Tunnel fire temperature field prediction and safe area division method and system

Abstract

The invention relates to the technical field of data processing, in particular to a tunnel fire temperature field prediction and safety area division method and system, and the method comprises the following steps: carrying out the preprocessing of the historical data of a tunnel fire, and obtaining the standardized data; establishing a combustion equation of the tunnel fire based on the standardized data; constructing a combustion model of the tunnel fire according to the combustion equation; using the standardized data to train the combustion model; and predicting a temperature field of a tunnel fire according to the trained combustion model, and imaging a prediction result to divide a safety area. According to the method, the combustion model of the tunnel fire is constructed based on the physical constraint neural network, the most matched combustion model is selected according to different tunnel fire conditions, the temperature field of the tunnel fire is predicted in real time, the prediction result is visualized, the accuracy and real-time performance of tunnel fire prediction are effectively improved, and the tunnel fire prediction efficiency is improved. And according to a visualization result, a safety area is divided, and powerful support is provided for emergency rescue.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, in particular to a method and system for predicting a tunnel fire temperature field and dividing a safe area. Background Art

[0002] Tunnel fires are usually highly dangerous and unpredictable due to the closed environment and complex flow field. The long and narrow geometric characteristics lead to fires accompanied by complex thermal fluid dynamics phenomena such as turbulence, thermal convection, radiation and chemical reactions. The strong coupling of these phenomena makes the temperature field, smoke diffusion and flame propagation particularly complex. Therefore, the development of accurate numerical simulation and prediction models is of great significance for temperature field prediction, real-time monitoring, emergency rescue and fire control of tunnel fires.

[0003] Traditional numerical simulation techniques mainly include Reynolds averaging, large eddy simulation, and direct numerical simulation. Among them, the Reynolds averaging method simplifies the calculation by averaging the transient behavior of large and small eddies, but it is difficult to capture the instantaneous turbulence details and local vortex structure, resulting in underestimation of the impact of turbulence on flame propagation and smoke diffusion; in contrast, large eddy simulation and direct numerical simulation can more accurately simulate the complex physical processes in tunnel fires. However, direct numerical simulation has extremely high demand for computing resources and is usually only applicable to small-scale or simplified scenes; although large eddy simulation performs well in reducing computing costs, its computing efficiency in large-scale complex scenes still needs to be improved. With the rapid development of computer technology, data-driven prediction technology has become a hot topic of research. This type of method can provide predictions quickly; however, pure data-driven models such as long short-term memory networks and artificial neural networks, in the absence of physical constraints, predict results that are inconsistent with the physical field. To solve this problem, a neural network algorithm based on physical information has been applied to engineering practice. By predicting flow field parameters, introducing and solving partial differential equations, the prediction accuracy is enhanced to ensure physical consistency, but the calculation speed cannot fully meet the real-time requirements of tunnel fire rescue.

[0004] Therefore, considering the advantages of numerical simulation technology and artificial intelligence algorithms, the tunnel fire combustion equation established based on the large eddy simulation numerical model is used as the physical control equation, and its physical equation residual is embedded in the loss function of the neural network, which not only improves the physical consistency of the model, but also significantly improves the computational efficiency. This method is suitable for complex tunnel fire environments, and can predict the tunnel fire temperature field and divide the safe area, providing a basis for emergency rescue. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for predicting the temperature field of a tunnel fire and dividing a safe area.

[0006] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides a method for predicting the temperature field of a tunnel fire and dividing a safe area, the method comprising the following steps: preprocessing the historical data of the tunnel fire to obtain standardized data; establishing a combustion equation for the tunnel fire based on the standardized data; constructing a combustion model for the tunnel fire according to the combustion equation; training the combustion model using the standardized data; predicting the temperature field of the tunnel fire based on the trained combustion model, and visualizing the prediction results for dividing the safe area. The present invention introduces physical constraints into a neural network to construct a combustion model, trains the combustion model based on historical data, so that it can adapt to different tunnel fire conditions, thereby predicting the temperature field of various complex tunnel fires in real time, and visualizing the prediction results, and then dividing the safe area according to the image data. In practical applications, it can provide a scientific reference basis for emergency rescue decisions in a timely manner, and has important practical value.

