Scene preprocessing method for fire simulation

Abstract

The invention discloses a scene preprocessing method for fire simulation, and relates to the technical field of building fire simulation, and the method comprises the steps: calculating a three-dimensional thermal weight through employing a fire propagation dynamics model, and generating a fire dangerous region thermal diagram; through a tensor field generation algorithm, fire behavior main diffusion direction and each axial anisotropic parameter are extracted, and fire behavior grid parameters are generated; through a hybrid grid generator, performing hexahedron dominant grid construction and increasing the grid density of a fire behavior dangerous area to obtain a fire behavior optimization hybrid grid; performing Reynolds number verification and boundary layer matching on the fire behavior optimization hybrid grid through a fire behavior feature adaptive algorithm, and performing iterative correction to generate a fire simulation correction grid; according to the invention, through the hybrid grid generator, the self-adaptive distribution of the grid density of the fire hazard area and the smooth connection of the transition area are realized, so that the grid distribution of the fire hazard area is accurately matched with the fire spreading characteristics.

Description

Technical Field

[0001] The invention relates to the technical field of building fire simulation, in particular to a scene preprocessing method for fire simulation. Background Art

[0002] Numerical fire simulation technology plays a vital role in building safety assessment and fire protection design. Existing techniques primarily generate simulation meshes based on BIM models and employ uniform meshes or simple meshing strategies to handle fire scenarios. In recent years, with the improvement in the accuracy of CFD solvers, researchers have begun introducing dynamic mesh optimization algorithms, such as localized meshing methods based on temperature gradients and parametric mesh generation techniques that incorporate combustion characteristics, significantly improving the accuracy of fire simulations.

[0003] Existing fire numerical simulation technologies fail to effectively quantify the dynamic coupling relationship between the main diffusion direction of the fire and the anisotropic parameters of the grid; traditional methods use uniform grids or static encryption strategies, which cannot accurately capture the flow characteristics of the fire along the main diffusion direction, resulting in insufficient resolution of the viscous bottom layer and turbulent transition zone of the high-temperature smoke flow, seriously affecting the simulation accuracy. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a scene preprocessing method for fire simulation to solve the problem of low simulation accuracy caused by insufficient dynamic coupling between fire directionality and grid parameters.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a scene preprocessing method for fire simulation, comprising:

[0008] The three-dimensional geometric data of the building is collected and corrected, and the standardized BIM file and component attribute table are output through component semantic annotation and material attribute binding; the three-dimensional thermal weight is calculated using the fire propagation dynamics model to generate a thermal map of the fire hazard area; the main fire diffusion direction and axial anisotropic parameters are extracted through the tensor field generation algorithm to generate the fire mesh parameters; the hexahedral dominant mesh is constructed through the hybrid mesh generator and the mesh density of the fire hazard area is increased to obtain the fire optimized hybrid mesh; the fire characteristic adaptive algorithm is used to check the Reynolds number and match the boundary layer of the fire optimized hybrid mesh, and it is iteratively corrected to generate a fire simulation correction mesh; the fire simulation correction mesh is encapsulated in FDS format, and the boundary conditions and material combustion parameters of the standardized BIM file and component attribute table are injected to output a high-quality input package that can directly drive the fire simulation.

[0009] As a preferred solution of the scene preprocessing method for fire simulation described in the present invention, the correction includes geometric repair and redundancy cleaning.

[0010] As a preferred solution of the scene preprocessing method for fire simulation described in the present invention, wherein: the standardized BIM file and component attribute table are output through component semantic annotation and material attribute binding, the specific steps are as follows:

[0011] Based on the corrected 3D building geometry data, the fire protection level and ventilation attribute semantic labels are annotated for key fire components, and a list of key fire components with classification labels is output;

[0012] Automatically match the combustion characteristic parameters of the fire key component list from the fire parameter library to generate a standardized BIM file containing fire parameters;

[0013] By parsing the attribute data of key fire components in standardized BIM files, the correlation between key fire components and combustion characteristic parameters is extracted, and a component attribute table is generated.

[0014] As a preferred embodiment of the scene preprocessing method for fire simulation described in the present invention, the three-dimensional thermal weight is calculated using the fire propagation dynamics model to generate a thermal map of the fire hazard area. The specific steps are as follows:

[0015] Based on historical standardized BIM files, a fire propagation dynamics model was trained using spatial topology deep learning methods;

[0016] The standardized BIM file is input into the trained fire propagation dynamics model to calculate the heat release rate distribution of the fire source;

[0017] Based on standardized BIM files, component attribute tables, and the heat release rate distribution of fire sources, the fire spread path is dynamically simulated and three-dimensional thermal weights are output;

[0018] The three-dimensional thermal weights are mapped to the three-dimensional Euclidean space of the building's three-dimensional geometric data, and the temperature field and smoke concentration hazard indicators are marked to generate a thermal map of the fire hazard area.

[0019] As a preferred solution of the scene preprocessing method for fire simulation described in the present invention, wherein: the main fire diffusion direction and various axial anisotropy parameters are extracted by the tensor field generation algorithm to generate the fire grid parameters, the specific steps are as follows:

[0020] The tensor field generation algorithm is used to extract the temperature field of the fire hazard area heat map and the gradient characteristics of the smoke concentration hazard index, and the main fire spread direction is analyzed to output the main fire diffusion direction vector field;

[0021] According to the main fire diffusion direction vector field and the three-dimensional thermal weight, the fire diffusion coefficient ratios of each axis in the three-dimensional Euclidean space of the building's three-dimensional geometric data are calculated, and the anisotropic parameters of each axis are output;

[0022] The fire main spread direction vector field and the axial anisotropy parameters are integrated to generate the fire grid parameters based on the spatial grid number.

[0023] As a preferred embodiment of the scene preprocessing method for fire simulation described in the present invention, the following steps are used to construct a hexahedron-dominated grid and increase the grid density of the fire hazard area to obtain a fire optimized hybrid grid.

