Structural performance fire-resistant design system and method considering personnel safety evacuation

Abstract

The invention relates to the technical field of fire-resistant design, and discloses a structural performance fire-resistant design system and method considering personnel safety evacuation, and the system comprises a fire simulation module which is used for dynamically simulating a fire development process through a fire dynamics tool; the structure performance analysis module is used for evaluating the structure fire resistance performance under the fire hazard condition; the personnel evacuation dynamic simulation module is used for simulating personnel evacuation behaviors in a fire environment; the collaborative optimization design module is used for balancing the fire resistance and evacuation efficiency of the structure; and the real-time monitoring and early warning module dynamically adjusts fire simulation and evacuation paths and is linked with a fire fighting system to implement emergency response. By integrating fire simulation, structural performance analysis and personnel evacuation dynamic simulation modules, the limitation of independent operation of traditional fire simulation and structural analysis is broken through. Structural analysis and evacuation simulation are driven by utilizing a dynamic fire temperature field and smoke data, so that comprehensive evaluation of a building structure, fire development and personnel behaviors in a fire is realized, and a basis is provided for collaborative design.

Description

Technical Field

[0001] The present invention relates to the technical field of fire-resistant design, and in particular to a structural performance-based fire-resistant design system and method that considers safe evacuation of personnel. Background Art

[0002] Current building fire resistance design methods are mainly based on standardized fire scenarios and traditional structural analysis methods, but these methods have certain limitations, especially when facing complex and dynamic fire situations, they cannot fully consider the interaction between structural performance and evacuation of personnel. The following are some problems existing in the current background technology:

[0003] (1) Traditional fire simulation and structural analysis are out of touch

[0004] Traditional fire-resistant design mostly relies on a single fire simulation or structural analysis model. Fire simulation focuses on the spread of fire and temperature distribution, while structural performance analysis focuses on the impact of high temperature on building materials. The two are usually conducted independently, lacking a comprehensive assessment of the structure and personnel behavior under the influence of fire. As a result, it is difficult to accurately assess the interaction between the building structure and the personnel evacuation process during a fire.

[0005] (2) Static assumptions and delayed emergency response

[0006] Most existing designs are modeled based on hypothetical fire scenarios (such as fire source location, fire load, etc.), assuming that the fire reaches a stable state at a certain moment or within a certain period of time. However, the spread and development of fires are dynamic, and the evacuation behavior of personnel is also affected by the progress of the fire. Traditional designs often ignore the real-time impact of factors such as temperature and smoke on personnel evacuation decisions during the development of fires, resulting in large deviations in the prediction of evacuation time and safety risks.

[0007] (3) Insufficient evacuation simulation

[0008] Most traditional evacuation simulation models assume that the evacuation process is uniform, ignoring the complex behavioral factors in a fire environment, such as the impact of panic, smoke, temperature changes, etc. on personnel decision-making. In addition, existing evacuation models mostly use simple statistical methods or assumptions based on experience, which cannot accurately simulate the dynamic behavior of personnel under fire.

[0009] (4) Lack of multi-objective optimization design

[0010] In fire safety design, there is a contradiction between structural performance and personnel evacuation. Increasing the fire resistance time of the structure may lead to narrowing of the evacuation passage, while optimizing the evacuation path may affect the fire resistance design of the structure. Most of the current fire resistance design methods focus on one aspect, lacking the coordinated optimization of structural and personnel safety. The existing design methods cannot effectively balance on the basis of multiple objectives and cannot achieve the best combination of structure and evacuation efficiency.

[0011] (5) Delayed emergency response and monitoring

[0012] Most fire simulation and fire resistance design tools lack linkage feedback with the actual fire environment after the design phase is completed, and cannot obtain fire data in real time for dynamic adjustment. When a fire occurs in a building, traditional design cannot provide timely emergency response and early warning, resulting in delayed response and inability to effectively coordinate fire response and personnel evacuation processes. Summary of the invention

[0013] In view of the shortcomings of the prior art, the present invention provides a structural performance-based fire-resistant design system and method that takes into account the safe evacuation of personnel, which solves the problems in the prior art such as the disconnection between fire simulation and structural analysis, delayed emergency response due to static assumptions, insufficient accuracy of personnel evacuation simulation, lack of multi-objective optimization, and delayed emergency response and monitoring.

[0014] To achieve the above objectives, the present invention is implemented through the following technical solutions: a structural performance-based fire resistance design system considering the safe evacuation of personnel, comprising:

[0015] The fire simulation module is used to dynamically simulate the fire development process through fire dynamics tools, combining simplified fire models and machine learning methods to generate fire temperature fields, smoke distribution and fire spread paths;

[0016] Structural performance analysis module, based on dynamic material performance database and thermal-mechanical coupled finite element analysis, evaluates the fire resistance performance of structures under fire conditions, including bearing capacity, deformation and buckling failure modes;

[0017] The dynamic simulation module for personnel evacuation uses a social force model to integrate physiological and psychological behavior characteristics, simulates personnel evacuation behavior in a fire environment, and dynamically optimizes the evacuation path in combination with real-time fire data;

[0018] The collaborative optimization design module uses a multi-objective optimization algorithm to integrate the results of fire simulation, structural performance analysis, and evacuation simulation, balance the structural fire resistance and evacuation efficiency, and generate a design solution;

[0019] The real-time monitoring and early warning module collects fire data in real time by installing sensors, dynamically adjusts fire simulation and evacuation routes, and links the fire protection system to implement emergency response.

[0020] Preferably, the fire simulation module comprises:

[0021] Use CFD tools to perform high-precision dynamic simulation of fire temperature field and smoke diffusion;

[0022] Introducing a meta-model method based on machine learning to quickly fit the laws of fire heat propagation and smoke diffusion through historical fire data;

[0023] Optimize the grid division strategy, use high-precision calculations in key areas, and use simplified calculations in other areas.

[0024] Preferably, the structural performance analysis module:

[0025] Dynamically call the high temperature performance parameters of building materials, including the curves of steel yield strength and concrete compressive strength changing with temperature;

[0026] Combined with the time-temperature curve of fire simulation, finite element tools are used to perform thermal-mechanical coupling analysis to evaluate the bearing capacity, deformation and failure mode of structural components.

[0027] Preferably, the personnel evacuation dynamic simulation module:

[0028] Based on the social force model, individual behavior and group behavior are simulated, wherein the individual behavior includes obstacle avoidance and path selection, and the group behavior includes congestion and flow distribution;

[0029] Considering the influence of physiological factors and psychological factors on evacuation behavior, the physiological factors include the influence of high temperature on walking speed, and the psychological factors include panic index;

[0030] Combine fire temperature field and smoke distribution data in real time to dynamically adjust evacuation routes and avoid high-risk areas.

[0031] Preferably, the collaborative optimization design module is implemented by the following steps:

[0032] Application of multi-objective optimization algorithms:

[0033] Genetic algorithms are used to comprehensively optimize the structural fire resistance and evacuation efficiency, taking maximizing the structural fire resistance time and minimizing the evacuation time as the objective function, and balancing the resource allocation weights between the two.

