point. Decide( If Err > 0, then Wrm = Err; otherwise, Cld = - Err. Wrms and Clds are calculated using virtual sensor nodes (virtual sensor nodes are set in the model, logically mimicking human temperature receptors, to calculate the deviation Err between the skin layer temperature and the setpoint temperature). The weighting coefficients are derived from the parameters of the classic human thermoregulation experimental model (which are the integrals of the human temperature receptor signals). get:

[0042]

[0043] The intensity of these signals is based on the core layer temperature calculated in real time by the model. Arterial blood temperature ) and skin temperature ( ) and their respective physiological set point temperatures ( deviation () It is determined by ( ).

[0044] Specifically, core layer temperature deviation Together with the skin layer temperature deviation Err, it serves as the input to the "thermal regulation signal generation module". This represents the setpoint temperature of the core layer. When the core layer or skin temperature rises (Err>0), the system generates a positive warming signal (Wrm), driving vasodilation. Enhance, thereby increase This enhances skin heat dissipation; conversely, it generates a cold signal (Cld), driving vasoconstriction signals. Enhance, reduce This mechanism helps preserve core heat. Through this mechanism, connections between different parts of the body are established via a virtual central nervous system, enabling the prediction of segmental skin burns under active thermoregulation.

[0045] 2.3 Traditional heat stress models usually assume that blood perfusion rate is only affected by temperature in one direction, while existing burn models usually ignore the feedback effect of blood circulation on injury evolution, thus failing to establish a real-time correlation between local tissue damage and systemic thermoregulation.

[0046] This invention reveals that under the high-temperature environment of a fire, localized skin burns not only alter tissue thermophysical parameters but also cause local blood flow obstruction, leading to the return of hot blood to the central circulatory system and further increasing the core layer temperature. This increased core layer temperature, in turn, alters blood perfusion rates and sweat evaporation in different areas through central nervous system regulation, thus negatively impacting skin burns. These processes constitute a bidirectional, dynamic, closed-loop coupling relationship between localized burns and heat stress.

[0047] To address the issue that traditional models cannot describe this closed-loop feedback, this invention further establishes a venous return mixing equation and a central blood pool heat balance equation, thereby achieving heat coupling and physiological linkage between different zones.

[0048] (1) Venous return mixing equation: Calculate the total enthalpy of venous blood mixture in all segments as the input for the heat balance of the central blood pool. The formula is as follows:

[0049] in, Total enthalpy (in W) after mixing venous blood from 20 segments; The instantaneous temperature (in °C) of the micro-element node representing the i-th partition and the j-th tissue layer; V represents the total volume of the i-th partition (unit: m³); V represents the volume of a human tissue micro-grid node (unit: m³). Arterial blood temperature (unit: K).

[0050] (2) Central blood pool heat balance equation: The central blood pool serves as a virtual core unit for the body's blood circulation, simulating the heat exchange process of the human heart and major blood vessels, and its temperature is updated in real time. It borrows the concept of the "core blood pool" from the classic Stolwijk model of human thermoregulation and optimizes it for fire environments: while the Stolwijk model's core blood pool only considers basic heat exchange, this invention's central blood pool adds a term for heat generation from the heart's work. The integral of venous return enthalpy across different sections is used to adapt to the physiological state of high metabolism and localized high temperatures in the human body under fire conditions. The formula is as follows:

[0051] in, Characterized by the total heat capacity of the central blood pool, Characterizes the real-time temperature of the central blood pool; , These are the heat dissipation from lung respiration and the heat generated by the heart's work, respectively.

[0052] (3) Segmental coupling output: Set the arterial blood temperature of each segment in the next time step. = ( t This step ensures that if the "legs" are severely exposed to heat from a fire, the hot blood flowing back from them will rapidly raise the temperature of the "central blood pool," which in turn will cause the temperature of the "head" and "internal organs" to rise. This is a systemic heat stress prediction that a single-segment model cannot achieve.

[0053] 2.4 Heat dissipation due to sweat evaporation under epidermal boundary conditions ( The calculation of ) is embedded in the integrated human thermal regulation simulation system constructed in step S1, specifically controlled by the sweating signal emitted by the "thermal regulation signal generation module" ( Dynamic regulation is used to achieve dynamic control of heat dissipation through sweat evaporation. The boundary condition equation is as follows: ( ) in, The net heat flow that penetrates the protective suit and reaches the skin surface; The amount of heat dissipated through sweat evaporation is determined by the sweating signal in the thermoregulation system. Dynamic regulation, changing in real time according to the thermal stress state. Simultaneously, the thermal conductivity of the skin layer... (W / m / k) is not a constant value, but rather depends on the degree of sweat wetting. The correlation reflects the impact of sweat accumulation on the skin's thermal conductivity, and its dynamic correction formula is:

[0054] in, It is related to the cumulative amount of sweat and epidermal porosity at the current moment. This reflects the physical phenomenon that sweat accumulation leads to increased thermal conductivity of the skin. The thermal conductivity of the skin layer in a dry state. is the thermal conductivity of water.

[0055] Through the construction of the above equations and modules, this invention forms a complete closed-loop physiological feedback control loop composed of system-level architecture and logic-level process. The two work together to realize the dynamic coupling simulation of human active thermoregulation and thermal damage process.

[0056] S3. Set precise initial and boundary conditions for the fire operation scenario.

