A traceable lightweight design method for fire-fighting protective clothing

Abstract

This invention belongs to the field of fire protection technology. It discloses a traceable, lightweight design method for fire-fighting protective clothing, comprising: performing muscle group zoning analysis on firefighters' physiological data to obtain a muscle group load distribution map; constructing a holographic model of human load based on this map; constructing a combat activity load network based on combat environment data and the holographic model of human load; simulating a high-temperature fire environment using the holographic model of human load and generating protective performance requirement data based on the simulation results; performing protective material adaptation analysis and combination optimization on the combat activity load network using the protective performance requirement data to obtain a material layering configuration scheme; comprehensively evaluating the material layering configuration scheme and verifying lightweight indicators to generate the final fire-fighting protective clothing design scheme and traceable data archive. This invention significantly reduces the weight of protective clothing while ensuring protective performance, improving the combat efficiency and safety of firefighters.

Description

Technical Field

[0001] This invention relates to the field of fire protection technology, and more specifically, to a lightweight design method for traceable fire-fighting protective clothing. Background Technology

[0002] In modern fire rescue operations, firefighters face increasingly complex and extreme fire environments, particularly under the combined effects of high temperatures, explosions, and toxic gases, placing ever higher demands on their personal protective equipment (PPE). Firefighting protective suits, as the most crucial PPE for firefighters, not only need to provide sufficient fire protection, heat insulation, and waterproofing, but also must ensure the firefighters' flexibility and comfort during high-intensity rescue operations. However, with enhanced protective performance, the weight of traditional firefighting protective suits has also increased, severely impacting firefighters' mobility and operational efficiency, and potentially leading to excessive physical exertion and heat stress, threatening their lives.

[0003] Traditional firefighting protective clothing designs often employ standardized, monolithic structures, failing to adequately consider the varying load distribution and protection needs of different body parts. This results in some areas being over-protected and excessively heavy, while critical areas may be underprotected. With the diversification of firefighting and rescue missions, traditional design methods struggle to effectively balance protective performance and weight, failing to provide precise protective configurations for different operational environments and rescue missions. This situation not only reduces firefighters' operational efficiency and endurance but also leaves them without targeted protective strategies in complex fire environments, increasing safety risks. Furthermore, traditional protective clothing lacks scientific data support and tracking mechanisms, hindering full lifecycle management and making it difficult to guarantee stable protective performance over long-term use.

[0004] In view of this, the present invention proposes a traceable lightweight design method for fire-fighting protective clothing to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution:

[0006] A traceable, lightweight design method for fire-fighting protective clothing, comprising:

[0007] Step S1: Obtain firefighters' human physiological data and combat environment data; perform muscle group zoning analysis on the firefighters' human physiological data to construct a human muscle group load distribution map;

[0008] Step S2: Identify local load reduction areas in the human muscle group load distribution map and reconstruct a holographic human body model to build a holographic human body load model;

[0009] Step S3: Based on the combat environment data, perform dynamic combat simulation processing on the holographic model of human body load and fit the human body load distribution to generate a combat activity load network;

[0010] Step S4: Simulate the high-temperature fire environment of the holographic model of human body load to obtain dynamic thermodynamic simulation data; perform flame retardant performance requirement analysis on the dynamic thermodynamic simulation data to generate protective performance requirement data.

[0011] Step S5: Utilize the protection performance requirement data to perform a protection material adaptation analysis on the combat activity load network, and optimize the material structure gradient combination to obtain a material layer configuration scheme;

[0012] Step S6: Conduct a multi-dimensional performance comprehensive evaluation of the material layering configuration scheme and verify the lightweight index to generate the final fire extinguishing protective clothing design scheme and traceable data archive.

[0013] Furthermore, the specific implementation steps of step S1 include:

[0014] Step S11: Obtain firefighters' physiological data and fire scene combat environment data;

[0015] Step S12: Extract multi-dimensional features from the firefighters' human physiological data to generate human physiological feature data;

[0016] Step S13: Perform muscle group partitioning on human physiological characteristic data to obtain muscle group partitioning data;

[0017] Step S14: Calculate the load intensity of firefighters' human physiological data based on muscle group zoning data, and construct a human muscle group load distribution map.

[0018] By configuring a high-precision wearable sensor network, various physiological parameters of firefighters in simulated or actual firefighting environments are collected in real time, including core indicators such as heart rate variability curves, dynamic body temperature distribution, electromyography (EMG) activity signals, and joint range of motion. Simultaneously, an environmental monitoring system is deployed to acquire operational environment data such as temperature gradients, humidity levels, harmful gas concentration distribution, and radiant heat intensity at the fire scene. The firefighters' physiological data is time-aligned and spatially matched with environmental data to form a complete data acquisition matrix. Deep learning algorithms are then applied to the collected firefighters' physiological data for feature extraction, identifying key physiological indicators such as heart rate variability patterns, body temperature distribution characteristics, and EMG signal spectral characteristics, thus constructing an individual physiological profile of each firefighter. The database comprehensively reflects the physiological response characteristics of firefighters in the combat environment, providing data support for subsequent muscle group zoning. Electromyography (EMG) analysis is used to accurately identify human muscle activity. Combined with anatomical knowledge, the human body is divided into multiple functional muscle group regions, such as the trunk core region, upper limb active region, and lower limb support region. The identified muscle group zoning information is labeled and stored, providing targeted data support for subsequent load analysis. The load intensity of each muscle group region during firefighting operations is calculated, including indicators such as muscle contraction intensity, duration, and fatigue level. A human muscle group load distribution map is constructed, visually displaying the changes in load intensity in different regions of the human body during firefighting operations in the form of a heat map.

[0019] Furthermore, step S14 specifically involves the following steps:

[0020] Based on muscle group partitioning data, muscle activity intensity is calculated from firefighters' human physiological data, and muscle group load intensity values ​​are extracted.

[0021] Time series analysis of muscle group load intensity values ​​was performed to obtain load intensity variation curves;

[0022] Peak values ​​are identified in the load intensity variation curve to determine the load concentration area.

[0023] By analyzing the joint movement angles of firefighters' human physiological data, the range of joint movement data was obtained.