[0007] Optionally, the preprocessing of the historical data of tunnel fires to obtain standardized data includes: obtaining the historical data of tunnel fires; performing interval mapping on the spatial temperature field of the historical data; and performing standardized processing on the historical data. The present invention uses the historical data of past tunnel fires as data support and performs standardized processing on the historical data, providing a rigorous and reliable data source for the determination and training of subsequent combustion models, effectively improving the prediction accuracy and generalization ability of the combustion model, and greatly enhancing the feasibility and practicality of the present invention.

[0008] Optionally, the combustion equation for tunnel fires is established based on the standardized data, including: obtaining a mass conservation expression during the combustion process; obtaining a momentum conservation expression affected by wind speed during the combustion process; constructing an energy equation expression affected by thermal radiation during the combustion process; and obtaining a mass fraction equation expression that takes into account the combustion rate during the combustion process. The present invention introduces physical constraints by establishing mass, momentum, energy, and mass fraction conservation equations for tunnel fires, which can effectively improve the physical consistency of the combustion model of the present invention, while significantly improving the computational efficiency. Combined with the constraints of physical conditions, the present invention can better adapt to complex tunnel fire environments, thereby improving the flexibility and practical value of the present invention.

[0009] Optionally, the combustion equation includes:

[0010] The mass conservation expression is:

[0011]

[0012] The momentum conservation expression is:

[0013]

[0014] Energy equation expression:

[0015]

[0016] The mass fraction equation expression is:

[0017]

[0018] in, is the fluid density, For time, is the divergence operator, For speed, is the fluid velocity vector, is the gradient operator, For pressure, is the molecular viscosity coefficient, is the subgrid viscosity coefficient, is the acceleration due to gravity, is the subgrid stress tensor, The wind force factor is is the velocity vector of the external wind, is the velocity vector of the wind inside the tunnel, is the enthalpy, is the molecular thermal conductivity, is the sub-grid eddy thermal conductivity, is the temperature, is the volume heat source term, is the radiation heat flux vector, is the quality score, is the molecular diffusion coefficient, is the sub-grid turbulent diffusion coefficient, The present invention describes the physical processes involved in the combustion process based on fluid dynamics, constructs conservation expressions of mass, momentum, energy and mass fraction according to standardized tunnel fire history data, and digitizes the complex tunnel fire situation to facilitate practical processing and analysis, providing physical condition constraints for the subsequent construction of a general tunnel fire combustion model, enhancing the applicability and physical consistency of the combustion model, and effectively improving the rigor and reliability of the method of the present invention.

[0019] Optionally, constructing a combustion model for tunnel fires according to the combustion equation includes: obtaining model parameters of the combustion model based on the historical data; obtaining residuals of mass, momentum, energy and mass fraction equations respectively; and constructing a loss function to obtain the combustion model. The present invention establishes model parameters of the combustion model based on historical data of tunnel fires, and at the same time establishes physical conservation equations to introduce physical constraints, so that the constructed combustion model can adapt to various complex tunnel fire environments, while enhancing the accuracy of the combustion model and providing more accurate and reliable prediction results.

[0020] Optionally, constructing the loss function to obtain the combustion model includes:

[0021]

[0022] in, is the loss function, is the mean square error between the predicted value and the true value, is the predicted value, is the true value, is the weight coefficient, , , and are the residuals of the equations for mass, momentum, energy, and mass fraction, respectively, , , and are the weights of each physical residual term respectively. The present invention obtains the residuals based on the obtained mass, momentum, energy and mass fraction equations, embeds the physical residuals into the loss function of the neural network with corresponding weights, and uses the physical residuals as regularization terms to prevent the constructed combustion model from overfitting. The physical constraints are introduced into the combustion model, which can effectively capture the process of vortex shedding during turbulent combustion, making the constructed combustion model more in line with the actual tunnel fire situation, and improving the generalization ability of the combustion model, so that the combustion model can better adapt to the complexity of the actual tunnel fire scene, and provide strong support for the practical application of the combustion model.

[0023] Optionally, the use of the standardized data to train the combustion model includes: using an optimizer to set the training parameters of the combustion model; importing the standardized data into the combustion model; and training the combustion model using the optimizer. The present invention imports a large amount of standardized historical data of tunnel fires into the combustion model, uses an optimizer to train the constructed combustion model, and optimizes the combustion model, which can effectively improve the prediction accuracy of the combustion model and enhance the reliability of the combustion model, so that the combustion model constructed by the present invention can be flexibly applied to various complex tunnel fire scenes in practical applications, greatly improving the practicality of the present invention.