[0034] Variable settings of design parameters:

[0035] The material selection, fireproofing layer thickness, cross-sectional dimensions in the structural parameters and the channel width, exit quantity and location in the evacuation parameters are used as optimization variables to ensure the integrity and feasibility of the scheme. The applicability of the design scheme is verified by combining fire simulation and evacuation path data.

[0036] Generation and output of comprehensive optimization solutions:

[0037] Based on the optimization calculation results, a comprehensive optimal solution including fire-resistant structure layout, evacuation route planning and channel design is generated, and a fire resistance design report and evacuation efficiency evaluation report are output to provide a decision-making basis for the overall performance-based fire resistance design of the building.

[0038] Preferably, the real-time monitoring and early warning module:

[0039] Deploy temperature sensors, smoke sensors and infrared monitoring equipment to collect fire scene data in real time;

[0040] Dynamically update fire simulation and evacuation route planning based on collected data;

[0041] The linkage fire protection system triggers the emergency devices for real-time intervention, and the emergency devices include a water spray system and a smoke exhaust device.

[0042] A structural performance-based fire resistance design method considering safe evacuation of personnel includes the following steps:

[0043] S1. Fire scene modeling and initial condition setting:

[0044] Construct a geometric model of the building to determine the location of the fire source, combustion conditions and related fire initial parameters;

[0045] Import the performance parameters of building materials under high temperature conditions and the distribution status of personnel to provide basic input for subsequent analysis;

[0046] S2. Fire simulation and key data generation:

[0047] Based on the set fire scenario, the fire development process is dynamically simulated to obtain the temperature distribution, smoke diffusion and fire spread path;

[0048] The simulated temperature and smoke data are used as input for performance analysis of building structures and dynamic adjustment of evacuation behavior of personnel;

[0049] S3. Structural performance evaluation and optimization suggestion generation:

[0050] Based on the fire simulation results, evaluate the bearing capacity, deformation law and fire resistance performance of the building structure under fire environment;

[0051] Generate failure mode and fire resistance time analysis results of key components and put forward targeted design optimization suggestions;

[0052] S4. Personnel evacuation behavior simulation and path optimization:

[0053] Combined with the dynamic data of fire development, analyze the evacuation behavior of personnel in a fire environment and dynamically adjust the evacuation route to avoid dangerous areas;

[0054] Calculate evacuation time, personnel flow distribution and congestion location to provide support for the design optimization of evacuation channels;

[0055] S5. Collaborative optimization design and comprehensive solution output:

[0056] The results of fire simulation, structural analysis and evacuation are integrated to balance the structural fire resistance and evacuation efficiency through multi-objective optimization method.

[0057] Generate the best design solution, including fire-resistant layout adjustment, fire-proof material optimization and evacuation route improvement, and provide design report and evaluation analysis;

[0058] S6. Real-time monitoring and dynamic feedback optimization:

[0059] Use real-time monitoring equipment to collect fire temperature and smoke concentration data, and feed the real-time data into fire development simulation and evacuation route adjustment;

[0060] Dynamically optimize fire control strategies and evacuation plans based on the latest data to ensure timely and effective fire emergency response.

[0061] Preferably, the optimal design solution in S5 includes:

[0062] Optimize structural layout and fireproof material selection for high temperature areas;

[0063] Dynamically adjust the design of evacuation passages and the distribution of exits;

[0064] Provide building performance-based fire resistance design report and evacuation efficiency assessment report.

[0065] Preferably, the dynamic adjustment includes:

[0066] Update the fire temperature field and smoke concentration distribution in real time based on sensor data;

[0067] Dynamically adjust evacuation routes to avoid high temperature and high smoke areas;

[0068] The fire protection system is linked to initiate emergency measures, including water spraying, smoke exhaust and personnel evacuation instructions.

[0069] Preferably, in step 6, the dynamic adjustment of the fire environment is achieved by the following steps:

[0070] Real-time fire data collection: deploy temperature sensors, smoke concentration sensors and infrared detection equipment to monitor the temperature, smoke distribution and fire spread trend of the fire scene in real time; input the collected data into the fire simulation process to dynamically update the temperature field, smoke concentration distribution and fire spread path;

[0071] Real-time update of structural performance and evacuation routes: Based on the real-time fire data collected, the temperature distribution and load-bearing capacity changes of key structural components are recalculated; evacuation route planning is dynamically adjusted to avoid high-temperature and high-smoke areas, and the traffic efficiency of personnel in the evacuation passage is optimized;

[0072] Emergency response and execution: trigger water spray and smoke exhaust fire fighting devices based on real-time fire data and updated evacuation routes; generate evacuation guidance strategies in real time, and notify personnel to adjust evacuation behavior through dynamic signs, voice or mobile information;

[0073] Iterative optimization and feedback: Continuously collect new data from the fire scene to further adjust fire development simulation, structural performance assessment and evacuation route planning; achieve continuous dynamic optimization during the fire emergency response process to improve evacuation efficiency and structural safety.

[0074] The present invention provides a structural performance-based fire resistance design system and method taking into account the safe evacuation of personnel.

[0075] It has the following beneficial effects:

[0076] 1. The present invention breaks the limitations of traditional fire simulation and structural analysis being independent of each other by integrating fire simulation module, structural performance analysis module and personnel evacuation dynamic simulation module. Through real-time data input of dynamic fire temperature field and smoke distribution, structural performance analysis and personnel evacuation simulation are directly driven, and a comprehensive evaluation of building structure, fire development and personnel behavior under fire conditions is achieved, providing a scientific basis for collaborative design and optimization during fire.

[0077] 2. Based on dynamic fire simulation and real-time monitoring technology, the present invention can update the temperature field and smoke concentration distribution as the fire develops, and dynamically adjust the evacuation path and optimize the emergency plan. By collecting real-time data through the sensor network and feeding it back to the design module, the limitations of static assumptions in traditional design are improved, and the real-time and reliability of fire response are significantly improved.

[0078] 3. This invention uses a social force model that integrates physiological and psychological behavioral characteristics, and combines the impact of environmental factors such as temperature and smoke on people's walking speed and evacuation path selection to accurately simulate the dynamic evacuation behavior of individuals and groups in a fire environment. Through this improvement, the shortcomings of ignoring the impact of panic and fire environment in traditional evacuation simulations are overcome, and the accuracy of evacuation time prediction and the scientific nature of strategy optimization are greatly improved.

[0079] 4. The present invention uses a collaborative optimization design module and a multi-objective optimization algorithm to comprehensively consider the balance between structural fire resistance and evacuation efficiency. For the conflicting objectives in fire (such as fire resistance time and evacuation efficiency), the present invention can generate multiple optimization solutions and achieve the best combination of structural safety and personnel evacuation efficiency through Pareto frontier analysis, filling the gap of single-objective optimization in existing methods.