[0024] The joint range of motion data is analyzed to identify the frequency of joint movement in order to obtain joint movement frequency characteristics.

[0025] Based on the joint activity frequency characteristics, load distribution is fitted to the load concentration area to construct a human muscle group load distribution map.

[0026] In this embodiment, by analyzing the amplitude, frequency, and other characteristics of electromyographic signal data, the activity intensity of each muscle group region is calculated and quantified into a muscle group load intensity value. The muscle group load intensity values ​​are arranged in chronological order to form time series data. The change pattern of muscle group load intensity values ​​over time is analyzed to generate a complete load intensity change curve. A peak detection algorithm is used to identify high-value regions in the load intensity change curve and mark key areas of load concentration to ensure coverage of all significant load points. Using joint angle sensor data, the range of motion of each joint during the movement process is calculated, including the maximum angle, minimum angle, and angle change amplitude. The joint range of motion data is statistically analyzed to form a structured joint activity feature set. The frequency characteristics of joint activity are analyzed, including the number of activities, repetition patterns, and rhythm characteristics, to generate joint activity frequency feature data. Combining the load concentration areas and joint activity characteristics, a nonlinear fitting algorithm is used to construct a complete human muscle group load distribution map. This map displays the load distribution of various regions of the human body in the form of a three-dimensional heat map, supports dynamic viewing and interactive analysis, and is regularly updated and optimized to ensure that it accurately reflects the latest muscle group load status.

[0027] Furthermore, the specific implementation steps of step S2 include:

[0028] Step S21: Identify high-load areas based on the human muscle group load distribution map to obtain data on key load reduction areas;

[0029] Step S22: Perform spatial correlation analysis on the data of key areas for burden reduction to obtain a regional collaborative relationship network;

[0030] Step S23: Perform quantitative analysis of protection requirements on the regional collaborative relationship network to generate protection requirement data;

[0031] Step S24: Based on the protection requirement data, perform quantitative analysis of the burden reduction optimization of the key burden reduction area data to generate burden reduction optimization parameters;

[0032] Step S25: Based on the load reduction optimization parameters, the human muscle group load distribution map is holographically reconstructed to construct a human load holographic model.

[0033] Based on the human muscle group load distribution map, a clustering analysis algorithm is used to identify areas where the load intensity exceeds a threshold. These high-load areas are marked as key load reduction areas. The area, average load intensity, and load distribution characteristics of each key load reduction area are calculated to form a complete dataset of key load reduction areas. The spatial relationship and functional connectivity between key load reduction areas are analyzed to identify mutually influencing area groups, establish a collaborative relationship model between areas, quantify the collaborative strength and influence mode between areas, construct a complete regional collaborative relationship network, analyze the protection requirements of each load reduction area, including multi-dimensional requirements such as fire protection, heat protection, waterproofing, and puncture resistance, quantify the protection requirement level of each area, generate regional protection requirement data, and establish a load reduction optimization model based on protection requirements and load reduction area characteristics. The key parameters of the best load reduction scheme are calculated, including material thickness, structural design, and functional configuration. The load reduction optimization parameters are combined with the human muscle group load distribution map, and holographic modeling technology is used to construct a three-dimensional interactive human load holographic model. This model not only displays the static load distribution but also simulates the dynamic load change process.

[0034] Furthermore, the specific implementation steps of step S3 include:

[0035] Step S31: Input the combat environment data into the human body load holographic model to conduct dynamic combat simulation in order to obtain combat dynamic load information;

[0036] Step S32: Perform combat action characteristic analysis on the combat dynamic load information to generate combat action characteristic data;

[0037] Step S33: Extract load transfer paths from the combat action feature data to generate multiple load transfer paths;

[0038] Step S34: Perform path fusion optimization on multiple load delivery paths to generate an operational load network.

[0039] The collected combat environment data is imported into a human load holographic model. During the import process, it is crucial to ensure data format compatibility with the model. Using a physics engine and biomechanical model, the process of firefighters performing various combat actions under different combat environments is simulated, and key parameter changes during the simulation are recorded to generate dynamic combat load information, including load distribution and intensity variations. The combat action characteristics within this dynamic load information are analyzed, extracting key parameters such as speed, frequency, and amplitude to identify typical combat action patterns, such as climbing, carrying, and firefighting. The extracted combat action features are then organized into a structured dataset to obtain corresponding combat action feature data for subsequent analysis. Using combat action characteristic data, this system tracks the transmission patterns of load across different parts of the human body, identifies load generation points, transmission paths, and aggregation points, and generates multiple complete load transmission paths describing load transmission, covering various typical combat actions. It comprehensively analyzes and optimizes these multiple load transmission paths, identifies repetitive paths and key nodes, and applies nonlinear optimization algorithms to merge these paths into a complete combat activity load network. This network presents the distribution and transmission relationships of the firefighter's entire body load in a graph structure, verifying its effectiveness and completeness. Simulation tests ensure that it accurately reflects the load distribution and transmission characteristics in a real combat environment.

[0040] Furthermore, the specific implementation steps of step S32 are as follows:

[0041] Calculate the speed of action based on the dynamic load information of human combat personnel to generate combat action speed parameters;

[0042] Extracting key active joints based on dynamic load information of human combat;

[0043] The activity frequency of key joints is statistically analyzed to generate the activity frequency between joints;

[0044] The force direction is identified from the dynamic load information of the human combat system to generate the direction of force transmission;

[0045] The motion trend analysis of the direction of force transmission is performed based on the activity frequency between joints to generate the trend of changes in combat actions;

[0046] Motion characteristic analysis is performed on the speed parameters and changing trends of combat actions to generate combat motion characteristic data.

[0047] The system calculates the movement speed of the human combat dynamic load information and generates combat movement speed parameters. The movement speed calculation obtains the angular velocity and linear velocity parameters of each joint by analyzing the rate of change of joint position over time.

[0048] Using kinematic analysis, joints with high activity frequency and large range of motion in combat actions, such as the shoulder joint, knee joint, and waist, are identified and marked as key active joints. Through time-frequency analysis technology, the activity frequency and rhythm of each key active joint under different simulated combat scenarios are obtained to generate the activity frequency between joints.