[0024] Optionally, the temperature field of the tunnel fire is predicted based on the trained combustion model, and the prediction results are visualized for dividing the safe area, including: selecting the combustion model that matches based on the monitoring data of the detection points in the tunnel; importing the monitoring data into the combustion model to predict the temperature field of the tunnel fire in real time; visualizing the prediction results to obtain a three-dimensional visualization image; and dividing the safe area according to the three-dimensional visualization image. The present invention selects the most suitable combustion model based on the monitoring data in the tunnel, which significantly improves the rigor of the present invention, and uses the combustion model to predict the temperature field of the tunnel fire in real time based on the real-time monitoring data, which effectively improves the real-time nature of the prediction, and further visualizes the prediction results, and divides the safe area according to the obtained image data, which enhances the practical value of the method of the present invention and has important reference value for emergency safety rescue decision-making.

[0025] Optionally, the processing the prediction result into a three-dimensional visualization image includes:

[0026]

[0027] in, For the three-dimensional data visualization process, Indicates three-dimensional image rendering. It is a three-dimensional grid structure. is the continuous temperature field function constructed, is the interpolation function, is a three-dimensional data point set, is the predicted temperature value, is the three-dimensional point coordinate, and The present invention constructs a continuous temperature field function through an interpolation function, and renders a three-dimensional image to obtain a three-dimensional temperature field visualization image, which can reflect the real-time situation of a tunnel fire, and enable emergency rescue personnel to quickly grasp the temperature field distribution, providing a reference basis for dividing safe areas, effectively improving the emergency response capability to tunnel fires, significantly enhancing the practicability of the method of the present invention, and providing a scientific basis for timely safety rescue decisions.

[0028] In the second aspect, the present invention provides a system for predicting the temperature field of a tunnel fire and dividing a safe area. The system executes the method for predicting the temperature field of a tunnel fire and dividing a safe area provided by the present invention. The system includes an input device, an output device, a processor, and a memory. The gain lies in that the hardware facilities integrated by the present invention have excellent performance, the input device, the output device, the processor, and the memory are interconnected, and the information transmission between the various components is smooth. Through the interaction of multiple hardware facilities, an efficient information processing system is constructed. The system of the present invention introduces the physical constraints of tunnel fires into neural networks, further constructs a combustion model of tunnel fires, improves the physical consistency of the combustion model, and makes the constructed combustion model applicable to various complex tunnel fire scenes, thereby performing real-time prediction of the temperature field of tunnel fires, and further visualizing the prediction results, thereby providing a reference for dividing safe areas and providing a reference for emergency safety rescue decisions for tunnel fires. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a method for predicting temperature field and dividing safe areas in a tunnel fire according to an embodiment of the present invention;

[0030] Figure 2 This is a front view comparison diagram of the actual value and predicted value of the tunnel fire temperature field according to an embodiment of the present invention;

[0031] Figure 3 A top view comparison diagram of the actual value and predicted value of the tunnel fire temperature field according to an embodiment of the present invention;

[0032] Figure 4 This is a comparison diagram of the actual value and predicted value of the tunnel fire temperature field according to an embodiment of the present invention;

[0033] Figure 5 This is a framework diagram of a tunnel fire temperature field prediction and safety area division system according to an embodiment of the present invention. Specific embodiments

[0034] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0035] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0036] See also Figure 1 An embodiment of the present invention provides a method for predicting a tunnel fire temperature field and dividing a safe area, the method comprising the following steps:

[0037] S1. Preprocess the historical data of tunnel fire to obtain standardized data.

[0038] Among them, S1 specifically includes the following steps:

[0039] S11. Obtain historical data of tunnel fires.

[0040] Specifically, a processor is used to obtain historical data of tunnel fires through numerical simulation software, and the historical data is stored in a memory, thereby ensuring the reliability of the data source of the present invention and providing accurate data support for the present invention.

[0041] S12, performing interval mapping on the spatial temperature field of the historical data.

[0042] Specifically, using a processor to establish a temperature interval mapping function, and storing the obtained temperature interval mapping function in a memory, includes:

[0043]

[0044] in, is the temperature interval mapping function, is the temperature, Indicates rounding down.

[0045] S13, standardizing the historical data.

[0046] Specifically, based on the mean and standard deviation of the historical data, the historical data of the tunnel fire is standardized by a processor, and the standardized data obtained after the standardization is stored in a memory, including:

[0047]

[0048] in, To standardize the data, For historical data, is the mean, is the standard deviation.

[0049] S2. Establishing a combustion equation for tunnel fire based on the standardized data.

