A fire control method, device and medium for highway tunnel environment

Abstract

The embodiments of this specification disclose a fire control method, device and medium for a highway tunnel environment, and relate to the technical field of highway tunnels. The method comprises: collecting real-time tunnel environment data and real-time tunnel traffic data of the highway tunnel, generating initial fire alarm information, predicting the tunnel fire, determining the predicted fire information, determining the initial fire node control scheme corresponding to the tunnel fire according to the predicted fire information and the tunnel information of the highway tunnel acquired in advance, so as to remotely control the fire-fighting equipment, collecting the corresponding real-time control fire information, synchronizing the real-time control fire information to the fire rescue terminal, and determining the rescue duration information and rescue entry route information corresponding to the fire rescue terminal; adjusting the initial fire node control scheme according to the rescue duration information and the real-time control fire information, determining the mid-term fire node control scheme, and performing fire control according to the mid-term fire node control scheme and the rescue entry route information.

Description

Technical Field

[0001] The present specification relates to the technical field of highway tunnels, and in particular to a fire control method, equipment and medium for use in a highway tunnel environment. Background Art

[0002] Due to the special construction structure, long distance, closed or semi-closed space environment of the expressway tunnel, once a fire occurs, the fire is very likely to spread rapidly. Vehicle fire is the main type of tunnel fire. Its burning and spreading speed is fast. In addition, the dense vehicles and diverse goods in the tunnel often contain flammable and explosive items, which further increases the risk of fire getting out of control. This rapid spread characteristic places extremely high demands on the initial control of the fire. The traditional method of relying on the alarm of on-site personnel often fails to activate the emergency response mechanism in a timely and effective manner due to the delay and possible inaccuracy of information transmission. In addition, the expressway tunnel is far away from the urban fire brigade, and there are high professional requirements for the activation and availability of on-site fire-fighting equipment. In the early stage of the fire, if the automatic fire extinguishing system, fire hydrants and other equipment in the tunnel are not started in time or accurately, the best time to extinguish the fire will be missed. In addition, when a fire occurs, the on-site personnel are often nervous and fearful, and it is often difficult to effectively use the on-site fire-fighting equipment for initial fire control. After the fire truck arrives at the scene, it still needs to face the complex traffic conditions and narrow passages in the tunnel, which further increases the difficulty of fire fighting and rescue.

[0003] In summary, the risk of fire spread in fire prevention and control in extra-long tunnels on highways is relatively high at present, and the use of on-site fire-fighting equipment is limited, so the fire cannot be effectively controlled in the early stage of the fire. Fire rescue personnel cannot grasp the real-time fire situation after arriving, and the efficiency of fire prevention and control needs to be improved. Summary of the invention

[0004] One or more embodiments of the present specification provide a fire control method, device and medium for use in a highway tunnel environment, which are used to solve the following technical problems: Currently, the risk of fire spread in fire prevention and control in extra-long tunnels on highways is relatively high, and the use of on-site firefighting equipment is limited, making it impossible to effectively control the fire in the early stages of the fire. Fire rescue personnel are unable to grasp the real-time fire situation after arriving, and the efficiency of fire prevention and control needs to be improved.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of the present specification provide a fire control method for a highway tunnel environment, the method comprising: collecting real-time tunnel environment data and real-time tunnel traffic data of a highway tunnel, providing a fire warning for the highway tunnel, generating initial fire alarm information, and predicting the tunnel fire through the initial fire alarm information, and determining predicted fire information, wherein the initial fire alarm information includes fire initial location information and fire initial source information, and the predicted fire information includes fire predicted spread area information and predicted fire environment parameters; based on the predicted fire information and pre-acquired tunnel information of the highway tunnel, determining an initial fire node control scheme corresponding to the tunnel fire to remotely control fire-fighting equipment, wherein In the method, the tunnel information includes fire-fighting equipment distribution data and tunnel distribution information, and the initial fire node control scheme includes at least one remote direct-start fire-fighting equipment in the highway tunnel and the initial control parameters of each remote direct-start fire-fighting equipment; the real-time fire control information corresponding to the initial fire node control scheme is collected, the real-time fire control information is synchronized to the fire rescue terminal, and the rescue duration information and rescue entry route information corresponding to the fire rescue terminal are determined; according to the rescue duration information and the real-time fire control information, the initial fire node control scheme is adjusted, the mid-term fire node control scheme is determined, and the highway tunnel is controlled according to the mid-term fire node control scheme and the rescue entry route information.