[0024] Based on the fire grid parameters, an octree adaptive subdivision algorithm is used to analyze the axial anisotropic parameters and generate a hexahedron-dominated hybrid grid strategy. The initial grid is then outputted in combination with the building's 3D geometric data to extract the coordinate set of the fire hazard area.

[0025] Based on the initial grid, hierarchical encryption is performed within the coordinate set of the fire hazard area to generate a non-uniform intermediate grid with a density gradient;

[0026] The non-uniform intermediate grid is subjected to Laplacian smoothing and transition zone connection processing, and the transition grid is output. The fire grid parameters are mapped to the transition grid nodes to generate the fire optimized hybrid grid.

[0027] As a preferred solution of the scene preprocessing method for fire simulation of the present invention, the specific steps of generating the fire simulation correction grid are as follows:

[0028] Calculate the Reynolds number distribution field based on the fire main diffusion direction vector field and the fire optimized hybrid grid;

[0029] According to the Reynolds number distribution field, the fire intensity is dynamically adjusted to optimize the number and thickness of hybrid grid layers, and the boundary layer encrypted grid is output;

[0030] Based on the boundary layer encrypted grid, multiple rounds of iterative correction are performed through the fire characteristic adaptive algorithm to generate the fire simulation correction grid.

[0031] As a preferred solution of the scene preprocessing method for fire simulation of the present invention, wherein: the output can directly drive the high-quality input package of fire simulation, the specific steps are as follows:

[0032] Convert the fire simulation correction grid into FDS format to generate a fire simulation correction grid file;

[0033] The boundary conditions of the standardized BIM file and the association relationship between the key fire components and combustion parameters in the component attribute table are injected into the fire simulation correction grid file to obtain a high-quality input package that can directly drive the fire simulation.

[0034] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the scene preprocessing method for fire simulation as described in the first aspect of the present invention is implemented.

[0035] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the scene preprocessing method for fire simulation as described in the first aspect of the present invention is implemented.

[0036] The beneficial effects of the present invention are as follows: through the hybrid grid generator, the adaptive distribution of grid density in the fire-hazardous area and the smooth connection of the transition area are realized, so that the grid distribution in the fire-hazardous area accurately matches the fire spread characteristics; through the fire characteristic adaptive algorithm, the dynamic optimization and iterative correction of the boundary layer grid parameters are completed, ensuring the accurate analysis of wall turbulence and smoke diffusion; the two technologies work together to improve the computational efficiency and prediction accuracy of fire simulation, and provide highly reliable dynamic fire evolution data for building fire protection design. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 Flowchart of the scene preprocessing method for fire simulation.

[0039] Figure 2 Flowchart for generating a heat map of fire hazard areas.

[0040] Figure 3 Flowchart for optimizing the hybrid mesh for output fire activity.

[0041] Figure 4 Flowchart of the input package for outputting high-quality fire simulations. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0045] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a scene preprocessing method for fire simulation, comprising the following steps:

[0046] S1. Collect the 3D geometric data of the building and make corrections.

[0047] Specifically, the three-dimensional geometric data of the building is collected by using a laser scanner to collect building point cloud data, and noise reduction and registration processing are performed on the building point cloud data to generate a triangular facet model. For example, the laser scanner uses a phase-type scanner to collect point cloud density of 5mm to obtain the three-dimensional geometric data of the building;

[0048] The correction process performs geometric repair using CAD repair tools to perform topological correction on non-manifold geometry, surface holes, and intersecting facets in the building's 3D geometric data. Redundancy cleanup is performed to remove decorative components, duplicate facets, and isolated vertices that are not related to fire simulation in the building's 3D geometric data. The 3D geometric data after geometric repair and redundancy cleanup must be tested by a BIM format validator to see if it complies with the IFC standard. The 3D geometric data that fails the test will be returned to the geometric repair step for reprocessing, and the 3D geometric data that passes the test will be output as a standardized BIM file. For example, non-manifold geometry repair involves stitching broken edges and filling missing faces. Redundancy cleanup examples include removing carved patterns and lamp models in the building's 3D geometric data to obtain the corrected 3D geometric data of the building.

[0049] Furthermore, the BIM format validator checks whether it complies with the IFC standard. The acceptance criteria are: the three-dimensional geometric data of the building after geometric repair and redundancy cleaning must completely retain the topological connection relationship of all building components, the geometric shapes of each component have no self-intersections or cracks, the material properties are consistent with the type properties defined by the IFC standard, the spatial coordinate accuracy reaches the millimeter-level error range required by the IFC format, and there are no redundant elements or duplicate entities in the model; the failure criteria are: there are missing components or topological breaks in the three-dimensional geometric data of the building, self-intersections or non-manifold edges in the geometric faces, the material properties are not classified according to the IFC standard or key parameters are missing, the spatial coordinate error exceeds the allowable range, or it contains uncleaned temporary construction lines, auxiliary surfaces, etc.

[0050] S2. Output standardized BIM files and component attribute tables through component semantic annotation and material attribute binding.

[0051] S2.1. Based on the corrected 3D building geometry data, label the fire protection level and ventilation attribute semantic labels for the key fire components, and output a list of key fire components with classification labels.

[0052] Specifically, based on the corrected three-dimensional building geometric data, wall components, door and window components, and ventilation duct components are selected in the BIM software; fire rating labels are added one by one in the property panel for the selected key fire components, and the fire rating labels are divided into three levels according to exemplary specifications: Class A corresponds to an exemplary fire resistance limit of ≥3.0 hours, Class B ≥1.5 hours, and Class C ≥0.5 hours; ventilation attribute labels are added one by one in the property panel for the key fire components, and the ventilation attribute labels are divided into natural ventilation, mechanical ventilation, and sealed according to exemplary conditions;

[0053] By traversing the fire rating label fields and ventilation attribute label fields of key fire components, the system checks for null or undefined values. Valid values ​​for the fire rating label are limited to three enumerations: A, B, and C. Valid values ​​for the ventilation attribute label are limited to natural ventilation, mechanical ventilation, and sealed. When a field value is null or outside the enumeration range, the corresponding component is highlighted in red in the BIM software's 3D view, triggering a verification failure message requiring manual re-marking or correction of the label value. A list of labeled key fire components is generated based on component number, component name, fire rating label, and ventilation attribute label. For example, when verifying the natural ventilation label, it checks whether door and window components contain valid opening parameters. When verifying the mechanical ventilation label, it checks whether ventilation duct components are bound to the smoke exhaust equipment number.