[0049] Inverse dynamics analysis is performed on the dynamic load information of the human body in combat, and the direction and magnitude of the force on each part of the human body are identified based on the analysis results to generate the direction of force transmission. Based on the activity frequency and force transmission direction between joints, machine learning algorithms are used to predict the trend of combat action changes, identify the conversion law and load transfer mode between different combat actions to generate the trend of combat action changes, and perform motion characteristic analysis on the speed parameters of combat actions and the trend of combat action changes to generate combat motion characteristic data.

[0050] Furthermore, the specific implementation steps of step S4 include:

[0051] Step S41: Simulate the high-temperature fire environment using the holographic model of human body load to obtain a distribution map of the thermal hazard exposure area;

[0052] Step S42: Assess the thermal hazard level of the hazard exposure area distribution map to generate the thermal protection requirement level for each area;

[0053] Step S43: Analyze the heat conduction path based on the thermal protection requirements of each region to obtain heat conduction simulation data;

[0054] Step S44: Analyze the human body heat dissipation requirements based on the combat environment data to obtain human body heat dissipation requirement data;

[0055] Step S45: Optimize the balance of protective performance based on heat conduction simulation data and human body heat dissipation demand data to generate protective performance demand data.

[0056] Based on computational fluid dynamics principles, a holographic model of human body load is used to simulate thermal exposure in a high-temperature fire environment. The simulation examines the thermal effects on various parts of the human body under different temperature gradients and thermal radiation intensities. Infrared thermal imaging analysis is used to accurately identify thermal hazard exposure areas in different parts of the body, generating a distribution map of these areas and quantifying the severity of each exposure. By considering factors such as temperature, duration, and heat flux density, the required thermal protection level for each exposure area is assessed, forming a regional thermal protection requirement dataset. The study analyzes the paths and modes of heat transfer from the fire environment to the human body, identifying the main heat conduction, radiation, and convection paths, and employs multiphysics coupling... Simulation technology simulates the heat transfer process in the various layers of fire-fighting protective clothing under high-temperature conditions, generating complete heat conduction simulation data. It analyzes the physiological heat dissipation needs of firefighters in high-temperature environments and, by considering factors such as heat generation, sweat evaporation, and respiration, combined with human thermal comfort theory, quantifies the heat dissipation needs of different body parts. The heat conduction simulation data and human heat dissipation needs are comprehensively analyzed to establish a multi-objective optimization model, balancing fire resistance and breathability. This generates regional and hierarchical protective performance requirement data, which accurately describes the required flame retardancy rating, thermal resistance, breathability, and moisture wicking performance for each area.

[0057] Furthermore, the specific implementation steps of step S5 include:

[0058] Step S51: Construct a database of protective material properties and collect data on the physicochemical properties of the materials;

[0059] Step S52: Spatially map the protection performance requirement data with the operational activity load network to identify the characteristics of the comprehensive performance requirements;

[0060] Step S53: Intelligently match the material performance database based on comprehensive performance requirement characteristics to generate a material candidate set;

[0061] Step S54: Perform multi-dimensional performance evaluation on the candidate material set to obtain material performance stratification data;

[0062] Step S55: Based on the material performance stratification data, perform gradient combination optimization on the combat activity load network to form a material stratification configuration scheme.

[0063] Detailed performance parameters of various fire-resistant fabrics, thermal insulation materials, and functional textiles are collected to establish a structured material performance database. This database includes comprehensive data on physical properties (such as density and thickness), chemical properties (such as flame retardancy and chemical corrosion resistance), and mechanical properties (such as tensile strength and abrasion resistance). Spatial correlation analysis is performed between protective performance requirements data and operational load networks to identify the comprehensive requirements of each muscle group region in terms of load distribution and thermal protection, forming a regional performance requirement feature map. Machine learning algorithms are then applied to intelligently filter and match the material database, generating optimal material requirements for each muscle group region. A candidate material set is established to ensure that the candidate materials can meet the protection and weight reduction requirements of the region. The candidate materials are evaluated from multiple dimensions, including protective performance, breathability, flexibility, weight and cost, and quantitative scores are generated to produce material performance stratification data, which includes the evaluation results of each candidate material in different performance dimensions. Based on genetic algorithm and simulated annealing algorithm, the material structure is optimized by gradient combination to maximize weight reduction and improve comfort while ensuring protective performance, forming a differentiated and gradient material stratification configuration scheme. The material stratification configuration scheme specifies in detail the material type, thickness and combination method to be used in each region.

[0064] Furthermore, the specific implementation steps of step S6 include:

[0065] Step S61: Conduct a quantitative test on the thermal protection performance of the material layering configuration scheme to generate a protection performance evaluation value;

[0066] Step S62: Construct a digital human body model based on the material layering configuration scheme, perform motion flexibility analysis, and generate flexibility assessment values;

[0067] Step S63: Perform precise weight calculations on the material layering configuration scheme to generate lightweight index values;

[0068] Step S64: Conduct a multi-dimensional comprehensive performance evaluation of the protective performance evaluation value, flexibility evaluation value, and lightweight index value to generate the final fire-fighting protective clothing design scheme and traceable data archive.

[0069] Based on national standards and industry specifications, thermal protection performance tests were conducted on the material layering configuration scheme, including heat conduction tests, heat radiation tests, and flame retardant performance tests. The protective performance indicators of each muscle group area were quantified, generating a complete protective performance evaluation report. The material layering configuration scheme was applied to a 3D human body model, simulating firefighters wearing the protective suit performing various typical actions, such as climbing, crawling, and carrying. Joint range of motion and movement flexibility were analyzed, generating flexibility evaluation data, including indicators such as the degree of joint movement restriction and movement completion time. By accurately calculating the weight of materials and structure in each area, the weight of the entire protective suit was evaluated, compared with traditional protective suits, and the percentage of weight reduction was calculated, generating a lightweight index report. A multi-dimensional evaluation model was established, weighted and calculated for protective performance, flexibility, and lightweight indicators to obtain a comprehensive performance score. Based on the evaluation results, local optimization and adjustments were made to form the final fire-fighting protective suit design scheme, including structural design drawings, material configuration tables, and manufacturing process parameters. A traceable data archive based on blockchain technology was established for the design scheme, recording complete information such as material performance parameters, structural design data, and test verification results, achieving "one file per piece" digital management and full life-cycle traceability.