[0080] 5. The present invention introduces a real-time monitoring and early warning module, combines the sensor network to collect temperature, smoke and other data, updates the fire simulation and evacuation route planning in real time, and simultaneously links emergency devices such as water spray and smoke exhaust to achieve dynamic optimization of the fire response process. By linking the design with the actual fire scene data, the defect of the traditional tools that the design stage is disconnected from the emergency response is overcome, and the emergency response capability in the fire environment is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 is a system architecture diagram of the present invention;

[0082] Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0085] Please see attached Figure 1 The embodiment of the present invention provides a structural performance-based fire resistance design system considering the safe evacuation of personnel, including:

[0086] The fire simulation module is used to dynamically simulate the fire development process through fire dynamics tools, combining simplified fire models and machine learning methods to generate fire temperature fields, smoke distribution and fire spread paths;

[0087] Structural performance analysis module, based on dynamic material performance database and thermal-mechanical coupled finite element analysis, evaluates the fire resistance performance of structures under fire conditions, including bearing capacity, deformation and buckling failure modes;

[0088] The dynamic simulation module for personnel evacuation uses a social force model to integrate physiological and psychological behavior characteristics, simulates personnel evacuation behavior in a fire environment, and dynamically optimizes the evacuation path in combination with real-time fire data;

[0089] The collaborative optimization design module uses a multi-objective optimization algorithm to integrate the results of fire simulation, structural performance analysis, and evacuation simulation, balance the structural fire resistance and evacuation efficiency, and generate a design solution;

[0090] The real-time monitoring and early warning module collects fire data in real time by installing sensors, dynamically adjusts fire simulation and evacuation routes, and links the fire protection system to implement emergency response.

[0091] In the present invention, the system includes four modules: fire simulation, structural performance analysis, dynamic simulation of personnel evacuation, and collaborative optimization design. Through data sharing and feedback mechanisms, the system works together to form a comprehensive performance-based fire-resistant design platform. The fire simulation module is based on fire dynamics tools, combined with simplified fire models and machine learning methods, dynamically simulates the fire development process, generates temperature fields, smoke distribution, and fire spread paths, and provides key data for subsequent analysis; the structural performance analysis module uses a dynamic material performance database and thermal-mechanical coupling finite element analysis to evaluate the bearing capacity, deformation, and failure mode of the building structure under fire conditions, and generates fire resistance optimization suggestions; the dynamic simulation module of personnel evacuation adopts a social force model, integrates physiological and psychological behavioral characteristics, simulates the evacuation behavior of personnel under fire conditions, and dynamically adjusts the evacuation path in combination with real-time fire data to improve evacuation efficiency and safety; the collaborative optimization design module integrates the results of fire simulation, structural analysis, and personnel evacuation through a multi-objective optimization algorithm, balances the structural fire resistance and evacuation efficiency, and generates a comprehensive design solution including layout optimization, material selection, and evacuation strategies. Through the close linkage and real-time response of each module, the system comprehensively improves the fire safety performance and emergency efficiency of the building.

[0092] The fire simulation module includes:

[0093] Use CFD tools to perform high-precision dynamic simulation of fire temperature field and smoke diffusion;

[0094] Introducing a meta-model method based on machine learning to quickly fit the laws of fire heat propagation and smoke diffusion through historical fire data;

[0095] Optimize the grid division strategy, use high-precision calculations in key areas, and use simplified calculations in other areas.

[0096] In the present invention, the fire simulation module, as the basic module of the system, provides key input data through dynamic simulation of fire temperature field, smoke diffusion and fire spread, and provides basic support for subsequent analysis and optimization. In this embodiment, the fire simulation module provides a high-precision, dynamically adaptable fire simulation solution based on CFD (computational fluid dynamics) tools, machine learning metamodel methods and grid optimization technology.

[0097] In this embodiment, the core goal of the fire simulation module is to generate accurate and dynamic fire temperature field, smoke distribution and fire spread path to support subsequent structural performance analysis and personnel evacuation simulation.

[0098] in:

[0099] 1. Use CFD tools to simulate fire temperature field and smoke diffusion

[0100] CFD tools (such as FDS) are widely used in fire dynamics simulation. In the present invention, the fire temperature field and smoke diffusion simulation adopts the joint solution of the unsteady heat conduction and mass conservation equations:

[0101]

[0102] Where ρ is the fluid density, c p is the specific heat capacity, T is the temperature, is the fluid velocity vector, k is the thermal conductivity, and Q is the heat source term released by the fire;

[0103] Specifically, this equation can be used to simulate the propagation of heat in space during a fire, combined with the smoke diffusion equation:

[0104]

[0105] Among them, Y i Indicates the mass fraction of flue gas components, D i is the diffusion coefficient, R i Indicates the mass rate of smoke produced by chemical reactions;

[0106] Through the above joint calculations, the temperature field and smoke diffusion data of fire development can be generated. These data can be used for performance evaluation of key areas (such as evacuation passages or load-bearing structures).

[0107] 2. Introducing a meta-model approach based on machine learning

[0108] As an option, in order to reduce the time complexity of CFD simulation calculation, the present invention introduces a meta-model method based on machine learning. Specifically, the method constructs a fitting model of temperature field and smoke distribution through historical fire data.

[0109] In one possible implementation, the meta-model fits the key parameters of the fire simulation through the following formula:

[0110]

[0111] Where T(x,y,z,t) is the fire temperature distribution, φ i (x, y, z, t) is a preset basis function (such as radial basis function or Gaussian kernel function), wi is the fitting weight, ∈ is the error term;

[0112] By training historical fire data through machine learning methods (such as support vector machines or neural networks), the temperature distribution and smoke diffusion laws of complex fire scenes can be quickly fitted. In actual operation, the fitting error of the metamodel is usually less than 5%, which can strike a balance between computational efficiency and accuracy.

[0113] 3. Optimization of mesh division strategy

[0114] In fire simulation, meshing has an important impact on computational efficiency and accuracy. In this invention, a non-uniform meshing strategy is adopted for fire scenes. High-precision fine mesh calculations are applied in key areas (such as fire source locations, evacuation passages, structural stress points, etc.), while simplified coarse mesh calculations are applied in other areas. The specific steps are as follows:

[0115] In the fire source and its surrounding areas, the grid size is set to 0.1-0.2 m to capture the heat conduction characteristics of the high-temperature area;

[0116] In non-critical areas farther away (such as external space or non-load-bearing structural areas), the grid size is set to 0.5-1.0m to reduce the computational burden.

[0117] It should be noted that the mesh optimization process uses an automated partitioning algorithm and combines it with multi-resolution computing technology to ensure the accuracy of calculations in key areas while reducing the global computational complexity.

[0118] 4. Output of simulation data

[0119] In some embodiments, the fire simulation module of the present invention generates the following output data through the above steps:

[0120] Fire temperature field at the time step;

[0121] Smoke concentration distribution;

[0122] Fire spread path and time series;

[0123] These data will be provided as input to the structural performance analysis module and the personnel evacuation dynamic simulation module;

[0124] In an extended implementation of the present invention, the fire simulation module can also be combined with airflow field analysis to evaluate the performance of the smoke exhaust system. For example, by adding the wind speed field simulation formula:

[0125]

[0126] Where p is the pressure, μ is the viscosity coefficient, For external forces (such as air flow provided by ventilation equipment).

[0127] Through the above expansion implementation, the air circulation environment in the evacuation channel can be further optimized and the evacuation efficiency can be improved.

[0128] Therefore, the fire simulation module of the present invention adopts a multi-dimensional technical means combining CFD tools, machine learning metamodels and grid optimization strategies, which not only ensures the accuracy of the simulation, but also significantly reduces the computing cost. By generating high-precision fire temperature field and smoke diffusion data, reliable input data is provided for other modules of the system (such as structural performance analysis and personnel evacuation simulation). The technical solution of the present invention is fully disclosed, which can provide a clear reproduction path for those skilled in the art and provide all-round technical support for fire safety design.