[0054] S2.3. Automatically match the combustion characteristic parameters of the fire key component list from the fire parameter library to generate a standardized BIM file containing the fire parameters.

[0055] Specifically, the material name field, fire rating label field, heat release rate peak field, smoke generation rate field, and CO generation rate field are set through the fire key component list; an enumeration value mapping relationship is established between the fire rating label field and the fire rating label field of the fire key component list, and the exemplary data entries are the material name field "rock wool board", the fire rating label field "Grade A", the heat release rate peak field exemplary value 300 kW / m², and the smoke generation rate field exemplary value 0.01 m² / kg; each fire rating label field in the fire parameter library is bound to at least one set of heat release rate peak field, smoke generation rate field, and CO generation rate field combination;

[0056] The component name field of the fire key component list and the material name field of the fire parameter library perform exact string matching; the fire protection grade label field of the fire key component list and the fire protection grade label field of the fire parameter library perform enumeration value matching; fire key components that do not match the corresponding parameters trigger parameter missing warnings and record logs; after the standardized BIM file with completed parameter writing performs IFC format verification to check the integrity of the attribute fields, a standardized BIM file containing fire parameters is generated.

[0057] S2.4. By parsing the attribute data of key fire components in the standardized BIM file, the correlation between key fire components and combustion characteristic parameters is extracted to generate a component attribute table.

[0058] Specifically, the standardized BIM file is parsed and the IFC parser is used to traverse the attribute data of key fire components to extract the component ID field, component name field, fire rating label field, ventilation attribute label field, heat release rate peak field, smoke generation rate field, and CO generation rate field; a mapping relationship is established between the component ID field and the heat release rate peak field, smoke generation rate field, and CO generation rate field; and a numerical association rule is established between the fire rating label field and the heat release rate peak field.

[0059] Generate a component attribute table based on the component ID field, component name field, fire rating label field, ventilation attribute label field, heat release rate peak field, smoke generation rate field, and CO generation rate field through the verified mapping relationship;

[0060] Furthermore, the process of establishing a mapping relationship between the component ID field and the peak heat release rate field, smoke generation rate field, and CO generation rate field is as follows: the combustion characteristic parameter data block corresponding to each component ID field in the standardized BIM file is extracted through the IFC parser, the heat release rate value in the combustion characteristic parameter data block is written into the peak heat release rate field, the smoke generation coefficient is written into the smoke generation rate field, and the carbon monoxide generation coefficient is written into the CO generation rate field, forming a one-to-one mapping relationship between the component ID and the combustion characteristic parameter; the process of establishing a numerical association rule between the fire protection grade label field and the peak heat release rate field is as follows: based on the material combustion performance limits corresponding to different fire protection grades in the building fire protection design code, the fire protection grade label field (such as "Grade A") is bound to the maximum allowable heat release rate value, and the label is mapped to a specific heat release rate threshold range through a table lookup method.

[0061] S3. Use the fire propagation dynamics model to calculate the three-dimensional thermal weight and generate a thermal map of the fire hazard area.

[0062] S3.1. Based on historical standardized BIM files, a fire propagation dynamics model is trained using a spatial topology deep learning method.

[0063] Specifically, the component attribute table is extracted from the historical standardized BIM file; the connection relationship of the historical standardized BIM file is used as the edge of the spatial topology graph, and the node attributes include room volume and heat release rate peak value, to generate a spatial topology graph. The nodes of the spatial topology graph are the room components and fire-critical components in the building's three-dimensional geometric data, and the edges are the connection relationships between door opening components and ventilation duct components between room components; the node feature vector of the spatial topology graph includes the volume field of the room component, the heat release rate peak field of the fire-critical component, the smoke generation rate field, and the CO generation rate field;

[0064] The fire spread time series data in historical fire cases are aligned with the corresponding spatial topology graph. The input of the graph neural network model is the node feature vector and edge feature vector of the spatial topology graph, and the output is the predicted temperature field value and smoke concentration predicted value of each room component in the next time step. The mean square error loss function is used to train the graph neural network model. The loss function calculates the difference between the predicted temperature field and the actual temperature field, and the difference between the predicted smoke concentration and the actual smoke concentration. The training termination condition is set to stop training when the validation set loss decreases by less than the exemplary stopping training threshold of 0.01 for three consecutive iterations. The trained graph neural network model is saved as a fire spread dynamics model.

[0065] S3.2. Input the standardized BIM file into the trained fire propagation dynamics model to calculate the heat release rate distribution of the fire source.

[0066] It should be noted that the expression for calculating the heat release rate distribution of the fire source is:

[0067] ;

[0068] in, It's location The heat release rate distribution at is the trained fire spread dynamics model, It is a topological diagram of the building space. is the material parameter matrix, is the initial fire source position marker vector, It is the maximum heat release rate reference value in the component property table;

[0069] Specifically, the room component volume field, door opening component opening area field, fire key component heat release rate peak field, smoke generation rate field, and CO generation rate field in the standardized BIM file are extracted to generate a building space topology map; the maximum value of the fire key component heat release rate peak field is read from the component attribute table as the maximum heat release rate benchmark value; and the material parameter matrix of the fire key component is extracted from the standardized BIM file;

[0070] The building space topology map, material parameter matrix, and initial fire source location marker vector are input into the trained fire propagation dynamics model. The fire propagation dynamics model aggregates the heat release rate peak field and smoke generation rate field data of adjacent nodes through graph convolution operations, and calculates the fire propagation path weight by combining the door opening component opening area field. The fire propagation dynamics model calculates the influence factor of the ventilation duct component cross-sectional area field and length field on fire spread through the edge attention mechanism.