[0070] The technical effects and advantages of the traceable lightweight design method for fire-fighting protective clothing of this invention are as follows:

[0071] This application analyzes muscle group load characteristics based on the actual load distribution of firefighters, allocates regional load reduction weights based on the load characteristic analysis results, analyzes the protective effect based on the regional load reduction weight allocation results, firefighter physiological data and combat environment data, and optimizes material configuration based on the obtained protective effect analysis results.

[0072] Meanwhile, this application conducts comprehensive data analysis on the human load during firefighting operations, accurately identifies areas requiring key protection and those that can be lightweighted, avoids the negative impact of traditional one-piece designs on the weight and flexibility of protective clothing, and improves the design accuracy and practicality of protective clothing.

[0073] Through dynamic combat simulation and high-temperature fire environment simulation, the actual protection needs of each area were systematically evaluated, achieving the design goal of "key parts are protected in a focused manner, and non-key parts are lightweighted". At the same time, the traceable data archive based on blockchain technology ensured the quality management of protective equipment throughout its entire life cycle, significantly improving the safety protection capabilities and combat effectiveness of firefighters in complex fire environments. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of a lightweight design method for traceable fire-fighting protective clothing according to the present invention. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see Figure 1 As shown in this embodiment, a lightweight design method for traceable fire-fighting protective clothing includes:

[0077] Step S1: Obtain firefighters' human physiological data and combat environment data; perform muscle group zoning analysis on the firefighters' human physiological data to construct a human muscle group load distribution map;

[0078] First, a wearable sensor network is used to collect various physiological parameters of firefighters in real time during firefighting and rescue operations, including heart rate changes, body temperature distribution, electromyographic activity, joint movement angles, and other multi-dimensional data. Simultaneously, environmental monitoring equipment is deployed to collect environmental parameters such as temperature gradients, humidity levels, harmful gas concentrations, and heat radiation intensity at the fire scene. Deep learning processing is applied to the acquired physiological data of the firefighters to identify the activity patterns and stress characteristics of different muscle groups during operations. A muscle group partitioning algorithm is used to divide the human body into multiple functional muscle group regions, and stress analysis and heat map display technology are used to analyze the load intensity and distribution of each muscle group region. Finally, a three-dimensional visualized human muscle group load distribution map is constructed using multimodal data fusion technology. This human muscle group load distribution map, presented as a heat map, visually displays the changes in load intensity in different areas of the human body during firefighting operations, providing a precise basis for subsequent load reduction design.

[0079] Step S2: Identify local load reduction areas in the human muscle group load distribution map and reconstruct a holographic human body model to build a holographic human body load model;

[0080] Specifically, based on the human muscle group load distribution map, a clustering analysis algorithm is used to identify areas with high load concentration and mark them as potential key points for load reduction. Edge detection and region segmentation techniques are used to accurately delineate the boundaries of the regions corresponding to these key points, obtaining a set of potential key load reduction regions. Spatial correlation analysis is performed on this set of potential key load reduction regions to calculate the interaction strength and influence range between different regions, identifying the set of key load reduction regions requiring focused load reduction and constructing a regional collaborative relationship network. Based on deep neural network technology, structural sizing quantitative analysis is performed on pre-acquired protection requirement data to balance protection performance and load reduction requirements. Holographic modeling technology is used, combined with the human muscle group load distribution map and the set of key load reduction regions, to reconstruct a three-dimensional space, generating a high-precision holographic model of human load. This holographic model not only includes static load distribution information but also integrates dynamic load transmission paths, providing a complete digital twin foundation for subsequent dynamic simulations.

[0081] Step S3: Based on the combat environment data, perform dynamic combat simulation processing on the holographic model of human body load and fit the human body load distribution to generate a combat activity load network.

[0082] Specifically, the collected combat environment data is input as parameters into the human body load holographic model. A physics engine simulates the limb movements and muscle coordination of firefighters under different combat environments, deeply analyzing the acquired dynamic load information and performing feature extraction and pattern recognition. This involves analyzing characteristic parameters such as the speed, frequency, and amplitude of firefighters' combat actions. Based on graph theory and network analysis methods, the dynamic combat simulation data generated during the physics engine simulation is transformed into multiple combat activity load paths. Each path represents a load transfer chain under a typical combat action sequence. A nonlinear optimization algorithm is used to fit and fuse multiple load paths, constructing a complete combat activity load network. This network expresses the distribution and transfer relationships of the firefighter's whole-body load in a graph structure, where nodes represent load accumulation points, edges represent load transfer paths, and weights represent load intensity, providing a precise basis for material configuration optimization based on load distribution.

[0083] Step S4: Simulate the high-temperature fire environment of the human body load holographic model to obtain dynamic thermodynamic simulation data; perform flame retardant performance requirement analysis on the dynamic thermodynamic simulation data to generate protective performance requirement data.

[0084] Specifically, based on computational fluid dynamics and heat conduction theory, a holographic model of human body load is used to simulate thermal exposure under high-temperature fire conditions. This allows for an in-depth assessment of the thermal exposure risk of the entire holographic model of human body load. Infrared thermal imaging analysis is used to accurately identify thermal hazard exposure areas of various parts of the human body, generating a distribution map of thermal hazard exposure risk areas. Heat conduction paths are traced and probabilities are calculated for each thermal hazard exposure area to quantify the thermal protection requirement level for different areas. This protection requirement level can quantitatively characterize the degree of thermal protection required for various parts of the human body. Multiphysics coupling simulation technology is used to simulate the heat exchange process between the human body and protective clothing under high-temperature fire conditions, obtaining dynamic thermodynamic simulation data. Combined with a human physiological model, the thermophysiological response characteristics of firefighters under high-temperature conditions are analyzed, including body temperature regulation, heat stress response, and heat dissipation requirements. Based on the dynamic thermodynamic simulation data and the human body's heat dissipation requirements, multi-objective optimization analysis is performed to balance flame-retardant protection performance and breathable heat dissipation performance, ultimately forming regional and hierarchical protection performance requirement data.

[0085] Step S5: Analyze the protective material adaptation of the combat activity load network using the protective performance requirement data, and optimize the material structure gradient combination to obtain a material layer configuration scheme.