[0129] Structural performance analysis module:

[0130] Dynamically call the high temperature performance parameters of building materials, including the curves of steel yield strength and concrete compressive strength changing with temperature;

[0131] Combined with the time-temperature curve of fire simulation, finite element tools are used to perform thermal-mechanical coupling analysis to evaluate the bearing capacity, deformation and failure mode of structural components.

[0132] In this embodiment, the implementation method of the structural performance analysis module is

[0133] 1. Dynamically call the high temperature performance parameters of building materials

[0134] As an option, the structural performance analysis module of the present invention calls the physical performance parameters of steel and concrete under different temperature conditions through a dynamic material performance database.

[0135] (1.1) Changes in high temperature properties of steel:

[0136] Yield strength of steel σ y The empirical formula for the change of (T) with temperature is:

[0137] σ y (T) = σ y (20)·(1-α·T)

[0138] Among them, σ y (20) is the yield strength at room temperature (20 °C), α is the temperature effect coefficient, and T is the temperature;

[0139] (1.2) Changes in high temperature performance of concrete:

[0140] Concrete compressive strengthσ c The degradation of (T) can be described as:

[0141] σ c(T) = σ c (20) β(T)

[0142] Among them, σ c (20) compressive strength at room temperature (20 °C), β(T) is the temperature dependence coefficient;

[0143] 2. In this embodiment, a finite element analysis (FEA) tool (such as ANSYS, ABAQUS) is used to perform a coupled analysis of the thermal-mechanical performance of the structure under fire conditions.

[0144] (2.1) Heat conduction equation:

[0145] The temperature distribution in the structure evolves with time and satisfies the following governing equation:

[0146]

[0147] Where ρ is the material density, c p is the material specific heat capacity, T is the temperature, k is the thermal conductivity, and Q is the internal heat source term;

[0148] (2.2) Thermal expansion effect:

[0149] The temperature increase causes thermal expansion of the structure, and its strain expression is:

[0150] ε T =α T ΔT

[0151] Among them, ε T is the thermal strain, α T is the thermal expansion coefficient of the material, ΔT is the temperature change;

[0152] (2.3) Mechanical equilibrium equation:

[0153] The stress distribution of the structure at high temperature satisfies the following equilibrium equation:

[0154]

[0155] Among them, σ is the stress tensor, ρ is the material density, and g is the gravitational acceleration;

[0156] (2.4) Total stress-strain relationship:

[0157] Under the condition of thermal-mechanical coupling, the total strain of the material includes the elastic strain ε e , plastic strain ε p and thermal strain ε T :

[0158] ε=ε e +ε p +ε T

[0159] The elastic strain ε e =σ / E(T), E(T) is the elastic modulus of the material at temperature T;

[0160] 3. Evaluation of failure modes

[0161] In some embodiments, the present invention provides recommendations for fire resistance design by analyzing the failure time and failure mode of key structural components.

[0162] (3.1) Failure time of key components:

[0163] According to the change of the bearing capacity of the structure, the failure time can be expressed as:

[0164]

[0165] Among them, t f is the failure time, R is the initial bearing capacity of the component, is the fire heat flux rate

[0166] (3.2) Classification of failure modes:

[0167] The failure modes of the structure include buckling instability, insufficient bearing capacity or excessive deformation. In the specific analysis, the following evaluation indicators are generated through finite element simulation:

[0168] Buckling critical temperature Tcr

[0169] Limit deformation Δmax

[0170] 4. Data Output

[0171] The structural performance analysis module of the present invention outputs the following key data:

[0172] Time variation curve of component bearing capacity,

[0173] Failure time t of key components f and failure modes,

[0174] Suggestions for optimizing fire resistance performance, including fire retardant coating thickness, component cross-section optimization, etc.

[0175] As an extension, the structural performance analysis module can be combined with dynamic monitoring data for real-time evaluation. Under fire scene conditions, the real-time temperature distribution data collected by sensors can be used to dynamically update the boundary conditions and heat source items of the finite element model to achieve online evaluation of structural performance. For example, the influence of high temperature changes on the interface bonding force of steel-concrete composite structures can be analyzed to further improve the applicability to complex structural systems.

[0176] Personnel evacuation dynamic simulation module:

[0177] Based on the social force model, individual and group behaviors are simulated. Individual behaviors include obstacle avoidance and path selection, while group behaviors include congestion and flow distribution.

[0178] Consider the impact of physiological and psychological factors on evacuation behavior. Physiological factors include the impact of high temperature on walking speed, and psychological factors include panic index.

[0179] Combine fire temperature field and smoke distribution data in real time to dynamically adjust evacuation routes and avoid high-risk areas.

[0180] The personnel evacuation dynamic simulation module of the present invention aims to dynamically simulate the evacuation behavior of personnel in a fire environment through a social force model. The module integrates individual and group behavior characteristics, considers the comprehensive influence of physiological and psychological factors, and combines the fire temperature field and smoke distribution data to dynamically adjust the evacuation path to maximize the evacuation efficiency and safety.

[0181] Implementation of dynamic simulation module for personnel evacuation

[0182] 1. Basic principles of the social force model

[0183] In this invention, the social force model is used to simulate the evacuation behavior of individuals and groups. The social force model assumes that individuals are affected by the following forces during the evacuation process:

[0184]

[0185] Where m is the individual mass, is the individual velocity vector, is the expected directional force of the individual, indicating the individual's intrinsic motivation to move toward the exit. is the interaction force between individuals and others, used to avoid collisions, is the force between the individual and the obstacle, indicating the individual's obstacle avoidance behavior.

[0186] 2. Individual behavior simulation

[0187] In this embodiment, the main characteristics of individual behavior include obstacle avoidance and path selection, which are simulated based on the following formula of the social force model:

[0188] (2.1) Obstacle avoidance behavior

[0189] The obstacle avoidance behavior is controlled by the repulsive force with the obstacle. The repulsive force formula is:

[0190]

[0191] Among them, A is the strength parameter of the repulsive force, d i is the distance between the individual and the obstacle, B is the distance attenuation coefficient of the repulsive force, is the unit normal vector pointing to the obstacle.

[0192] (2.2) Path selection behavior

[0193] The path selection is controlled by the expected directional force, and the expected directional force formula is:

[0194]

[0195] in, is the expected speed of the individual, τ is the time constant for the individual to adjust the speed, is the unit normal vector pointing into the path.

[0196] 3. Group behavior simulation

[0197] Group behavior includes congestion and flow distribution, which are mainly simulated by the interaction between individuals:

[0198] (3.1) Congestion behavior

[0199] When individuals gather in the evacuation channel, the interaction force between individuals represents mutual repulsion:

[0200]

[0201] Where C is the strength parameter of the interaction force, d ij is the distance between individuals i and j, D is the distance attenuation coefficient of the interaction force, The unit normal vector pointing towards other entities.