[0057] Based on the predicted firefighting scenario, precise initial and boundary conditions are set for the constructed human body model. The initial conditions are set as the thermoneutral steady-state temperature distribution of each layer of the human body before entering the fire environment, for example, 37°C for the core layer and 34°C for the skin surface. The boundary conditions are the fire environment load applied to the outer surface of the epidermis, i.e., the net heat flux (…). The heat flux should take into account the heat insulation attenuation effect of protective clothing to realistically simulate the actual heat load borne by firefighters and other personnel working in complex thermal environments in a fire scene. This step ensures that the model can accurately reflect the safety characteristics of firefighters and other personnel working in complex thermal environments in a fire scene.

[0058] S4. Numerical solution of the dynamically coupled equations.

[0059] In terms of numerical solution methods, the implicit finite difference method (FDM) is used to discretize the coupled partial differential equations in time steps, and the temperature distribution of each segment and node is obtained through iterative calculation within each time step, realizing the dynamic solution of the human body temperature field T(x,t). This method unifies the modeling and synchronous solution of the human body's thermal regulation process and heat transfer process, avoiding the error accumulation problem caused by traditional step-by-step calculations.

[0060] S5. Perform simultaneous assessment and output of dual risks.

[0061] This step is a key manifestation of the practicality of this invention, effectively overcoming the limitations of existing methods in accurately and simultaneously evaluating the risks of heat stress and skin burns, and providing quantifiable and engineerable early warning indicators. Within the same iterative calculation loop of step S4, based on real-time updated, segmented tissue temperature data, the following two risk assessment tasks are performed synchronously and in parallel: i. Segmental skin burn risk assessment: At each time step, for each segment, extract the real-time temperature at the dermal basal layer. This value is then substituted into the Henriques damage integral model to calculate the thermal damage value Ω of the skin tissue in that segment in real time. When the Ω value of any segment first reaches or exceeds the critical damage threshold of 1.0, the current time t is recorded as the "skin burn prediction time" for that segment. ).

[0062] ii. Overall human body heat stress risk assessment: Simultaneously monitor the core layer temperature at each time step. .when When the temperature first reaches or exceeds the preset upper limit of human body heat stress safety, the current time t is recorded as the "heat stress prediction time" (t). ).

[0063] Finally, the system comprehensively outputs two key time values: the earliest predicted time for skin burns and the earliest predicted time for heat stress across all zones. The minimum of these two values ​​is then used as the recommended final safe operating time limit for firefighters in that specific operational scenario. This comprehensive indicator will provide firefighters with accurate, reliable, and practical decision-making support for on-site command and personal protective equipment, significantly improving the safety of fire rescue missions.

[0064] A dynamic coupling model reveals a bidirectional interaction mechanism between skin burns and heat stress. Heat stress affects the skin's heat dissipation capacity by altering skin blood perfusion rate, thereby promoting burns. Conversely, skin burns lead to local blood flow obstruction and reduced heat dissipation capacity, causing hot blood to flow back to the central blood pool, accelerating the rise in core body temperature and further exacerbating heat stress. By constructing a dynamic coupling model that simultaneously considers the interaction between the two, the accuracy of predicting physiological damage to humans in fire environments is improved.

[0065] A human body thermal regulation simulation system is divided into the following modules according to its functions: 1) Geometric and physical property parameter library module: Stores the geometric dimensions and thermophysical parameters of 20 segments and 5 layers of human body tissue; 2) Thermal Regulation Signal Generation Module: This thermal regulation signal generation module is the core processing unit, equivalent to a "central nervous system control unit." It further includes: a) Temperature deviation calculation unit: Receives virtual temperature sensor signals from the core layer and skin layer of each partition and calculates the temperature deviation from the physiological set point.

[0066] b) Regulation signal generation unit: Generates vasodilation signals based on temperature deviation. vasoconstriction signals and sweating signals .

[0067] c) Signal distribution unit: Distributes the generated regulatory signals to each peripheral physiological regulatory unit according to the regional segments.

[0068] 3) Dynamic Coupled Solver Module: Used to solve the dynamic coupled biological heat transfer equations established in step S2, which include dynamic physiological regulation terms.

[0069] 4) Risk assessment and output module: used to simultaneously assess the risk of skin burns in the segment and the overall heat stress risk in step S5, and output the comprehensive safe operation time limit.

[0070] Corresponding to the above system layer architecture, the logical flow of model execution within each computation time step Δt is as follows: 1) Parameter loading: Read the geometric dimensions, thermophysical properties, and initial physiological parameters of the human body in 20 segments and 5 layers at the current time step from the geometry and physical property parameter library module; 2) Sensing and Deviation Calculation: The thermal regulation signal generation module collects real-time temperatures of the skin layer and core layer in each segment using virtual temperature sensors, and calculates the temperature deviation. ; 3) Regulation signal generation and distribution: The thermal regulation signal generation module generates a vasodilatory signal based on the temperature deviation. vasoconstriction signals and sweating signals And assign them to the corresponding partitions and organizational layers; 4) Dynamically Coupled Solution: The dynamically coupled solver module is substituted with the updated dynamic blood perfusion rate. Heat dissipation through sweat evaporation Solve the coupled biological heat transfer equations to obtain the complete temperature field T(x,t) at the current time step; 5) Dual risk assessment: The risk assessment and output module simultaneously performs segmented skin burn risk and whole-body heat stress risk assessment based on the temperature field T(x,t); 6) Iteration and closed-loop feedback: The temperature field T(x,t) of the current time step is used as the initial temperature of the next time step, updated to the geometry and physical property parameter library module, and the loop of the next time step is entered until any risk reaches the critical threshold, at which point the calculation is terminated and the comprehensive safe operation time limit is output.