[0050] Specifically, the combustion equation is obtained by using a processor based on the standardized data, and the obtained combustion equation expression is stored in a memory, including:

[0051] The mass conservation expression is:

[0052]

[0053] The momentum conservation expression is:

[0054]

[0055] Energy equation expression:

[0056]

[0057] The mass fraction equation expression is:

[0058]

[0059] in, is the fluid density, For time, is the divergence operator, For speed, is the fluid velocity vector, is the gradient operator, For pressure, is the molecular viscosity coefficient, is the subgrid viscosity coefficient, is the acceleration due to gravity, is the subgrid stress tensor, The wind force factor is is the velocity vector of the external wind, is the velocity vector of the wind inside the tunnel, is the enthalpy, is the molecular thermal conductivity, is the sub-grid eddy thermal conductivity, is the temperature, is the volume heat source term, is the radiation heat flux vector, is the quality score, is the molecular diffusion coefficient, is the sub-grid turbulent diffusion coefficient, is the generation rate.

[0060] Furthermore, the Arrhenius equations include:

[0061]

[0062] in, is the generation rate, is the fluid density, is the reaction rate prefactor, is the activation energy, is the gas constant, is the temperature, is the reactant concentration, is the reaction order.

[0063] Furthermore, the sub-network structure includes:

[0064]

[0065]

[0066]

[0067] in, is the subgrid viscosity coefficient, is the fluid density, is a constant, is the filter width, is the reaction order, is the modulus of shear strain rate, is the sub-grid eddy thermal conductivity, is the specific heat capacity at constant pressure, is the turbulent Prandtl number, is the sub-grid turbulent diffusion coefficient, is the subgrid viscosity coefficient, is the turbulent Schmidt number.

[0068] S3. Constructing a combustion model of tunnel fire according to the combustion equation.

[0069] Among them, S3 specifically includes the following steps:

[0070] S31. Obtaining model parameters of the combustion model based on the historical data.

[0071] Specifically, the parameters of the neural network are obtained by using a processor according to the nonlinear activation function of the neural network, and the obtained parameters of the neural network are stored in a memory as a data basis for further constructing a combustion model, including:

[0072] Input Layer:

[0073]

[0074] Hidden Layer 1:

[0075]

[0076] Hidden Layer 2:

[0077]

[0078] Hidden Layer 3:

[0079]

[0080] Hidden layer 4:

[0081]

[0082] Output layer:

[0083]

[0084] in, For input data, is the output of each hidden layer, is a nonlinear activation function, is the weight, is the bias parameter, is the predicted value.

[0085] S32. Obtain the residuals of the mass, momentum, energy and mass fraction equations respectively.

[0086] Specifically, the processor is used to obtain the residuals of each equation according to the physical constraint equation, and the obtained residuals are stored in a memory.

[0087] S33, constructing a loss function to obtain the combustion model.

[0088] Specifically, the processor is used to introduce the residual of the physical constraint equation into the loss function as a regularization term with a certain weight, including:

[0089]

[0090] in, is the loss function, is the mean square error between the predicted value and the true value, is the predicted value, is the true value, is the weight coefficient, , , and are the residuals of the equations for mass, momentum, energy, and mass fraction, respectively, , , and are the weights of each physical residual term.

[0091] S4. Using the standardized data to train the combustion model.

[0092] Specifically, the optimizer is used to set the training parameters, and the combustion model is trained based on the standardized historical data, and the processor is used to minimize the loss function, including:

[0093]

[0094] in, represents minimization, is the weight, is the bias parameter, is the loss function, For input data, is the true value.

[0095] Further, using a processor to evaluate the prediction performance of the combustion model on the validation set, calculating the mean square error and storing it in a memory, including:

[0096]

[0097] in, is the mean square error, is the total number of samples, is the predicted value, is the true value.

[0098] S5. Predicting the temperature field of the tunnel fire based on the trained combustion model, and visualizing the prediction results for dividing the safety area.

[0099] Among them, S5 specifically includes the following steps:

[0100] S51. Selecting a suitable combustion model based on monitoring data of detection points in the tunnel.

[0101] Specifically, the processor is used to select the most suitable combustion model according to different input feature combinations of tunnel fire, including:

[0102]

[0103] in, For the combustion model, For input data, is the fluid velocity vector, are tunnel parameters, For pressure.