[0071] The fire spread dynamics model outputs the heat release rate weight coefficient of the three-dimensional space grid node, and the exemplary value of the weight coefficient range is 0-1; the heat release rate weight coefficient is multiplied by the maximum heat release rate reference value to obtain the heat release rate distribution value of the three-dimensional space grid node; the heat release rate distribution value is mapped according to the coordinate grid of the building's three-dimensional geometric data to generate the heat release rate distribution of the fire source including the position coordinate field and the heat release rate value field.

[0072] S3.3. Based on the standardized BIM file, component attribute table, and heat release rate distribution of the fire source, dynamically simulate the fire spread path and output the three-dimensional thermal weight.

[0073] Specifically, the spatial coordinates and connection relationships of room components of the building's three-dimensional geometric data are parsed from standardized BIM files; the fire protection level, peak heat release rate, and ventilation property data of key fire components are obtained from the component attribute table; the temperature field and smoke concentration parameters of the room are initialized, and the temperature field and smoke concentration parameters of the room are iteratively updated according to the time step. The temperature field update takes into account the influence of the door opening area of ​​the adjacent room and the peak heat release rate of the fire protection level. The smoke concentration update determines the diffusion path based on the natural ventilation opening area or mechanical ventilation number of the ventilation attribute;

[0074] The temperature field value of each room component is monitored in real time. When the temperature field value of the room component reaches or exceeds the corresponding fire protection level label field, the coordinates of the room component whose temperature field value reaches or exceeds the corresponding fire protection level label field are marked as red polygonal areas in the building's three-dimensional geometric data and recorded as active fire spread areas; the heat release rate distribution value of the active fire spread area is adjusted according to the heat release rate peak field of the adjacent room components; the temperature field value and the smoke concentration value of each time step are weighted and accumulated according to the spatial position; and the three-dimensional thermal weight is generated after the time step iteration is completed.

[0075] S3.4. Map the three-dimensional thermal weights to the three-dimensional Euclidean space of the building's three-dimensional geometric data, and mark the temperature field and smoke concentration hazard indicators to generate a thermal map of the fire hazard area.

[0076] Specifically, the three-dimensional thermal weight is matched point by point with the three-dimensional geometric data of the building; in the three-dimensional Euclidean space of the three-dimensional geometric data of the building, the grid nodes are divided into equally spaced grids according to the grid resolution parameter of the three-dimensional thermal weight within the bounding box of the three-dimensional geometric data of the building, and a mapping relationship table between the grid node coordinates and the row and column numbers of the three-dimensional thermal weight data nodes is established to obtain the spatial grid index;

[0077] The temperature field values ​​are converted into RGB color codes through linear interpolation, and the color gradient changes from blue to red from low temperature to high temperature. The smoke concentration values ​​are converted into transparency parameters through linear interpolation, and the transparency parameters change from high transparency to low transparency from low concentration to high concentration. The triangular mesh surface data of the building's three-dimensional geometric data is imported into the three-dimensional rendering engine, and the temperature field color code and the smoke concentration transparency parameter are superimposed on the mesh surface of the building's three-dimensional geometric data. The high-temperature area is identified based on the temperature field color code and the polygonal boundary contour line is drawn. The polygonal boundary contour line, the smoke concentration transparency parameter and the spatial coordinates of the components of the building's three-dimensional geometric data are associated to generate a thermal map of the fire hazard area containing three-dimensional coordinates, temperature field color and smoke concentration transparency.

[0078] S4. The main fire diffusion direction and axial anisotropy parameters are extracted through the tensor field generation algorithm to generate the fire grid parameters.

[0079] S4.1. Use the tensor field generation algorithm to extract the temperature field of the fire hazard area thermal map and the gradient characteristics of the smoke concentration hazard index, analyze the main fire spread direction, and output the main fire diffusion direction vector field.

[0080] Specifically, the numerical distribution of the temperature field weight field and the smoke concentration hazard index is extracted from the thermal map of the fire hazard area; a three-dimensional spatial gradient analysis is performed on the temperature field weight field value to generate a temperature field gradient vector; a three-dimensional spatial gradient analysis is performed on the smoke concentration hazard index field value to generate a smoke concentration gradient vector; the temperature field gradient vector and the smoke concentration gradient vector are superimposed into a joint gradient tensor according to the spatial coordinates; a principal component analysis is performed on the joint gradient tensor to extract the dominant direction vector as a candidate for the main diffusion direction of the fire; a gradient amplitude threshold is set to filter out noise interference, and the gradient amplitude threshold is set according to the maximum gradient ratio of the temperature field weight to the smoke concentration weight, and an exemplary ratio is 70% for the temperature field gradient and 30% for the smoke concentration gradient; effective candidates for the main diffusion direction of the fire are screened according to the gradient amplitude threshold; the effective main diffusion direction candidates of the fire are mapped to direction vectors according to the three-dimensional coordinates of the three-dimensional geometric data of the building; the direction vector generates a main diffusion direction vector field of the fire according to the spatial coordinates and the direction angle.

[0081] S4.2. Calculate the fire diffusion coefficient ratios of each axis in the three-dimensional Euclidean space of the building's three-dimensional geometric data based on the main fire diffusion direction vector field and the three-dimensional thermal weight, and output the anisotropic parameters of each axis.