[0071] The beneficial effects of this invention are as follows: The beneficial effects of this invention lie in providing a method for synchronously predicting skin burns and heat stress based on a multi-node, multi-layer, dynamically coupled physiological thermoregulation model of the human body. Its beneficial effects are significant and groundbreaking, mainly reflected in the following aspects: 1. This invention achieves unified, segmented, and dynamically coupled prediction of skin burns and heat stress, overcoming the limitations of existing separate systems. It is not a simple superposition of existing burn and heat stress models, but rather a physiological thermal model constructed with refined segmentation, multi-layered tissue structures, and an embedded active thermoregulation mechanism mediated by the human central nervous system. For the first time, within a unified numerical simulation framework, it achieves simultaneous and high-precision prediction of skin burn risk in various parts of the human body and overall heat stress risk. Compared to traditional serial models, this invention avoids problems such as inconsistent parameter systems, inconsistent time steps, and disconnect between local and overall thermal states between different models, thus significantly improving the accuracy of predicting human thermal injury in fire environments.

[0072] 2. Significantly improves the accuracy of skin burn prediction and its applicability to fire environments. This invention incorporates dynamically changing blood perfusion rate and other physiological regulation (such as adjustments to metabolic rate) as variables, rather than constants, into the biological heat transfer equation. When the human body enters a state of heat stress, real-time changes in core and skin temperature dynamically adjust blood flow rate, affecting heat transport between tissues. This deep coupling of physiological feedback allows the model to accurately simulate the real physiological response of the human body under extreme heat conditions in a fire, especially in long-term, low-to-medium intensity heat exposure scenarios, greatly improving the accuracy and reliability of skin burn prediction. This effectively improves the prediction bias problem caused by neglecting physiological regulation in traditional static models, breaking the limitation of existing models that are only applicable to non-fire environment testing, and can be effectively adapted to complex thermal environments such as fire scenes.

[0073] 3. This invention enables the correlational prediction of burn risks across different parts of the human body, improving the accuracy of local risk assessment. By dividing the human body into 20 independent segments and correlating the physiological regulatory parameters of each segment with core layer temperature deviations and central nervous system signals, it achieves the correlational and segmental prediction of burn risks across different parts of the body. This overcomes the shortcomings of existing models that typically use constant parameters for burn prediction and struggle to predict different parts of the body, significantly improving the precision and accuracy of local skin burn risk assessment. It can provide a scientific basis for the localized optimization design of personal protective equipment for firefighters.

[0074] 4. Provides clear, practical, and operable comprehensive safety operation time limit decision indicators. This invention simultaneously outputs two key quantitative indicators—"predicted earliest skin burn time" and "predicted heat stress time"—through a single numerical calculation. Furthermore, the smaller of the two values ​​is used as the final "comprehensive safety operation time limit recommendation" for the specific operational scenario. This single, clear, and comprehensive decision indicator helps simplify the decision-making process for on-site commanders, reduces the interpretation difficulties caused by complex information, and improves the timeliness and accuracy of decisions. It effectively fills the gaps in existing heat stress models, which lack practical early warning indicators, and in burn models, which cannot be used in conjunction with heat stress assessment. It provides firefighters with a new technical solution that features a unified model, accurate predictions, and practical results for on-site command, operational procedure development, and personal protective equipment management.

[0075] 5. Deepen the understanding of the mechanisms of firefighters' protection against heat injury. This invention, by simultaneously assessing skin burn and heat stress levels under the same model and experimental conditions, will help clarify whether firefighters experience skin burns first or reach their heat stress limits first in a fire, and whether there is a complex interaction mechanism between the two. This will provide a powerful new tool for research on the mechanisms of firefighters' protection against heat injury in fire environments.

[0076] In summary, this invention not only theoretically constructs a dynamic coupling model that is more in line with the actual physiological reality of the human body, but also provides a more adaptable, accurate, and decision-making-oriented method for predicting thermal damage in practice. It has significant theoretical and practical value for ensuring the safety of workers, improving work efficiency, and promoting technological progress in the field of occupational safety and health. Attached Figure Description

[0077] Figure 1 This is a schematic diagram of the structure and main heat exchange mechanism of the multi-node physiological model provided in an embodiment of the present invention; Figure 2 The flowchart illustrates the overall process of the method for simultaneous prediction of skin burns and heat stress provided in this embodiment of the invention. Detailed Implementation

[0078] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0079] In this invention, the classical models directly reference the equations: Pennes' biological heat transfer equation and Henriques' damage integral formula.

[0080] Improved equation: 1. Dynamic blood perfusion rate w b Based on the Fiala human thermoregulation model, a burn-related adverse effect mechanism was introduced. 2. A new sweat evaporation heat dissipation term q was added. evap α sweat infiltration sweat 3. The heat balance equation of the central blood pool.

[0081] The synchronous prediction method provided in this embodiment of the invention has the following overall process: Figure 2 As shown, the core is to construct a refined, segmented, and dynamically coupled multi-layered physiological thermal model of the human body, and to simultaneously output the early warning time of skin burns and heat stress through numerical solution.