[0202] (3.2) Flow distribution,The group flow distribution is determined by the utilization rate of the evacuation channel and is calculated by the following formula:

[0203]

[0204] Among them, F is the channel flow, N is the total number of people passing through the channel, t s The time to complete the evacuation;

[0205] 4. Consideration of physiological and psychological factors

[0206] (4.1) Physiological factors

[0207] Physiological factors include the effect of high temperature on gait speed, which is expressed by the following formula: i :

[0208]

[0209] in, is the normal walking speed of the individual, k T is the attenuation coefficient of temperature to velocity, Ti is the temperature at the current location.

[0210] (4.2) Psychological factors

[0211] Psychological factors are mainly represented by the panic index λ, which affects the individual's path selection and speed adjustment:

[0212]

[0213] Among them, the λ panic index is determined by the individual's congestion level and the fire development stage.

[0214] 5. Real-time combination of fire temperature field and smoke distribution data

[0215] In this embodiment, the dynamic adjustment of the evacuation path is achieved by combining the fire temperature field and smoke distribution data in real time.

[0216] (5.1) Determination of hazardous areas

[0217] The hazard of each area R is evaluated based on the temperature field T(x,y,z,t) and smoke concentration distribution S(x,y,z,t) generated by the fire simulation:

[0218] R=w T T+w S S

[0219] Where T is temperature, S is smoke concentration, w T , w S are the risk weight factors of temperature and smoke,

[0220] (5.2) Path adjustment algorithm

[0221] The dynamic adjustment of evacuation routes adopts an algorithm based on the shortest path and risk weighting:

[0222]

[0223] Among them, D adjusted is the adjusted path length, d i is the path segment length, R i is the hazard of the path segment.

[0224] As an extension, the dynamic simulation module for evacuation can also be combined with virtual reality (VR) technology for evacuation training. By simulating fire scenes in real time and combining social force models, it can provide building managers with more intuitive evacuation guidance solutions.

[0225] Therefore, the dynamic simulation module of personnel evacuation can accurately describe the evacuation behavior of individuals and groups under fire conditions by combining the social force model with the comprehensive simulation of physiological and psychological factors. At the same time, the dynamic optimization of the evacuation path is achieved by combining the fire temperature field and smoke distribution data in real time. The above technical solution is fully disclosed and has good reproducibility, providing scientific support for the safe evacuation of personnel under fire conditions.

[0226] The collaborative optimization design module is implemented through the following steps:

[0227] Application of multi-objective optimization algorithms:

[0228] Genetic algorithms are used to comprehensively optimize the structural fire resistance and evacuation efficiency, taking maximizing the structural fire resistance time and minimizing the evacuation time as the objective function, and balancing the resource allocation weights between the two.

[0229] Variable settings of design parameters:

[0230] The material selection, fireproofing layer thickness, cross-sectional dimensions in the structural parameters and the channel width, exit quantity and location in the evacuation parameters are used as optimization variables to ensure the integrity and feasibility of the scheme. The applicability of the design scheme is verified by combining fire simulation and evacuation path data.

[0231] Generation and output of comprehensive optimization solutions:

[0232] Based on the optimization calculation results, a comprehensive optimal solution including fire-resistant structure layout, evacuation route planning and channel design is generated, and a fire resistance design report and evacuation efficiency evaluation report are output to provide a decision-making basis for the overall performance-based fire resistance design of the building.

[0233] The collaborative optimization design module of the present invention aims to collaboratively optimize the fire resistance and evacuation efficiency of building structures by integrating data from fire simulation, structural performance analysis, and personnel evacuation simulation through a multi-objective optimization algorithm, and generate a comprehensive optimal design solution that takes into account both safety and functionality.

[0234] Implementation of collaborative optimization design module

[0235] 1. Application of multi-objective optimization algorithm

[0236] In the present invention, the collaborative optimization design module adopts a genetic algorithm (GA) to achieve multi-objective optimization, with the goal of maximizing the structural fire resistance time and minimizing the personnel evacuation time, and balancing the resource distribution between the two.

[0237] (1.1) Definition of optimization objective function

[0238] The multi-objective optimization problem is centered around the following objective function:

[0239]

[0240] Among them, F is the optimization target value, t s is the actual evacuation time, t f is the actual structural fire resistance time, are the benchmark values ​​of evacuation time and fire resistance time respectively, w1 and w2 are target weight coefficients, which satisfy w1+w2=1

[0241] (1.2) Implementation steps of genetic algorithm

[0242] Genetic algorithm achieves multi-objective optimization through the following steps:

[0243] Initial population generation: Based on the upper and lower limits of the design parameters, a set of design variable combinations is randomly generated as the initial population.

[0244] Fitness evaluation: Using fire simulation and evacuation path data, calculate the objective function value of each population individual.

[0245] Selection operation: Based on the fitness value, high-quality individuals are selected using a roulette wheel or tournament selection mechanism.

[0246] Crossover operation: Perform genetic crossover operation on selected individuals to generate new individual combinations.

[0247] Mutation operation: Randomly mutate some design variables to increase population diversity.

[0248] Iteration convergence: Repeat the above process until the objective function value converges or reaches the preset number of iterations

[0249] (1.3) Generation of Pareto solution set

[0250] The output of the genetic algorithm is the Pareto frontier solution set, each of which is a compromise between the structural fire resistance and the evacuation efficiency.

[0251] 2. Variable setting of design parameters

[0252] In the present invention, the variable setting of design parameters covers the core elements of structure and evacuation:

[0253] Structural parameters

[0254] Material selection (e.g. refractory coating type),

[0255] Thickness of fireproof layer (unit: mm),

[0256] Component cross-sectional dimensions (such as cross-sectional width and height of columns and beams, unit: mm).

[0257] Evacuation parameters

[0258] Width of evacuation passage (unit: m),

[0259] Number of evacuation exits (unit: number),

[0260] Exit location (coordinate point on the building plan)

[0261] In a possible implementation, these design variables are set as continuous or discrete decision variables, and the optimal solution is gradually found through iterative optimization using a genetic algorithm.

[0262] Setting constraints

[0263] Structural constraints: fire resistance time t f Must meet minimum building code requirements

[0264]

[0265] Evacuation constraint: evacuation time t s The maximum allowed time must be met

[0266]

[0267] 3. Generation and output of comprehensive optimization plan

[0268] Generation of optimization results

[0269] According to the Pareto solution set, the optimal solution suitable for the specific building conditions (such as equilibrium solution or safety priority solution) is selected to generate the fire-resistant structure layout and evacuation path planning scheme.

[0270] For example, the outputted optimization solution may include:

[0271] Increase the thickness of fire retardant coating by 20%,

[0272] Adjust the exit location to the crowded area.

[0273] Optimize the width of the evacuation channel to 1.5m.

[0274] Output form of optimization plan

[0275] Fire-resistant design report: including structural design parameters such as fire-resistant layer thickness, material selection, and component section optimization.

[0276] Evacuation efficiency evaluation report: including evacuation route planning map, congestion distribution map and evacuation time analysis data.

[0277] Comprehensive performance index: Quantify the optimization results of structural fire resistance and evacuation efficiency into an overall performance score to facilitate designer decision-making.

[0278] As an extension, the collaborative optimization design module of the present invention can also be combined with building information modeling (BIM) technology to achieve visual integration of three-dimensional building models and optimization results. For example, the optimization plan is automatically loaded through the BIM platform, and the layout of the evacuation channel or the material selection is dynamically adjusted to improve the design efficiency.