[0082] It should be noted that the expression for calculating the ratio of the fire diffusion coefficients of each axis in the three-dimensional Euclidean space of the building's three-dimensional geometric data is:

[0083] ;

[0084] in, , , yes The fire diffusion coefficient ratio of the axis, , , The main spreading direction of the fire is in The component modulus of the axis, is the three-dimensional thermal weight of the grid node in the exemplary range [0-1], It is a very small positive number, exemplarily 1e-6;

[0085] Specifically, the direction vector field of each three-dimensional coordinate point is extracted from the main diffusion direction vector field of the fire; the temperature field weight field and smoke concentration weight field of the corresponding coordinate point are extracted from the three-dimensional thermal weight; the direction vector field is decomposed into Axis component, Axis component, Axis component; The axis component and the temperature field weight field and the smoke concentration weight field are weighted according to the weight ratio (for example, the temperature field weight accounts for 60%, and the smoke concentration weight accounts for 40%). Axial fire diffusion coefficient component; The axis component, temperature field weight field, and smoke concentration weight field are weighted and calculated according to the same weight ratio. Axial fire diffusion coefficient component; The axis component, temperature field weight field, and smoke concentration weight field are weighted and calculated according to the same weight ratio. Axial fire diffusion coefficient component; The three axial fire diffusion coefficient components are normalized to obtain The percentage value of the axial fire diffusion coefficient ratio; the fire diffusion coordinate point The axial fire diffusion coefficient ratio is associated with the spatial structure of the building's three-dimensional geometric data according to the three-dimensional coordinates; the output includes the three-dimensional coordinate fields, Axial fire diffusion coefficient ratio field, Axial fire diffusion coefficient ratio field, Axial fire diffusion coefficient ratio field axial anisotropy parameter.

[0086] S4.3. Integrate the fire main spread direction vector field and the axial anisotropy parameters to generate fire grid parameters based on the spatial grid number.

[0087] Specifically, the direction vector field of the three-dimensional coordinate point is extracted from the main fire diffusion direction vector field; the corresponding three-dimensional coordinate point is extracted from the axial anisotropy parameter data table. Axial fire diffusion coefficient ratio field, Axial fire diffusion coefficient ratio field, Axial fire diffusion coefficient ratio field; the direction vector field Quantity and Axial fire diffusion coefficient ratio field combined to generate Axially weighted direction component; the direction vector field Quantity and Axial fire diffusion coefficient ratio field combined to generate Axially weighted direction component; the direction vector field Quantity and Axial fire diffusion coefficient ratio field combined to generate Axial weighted direction component; Axial weighted direction component, Axial weighted direction component, The axial weighted direction components are integrated into a comprehensive fire spread vector according to three-dimensional coordinate points; a fire spread direction vector is generated;

[0088] The temperature field weight field and the smoke concentration weight field in the three-dimensional thermal weight are combined according to the three-dimensional coordinate points and the fire spread direction vector; the integration of the temperature field weight field and the smoke concentration weight field is used as the fire comprehensive hazard index field; the three-dimensional coordinate points, the unit fire spread direction vector, the fire spread intensity field and the fire comprehensive hazard index field are sorted according to the spatial grid number to generate the fire grid parameters including the spatial grid number, the three-dimensional coordinate field, the unit fire spread direction vector field, the fire spread intensity field and the fire comprehensive hazard index field.

[0089] S5. Use the hybrid mesh generator to construct a hexahedron-dominated mesh and increase the mesh density in the fire hazard area to obtain a fire optimized hybrid mesh.

[0090] S5.1. Based on the fire grid parameters, the octree adaptive subdivision algorithm is used to analyze the axial anisotropic parameters and generate a hexahedron-dominated hybrid grid strategy. The initial grid is output in combination with the building's 3D geometric data to extract the coordinate set of the fire hazard area.

[0091] Specifically, the spatial grid number, fire comprehensive hazard index field, and unit fire spread direction vector field are extracted from the fire grid parameters; an octree root node is created within the bounding box of the building's three-dimensional geometric data, and the root node size covers the complete spatial range of the building's three-dimensional geometric data; the fire comprehensive hazard index field of the fire grid parameters is traversed, and the octree node subdivision operation is triggered according to the numerical distribution of the fire comprehensive hazard index field in the fire grid parameters, splitting the node into eight child nodes; the subdivision process is repeated until the child node size meets the octree subdivision termination condition;

[0092] Mark the final octree node as a hexahedral mesh; convert the final octree node of the surface boundary in the building's 3D geometric data into a tetrahedral mesh; merge the hexahedral mesh and the tetrahedral mesh to generate a hybrid mesh set; associate each mesh in the hybrid mesh set with the fire comprehensive hazard index field and the unit fire spread direction vector field in the fire grid parameters; filter the mesh space coordinate fields that meet the fire grid screening conditions based on the fire comprehensive hazard index field; match the filtered mesh space coordinate fields with the component space coordinates of the building's 3D geometric data to generate a fire hazard area coordinate set;

[0093] Furthermore, the octree subdivision termination condition is as follows: when the child node size is ≤ the preset minimum grid size (e.g. 0.5m³) or the variance of the fire danger index within the node is less than the octree subdivision termination condition (e.g. 10%), the subdivision is stopped;

[0094] Fire intensity grid screening conditions: Grids whose comprehensive fire hazard index exceeds the critical value (such as ≥0.7) are judged to meet the fire intensity grid screening conditions and are used to match component coordinates to generate dangerous areas.

[0095] S5.2. Based on the initial grid, perform hierarchical encryption within the coordinate set of the fire hazard area to generate a non-uniform intermediate grid with a density gradient.

[0096] Specifically, the three-dimensional coordinate field and the fire comprehensive hazard index field of the fire hazard area coordinate set are extracted from the initial grid; the hierarchical encryption rule is set so that the value of the fire comprehensive hazard index field is positively correlated with the encryption level; each grid coordinate of the fire hazard area coordinate set is traversed, the fire comprehensive hazard index range is determined and the corresponding subdivision level is assigned; and the octree subdivision operation is performed on the grids with the assigned level.

[0097] The subdivided subgrid inherits the fire comprehensive hazard index field of the parent grid; the density gradient parameter is set as the subdivision level exponential function, and the exemplary density gradient parameter is that level 0 corresponds to density base 1, level 1 corresponds to density base 4, level 2 corresponds to density base 16, and level 3 corresponds to density base 64; the original grid and the subdivided subgrid are merged according to the spatial coordinates to generate a non-uniform intermediate grid set; each grid in the non-uniform intermediate grid set is associated with the fire comprehensive hazard index field and the density gradient field; the non-uniform intermediate grid data file is output, and the file fields include the grid number field, the three-dimensional coordinate field, the density gradient field, and the fire comprehensive hazard index field. The exemplary format is a CSV table, and the field order is number, coordinate, coordinate, Coordinates, density gradient, and comprehensive fire danger index are used to generate a non-uniform intermediate grid with density gradient.