[0104] Further, a processor is used to construct a hierarchical decision tree for selecting a combustion model, including:

[0105] Determine input features:

[0106]

[0107] in, is the input feature, are different physical quantities in tunnel fire.

[0108] It should be noted that the different physical quantities in the tunnel fire are measured tunnel fire characteristic quantities such as wind speed, temperature and smoke concentration.

[0109] Construct the first-level decision tree to classify the input features of the tunnel fire and determine the general direction of selecting the combustion model, including:

[0110]

[0111] in, is the output category, It is the first layer of decision tree.

[0112] The second-level decision tree is constructed based on the first-level decision tree to further determine the direction of the combustion model, including:

[0113]

[0114] in, is the output category, It is the second level decision tree.

[0115] Then construct the subsequent decision trees at each level, including:

[0116]

[0117] in, is the output category, For the Layer decision tree.

[0118] Furthermore, a processor is used to connect the combustion model with a multi-layer decision tree in series, so as to quickly select a suitable combustion model, including:

[0119]

[0120] in, For output, For the combustion model, For input, For decision tree.

[0121] S52: Import the monitoring data into the combustion model to perform real-time prediction on the temperature field of the tunnel fire.

[0122] Specifically, the monitoring data of the detection points in the tunnel are inputted by the input device, and the combustion model selected by the processor through the hierarchical decision tree is used to make a real-time prediction of the temperature field of the tunnel fire.

[0123] S53, performing image processing on the prediction result to obtain a three-dimensional visualization image.

[0124] Specifically, a processor is used to construct a continuous temperature field function through an interpolation function, and three-dimensional grid temperature data is rendered in a three-dimensional image to generate a three-dimensional temperature field visualization image of a tunnel fire and save it in a memory, and the three-dimensional temperature field visualization image is displayed through an output device, including:

[0125]

[0126] in, For the three-dimensional data visualization process, Indicates three-dimensional image rendering. It is a three-dimensional grid structure. is the continuous temperature field function constructed, is the interpolation function, is a three-dimensional data point set, is the predicted temperature value, is the three-dimensional point coordinate, and is the total number of traversal points.

[0127] See also Figure 2 , the figure is a front view comparison diagram of the actual value and the predicted value of the tunnel fire temperature field, which is a front view visualization image of the three-dimensional temperature field of the tunnel fire obtained by the processor, including the real value temperature field distribution diagram of the tunnel fire and the predicted value temperature field distribution diagram obtained by the present invention.

[0128] See also Figure 3 , the figure is a top-down comparison diagram of the actual value and the predicted value of the tunnel fire temperature field, which is a top-down visualization image of the three-dimensional temperature field of the tunnel fire obtained by the processor, including the actual value temperature field distribution diagram of the tunnel fire and the predicted value temperature field distribution diagram obtained by the present invention.

[0129] See also Figure 4 , the figure is a comparison diagram of the actual value and predicted value of the temperature field of a tunnel fire, which is a three-dimensional oblique visualization image of the temperature field of a tunnel fire obtained by the processor, including a distribution diagram of the actual value temperature field of the tunnel fire and a distribution diagram of the predicted value temperature field obtained by the present invention.

[0130] according to Figure 2 , Figure 3 and Figure 4 The actual value temperature field distribution of the tunnel fire is well consistent with the predicted value temperature field distribution obtained by the method of the present invention, which fully reflects the accuracy and feasibility of the method of the present invention and greatly improves the practical application value of the method of the present invention.

[0131] S54: Divide the safety area according to the three-dimensional visualization image.

[0132] Specifically, a processor is used to divide the safe area in the tunnel fire according to the obtained three-dimensional temperature field visualization image of the tunnel fire, and the safe area is displayed through an output device to provide a scientific reference basis for emergency safety rescue.

[0133] See also Figure 5 In an optional embodiment, in order to efficiently execute the tunnel fire temperature field prediction and safety area division method provided by the present invention, the present invention provides a tunnel fire temperature field prediction and safety area division system, the system includes an input device, an output device, a processor and a memory, the hardware facilities are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the specific steps of the embodiment related to the tunnel fire temperature field prediction and safety area division method provided by the present invention. The tunnel fire temperature field prediction and safety area division system provided by the present invention has a complete structure, objective and stable, and can efficiently execute the tunnel fire temperature field prediction and safety area division method provided by the present invention, thereby improving the overall applicability and practical application ability of the present invention.