[0279] Real-time monitoring and early warning module:

[0280] Deploy temperature sensors, smoke sensors and infrared monitoring equipment to collect fire scene data in real time;

[0281] Dynamically update fire simulation and evacuation route planning based on collected data;

[0282] The linked fire protection system triggers emergency devices for real-time intervention, including water sprinkler systems and smoke exhaust equipment.

[0283] The real-time monitoring and early warning module of the present invention collects fire scene data in real time by deploying multiple types of sensors, realizes dynamic updates by combining fire simulation and evacuation route planning, and implements real-time intervention by linking the fire protection system. The module provides full-process support for data collection, dynamic feedback, optimization adjustment and emergency response during the fire process, ensuring the safety and emergency response capability of the building in a fire environment.

[0284] Realization method of real-time monitoring and early warning module

[0285] 1. Data collection and sensor deployment

[0286] In the present invention, real-time monitoring is carried out by deploying temperature sensors, smoke sensors and infrared monitoring equipment to collect key data of the fire scene.

[0287] (1.1) Application of temperature sensors

[0288] Temperature sensors are distributed in key structural areas (such as evacuation passages, stairwells, and near load-bearing components).

[0289] Used to monitor local temperature changes in real time.

[0290] The formula description of temperature data:

[0291]

[0292] Where T(x, y, z, t) is the real-time temperature at the monitoring location, T0 is the initial temperature, and Q(t′) is the thermal power of the fire heat source at time t′;

[0293] (1.2) Application of smoke sensors

[0294] Smoke sensors monitor the smoke concentration on site and are deployed in air flow areas (such as smoke exhaust vents and stairwells);

[0295] Real-time monitoring model of smoke diffusion:

[0296]

[0297] Among them, S(x,y,z,t) is the smoke concentration at the monitoring location, S0 is the smoke concentration at the fire source, and d is the smoke concentration.

[0298] The propagation distance, λ is the diffusion coefficient;

[0299] (1.3) Application of infrared monitoring equipment

[0300] Infrared monitoring equipment is used to identify the location of fire source, flame range and spread speed.

[0301] The calculation formula of fire source expansion speed is:

[0302]

[0303] Among them, v f is the flame propagation speed, Δd is the distance the flame expands, and Δt is the distance the flame expands;

[0304] 2. Dynamic optimization of evacuation route planning

[0305] Through real-time updated fire simulation data, the evacuation route is optimized and adjusted to avoid high temperature and high smoke areas. Path adjustment formula:

[0306]

[0307] Among them, D dynamic is the adjusted evacuation path length, d i is the physical length of the path segment, R i is the hazard weight of the path segment.

[0308] 3. Link fire protection systems to implement real-time intervention

[0309] In the present invention, the monitoring data is also used to link the building fire protection system to achieve rapid control and risk mitigation of fire by triggering emergency devices;

[0310] (3.1) Startup of the water spray system

[0311] When the temperature sensor detects that the temperature in a certain area exceeds the set threshold, the sprinkler system is automatically triggered:

[0312] Q w =C w ·A w

[0313] Among them, Q w is the water spraying amount, C w is the water spray intensity, A w The area covered by the water spray;

[0314] (3.2) Control of smoke exhaust system

[0315] When the smoke concentration exceeds the safety threshold, the smoke exhaust system will automatically start. The smoke exhaust flow rate formula is:

[0316] Q s =v s ·A s

[0317] Among them, Q s is the smoke exhaust volume, v s is the smoke exhaust wind speed, A is the smoke exhaust port area;

[0318] (3.3) Alarm and evacuation instructions

[0319] Trigger the sound and light alarm system and provide real-time evacuation guidance through dynamic signs and voice broadcasts

[0320] As an extension, the real-time monitoring and early warning module of the present invention can be integrated into the intelligent building management system (BMS) to further realize the all-round linkage between fire scene data and building control. For example, by optimizing the distribution of the sensor network, the coverage of high-risk areas (such as large shopping malls and subway stations) can be enhanced.

[0321] Therefore, the real-time monitoring and early warning module collects fire scene data by deploying sensors, updates fire simulation and evacuation route planning in real time, and links the fire protection system to achieve rapid response. This module is comprehensive, dynamic and highly automated, providing strong technical support for fire emergency management. This technical solution is fully disclosed to ensure that technicians in this field can fully reproduce it.

[0322] Please see attached Figure 2 , the above system design method comprises the following steps:

[0323] S1. Fire scene modeling and initial condition setting:

[0324] Construct a geometric model of the building to determine the location of the fire source, combustion conditions and related fire initial parameters;

[0325] Import the performance parameters of building materials under high temperature conditions and the distribution status of personnel to provide basic input for subsequent analysis;

[0326] S2. Fire simulation and key data generation:

[0327] Based on the set fire scenario, the fire development process is dynamically simulated to obtain the temperature distribution, smoke diffusion and fire spread path;

[0328] The simulated temperature and smoke data are used as input for performance analysis of building structures and dynamic adjustment of evacuation behavior of personnel;

[0329] S3. Structural performance evaluation and optimization suggestion generation:

[0330] Based on the fire simulation results, evaluate the bearing capacity, deformation law and fire resistance performance of the building structure under fire environment;

[0331] Generate failure mode and fire resistance time analysis results of key components and put forward targeted design optimization suggestions;

[0332] S4. Personnel evacuation behavior simulation and path optimization:

[0333] Combined with the dynamic data of fire development, analyze the evacuation behavior of personnel in a fire environment and dynamically adjust the evacuation route to avoid dangerous areas;

[0334] Calculate evacuation time, personnel flow distribution and congestion location to provide support for the design optimization of evacuation channels;

[0335] S5. Collaborative optimization design and comprehensive solution output:

[0336] The results of fire simulation, structural analysis and evacuation are integrated to balance the structural fire resistance and evacuation efficiency through multi-objective optimization method.

[0337] Generate the best design solution, including fire-resistant layout adjustment, fire-proof material optimization and evacuation route improvement, and provide design report and evaluation analysis;

[0338] S6. Real-time monitoring and dynamic feedback optimization:

[0339] Use real-time monitoring equipment to collect fire temperature and smoke concentration data, and feed the real-time data into fire development simulation and evacuation route adjustment;

[0340] Dynamically optimize fire control strategies and evacuation plans based on the latest data to ensure timely and effective fire emergency response.

[0341] In this embodiment, a complete process from fire modeling to emergency response is formed by combining fire simulation, structural performance evaluation, personnel evacuation optimization and real-time monitoring. The method includes multiple steps, each of which is interrelated and progressively advanced, and finally generates an optimized building fire resistance design plan. The optimization content can be dynamically adjusted according to real-time fire data to ensure the scientificity and operability of the design. The following is a detailed description of the specific steps:

[0342] 1. Fire scene modeling and initial condition setting

[0343] First, a geometric model is established based on the actual structural structure of the building to provide a basic framework for fire simulation. In this process, it is necessary to accurately describe the geometric features of the building's walls, floors, doors and windows, evacuation passages, etc., and consider environmental conditions that may affect the development of the fire.