[0098] S5.3. Perform Laplacian smoothing and transition zone connection processing on the non-uniform intermediate grid, output the transition grid, and map the fire grid parameters to the transition grid nodes to generate the fire optimized hybrid grid.

[0099] Specifically, each non-uniform intermediate grid node is traversed, the coordinates of adjacent nodes are obtained, the average value of adjacent node coordinates is calculated, and the current node coordinates are updated to the average value; this operation is repeated until the node displacement converges. Regions with large differences in density gradients between adjacent grids in the non-uniform intermediate grid set are detected and marked as transition zone boundaries; intermediate layer grid nodes are inserted at the transition zone boundaries, and the coordinates of the intermediate layer grid nodes are generated by linear interpolation. The density gradient is uniformly transitioned according to the difference between adjacent grids; the smoothed grid nodes are merged with the inserted intermediate layer grid nodes to generate a transition grid set. The fire comprehensive hazard index field and the unit fire spread direction vector field are extracted from the fire grid parameters, and bilinear interpolation mapping is performed according to the three-dimensional coordinates of the transition grid nodes. The consistency of the mapped fire comprehensive hazard index field and the unit fire spread direction vector data is verified, and the data of abnormal nodes is re-interpolated and updated. The verified transition grid nodes are integrated with the mapped fire comprehensive hazard index field and the unit fire spread direction vector field to generate a fire optimized hybrid grid.

[0100] S6. Through the fire characteristic adaptive algorithm, the fire optimized hybrid grid is checked for Reynolds number and boundary layer matching, and iterative correction is performed to generate a fire simulation correction grid.

[0101] S6.1. Calculate the Reynolds number distribution field based on the fire main spread direction vector field and the fire optimized hybrid grid.

[0102] It should be noted that the expression for calculating the Reynolds number distribution field is:

[0103] ;

[0104] in, Fire Optimized Hybrid Grid The Reynolds number distribution field at is the smoke density, is the flue gas dynamic viscosity, Fire Optimized Hybrid Grid The fire spreading velocity vector at Fire Optimized Hybrid Grid The scale parameter along the main direction of the fire;

[0105] Specifically, the unit fire spread direction vector field and the fire spread intensity field of the fire optimization hybrid grid node are extracted from the main fire spread direction vector field; the three-dimensional coordinate field and the fire comprehensive hazard index field of the node are extracted from the fire optimization hybrid grid; the modulus of the fire spread velocity vector is defined as the product of the modulus of the unit fire spread direction vector field and the fire spread intensity field; the spatial interval average value of the fire optimization hybrid grid node is calculated to obtain the scale parameter, and the spatial interval average value is obtained by the arithmetic average of the three-dimensional coordinates of the node and the distance between the adjacent nodes; the smoke density parameter is defined as the corresponding value of the fire comprehensive hazard index field mapped to the preset smoke density relationship table; the smoke dynamic viscosity parameter is defined as a constant experimental measurement value; each node of the fire optimization hybrid grid is traversed, and the modulus, scale parameter, smoke density parameter and smoke dynamic viscosity parameter of the fire spread velocity vector are substituted into the Reynolds number calculation formula to generate the Reynolds value of the current node, and the calculation of the Reynolds number distribution field is completed;

[0106] Furthermore, the specific construction process of the preset smoke density relationship table is as follows: based on historical fire case data, the measured smoke density values ​​corresponding to different fire comprehensive hazard index intervals are extracted; the fire comprehensive hazard index is divided into several continuous intervals, each interval corresponds to a median smoke density value; the mapping relationship table between the fire comprehensive hazard index and the smoke density is obtained through statistical analysis methods; the linear interpolation method is used to process the transition values ​​at the interval boundary to ensure the continuity of the mapping relationship; and finally a preset smoke density relationship table containing the fire comprehensive hazard index field and the smoke density field is generated.

[0107] S6.2. Dynamically adjust the number and thickness of the fire-optimized hybrid grid based on the Reynolds number distribution field, and output the boundary layer encrypted grid.

[0108] Specifically, the Reynolds value field and three-dimensional coordinate field of the fire optimization hybrid grid nodes are extracted from the Reynolds number distribution field; the near-wall refinement rule is defined as a dynamic association between the Reynolds value and the grid layer number and thickness parameters, with areas with higher Reynolds values ​​corresponding to larger layer parameters and smaller thickness parameters; each node of the fire optimization hybrid grid is traversed, and the layer number and thickness parameters are matched according to the Reynolds value; an octree subdivision operation is performed on the nodes with matching parameters, with the number of subdivisions equal to the layer number parameter, and each subdivision generates a subgrid and updates the three-dimensional coordinate field; the subdivided subgrid inherits the Reynolds value field of the parent grid; the thickness parameter is adjusted so that the grid spacing of each layer is gradually reduced according to the thickness parameter; transition grids are inserted into areas where the spacing between adjacent grid layers differs significantly, and the transition grid spacing is generated by linear interpolation; the subdivided refined grid and the transition grid are merged to generate a boundary layer refined grid set; each grid in the boundary layer refined grid set is associated with the Reynolds value field, the layer number field, and the thickness field; the difference in the number of adjacent grid layers is detected and smoothed by redistributing the number of grid layers in the difference area according to the adjacent average value; the smoothed boundary layer refined grid set is output as a structured data file to generate the boundary layer refined grid.

[0109] S6.3. Based on the boundary layer encrypted grid, multiple rounds of iterative correction are performed through the fire characteristic adaptive algorithm to generate a fire simulation correction grid.