[0086] Specifically, a material performance database is constructed, containing physical, chemical, and mechanical properties of various flame-retardant fabrics, thermal insulation materials, and functional fabrics. Protective performance requirements data are spatially mapped to the operational load network to identify the comprehensive performance requirements of each region. Through in-depth analysis of material compatibility data, various indicators describing the matching degree between materials and protective requirements are extracted, including material characteristics across multiple dimensions such as protective performance, breathability, flexibility, and weight. Based on machine learning algorithms, the material database is intelligently matched and filtered to generate the optimal material candidate set for each region, forming material performance matching data. Based on the material performance matching data, an optimization method combining genetic algorithms and simulated annealing algorithms is used to predict and simulate the performance of different material combinations in protective clothing, identifying the optimal material structure combination path. Based on the optimal combination path, precise material allocation is performed for each region in the operational load network, forming a differentiated and gradient material layering configuration scheme to achieve the design goal of "focused protection for critical parts and lightweighting for non-critical parts."

[0087] Step S6: Conduct a multi-dimensional comprehensive performance evaluation of the material layering configuration scheme and verify the lightweight index to generate the final fire extinguishing protective clothing design scheme and traceable data archive.

[0088] Specifically, based on thermal protection performance testing standards, the thermal protection performance of the material layering configuration scheme is quantified, including tests on heat conduction, heat radiation, and flame retardancy, generating a protection performance evaluation value. Using a pre-constructed three-dimensional human body model, various typical movements are simulated while firefighters wear the design scheme to assess joint mobility and flexibility, generating a flexibility evaluation value. By precisely calculating the weight of each material and the structural weight, and comparing it with traditional protective clothing, the weight reduction ratio is calculated, generating a lightweight index value. Using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, a multi-dimensional comprehensive performance evaluation of the protection performance evaluation value, flexibility evaluation value, and lightweight index value is conducted, resulting in a comprehensive performance score and optimization suggestions. Based on the evaluation results, local adjustments and optimizations are made to form the final fire-fighting protective clothing design scheme, including structural design drawings, material configuration tables, and manufacturing process parameters. A unique identification code based on blockchain technology is established for the final design scheme to record information such as material performance parameters, structural design data, test verification results and expected service life of the design scheme, and to build a complete traceable data archive. The traceable data archive is connected to the Internet of Things information platform to realize digital management of "one item, one file", real-time monitoring of "status can be checked" and maintenance early warning functions of "automatic reminder", thereby improving the efficiency of the whole life cycle management of protective equipment.

[0089] It should be further explained that, in the specific implementation process, step S1 includes the following steps:

[0090] Step S11: Obtain firefighters' physiological data and fire scene combat environment data;

[0091] Step S12: Extract multi-dimensional features from the firefighters' human physiological data to generate human physiological feature data;

[0092] Step S13: Perform muscle group partitioning on human physiological characteristic data to obtain muscle group partitioning data;

[0093] Step S14: Calculate the load intensity of firefighters' human physiological data based on muscle group zoning data, and construct a human muscle group load distribution map.

[0094] In this embodiment, a high-precision wearable sensor network is configured to collect various physiological parameters of firefighters in simulated or actual firefighting environments in real time, obtaining corresponding firefighter physiological data, including core indicators such as heart rate change curves, dynamic body temperature distribution, electromyographic activity signals, and joint range of motion. Simultaneously, environmental monitoring equipment is deployed to acquire operational environment data such as temperature gradients, humidity levels, harmful gas concentration distribution, and radiant heat intensity at the fire scene. The firefighter physiological data and environmental data are time-aligned and spatially matched to form a complete data acquisition matrix. Deep learning algorithms are then applied to the collected firefighter physiological data for feature extraction, identifying key physiological indicators such as heart rate change patterns, body temperature distribution characteristics, and electromyographic signal spectral characteristics. A database of individual physiological characteristics of firefighters was constructed, which comprehensively reflects the physiological response characteristics of firefighters in the combat environment, providing data support for subsequent muscle group zoning. Electromyography (EMG) analysis technology was used to accurately identify human muscle activity. Combined with anatomical knowledge, the human body was divided into multiple functional muscle group regions, such as the trunk core region, the upper limb active region, and the lower limb support region. The identified muscle group zoning information was labeled and stored to provide targeted data support for subsequent load analysis. The load intensity of each muscle group region during firefighting operations was calculated, including indicators such as muscle contraction intensity, duration, and fatigue level. A human muscle group load distribution map was constructed, which visually displays the changes in load intensity of various regions of the human body during firefighting operations in the form of a heat map.

[0095] In this embodiment, step S14 specifically involves the following steps:

[0096] Based on muscle group partitioning data, muscle activity intensity is calculated from firefighters' human physiological data, and muscle group load intensity values ​​are extracted.

[0097] Time series analysis of muscle group load intensity values ​​was performed to obtain load intensity variation curves;

[0098] Peak values ​​are identified in the load intensity variation curve to determine the load concentration area.

[0099] By analyzing the joint movement angles of firefighters' human physiological data, the range of joint movement data was obtained.

[0100] The joint range of motion data is analyzed to identify the frequency of joint movement in order to obtain joint movement frequency characteristics.

[0101] Based on the joint activity frequency characteristics, load distribution is fitted to the load concentration area to construct a human muscle group load distribution map.

[0102] In this embodiment, by analyzing the amplitude, frequency, and other characteristics of electromyographic signal data, the activity intensity of each muscle group region is calculated and quantified into a muscle group load intensity value. The muscle group load intensity values ​​are arranged in chronological order to form time series data. The change pattern of muscle group load intensity values ​​over time is analyzed to generate a complete load intensity change curve. A peak detection algorithm is used to identify high-value regions in the load intensity change curve and mark key areas of load concentration to ensure coverage of all significant load points. Using joint angle sensor data, the range of motion of each joint during the movement process is calculated, including the maximum angle, minimum angle, and angle change amplitude. The joint range of motion data is statistically analyzed to form a structured joint activity feature set. The frequency characteristics of joint activity are analyzed, including the number of activities, repetition patterns, and rhythm characteristics, to generate joint activity frequency feature data. Combining the load concentration areas and joint activity characteristics, a nonlinear fitting algorithm is used to construct a complete human muscle group load distribution map. This map displays the load distribution of various regions of the human body in the form of a three-dimensional heat map, supports dynamic viewing and interactive analysis, and is regularly updated and optimized to ensure that it accurately reflects the latest muscle group load status.