[0082] Step 1: Construction of a refined multi-node, segmented, and layered human physiological thermal model and its mathematical equations Reference Figure 1As shown, this invention first constructs a five-layer, multi-node physiological model along the thickness direction of human tissue. Macroscopically, this model divides the human body into 20 independent segments (denoted as segment index i), covering key areas such as the head, trunk, left upper limb, right upper limb, left lower limb, and right lower limb. In terms of depth, each segment is further divided into five layers: epidermis, dermis, adipose tissue, muscle layer, and core layer (denoted as tissue layer index j), and each tissue layer is discretized radially into N micro-grid nodes. Based on this refined model architecture, for the j-th layer of tissue in the i-th segment, the following coupled biological heat transfer equations containing dynamic physiological regulatory mechanisms are established: 1. Heat transfer equation of the epidermis: The epidermis, as the outermost layer of the human body, is primarily responsible for heat conduction and basal metabolism. Its heat transfer equation is: +

[0083] in It is the density of the epidermis (kg / m³) 3 ), It is the specific heat capacity of the epidermis (J / kg / K). Basal metabolic rate (W / m 3 Thermal conductivity It is determined by the degree of sweat penetration. Modified dynamic function:

[0084] in, It is related to the cumulative amount of sweat and the porosity of the epidermis at the current moment. The thermal conductivity of the skin layer in a dry state. is the thermal conductivity of water.

[0085] 2. Heat transfer equation of the dermis: The dermis is a crucial area for skin burns and a key layer for the dynamic regulation of blood perfusion. Its heat transfer equation incorporates dynamically regulated blood perfusion and additional metabolic rate terms, fundamentally different from traditional static models.

[0086] in Blood density (kg / m³) 3 ), It is the specific heat capacity of blood (J / kg / K). Additional metabolic rate (W / m 3 ), where a is the countercurrent heat exchange ratio. It's blood temperature. It is a dynamically changing blood flow rate (m 3 / s / m 3 This is one of the core innovations of this invention. It is no longer a constant value, but rather determined by the baseline blood flow rate (…). Additional blood flow velocity () It also contributes to the dilation or contraction of blood vessels regulated by the human central nervous system.

[0087] It is a dynamic additional metabolic rate (W / m 3 It is closely related to the real-time temperature of the dermis and its set point, reflecting the enhanced metabolism caused by increased temperature.

[0088] It is the difference between the baseline blood flow rate under high temperature conditions and the baseline blood flow rate under neutral temperature conditions. Its dynamic response is also related to the local dermal temperature and its set point, and can be written as:

[0089] Secondly, in order to more accurately consider the dynamic effects of vasodilation or vasoconstriction on dermal blood flow velocity, It can be represented as:

[0090] in and These are the vasodilation and vasoconstriction coefficients of the dermis, respectively. and These are the vasodilatory and vasoconstrictive signals output by the central nervous system based on the temperature difference between the core layer and the skin layer (m 3 / s / m 3 Its expression is:

[0091]

[0092] in and These are the control coefficients for vasodilation (l / h / ℃) and vasoconstriction (l / ℃) in the core layer, respectively. and These are the control coefficients for dermal vasodilation (l / h / ℃) and vasoconstriction (l / ℃), respectively. and These are vasodilations in the core layer and dermis of the head (l / h / ℃). 2 ) and vasoconstriction (l / ℃) 2The control coefficients are: Wrm and Cld, which are the warm and cold signals (°C) determined by the temperature difference between the core layer temperature and its set point, respectively; and Wrms and Clds, which are the weighting coefficients (°C) of the warm and cold signals, respectively. Wrm and Cld are determined by the temperature difference between the dermal layer temperature and its set point (Err=T-Tset, if Err>0, then Wrm=Err, otherwise Cld=-Err). Wrms and Clds are obtained through the weighting coefficients (Sr) of the skin receptor signal integral. This mechanism realizes the connection between different parts of the human body through the central nervous system, thereby realizing the prediction of segmental skin burns under active thermoregulation.

[0093]

[0094]

[0095] Since the dermis is a critical area for burns, the reverse effect of burns on blood flow needs to be considered. Therefore, a damage inhibition term Ψ(Ω) is introduced on top of the basic blood flow regulation model, and the formula for dynamic blood perfusion rate in the dermis is constructed as follows:

[0096] in, This represents the baseline blood perfusion rate of the dermis. Let Ω be the vasodilation coefficient, Ω be the thermal damage value of the skin tissue in this segment, and Ψ(Ω) be the vascular function impairment function. When Ω < 0.5 (no burn), Ψ ≈ 1 (normal adjustment); When 0.5≤Ω<1.0 (inflammation occurs), Ψ>1 (vasodilation and congestion, accelerating heat transfer). When Ω≥1.0 (second-degree burn necrosis), Ψ→0 (vascular embolism, blood flow cessation).

[0097] This equation simulates the instant when the skin is burned, the blockage of blood flow causes local heat to be unable to dissipate in time, resulting in a sharp increase in tissue temperature, which in turn nonlinearly exacerbates deep tissue damage, thus realizing a dynamic coupling characterization of burns and blood flow regulation.

[0098] 3. Heat transfer equation of the fat layer The fat layer, as an insulating layer, has similar heat transfer characteristics to the dermis, but generally has lower blood perfusion and metabolic heat production levels.

[0099]

[0100] in, Composition in the fat layer and basal blood perfusion rate in the dermis Similarly, it is mainly determined by the baseline blood flow rate ( ) and additional blood flow rate ( It is composed of and indirectly influenced by central regulation.