[0134] In summary, the tunnel fire temperature field prediction and safety area division method and system provided by the method of the present invention introduce physical constraints into the neural network to construct a combustion model, improve the physical consistency of the combustion model, make the constructed combustion model applicable to various complex tunnel fire scenes, and significantly improve the calculation efficiency, by introducing physical constraints and data-driven to achieve real-time prediction of tunnel fires and further visualize the prediction results, so as to provide a scientific basis for dividing safety areas and timely safety rescue decisions for tunnel fires. The method of the present invention is easy to understand, simple in calculation, small in workload, easy to apply in practice, and provides a theoretical basis and technical support for timely safety rescue in tunnel fires.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. Tunnel fire temperature field prediction and safety area division method, It is characterized in that The steps include: Preprocess the historical data of tunnel fire to obtain standardized data; Establishing a combustion equation for tunnel fire based on the standardized data; Constructing a combustion model of tunnel fire according to the combustion equation; Using the standardized data to train the combustion model; The temperature field of the tunnel fire is predicted based on the trained combustion model, and the prediction results are visualized to divide the safe area.

2. According to the method for predicting temperature field and dividing safe areas of tunnel fires according to claim 1, It is characterized in that The preprocessing of the historical data of tunnel fire to obtain standardized data includes: Obtain historical data on tunnel fires; Performing interval mapping on the spatial temperature field of the historical data; The historical data is standardized.

3. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 2, It is characterized in that The combustion equation of tunnel fire is established based on the standardized data, including: Obtain the mass conservation expression during combustion; The momentum conservation expression of wind speed effect during combustion is obtained; Construct the energy equation expression for the effect of thermal radiation during combustion; Obtain the mass fraction equation expression considering the burning rate during the combustion process.

4. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 3, It is characterized in that The combustion equation includes: The mass conservation expression is: ； The expression for conservation of momentum is: ； Energy equation expression: ； The mass fraction equation expression is: ； in, is the fluid density, For time, is the divergence operator, For speed, is the fluid velocity vector, is the gradient operator, For pressure, is the molecular viscosity coefficient, is the subgrid viscosity coefficient, is the acceleration due to gravity, is the subgrid stress tensor, The wind force factor is is the velocity vector of the external wind, is the velocity vector of the wind inside the tunnel, is the enthalpy, is the molecular thermal conductivity, is the sub-grid eddy thermal conductivity, is the temperature, is the volume heat source term, is the radiation heat flux vector, is the quality score, is the molecular diffusion coefficient, is the sub-grid turbulent diffusion coefficient, is the generation rate.

5. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 3, It is characterized in that The method of constructing a combustion model of a tunnel fire according to the combustion equation comprises: Obtaining model parameters of the combustion model based on the historical data; Obtain the residuals of the mass, momentum, energy, and mass fraction equations respectively; A loss function is constructed to obtain the combustion model.

6. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 5, It is characterized in that The step of constructing a loss function to obtain the combustion model includes: ； in, is the loss function, is the mean square error between the predicted value and the true value, is the predicted value, is the true value, is the weight coefficient, , , and are the residuals of the equations for mass, momentum, energy, and mass fraction, respectively, , , and are the weights of each physical residual term.

7. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 5, It is characterized in that The step of training the combustion model using the standardized data comprises: Using an optimizer to set training parameters of the combustion model; importing the standardized data into the combustion model; The combustion model is trained using the optimizer.

8. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 7, It is characterized in that The temperature field of the tunnel fire is predicted based on the trained combustion model, and the prediction result is visualized for dividing the safety area, including: Selecting a suitable combustion model based on monitoring data of detection points in the tunnel; Importing the monitoring data into the combustion model to perform real-time prediction of the temperature field of the tunnel fire; Performing image processing on the prediction results to obtain a three-dimensional visualization image; A safe area is divided according to the three-dimensional visualization image.

9. The method for predicting temperature field and dividing safe areas of tunnel fire according to claim 8, It is characterized in that The step of processing the prediction result into a three-dimensional visualization image includes: ； in, For the three-dimensional data visualization process, Indicates three-dimensional image rendering. It is a three-dimensional grid structure. is the continuous temperature field function constructed, is the interpolation function, is a three-dimensional data point set, is the predicted temperature value, are the three-dimensional point coordinates, and is the total number of traversal points.

10. Tunnel fire temperature field prediction and safety area division system, It is characterized in that The system includes an input device, an output device, a processor and a memory, wherein the input device, the output device, the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the tunnel fire temperature field prediction and safety area division method according to any one of claims 1 to 9.

Images