[0344] As a basic input, the initial conditions of the fire need to be set, including information such as the location of the fire source, the type of burning material, and the initial heat power. In addition, relevant performance data of building materials, such as the high-temperature characteristics of steel and concrete, need to be imported so that they can be used in subsequent structural analysis. At the same time, considering the needs of personnel evacuation simulation, the initial distribution status of personnel, including crowd density and location, should be provided in combination with the building usage scenario.

[0345] These data provide a complete input basis for fire simulation, structural performance analysis and evacuation route planning, ensuring that subsequent steps can be accurately calculated and analyzed based on actual scenarios.

[0346] 2. Fire simulation and key data generation

[0347] After completing the initial modeling, the fire simulation module is used to simulate the fire development and dynamically simulate the fire expansion, temperature changes and smoke distribution. In this process, high-precision calculation tools are used to generate the fire temperature field, smoke concentration distribution and fire spread path, providing key data support for subsequent analysis.

[0348] At the same time, the simulation results will be output in the form of time steps to show the dynamic changes in the development of the fire. These data will be passed as input to the structural performance analysis module and the personnel evacuation dynamic simulation module to evaluate the structural safety and personnel evacuation efficiency of the building under fire conditions.

[0349] The generation of simulation data not only supports static analysis, but also lays a data foundation for real-time adjustment and dynamic optimization, ensuring that the design plan can adapt to the development and changes of the fire.

[0350] 3. Structural performance evaluation and optimization suggestion generation

[0351] Based on the fire simulation results, the structural performance analysis module is used to conduct a detailed assessment of the building's structural components, focusing on the changes in their mechanical properties under fire conditions. The analysis includes the component's load-bearing capacity, deformation characteristics, and possible failure modes.

[0352] In the assessment, the fire temperature data is combined with the material performance parameters to dynamically calculate the fire resistance of the components, generate a curve of the load-bearing capacity over time, and the failure time of the structural components. In addition, based on the analysis results, optimization suggestions are put forward, such as strengthening the thickness of the fireproof layer in key parts, adjusting the material selection, etc., to improve the overall fire resistance performance.

[0353] Through structural performance analysis, the weak areas of the building under fire conditions can be identified and targeted improvement measures can be provided to designers.

[0354] 4. Personnel evacuation behavior simulation and path optimization

[0355] The dynamic simulation module for personnel evacuation analyzes the behavior patterns and evacuation paths of personnel in a fire environment by combining fire simulation results. During the simulation process, not only individual behaviors such as obstacle avoidance and path selection are considered, but also group characteristics such as congestion effects and flow distribution are integrated to provide a comprehensive analysis of personnel evacuation.

[0356] Through this module, the evacuation time, personnel flow and congestion hotspots in different areas can be calculated. At the same time, based on real-time fire data, the evacuation route can be dynamically adjusted to avoid high temperature or high smoke areas and improve evacuation efficiency.

[0357] The simulation results provide reliable data support for evacuation channel design and strategy optimization, ensuring rapid and efficient evacuation of personnel in fire scenarios.

[0358] 5. Collaborative optimization design and comprehensive solution output

[0359] Combining fire simulation, structural performance analysis and personnel evacuation data, the collaborative optimization design module is used to generate an overall design solution through a multi-objective optimization algorithm. During the optimization process, the balance between structural fire resistance and evacuation efficiency is comprehensively considered to ensure that both meet the design goals.

[0360] The optimization involves fire-resistant structure layout, fire-proof material selection, component size optimization, and evacuation route layout design. The optimization plan is output in various forms, including the building's fire-resistant design plan, evacuation route planning map, and efficiency evaluation report, providing comprehensive decision-making support for building designers.

[0361] Through collaborative optimization, the final design solution can take into account both structural safety and personnel evacuation efficiency, providing protection for the overall fire resistance of the building.

[0362] 6. Real-time monitoring and dynamic feedback optimization

[0363] During a fire, the real-time monitoring module collects data such as temperature and smoke concentration at the fire scene through a sensor network. These real-time data are input into the fire simulation module and the evacuation route planning module to dynamically adjust the fire development model and evacuation plan.

[0364] The real-time monitoring module also links with the fire protection system, triggering emergency devices such as water spray and smoke exhaust according to the dynamic changes of the fire scene, helping to control the development of the fire and reduce obstacles to the evacuation of personnel. At the same time, through sound and light alarms, voice guidance and other methods, the evacuation strategy is updated in real time to guide personnel to evacuate quickly.

[0365] Through real-time feedback and dynamic adjustment, this method can effectively respond to the development and changes of fire and enhance the flexibility and adaptability of the design scheme.

[0366] Therefore, the fire-resistant design method of the present invention forms a complete process from theoretical design to practical application through six steps: fire scene modeling, dynamic simulation, structural performance evaluation, personnel evacuation optimization, collaborative optimization design, and real-time monitoring feedback. The organic combination of each step ensures the scientificity, adaptability, and practicality of the scheme. The technology is fully disclosed and can provide clear implementation guidance for those skilled in the art.

[0367] The optimized design solutions in S5 include:

[0368] Optimize structural layout and fireproof material selection for high temperature areas;

[0369] Dynamically adjust the design of evacuation passages and exit distribution locations;

[0370] Provide building performance-based fire resistance design report and evacuation efficiency assessment report.

[0371] Dynamic adjustments include:

[0372] Update the fire temperature field and smoke concentration distribution in real time based on sensor data;

[0373] Dynamically adjust evacuation routes to avoid high temperature and high smoke areas;

[0374] The fire protection system is linked to initiate emergency measures, including water spraying, smoke exhaust and personnel evacuation instructions.

[0375] In step 6, the dynamic adjustment of the fire environment is achieved through the following steps:

[0376] Real-time fire data collection: deploy temperature sensors, smoke concentration sensors and infrared detection equipment to monitor the temperature, smoke distribution and fire spread trend of the fire scene in real time; input the collected data into the fire simulation process to dynamically update the temperature field, smoke concentration distribution and fire spread path;

[0377] Real-time update of structural performance and evacuation routes: Based on the real-time fire data collected, the temperature distribution and load-bearing capacity changes of key structural components are recalculated; evacuation route planning is dynamically adjusted to avoid high-temperature and high-smoke areas, and the traffic efficiency of personnel in the evacuation passage is optimized;

[0378] Emergency response and execution: trigger water spray and smoke exhaust fire fighting devices based on real-time fire data and updated evacuation routes; generate evacuation guidance strategies in real time, and notify personnel to adjust evacuation behavior through dynamic signs, voice or mobile information;

[0379] Iterative optimization and feedback: Continuously collect new data from the fire scene to further adjust fire development simulation, structural performance assessment and evacuation route planning; achieve continuous dynamic optimization during the fire emergency response process to improve evacuation efficiency and structural safety.

[0380] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A structural performance-based fire resistance design system considering safe evacuation of personnel, characterized in that: include: The fire simulation module is used to dynamically simulate the fire development process through fire dynamics tools, combining simplified fire models and machine learning methods to generate fire temperature fields, smoke distribution and fire spread paths; Structural performance analysis module, based on dynamic material performance database and thermal-mechanical coupled finite element analysis, evaluates the fire resistance performance of structures under fire conditions, including bearing capacity, deformation and buckling failure modes; The dynamic simulation module for personnel evacuation uses a social force model to integrate physiological and psychological behavior characteristics, simulates personnel evacuation behavior in a fire environment, and dynamically optimizes the evacuation path in combination with real-time fire data; The collaborative optimization design module uses a multi-objective optimization algorithm to integrate the results of fire simulation, structural performance analysis, and evacuation simulation, balance the structural fire resistance and evacuation efficiency, and generate a design solution; The real-time monitoring and early warning module collects fire data in real time by installing sensors, dynamically adjusts fire simulation and evacuation routes, and links the fire protection system to implement emergency response.