[0101] 4. Heat transfer equation of the muscle layer The muscle layer is the primary heat-generating region, and its equations additionally include the work done by external forces. ) and chills ( The dynamic heat production term caused by:

[0102] in, It is blood temperature; and These are the heat generated by external work and tremors (W / m²). 3 The calculation formula is as follows:

[0103]

[0104] Met is the metabolic heat rate (met, 1 Met = 58.15 W / m²) generated by external work. 2 Merf is the distribution coefficient of the muscle layer. It is the area of ​​body segments (m²) 2 Chit is the distribution coefficient of the muscle layer. In the muscle layer... Based on baseline blood flow rate ( Additional blood flow velocity () ) and external work ( ) and trembling ( The contribution of this component is assumed to be 1.0 l / h of blood flow, which is required to generate 1.16 W of heat.

[0105]

[0106] 5. Heat transfer equation of the core layer The core layer typically employs a lumped parameter model, where its temperature variation is determined by heat exchange with the muscle layer, systemic metabolic heat production, and respiratory heat dissipation. This invention addresses the core layer temperature... With arterial blood temperature It is considered to be the same (this is a modeling assumption intended to simplify calculations, directly applying the heating / cooling effects of blood circulation to the tissue through the blood perfusion term) to achieve dynamic coupling of blood circulation.

[0107]

[0108] Where C is the heat capacity of the core layer (J / K), and T is the temperature of the core layer (°C). It is the thermal conductivity (W / k) of the core layer. and These are basal metabolic rate and additional metabolic rate (W), respectively. In the fat layer... Based on baseline blood flow rate ( ) and additional blood flow rate ( Composition (m) 3 / s). This refers to the heat lost through respiration in the core layer of the chest, and its calculation formula is as follows:

[0109] in and These are ambient temperature (°C) and water vapor pressure (kPa); and These are the heat generated (W) due to external work and tremors, respectively; sum is the total heat generated by all segments.

[0110] 6. Whole-body segmental thermal coupling equations To achieve coordinated thermal interaction and physiological regulation across 20 tissue segments, a systemic segmental thermal coupling equation is established based on the heat transfer equations of each tissue layer. This equation facilitates the transfer of local temperature to the overall physiological state, specifically including: (1) Equation for mixing venous return The formula used to integrate the venous blood enthalpy across all segments, reflecting the thermal effect of local tissue temperature on systemic blood circulation, is as follows:

[0111] (2) Central Blood Pool Heat Balance Equation The real-time thermal balance of the central blood pool is simulated to achieve dynamic distribution of body heat, as shown in the following formula:

[0112] Step 2: Setting Boundary Conditions This step sets the initial and boundary conditions required for the model to run, simulating the actual situation of firefighters in a fire.

[0113] 1. Initial conditions: At the start of the simulation (t=0), the human body is set to be in a steady state under a thermoneutral environment. For example, the core layer temperature is set to 37℃, the skin surface temperature is set to 34℃, and a stable temperature gradient distribution exists between the layers.

[0114] 2. Boundary Conditions: On the outer surface of the epidermis (x=0), a dynamic thermal balance equation is used as the boundary condition to accurately simulate the heat load in a fire and the heat dissipation mechanism of the human body.

[0115] in, It is the thermal conductivity of the skin layer (W / m / K). The net heat flow that penetrates fire protective clothing and reaches the skin surface; The amount of heat dissipated by sweat evaporation is controlled by skin humidity and thermoregulation signals.

[0116] Step 3: Numerical Solution The implicit finite difference method is used to numerically discretize and solve the multi-node, multi-layer, dynamically coupled biological heat transfer equations established in steps one and two. The calculation steps are as follows: (1) Spatial and temporal discretization Discretize each tissue layer j in each human body segment i (i=1~20) along the radial depth direction in one-dimensional space and establish a grid node: let the spatial coordinate be x (radial depth), the time coordinate be t, the spatial step size be Δx, and the time step size be Δt. Discretize the continuous temperature field T(x,t) as follows: , where i is the partition index, j is the organizational layer index, n is the time step index, and m is the spatial node index.

[0117] (2) Discretization of governing equations and boundary conditions The unsteady-state biological heat transfer equations and boundary condition equations are discretized for each partition i and each tissue layer j. Taking the discretization of the heat transfer equation for the epidermis as an example:

[0118] in, c and k are the tissue density, specific heat capacity, and thermal conductivity of the epidermis, respectively; This represents the temperature at the m-th spatial node at time step n; These represent the basal metabolic heat production rate and sweat evaporation heat dissipation rate of the epidermis at time step n+1, respectively.

[0119] (3) Dynamic physiological parameter updates By employing a time-stepping solution strategy, within each preset time step Δt, a system of linear equations consisting of discrete equations from all nodes is solved, yielding... The temperature values ​​of all segments, all tissue layers, and all spatial nodes are obtained at all times, while dynamic physiological parameters (such as blood perfusion rate and metabolic heat production rate) are updated simultaneously, thereby obtaining the complete, segmented spatiotemporal distribution of tissue temperature field T(x,t).

[0120] To ensure the physical realism and predictive accuracy of the model, the thermophysical and physiological parameters of each tissue layer were set with reference to relevant experimental measurement data and literature, including: ASTM D 1777 standard test, ISO 8996 standard, and "Development of a numerical model to predict physiological strain of firefighter in fire hazard" (Scientific Reports, 2018).