[0110] Specifically, the grid number, three-dimensional coordinates, Reynolds number, number of layers and thickness data are extracted from the boundary layer encrypted grid; the temperature field and smoke concentration time series of historical fire cases are loaded as verification benchmarks; the three-dimensional coordinates, number of layers and thickness parameters of the boundary layer encrypted grid are input; the absolute percentage error between the temperature field and smoke concentration prediction values ​​and the benchmark values ​​is calculated; the grid nodes of the boundary layer encrypted grid are traversed, and the error weight coefficient is generated based on the temperature field prediction error percentage and the smoke concentration prediction error percentage, and the error weight coefficient is the average of the two percentages; the layer number field parameter is dynamically adjusted according to the error weight coefficient, for example: every 1% increase in the error weight coefficient corresponds to an increase of 0.1 layers, and every 1% decrease in the error weight coefficient corresponds to a decrease of 0.1 layers; the thickness field is dynamically adjusted according to the error weight coefficient, for example, every 1% increase in the error weight coefficient corresponds to a decrease of 0.1% in thickness, and every 1% decrease in the error weight coefficient corresponds to an increase of 0.1% in thickness. Perform octree subdivision operation on the adjusted layer number field and thickness field parameters to generate a new sub-grid and update the three-dimensional coordinate field; input the new grid data into the fire simulation model to perform the next round of iteration; repeat the process of error weight allocation, dynamic parameter adjustment, grid subdivision, and simulation verification until the rate of change of the error weight coefficient is lower than the preset convergence condition or the preset iteration round is reached; extract the three-dimensional coordinate field, layer number field, and thickness field of the final grid node; generate a fire simulation correction grid data file in CSV format, with the field order being grid number, Coordinates, number of layers and thickness; output fire simulation correction grid;

[0111] It should be noted that the process of setting the preset convergence conditions is as follows: based on the error weight coefficient change rate data of historical fire simulation cases, the average change rate of the error weight coefficient in multiple rounds of iterations is obtained; the convergence condition is defined as the absolute value of the error weight coefficient change rate in three consecutive rounds of iterations is less than 1% of the historical average change rate, which is regarded as the error change tending to be stable; the process of setting the preset iteration rounds is as follows: the average number of iterations required to reach the convergence conditions in historical fire simulation cases is statistically calculated, and the exemplary statistical result is 8 rounds; the preset iteration rounds are set to 1.5 times the average number of iterations, and the exemplary value is 12 rounds; it is verified through experiments that the proportion of cases that have not converged when the iteration rounds reach 12 rounds is less than 5%, confirming the validity of the upper limit of this round.

[0112] S7. Encapsulate the fire simulation corrected mesh into FDS format, inject the boundary conditions and material combustion parameters of the standardized BIM file and component attribute table, and output a high-quality input package that can directly drive the fire simulation.

[0113] S7.1. Convert the fire simulation correction grid into FDS format to generate a fire simulation correction grid file.

[0114] Specifically, the grid number field, three-dimensional coordinate field, layer number field, thickness field, temperature field prediction value field, and smoke concentration prediction value field in the fire simulation correction grid data file are extracted; the grid structure parameters are defined according to the FDS format specification, and the grid structure parameters include the grid start point coordinate field, the grid end point coordinate field, and the grid unit size field; the three-dimensional coordinate field of the fire simulation correction grid is converted into the grid start point coordinate field and the grid end point coordinate field in the FDS format, and the conversion rule is that the start point coordinate of each grid is the three-dimensional coordinate field minus 50% of the thickness field, and the end point coordinate is the three-dimensional coordinate field plus 50% of the thickness field; the layer number field is converted into the grid unit size field in the FDS format, and the grid unit size field is converted into the grid start point coordinate field and the grid end point coordinate field in the FDS format. The element size field is equal to the thickness field divided by the layer number field; the temperature field prediction value field is mapped to the temperature field data field in FDS format, and the smoke concentration prediction value field is mapped to the smoke concentration data field in FDS format; the grid structure parameters, temperature field data field, and smoke concentration data field are written to the text file according to the FDS input file syntax rules; the grid unit size field of the text file is verified to see if it meets the grid continuity requirements of the FDS format, and the grid continuity requirement is that the size difference of adjacent grid units does not exceed 10% of the size field; the verified text file is saved as a fire simulation correction grid file in FDS format with a file extension of .fds; the fire simulation correction grid file is output to the preset storage path.

[0115] S7.2. Inject the boundary conditions of the standardized BIM file and the association between the key fire components and combustion parameters in the component attribute table into the fire simulation correction grid file to obtain a high-quality input package that can directly drive the fire simulation.

[0116] It should be explained that the boundary condition field of the standardized BIM file and the fire key component number field, fire rating label field, heat release rate peak field, and ventilation attribute label field of the component attribute table are extracted; the grid number field, three-dimensional coordinate field, temperature field prediction value field, and smoke concentration prediction value field of the fire simulation correction grid file are extracted; a spatial association is established between the fire key component number field of the standardized BIM file and the grid number field of the fire simulation correction grid file through three-dimensional coordinate field matching; the fire rating label field is mapped to the material thermal conductivity coefficient field of the fire simulation correction grid file, the heat release rate peak field is mapped to the heat release rate distribution field of the fire simulation correction grid file, and the ventilation attribute label field is mapped to the ventilation opening area field of the fire simulation correction grid file; the material thermal conductivity coefficient field is mapped to the material thermal conductivity coefficient field according to the FDS format specification. , heat release rate distribution field, and ventilation opening area field are converted into FDS input parameter fields; verify whether the converted FDS input parameter fields meet the input constraints of the fire simulation engine, including the numerical non-negativity of the heat release rate distribution field, the numerical range validity of the ventilation opening area field, and the spatial topological consistency of the grid unit and the boundary conditions; merge the verified material thermal conductivity coefficient field, heat release rate distribution field, ventilation opening area field, temperature field prediction value field, and smoke concentration prediction value field into a structured data table according to the FDS input file format; integrate the structured data table with the grid number field and three-dimensional coordinate field of the fire simulation correction grid file and write them into the FDS input file; package the FDS input file and the component attribute table and boundary condition table of the standardized BIM file into a ZIP compressed file to generate a high-quality input package.