[0103] It should be further explained that, in the specific implementation process, step S2 includes the following steps:

[0104] Step S21: Identify high-load areas based on the human muscle group load distribution map to obtain data on key load reduction areas;

[0105] Step S22: Perform spatial correlation analysis on the data of key areas for burden reduction to obtain a regional collaborative relationship network;

[0106] Step S23: Perform quantitative analysis of protection requirements on the regional collaborative relationship network to generate protection requirement data;

[0107] Step S24: Based on the protection requirement data, perform quantitative analysis of the burden reduction optimization of the key burden reduction area data to generate burden reduction optimization parameters;

[0108] Step S25: Based on the load reduction optimization parameters, the human muscle group load distribution map is holographically reconstructed to construct a human load holographic model.

[0109] In this embodiment, based on the human muscle group load distribution map, a clustering analysis algorithm is used to identify areas where the load intensity exceeds a threshold. These high-load areas are marked as key load reduction areas. The area, average load intensity, and load distribution characteristics of each key load reduction area are calculated to form a complete dataset of key load reduction areas. The spatial relationship and functional connectivity between key load reduction areas are analyzed to identify mutually influencing area groups. A collaborative relationship model between areas is established, and the collaborative strength and influence mode between areas are quantified to construct a complete regional collaborative relationship network. The protection requirements of each load reduction area are analyzed, including multi-dimensional requirements such as fire protection, heat protection, waterproofing, and puncture resistance. The protection requirement level of each area is quantified, and regional protection requirement data is generated. Based on the protection requirements and the characteristics of the load reduction areas, a load reduction optimization model is established to calculate the key parameters of the best load reduction scheme, including material thickness, structural design, and functional configuration. The load reduction optimization parameters are combined with the human muscle group load distribution map, and holographic modeling technology is used to construct a three-dimensional interactive human load holographic model. This model not only displays the static load distribution but also simulates the dynamic load change process.

[0110] It should be further explained that, in the specific implementation process, step S3 includes the following steps:

[0111] Step S31: Input the combat environment data into the human body load holographic model to conduct dynamic combat simulation in order to obtain combat dynamic load information;

[0112] Step S32: Perform combat action characteristic analysis on the combat dynamic load information to generate combat action characteristic data;

[0113] Step S33: Extract load transfer paths from the combat action feature data to generate multiple load transfer paths;

[0114] Step S34: Perform path fusion optimization on multiple load delivery paths to generate an operational load network.

[0115] In this embodiment, the collected combat environment data is imported into a human load holographic model. During the data import process, it is crucial to ensure data format compatibility with the model. Through a physics engine and biomechanical model, the process of firefighters performing various combat actions under different combat environments is simulated, and key parameter changes during the simulation are recorded to generate dynamic combat load information, including load distribution and intensity changes. The combat action characteristics within the dynamic combat load information are analyzed, and key parameters such as action speed, frequency, and amplitude are extracted to identify typical combat action patterns, such as climbing, carrying, and firefighting. The extracted combat action features are then organized into a structured dataset to obtain corresponding combat action feature data for subsequent processing. Based on combat action characteristic data, the system analyzes and utilizes data to track the transmission patterns of load across different parts of the human body, identifying load generation points, transmission paths, and aggregation points. It generates multiple complete load transmission paths describing load transmission, covering various typical combat actions. These paths are then comprehensively analyzed and optimized to identify repetitive paths and key nodes. A nonlinear optimization algorithm is applied to merge these paths into a complete combat activity load network. This network presents the distribution and transmission relationships of the firefighter's entire body load in a graph structure. The effectiveness and completeness of the combat activity load network are verified, and simulation tests ensure that it accurately reflects the load distribution and transmission characteristics in a real combat environment.

[0116] The specific implementation steps of step S32 are as follows:

[0117] Calculate the speed of action based on the dynamic load information of human combat personnel to generate combat action speed parameters;

[0118] Extracting key active joints based on dynamic load information of human combat;

[0119] The activity frequency of key joints is statistically analyzed to generate the activity frequency between joints;

[0120] The force direction is identified from the dynamic load information of the human combat system to generate the direction of force transmission;

[0121] The motion trend analysis of the direction of force transmission is performed based on the activity frequency between joints to generate the trend of changes in combat actions;

[0122] Motion characteristic analysis is performed on the speed parameters and changing trends of combat actions to generate combat motion characteristic data.

[0123] Specifically, the dynamic load information of human combat is used to calculate the speed of movement and generate combat movement speed parameters. The speed of movement is calculated by analyzing the rate of change of joint position over time to obtain the angular velocity and linear velocity parameters of each joint.

[0124] Using kinematic analysis, joints with high activity frequency and large range of motion in combat actions, such as the shoulder joint, knee joint, and waist, are identified and marked as key active joints. Through time-frequency analysis technology, the activity frequency and rhythm of each key active joint under different simulated combat scenarios are obtained to generate the activity frequency between joints.

[0125] Inverse dynamics analysis is performed on the dynamic load information of the human body in combat, and the direction and magnitude of the force on each part of the human body are identified based on the analysis results to generate the direction of force transmission. Based on the activity frequency and force transmission direction between joints, machine learning algorithms are used to predict the trend of combat action changes, identify the conversion law and load transfer mode between different combat actions to generate the trend of combat action changes, and perform motion characteristic analysis on the speed parameters of combat actions and the trend of combat action changes to generate combat motion characteristic data.

[0126] It should be further explained that, in the specific implementation process, step S4 includes the following steps:

[0127] Step S41: Simulate the high-temperature fire environment using the holographic model of human body load to obtain a distribution map of the thermal hazard exposure area;

[0128] Step S42: Assess the thermal hazard level of the hazard exposure area distribution map to generate the thermal protection requirement level for each area;

[0129] Step S43: Analyze the heat conduction path based on the thermal protection requirements of each region to obtain heat conduction simulation data;

[0130] Step S44: Analyze the human body heat dissipation requirements based on the combat environment data to obtain human body heat dissipation requirement data;

[0131] Step S45: Optimize the balance of protective performance based on heat conduction simulation data and human body heat dissipation demand data to generate protective performance demand data.