[0121] Step 4: Synchronous Prediction and Output Reference Figure 2 The process shown in the invention is as follows: at each time step After the calculations are completed, the system executes the following two key risk assessment tasks synchronously and in parallel, ensuring the comprehensiveness and timeliness of the predictions: 1. Segmental skin burn risk assessment: For each segment of the human body, the real-time temperature of the dermis is extracted. And substitute it into the classic Henriques damage integral formula:

[0122] Accumulated calculations are performed. Once the damage integral value Ω of any segment first reaches or exceeds the critical damage threshold of 1.0 (typically corresponding to second-degree burns), the current time step is recorded. For this segment, the "predicted time for skin burns" ( ), and stop the cumulative burn damage assessment of that section.

[0123] 2. Overall human body heat stress risk assessment: Simultaneously monitor core layer temperature at each time step. .when When the preset upper limit of human body heat stress temperature is reached or exceeded for the first time, the current time step is recorded. For "heat stress prediction time" ( ), and stop the judgment on that path.

[0124] The program continues to run until the burn time and heat stress time for all segments are determined, or the preset maximum simulation duration is reached. Finally, the method outputs two key time values: the earliest occurring "predicted skin burn time" and the earliest "predicted heat stress time" for all segments. The minimum of these two values ​​is then used as the final comprehensive safe operating time limit recommendation for firefighters in this specific operational scenario.

[0125] Example Demonstration Taking a fire rescue scenario as an example, the input environmental heat flux is 8.5 kW / m², and the firefighter's metabolic rate is 4 Met (equivalent to moderate-intensity work). Using the method of this invention, the results for a specific segment (e.g., the left upper arm) are calculated as follows: = 480 seconds; simultaneously, the predicted heat stress for the entire body is = 650 seconds. This means that in this scenario, skin burns to the left upper arm are the primary limiting risk, and the overall safe working time should not exceed 480 seconds.

[0126] In another scenario, such as a confined space with lower heat flux (3 kW / m²) but higher humidity and motion intensity (6 Met), simulation results show that: all zones All exceeded 900 seconds, but = 720 seconds. At this point, heat stress becomes a limiting risk, and the recommended safe working time is 720 seconds.

[0127] Through the above-described refined and dynamically coupled implementation methods, this invention can clearly distinguish and quantify these two or even more complex risk scenarios, providing comprehensive and accurate safety guidance, thereby playing an important role in protecting the lives of firefighters and improving operational efficiency.

[0128] The above-described embodiments are merely one implementation of the present invention, and while the descriptions are 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 all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model, characterized in that, include: S1. Constructing a refined multi-node segmented and layered human physiological thermal model: Construct a multi-node geometric model of the human body, dividing the human body into 20 independent segments. Each segment is radially divided into the epidermis, dermis, fat layer, muscle layer, and core layer. The tissue layer index is denoted as j. Each tissue layer is divided into N micro-mesh nodes along the radial depth direction. The thermal properties and physiological parameters of human tissues are set according to each segment and each tissue layer, unifying the segment division, tissue layer division, and physiological parameter setting issues in skin burn prediction and heat stress prediction. S2. Establish a dynamic coupled biological heat transfer equation system with an embedded active thermoregulation mechanism: For any segment of the human body, let the segment index be i, where i ranges from 1 to 20, corresponding to 20 independent segments, and establish an unsteady biological heat transfer equation for the j-th tissue layer; for the epidermal grid unit, introduce the regulation mechanism of sweat accumulation on skin thermophysical parameters, considering the dynamic changes of thermal conductivity and specific heat capacity with the amount of sweat accumulation; for the dermis, fat layer, muscle layer and core layer, incorporate a dynamic blood perfusion term into their unsteady biological heat transfer equations. This dynamic blood perfusion term is not a constant value and is a function of the human body's thermoregulation control signal. At the same time, introduce the reverse influence mechanism of burn on human physiological regulation function to realize the coupling relationship between burn factors and physiological regulation processes; The human body thermal regulation control signal is based on a weighted calculation of the core layer temperature deviation and the skin layer temperature deviation, taking into account the effect of skin burns; when the temperature rises, a vasodilation signal is generated to increase the value of the dynamic blood perfusion term, while changing the human body's sweating rate and metabolic heat production; in order to simulate the thermal interaction between different segments of the human body, an energy conservation equation for the central circulating blood pool is constructed to update the arterial blood temperature in the above equation. S3. Set precise initial and boundary conditions for fire operation scenarios: Set the initial steady-state temperature field and thermal boundary conditions of the fire environment, and establish the boundary conditions for the continuity between human tissue layers; S4. Numerical solution of dynamic coupling equations: The implicit finite difference method is used to solve the coupling equations in time steps. In each time step, the current physiological parameters are updated according to the temperature field calculated at the previous moment, and then the transient temperature of each node at the current moment is calculated. S5. Perform simultaneous assessment and output of dual risks: Utilize the transient temperature calculated at each time step and execute in parallel; calculate the cumulative damage value based on the temperature of different dermal segments to assess the risk of skin burns; The risk assessment of whole-body heat stress is based on the core temperature, and the core temperature threshold is monitored; the earliest time to reach the critical threshold is taken as the comprehensive safe operation time limit.

2. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S1 specifically includes: Based on numerical simulation methods, a human thermal regulation simulation system was constructed on a numerical simulation platform. The system uses a multi-node geometric model of the human body as its core. The specific construction process and system architecture are as follows: Core Concept: Construction of a Multi-Node Geometric Model of the Human Body The human body is meticulously divided into 20 representative independent segments, denoted as segment index i, specifically including the face, head, left upper arm, right upper arm, left lower arm, right lower arm, left hand, right hand, chest, shoulder, abdomen, back, left hip, right hip, left thigh, right thigh, left calf, right calf, left foot, and right foot. For each segment, a multi-level physiological thermal structure model is constructed along the radial thickness direction of the human tissue, denoted as tissue layer index j, containing five layers of human tissue: epidermis, dermis, fat layer, muscle layer, and core layer. Each tissue layer is further discretized into N micro-element mesh nodes along the radial depth direction, forming a refined multi-node model architecture of "segment-level-micro-element node". Each layer of the model is assigned accurate inherent thermal property parameters corrected for fire environment conditions, including density. Specific heat capacity and thermal conductivity This provides an accurate physical basis for subsequent heat transfer calculations and dynamic response analysis; Simulation System Architecture Based on the constructed multi-node segmented hierarchical human physiological thermal model, the multi-node segmented hierarchical human physiological thermal model is embedded as a unified physiological thermal calculation kernel into the simulation platform to construct a human thermal regulation simulation system for simultaneous prediction of skin burns and heat stress.

3. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S2 specifically includes: The unsteady heat transfer equation of the epidermis is: in, , , These represent the density of the epidermis (kg / m³). 3 ), specific heat capacity (J / kg / K) and thermal conductivity (W / (m·K)), T is tissue temperature, t is time, x is depth, Basal metabolic rate, It is evaporative heat transfer in the epidermis; The unsteady biological heat transfer equation of the muscle layer is: in, and These are the heat generated by external work and tremors (W / m²). 3 ), It's blood temperature; The unsteady biological heat transfer equation of the core layer is: Where C is the heat capacity of the core layer (J / K), and T is the heat capacity of the core layer. c It is the temperature (°C) of the core layer. It is a dynamically changing blood flow rate (m 3 / s / m 3 ), It is the thermal conductivity (W / k) of the core layer; and These are basal metabolic rate and additional metabolic rate (W); It is the heat lost by the core layer of the chest through respiration; In the dermis and fat layer, the unsteady biological heat transfer equation is expressed as: in, , , These represent the density, specific heat capacity, and thermal conductivity of the corresponding layer of tissue, respectively. The heat capacity of blood is determined by blood density. Specific heat capacity of blood Multiplying them together yields the result. Basal metabolic rate (W / m 3 ), Additional metabolic rate (W / m 3 ), where a is the countercurrent heat exchange ratio; Related to the temperature of the dermis, specifically as follows: in These are the setpoint temperatures for various points on the human body.

4. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S2 also includes: Dynamic physiological regulatory mechanisms: Dynamic blood perfusion rate The dynamic blood perfusion rate of each tissue layer is a function of the human body's thermoregulation control signal. Dermis: Introducing a burn damage inhibition term ( The formula is: in, The baseline blood perfusion rate of the dermis. Let Ω be the vasodilation coefficient, Ω be the thermal damage value of the skin tissue in this segment, and Ψ(Ω) be the vascular function impairment function. No burn occurred when Ω < 0.5, Ψ ≈ 1, normal adjustment is possible; Inflammation occurs when 0.5 ≤ Ω < 1.0, Ψ > 1, vasodilation and congestion occur, and heat transfer is accelerated; When Ω≥1.0, second-degree burns result in necrosis, Ψ→0, vascular embolism, and cessation of blood flow. Muscle layer: Including contributions from external work and shivering heat production, the formula is: in, Based on blood flow rate, To increase blood flow rate, Performing work on the outside and trembling; Fat layer / core layer: The formula is: Only the regulation of temperature and central nervous system signals is considered, without introducing other factors, to align with its physiological functions; among which, and These are the vasodilation coefficient and the vasoconstriction coefficient, respectively. and Err represents the vasodilatory and vasoconstrictive signals emitted by the central nervous system, and Err represents the local temperature deviation term. It is the difference between the baseline blood flow rate under high temperature conditions and the baseline blood flow rate under neutral temperature conditions, written as: 。 5. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 4, characterized in that, and The expression is closely related to the real-time temperature deviation between the core layer and the skin layer: in, and These are the control coefficients for vasodilation (l / h / ℃) and vasoconstriction (l / ℃) in the core layer of the head, respectively. and These are the control coefficients for vasodilation (l / h / ℃) and vasoconstriction (l / ℃) in the skin layers of each location; and These are vasodilations of the core layer of the head and other skin layers (l / h / ℃). 2 ) and vasoconstriction (l / ℃) 2 The control coefficients are: (1) represents the core layer; Wrm(1) and Cld(1) are the warm and cold signals (°C) of the core layer, respectively; Wrms and Clds are the weighting coefficients (°C) of the warm and cold signals, respectively; Wrm and Cld are the temperature differences between the skin layer temperature of each part and its set point. Decide, If Err > 0, then Wrm = Err; otherwise, Cld = -Err. Wrms and Clds are weighting coefficients obtained through integration by the virtual sensor nodes. get: The intensity of these signals is based on the core layer temperature calculated in real time by the model. Arterial blood temperature and skin temperature and their respective physiological set point temperatures deviation The decision; Core layer temperature deviation Together with the skin layer temperature deviation Err, it serves as the input to the thermal regulation signal generation module. This represents the setpoint temperature of the core layer; when the core layer or skin temperature rises (Err>0), the system generates a positive warming signal (Wrm), driving vasodilation. Enhance, thereby increase This enhances skin heat dissipation; conversely, it generates a cold signal (Cld), driving vasoconstriction signals. Enhance, reduce To conserve core heat.

6. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S2 also includes: Venous return mixing equation: Calculate the total enthalpy of venous blood mixture across all segments, as the input for the central blood pool's heat balance. The formula is as follows: in, The instantaneous temperature (°C) of the micro-element node in the i-th partition and the j-th tissue layer; V represents the total volume (m³) of the i-th partition; V represents the volume (m³) of the micro-grid node of human tissue. The total enthalpy (W) of the venous blood from 20 segments is calculated. Arterial blood temperature (K); Central Blood Pool Heat Balance Equation: The central blood pool is a virtual core unit for whole-body blood circulation, used to simulate the heat exchange process of blood in the human heart and major blood vessels, and its temperature is updated in real time; its formula is as follows: in, Characterized by the total heat capacity of the central blood pool, Characterizes the real-time temperature of the central blood pool; , These are the heat loss from lung respiration and the heat generated by the heart's work, respectively. The arterial blood temperature of each segment at the next time step is assigned by the temperature of the central blood pool, realizing thermal coupling and linkage of the whole body segments; Segment coupling output: Sets the arterial blood temperature of each segment in the next time step. = ( t ).

7. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S2 also includes: In the thermal equilibrium boundary conditions of the outer surface of the epidermis, the heat loss due to sweat evaporation The calculation is embedded in the human body thermal regulation simulation system, and is influenced by the sweating signals emitted by its internal thermal regulation signal generation module. Dynamic regulation is used to achieve dynamic control of heat dissipation through sweat evaporation. The boundary condition equation is as follows: ( ) in, The net heat flow that penetrates the protective suit and reaches the skin surface; The amount of heat dissipated through sweat evaporation is determined by the sweating signal in the thermoregulation system. Dynamic regulation, changing in real time with the thermal stress state; simultaneously, the thermal conductivity of the skin layer... (W / m / k) and sweat wetting The correlation reflects the impact of sweat accumulation on the skin's thermal conductivity, and its dynamic correction formula is: in, It is related to the cumulative amount of sweat and the porosity of the epidermis at the current moment; The thermal conductivity of the skin layer in a dry state. is the thermal conductivity of water.

8. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S3 specifically includes: Based on the firefighting scenario to be predicted, precise initial and boundary conditions are set for the constructed human body model. The initial condition is set as the thermoneutral steady-state temperature distribution of each layer of the human body before entering the fire environment. The boundary condition is the fire thermal environment load applied to the outer surface of the epidermis, i.e., the net heat flux. The heat flow should take into account the heat insulation attenuation effect of the protective clothing, to reflect the actual heat load that workers in a real, complex thermal environment would experience in a fire.

9. The method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model according to claim 1, characterized in that, Step S5 specifically includes: For each segment of the human body, the real-time temperature at the basal layer of the dermis is extracted. The skin burn risk assessment uses the Henriques burn integral model: in, : Integral variable, representing any moment from the start to the current time; when Ω ≥ 1.0, it is determined that a second-degree burn has occurred, and the current time step is recorded. "Predicted time for skin burns" In addition, the average temperature of the core layer of all partitions is monitored simultaneously. ;when When the temperature is ≥ 38.5℃, record the current time step. For "heat stress prediction time" ( Finally, take... and The minimum value between these two values ​​is the recommended safe working time for firefighters.

10. A human body thermal regulation simulation system for implementing the synchronous prediction method for human body thermal injury based on a dynamically coupled physiological model as described in any one of claims 1-9, comprising: The geometry and physical property parameter library module stores the geometric dimensions and thermophysical parameters of 20 segments and 5 layers of human tissue; Thermal regulation signal generation module: This thermal regulation signal generation module is the core processing unit; it further includes: Temperature deviation calculation unit: Receives virtual temperature sensor signals from the core layer and skin layer of each partition and calculates the temperature deviation from the physiological set point; The signal generation unit regulates the generation of vasodilation signals based on temperature deviation. vasoconstriction signals and sweating signals ; Signal distribution unit: Distributes the generated regulatory signals to each peripheral physiological regulatory unit according to the regional segments; Dynamic Coupled Solver Module: Used to solve the dynamic coupled biological heat transfer equations established in step S2; Risk assessment and output module: used to simultaneously assess the risk of skin burns in the segment and the overall heat stress risk in step S5, and output the comprehensive safe operation time limit; In the corresponding system layer architecture, the logical flow of model execution within each computation time step Δt is as follows: Parameter loading: Read the geometric dimensions, thermophysical properties, and initial physiological parameters of 20 segments and 5 layers of tissue in the human body at the current time step from the geometry and physical property parameter library module; Sensing and Deviation Calculation: The thermal regulation signal generation module collects real-time temperatures of the skin layer and core layer in each segment using virtual temperature sensors, and calculates the temperature deviation. ; Regulation signal generation and distribution: The thermal regulation signal generation module generates vasodilation signals based on temperature deviation. vasoconstriction signals and sweating signals And assign them to the corresponding partitions and organizational layers; Dynamic coupling solution: The dynamic coupling solver module is substituted with the updated dynamic blood perfusion rate. Heat dissipation through sweat evaporation Solve the coupled biological heat transfer equations to obtain the complete temperature field T(x,t) at the current time step; Dual risk assessment: The risk assessment and output module simultaneously performs the assessment of the risk of skin burns in different segments and the risk of heat stress throughout the body based on the temperature field T(x,t); Iteration and closed-loop feedback: The temperature field T(x,t) at the current time step is used as the initial temperature at the next time step. This is updated to the geometry and physical property parameter library module, and the cycle continues until any risk reaches the critical threshold. The calculation is then terminated and the comprehensive safe operation time limit is output.

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