[0117] This embodiment also provides a computer device suitable for the scene preprocessing method for fire simulation, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the scene preprocessing method for fire simulation proposed in the above embodiment.

[0118] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0119] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the scene preprocessing method for fire simulation proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0120] In summary, the present invention achieves adaptive distribution of grid density in fire-hazardous areas and smooth connection of transition areas through: a hybrid grid generator, so that the grid distribution in fire-hazardous areas accurately matches the fire spread characteristics; through a fire characteristic adaptive algorithm, dynamic optimization and iterative correction of boundary layer grid parameters are completed, ensuring accurate analysis of wall turbulence and smoke diffusion; the two technologies work together to improve the computational efficiency and prediction accuracy of fire simulation, providing highly reliable dynamic fire evolution data for building fire protection design.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A scene preprocessing method for fire simulation, characterized by: include, Collect and modify the building's 3D geometric data, and output standardized BIM files and component attribute tables through component semantic annotation and material attribute binding; The fire spread dynamics model is used to calculate the three-dimensional thermal weight and generate a thermal map of the fire hazard area; The main fire diffusion direction and axial anisotropy parameters are extracted through the tensor field generation algorithm to generate the fire grid parameters; Through the hybrid mesh generator, hexahedron-dominated mesh is constructed and the mesh density of the fire hazard area is increased to obtain the fire optimized hybrid mesh; The fire characteristic adaptive algorithm is used to perform Reynolds number verification and boundary layer matching on the fire optimized hybrid grid, and then iterative correction is performed to generate a fire simulation correction grid. The fire simulation corrected mesh is encapsulated in FDS format, and the boundary conditions and material combustion parameters of the standardized BIM file and component attribute table are injected to output a high-quality input package that can directly drive the fire simulation.

2. The scene preprocessing method for fire simulation according to claim 1, characterized in that: The correction includes geometric repair and redundancy cleaning.

3. The scene preprocessing method for fire simulation according to claim 2, characterized in that: The steps of outputting standardized BIM files and component attribute tables through component semantic annotation and material attribute binding are as follows: Based on the corrected 3D building geometry data, the fire protection level and ventilation attribute semantic labels are annotated for key fire components, and a list of key fire components with classification labels is output; Automatically match the combustion characteristic parameters of the fire key component list from the fire parameter library to generate a standardized BIM file containing fire parameters; By parsing the attribute data of key fire components in standardized BIM files, the correlation between key fire components and combustion characteristic parameters is extracted, and a component attribute table is generated.

4. The scene preprocessing method for fire simulation according to claim 3, characterized in that: The fire spread dynamics model is used to calculate the three-dimensional thermal weight and generate a thermal map of the fire hazard area. The specific steps are as follows: Based on historical standardized BIM files, a fire propagation dynamics model was trained using spatial topology deep learning methods; The standardized BIM file is input into the trained fire propagation dynamics model to calculate the heat release rate distribution of the fire source; Based on standardized BIM files, component attribute tables, and the heat release rate distribution of fire sources, the fire spread path is dynamically simulated and three-dimensional thermal weights are output; The three-dimensional thermal weights are mapped to the three-dimensional Euclidean space of the building's three-dimensional geometric data, and the temperature field and smoke concentration hazard indicators are marked to generate a thermal map of the fire hazard area.

5. The scene preprocessing method for fire simulation according to claim 4, characterized in that: The tensor field generation algorithm is used to extract the main diffusion direction of the fire and the axial anisotropy parameters to generate the fire grid parameters. The specific steps are as follows: The tensor field generation algorithm is used to extract the temperature field of the fire hazard area heat map and the gradient characteristics of the smoke concentration hazard index, and the main fire spread direction is analyzed to output the main fire diffusion direction vector field; According to the main fire diffusion direction vector field and the three-dimensional thermal weight, the fire diffusion coefficient ratios of each axis in the three-dimensional Euclidean space of the building's three-dimensional geometric data are calculated, and the anisotropic parameters of each axis are output; The fire main spread direction vector field and the axial anisotropy parameters are integrated to generate the fire grid parameters based on the spatial grid number.

6. The scene preprocessing method for fire simulation according to claim 5, characterized in that: The hybrid grid generator is used to construct a hexahedron-dominated grid and increase the grid density in the fire hazard area to obtain a fire optimized hybrid grid. The specific steps are as follows: Based on the fire grid parameters, an octree adaptive subdivision algorithm is used to analyze the axial anisotropic parameters and generate a hexahedron-dominated hybrid grid strategy. The initial grid is then outputted in combination with the building's 3D geometric data to extract the coordinate set of the fire hazard area. Based on the initial grid, hierarchical encryption is performed within the coordinate set of the fire hazard area to generate a non-uniform intermediate grid with a density gradient; The non-uniform intermediate grid is subjected to Laplacian smoothing and transition zone connection processing, and the transition grid is output. The fire grid parameters are mapped to the transition grid nodes to generate the fire optimized hybrid grid.

7. The scene preprocessing method for fire simulation according to claim 6, characterized in that: The specific steps of generating the fire simulation correction grid are as follows: Calculate the Reynolds number distribution field based on the fire main diffusion direction vector field and the fire optimized hybrid grid; According to the Reynolds number distribution field, the fire intensity is dynamically adjusted to optimize the number and thickness of hybrid grid layers, and the boundary layer encrypted grid is output; Based on the boundary layer encrypted grid, multiple rounds of iterative correction are performed through the fire characteristic adaptive algorithm to generate the fire simulation correction grid.

8. The scene preprocessing method for fire simulation according to claim 7, characterized in that: The output can directly drive a high-quality input package for fire simulation, as follows: Convert the fire simulation correction grid into FDS format to generate a fire simulation correction grid file; The boundary conditions of the standardized BIM file and the association relationship between the key fire components and combustion parameters in the component attribute table are injected into the fire simulation correction grid file to obtain a high-quality input package that can directly drive the fire simulation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the scene preprocessing method for fire simulation described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the scene preprocessing method for fire simulation according to any one of claims 1 to 8 are implemented.

Images