[0132] In this embodiment, based on the principles of computational fluid dynamics, a holographic model of human body load is used to simulate thermal exposure in a high-temperature fire environment. The simulation measures the thermal impact on various parts of the human body under different temperature gradients and thermal radiation intensities. Infrared thermal imaging analysis is used to accurately identify thermal hazard exposure areas of various parts of the human body, generating a distribution map of these areas and quantifying the severity of each exposure. By considering factors such as temperature, duration, and heat flux density, the required thermal protection level for each exposure area is assessed, forming a regional thermal protection requirement dataset. The path and mode of heat transfer from the fire environment to the human body are analyzed, identifying the main heat conduction, heat radiation, and heat convection paths. Multiphysics analysis is then applied. Field coupling simulation technology simulates the heat transfer process in the various layers of fire-fighting protective clothing under high-temperature conditions, generating complete heat conduction simulation data. It analyzes the physiological heat dissipation needs of firefighters in high-temperature environments and, by considering factors such as heat generation, sweat evaporation, and respiration, combined with human thermal comfort theory, quantifies the heat dissipation needs of different body parts. The heat conduction simulation data and human heat dissipation needs are comprehensively analyzed to establish a multi-objective optimization model, balancing fire resistance and breathability. This generates regional and hierarchical protective performance requirement data, which accurately describes the required flame retardancy rating, thermal resistance, breathability, and moisture wicking performance for each area.

[0133] It should be further explained that, in the specific implementation process, step S5 includes the following steps:

[0134] Step S51: Construct a database of protective material properties and collect data on the physicochemical properties of the materials;

[0135] Step S52: Spatially map the protection performance requirement data with the operational activity load network to identify the characteristics of the comprehensive performance requirements;

[0136] Step S53: Intelligently match the material performance database based on comprehensive performance requirement characteristics to generate a material candidate set;

[0137] Step S54: Perform multi-dimensional performance evaluation on the candidate material set to obtain material performance stratification data;

[0138] Step S55: Based on the material performance stratification data, perform gradient combination optimization on the combat activity load network to form a material stratification configuration scheme.

[0139] In this embodiment, detailed performance parameters of various fire-resistant fabrics, heat-insulating materials, and functional fabrics are collected to establish a structured material performance database. This database contains comprehensive data on the physical properties (such as density and thickness), chemical properties (such as flame retardancy and chemical corrosion resistance), and mechanical properties (such as tensile strength and abrasion resistance). Spatial correlation analysis is performed between the protective performance requirement data and the operational load network to identify the comprehensive demand characteristics of each muscle group region in terms of load distribution and thermal protection, forming a regional performance requirement feature map. Machine learning algorithms are then applied to intelligently filter and match the material database, generating data for each muscle group region. The optimal material candidate set ensures that the candidate materials can meet the protection and weight reduction requirements of the region. The material candidate set is evaluated from multiple dimensions, and quantitative scores are given from multiple aspects such as protective performance, breathability, flexibility, weight and cost. Material performance stratification data is generated, which includes the evaluation results of each candidate material in different performance dimensions. Based on genetic algorithm and simulated annealing algorithm, the material structure is optimized by gradient combination. While ensuring protective performance, the weight reduction and comfort are maximized, forming a differentiated and tiered material stratification configuration scheme. The material stratification configuration scheme specifies in detail the material type, thickness and combination method to be used in each region.

[0140] It should be further explained that, in the specific implementation process, step S6 includes the following steps:

[0141] Step S61: Conduct a quantitative test on the thermal protection performance of the material layering configuration scheme to generate a protection performance evaluation value;

[0142] Step S62: Construct a digital human body model based on the material layering configuration scheme, perform motion flexibility analysis, and generate flexibility assessment values;

[0143] Step S63: Perform precise weight calculations on the material layering configuration scheme to generate lightweight index values;

[0144] Step S64: Conduct a multi-dimensional comprehensive performance evaluation of the protective performance evaluation value, flexibility evaluation value, and lightweight index value to generate the final fire-fighting protective clothing design scheme and traceable data archive.

[0145] In this embodiment, based on national standards and industry specifications, the thermal protection performance of the material layering configuration scheme is tested, including heat conduction testing, heat radiation testing, and flame retardant performance testing. The protective performance indicators of each muscle group area are quantified, generating a complete protective performance evaluation report. The material layering configuration scheme is applied to a three-dimensional human body model, simulating firefighters wearing the protective suit performing various typical actions, such as climbing, crawling, and carrying. Joint range of motion and movement flexibility are analyzed, generating flexibility evaluation data, including indicators such as the degree of joint movement restriction and movement completion time. By accurately calculating the weight of the materials and structural weight of each area, the overall protective suit is evaluated. The protective suit undergoes a weight assessment, is compared with traditional protective suits, calculates the percentage of weight reduction, generates a lightweight index report, establishes a multi-dimensional evaluation model, and weights protective performance, flexibility, and lightweight indexes to obtain a comprehensive performance score. Based on the evaluation results, local optimizations and adjustments are made to form the final design scheme for the fire extinguishing protective suit, including structural design drawings, material configuration tables, and manufacturing process parameters. A traceable data archive based on blockchain technology is established for the design scheme, recording complete information such as material performance parameters, structural design data, and test verification results, realizing digital management and full life cycle traceability of "one piece, one file".

[0146] This invention achieves a precise match between protective clothing design and the physiological characteristics and operational needs of firefighters through scientific data acquisition and analysis techniques; based on dynamic combat simulation and thermodynamic analysis, it ensures that the protective performance meets actual requirements; by adopting material gradient combination optimization technology, it achieves lightweight design while ensuring protective performance, reducing the burden on firefighters and improving operational efficiency; by introducing traceable data archives and Internet of Things technology, it realizes quality tracking and management of the protective clothing throughout its entire life cycle, providing firefighters with more reliable and intelligent personal protective equipment, effectively improving their safety protection capabilities and operational effectiveness in complex fire environments.