2. A structural performance-based fire resistance design system considering safe evacuation of personnel according to claim 1, characterized in that: The fire simulation module comprises: Use CFD tools to perform high-precision dynamic simulation of fire temperature field and smoke diffusion; Introducing a meta-model method based on machine learning to quickly fit the laws of fire heat propagation and smoke diffusion through historical fire data; Optimize the grid division strategy, use high-precision calculations in key areas, and use simplified calculations in other areas.

3. A structural performance-based fire resistance design system considering safe evacuation of personnel according to claim 1, characterized in that: The structural performance analysis module: Dynamically call the high temperature performance parameters of building materials, including the curves of steel yield strength and concrete compressive strength changing with temperature; Combined with the time-temperature curve of fire simulation, finite element tools are used to perform thermal-mechanical coupling analysis to evaluate the bearing capacity, deformation and failure mode of structural components.

4. A structural performance-based fire resistance design system considering safe evacuation of personnel according to claim 1, characterized in that: The personnel evacuation dynamic simulation module: Based on the social force model, individual behavior and group behavior are simulated, wherein the individual behavior includes obstacle avoidance and path selection, and the group behavior includes congestion and flow distribution; Considering the influence of physiological factors and psychological factors on evacuation behavior, the physiological factors include the influence of high temperature on walking speed, and the psychological factors include panic index; Combine fire temperature field and smoke distribution data in real time to dynamically adjust evacuation routes and avoid high-risk areas.

5. The structural performance-based fire resistance design system considering safe evacuation of personnel according to claim 1 is characterized in that: The collaborative optimization design module is implemented by the following steps: Application of multi-objective optimization algorithms: Genetic algorithms are used to comprehensively optimize the structural fire resistance and evacuation efficiency, taking maximizing the structural fire resistance time and minimizing the evacuation time as the objective function, and balancing the resource allocation weights between the two. Variable settings of design parameters: The material selection, fireproofing layer thickness, cross-sectional dimensions in the structural parameters and the channel width, exit quantity and location in the evacuation parameters are used as optimization variables to ensure the integrity and feasibility of the scheme. The applicability of the design scheme is verified by combining fire simulation and evacuation path data. Generation and output of comprehensive optimization solutions: Based on the optimization calculation results, a comprehensive optimal solution including fire-resistant structure layout, evacuation route planning and channel design is generated, and a fire resistance design report and evacuation efficiency evaluation report are output to provide a decision-making basis for the overall performance-based fire resistance design of the building.

6. A structural performance-based fire resistance design system considering safe evacuation of personnel according to claim 1, characterized in that: The real-time monitoring and early warning module: Deploy temperature sensors, smoke sensors and infrared monitoring equipment to collect fire scene data in real time; Dynamically update fire simulation and evacuation route planning based on collected data; The linkage fire protection system triggers the emergency devices for real-time intervention, and the emergency devices include a water spray system and a smoke exhaust device.

7. A structural performance-based fire resistance design method considering safe evacuation of personnel, based on a structural performance-based fire resistance design system considering safe evacuation of personnel according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Fire scene modeling and initial condition setting: Construct a geometric model of the building to determine the location of the fire source, combustion conditions and related fire initial parameters; Import the performance parameters of building materials under high temperature conditions and the distribution status of personnel to provide basic input for subsequent analysis; S2. Fire simulation and key data generation: Based on the set fire scenario, the fire development process is dynamically simulated to obtain the temperature distribution, smoke diffusion and fire spread path; The simulated temperature and smoke data are used as input for performance analysis of building structures and dynamic adjustment of evacuation behavior of personnel; S3. Structural performance evaluation and optimization suggestion generation: Based on the fire simulation results, evaluate the bearing capacity, deformation law and fire resistance performance of the building structure under fire environment; Generate failure mode and fire resistance time analysis results of key components and put forward targeted design optimization suggestions; S4. Personnel evacuation behavior simulation and path optimization: Combined with the dynamic data of fire development, analyze the evacuation behavior of personnel in a fire environment and dynamically adjust the evacuation route to avoid dangerous areas; Calculate evacuation time, personnel flow distribution and congestion location to provide support for the design optimization of evacuation channels; S5. Collaborative optimization design and comprehensive solution output: The results of fire simulation, structural analysis and evacuation are integrated to balance the structural fire resistance and evacuation efficiency through multi-objective optimization method. Generate the best design solution, including fire-resistant layout adjustment, fire-proof material optimization and evacuation route improvement, and provide design report and evaluation analysis; S6. Real-time monitoring and dynamic feedback optimization: Use real-time monitoring equipment to collect fire temperature and smoke concentration data, and feed the real-time data into fire development simulation and evacuation route adjustment; Dynamically optimize fire control strategies and evacuation plans based on the latest data to ensure timely and effective fire emergency response.

8. A structural performance-based fire resistance design method considering safe evacuation of personnel according to claim 7, characterized in that: The optimal design scheme in S5 includes: Optimize structural layout and fireproof material selection for high temperature areas; Dynamically adjust the design of evacuation passages and exit distribution locations; Provide building performance-based fire resistance design report and evacuation efficiency assessment report.

9. A structural performance-based fire resistance design method considering safe evacuation of personnel according to claim 7, characterized in that: The dynamic adjustment includes: Update the fire temperature field and smoke concentration distribution in real time based on sensor data; Dynamically adjust evacuation routes to avoid high temperature and high smoke areas; The fire protection system is linked to initiate emergency measures, including water spraying, smoke exhaust and personnel evacuation instructions.

10. A structural performance-based fire resistance design method considering safe evacuation of personnel according to claim 7, characterized in that: In step 6, the dynamic adjustment of the fire environment is achieved through the following steps: Real-time fire data collection: deploy temperature sensors, smoke concentration sensors and infrared detection equipment to monitor the temperature, smoke distribution and fire spread trend of the fire scene in real time; input the collected data into the fire simulation process to dynamically update the temperature field, smoke concentration distribution and fire spread path; Real-time update of structural performance and evacuation routes: Based on the real-time fire data collected, the temperature distribution and load-bearing capacity changes of key structural components are recalculated; evacuation route planning is dynamically adjusted to avoid high-temperature and high-smoke areas, and the traffic efficiency of personnel in the evacuation passage is optimized; Emergency response and execution: trigger water spray and smoke exhaust fire fighting devices based on real-time fire data and updated evacuation routes; generate evacuation guidance strategies in real time, and notify personnel to adjust evacuation behavior through dynamic signs, voice or mobile information; Iterative optimization and feedback: Continuously collect new data from the fire scene to further adjust fire development simulation, structural performance assessment and evacuation route planning; achieve continuous dynamic optimization during the fire emergency response process to improve evacuation efficiency and structural safety.

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