[0147] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0148] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0149] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0150] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0151] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0152] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0153] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0154] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A traceable fire protection suit lightweight design method, characterized in that, The method comprises the following steps: Step S1: obtaining firefighter physiological data and combat environment data; Step S2: identifying local load reduction areas of the human muscle load distribution atlas and reconstructing a holographic human body model to construct a human load holographic model; Step S3: performing dynamic combat simulation processing on the human load holographic model based on the combat environment data, and fitting the human load distribution to generate a combat activity load network; Step S4: simulating the high-temperature fire field environment of the human load holographic model to obtain dynamic thermodynamic simulation data; and performing flame retardant performance demand analysis on the dynamic thermodynamic simulation data to generate protection performance demand data; Step S5: performing protection material adaptation analysis on the combat activity load network using the protection performance demand data, and performing material structure gradient combination optimization to obtain a material layering configuration scheme; Step S6: performing multi-dimensional performance comprehensive evaluation on the material layering configuration scheme, and verifying lightweight indicators to generate an ultimate fire extinguishing protective clothing design scheme and traceable data archives. The specific implementation steps of step S1 include:

2. The traceable fire protection garment lightweight design method according to claim 1, wherein, Step S11: obtaining firefighter physiological data and fire field combat environment data; Step S12: performing multi-dimensional feature extraction on the firefighter physiological data to generate human physiological feature data; Step S13: performing muscle group partition identification on the human physiological feature data to obtain muscle group partition data; Step S14: performing load intensity calculation on the firefighter physiological data based on the muscle group partition data to construct a human muscle load distribution atlas. The specific implementation steps of step S14 include:

3. The traceable fire protection garment lightweight design method according to claim 2, wherein, Performing muscle activity intensity calculation on the firefighter physiological data based on the muscle group partition data to extract muscle load intensity values; Performing time series analysis on the muscle load intensity values to obtain a load intensity change curve; Performing peak value identification on the load intensity change curve to obtain a load concentration area; Performing joint activity angle analysis on the firefighter physiological data to obtain joint activity range data; Performing activity frequency identification on the joint activity range data to obtain joint activity frequency characteristics; Performing load distribution fitting on the load concentration area based on the joint activity frequency characteristics to construct a human muscle load distribution atlas. The specific implementation steps of step S2 include:

4. The traceable fire protection garment lightweight design method of claim 2, wherein, Step S21: identifying high load areas based on the human muscle load distribution atlas to obtain key load reduction area data; Step S22: performing spatial correlation analysis on the key load reduction area data to obtain a regional collaborative relationship network; Step S23: performing protection demand quantification analysis on the regional collaborative relationship network to generate protection demand data; Step S24: performing load reduction optimization quantitative analysis on the key load reduction area data based on the protection demand data to generate load reduction optimization parameters; Step S25: reconstructing the human muscle load distribution atlas based on the load reduction optimization parameters to construct a human load holographic model. The specific implementation steps of step S3 include:

5. The traceable fire protection garment lightweight design method according to claim 4, wherein, ​ Step S31: input the combat environment data into the human load holographic model, perform dynamic combat simulation to obtain combat dynamic load information; Step S32: analyze the combat action characteristics of the combat dynamic load information to generate combat action characteristic data; Step S33: extract the load transmission path of the combat action characteristic data to generate multiple load transmission paths; Step S34: optimize the path fusion of the multiple load transmission paths to generate a combat activity load network.

6. The traceable fire protection garment lightweight design method of claim 4, wherein, The specific steps of step S32 are: Calculate the action speed of the human combat dynamic load information to generate combat action speed parameters; Extract key activity joints based on human combat dynamic load information; Statistical analysis of the activity frequency of the key activity joints to generate the activity frequency between joints; Identify the force direction of the human combat dynamic load information to generate the mechanical transmission direction; Analyze the motion trend of the mechanical transmission direction according to the activity frequency between joints to generate the combat action change trend; Analyze the motion characteristics of the combat action speed parameters and the combat action change trend to generate combat motion characteristic data.

7. The traceable fire protection garment lightweight design method of claim 5, wherein, The specific implementation steps of step S4 include: Step S41: simulate the high-temperature fire environment of the human load holographic model to obtain a thermal hazard exposure area distribution map; Step S42: evaluate the thermal hazard level of the hazard exposure area distribution map to generate the thermal protection requirement level of each region; Step S43: analyze the heat conduction path based on the thermal protection requirement level of each region to obtain heat conduction simulation data; Step S44: analyze the human heat dissipation requirement of the combat environment data to obtain human heat dissipation requirement data; Step S45: balance and optimize the protection performance according to the heat conduction simulation data and the human heat dissipation requirement data to generate protection performance requirement data.

8. The traceable fire protection garment lightweight design method of claim 7, wherein, The specific implementation steps of step S5 include: Step S51: build a protection material performance database to collect material physical and chemical performance data; Step S52: spatially map the protection performance requirement data and the combat activity load network to identify the comprehensive performance requirement characteristics; Step S53: intelligently match the material performance database based on the comprehensive performance requirement characteristics to generate a material candidate set; Step S54: perform multi-dimensional performance evaluation on the material candidate set to obtain material performance hierarchical data; Step S55: perform gradient combination optimization on the combat activity load network based on the material performance hierarchical data to form a material hierarchical configuration scheme.

9. The traceable fire protection garment lightweight design method of claim 8, wherein, The specific implementation steps of step S6 include: Step S61: perform thermal protection performance quantification testing on the material hierarchical configuration scheme to generate protection performance evaluation values; Step S62: build a digital human model based on the material hierarchical configuration scheme to perform motion flexibility analysis and generate flexibility evaluation values; Step S63: perform accurate weight calculation on the material hierarchical configuration scheme to generate lightweight index values; Step S64: perform multi-dimensional performance comprehensive evaluation on the protection performance evaluation values, flexibility evaluation values, and lightweight index values to generate a final fire extinguishing protective clothing design scheme and traceable data archive.

10. The traceable fire protection garment lightweight design method of claim 9, wherein, The traceable data file is a unique identification code based on a blockchain technology, which records material performance parameters, structural design data, test verification results and expected service life of the design scheme.

Images