[0007] One or more embodiments of the present specification provide a fire control device for a highway tunnel environment, comprising:

[0008] at least one processor; and,

[0009] a memory communicatively connected to the at least one processor; wherein,

[0010] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0011] One or more embodiments of the present specification provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the above method.

[0012] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects: through the above technical solution, by collecting real-time environmental data and traffic data of highway tunnels, the system can detect fires early and generate initial fire alarm information, shortening the time from the occurrence of fire to its detection, and gaining valuable time for subsequent rapid response and effective control; based on the initial fire alarm information, the system can further predict the development trend of the fire, including the predicted spread area of ​​the fire and the predicted fire environmental parameters, providing a scientific basis for formulating the control plan for the initial node of the fire, making the control plan more accurate and effective; the control plan for the initial node of the fire includes the selection of remote direct-start fire-fighting equipment and the setting of initial control parameters. Through remote control, it can Initial fire control is carried out before the arrival of fire rescue personnel to reduce the risk of fire spread. The automated management method improves the efficiency and reliability of fire prevention and control. The real-time fire control information is synchronized to the fire rescue terminal, and the rescue duration and entry route information are determined, which helps fire rescue personnel to grasp the fire dynamics in real time and formulate a more reasonable rescue plan. According to the rescue duration and real-time fire control information, the control plan for the initial node of the fire is dynamically adjusted, and the control plan for the mid-term node of the fire is determined. The dynamic adjustment mechanism can ensure that the control plan is always consistent with the changes in the fire situation, which improves the flexibility and effectiveness of fire control, and can ensure that the tunnel fire is effectively controlled before the arrival of fire rescue, creating a more favorable environment for rescue operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0014] Figure 1 A flow chart of a fire control method for a highway tunnel environment provided in an embodiment of this specification;

[0015] Figure 2 A schematic diagram of the structure of a fire control device for use in a highway tunnel environment provided in an embodiment of this specification. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0017] The embodiments of this specification provide a fire control method for a highway tunnel environment. It should be noted that the execution subject in the embodiments of this specification can be a server or any device with data processing capabilities. Figure 1 A flow chart of a fire control method for a highway tunnel environment provided in an embodiment of this specification is shown as follows: Figure 1 As shown, it mainly includes the following steps:

[0018] Step S101, collect real-time tunnel environment data and real-time tunnel traffic data of the highway tunnel, issue fire warning for the highway tunnel, generate initial fire alarm information, and predict the tunnel fire through the initial fire alarm information to determine the predicted fire information.

[0019] The initial fire alarm information includes the initial fire location information and the initial fire source information, and the predicted fire information includes the predicted fire spread area information and the predicted fire environment parameters;

[0020] In one embodiment of the present specification, real-time tunnel environment data and real-time tunnel traffic data of a highway tunnel are collected by presetting multiple types of sensors and image acquisition devices in a highway tunnel. Real-time tunnel environment data mainly includes smoke concentration, temperature, humidity, wind speed and direction, and visibility in the tunnel. Various sensors are deployed in the tunnel, such as smoke concentration monitors, temperature and humidity sensors, wind speed and direction meters, visibility detectors, etc., which can collect environmental parameters in the tunnel in real time and transmit the data to the data processing center. The application of sensor technology makes the collection of environmental data more accurate and comprehensive, and can timely detect environmental changes in the tunnel, providing a basis for fire prediction. By installing high-definition cameras in the tunnel, image information in the tunnel can be captured in real time, including vehicle driving conditions, personnel activities, etc., which can be used to analyze traffic conditions and environmental changes in the tunnel. In the embodiment of this specification, it is used to identify fire-fighting equipment and passing vehicles in the tunnel.

[0021] The fire warning is carried out on the highway tunnel and the initial fire alarm information is generated, which specifically includes: obtaining the real-time tunnel environment data, wherein the tunnel environment data includes the real-time temperature data of multiple temperature collection points and the real-time smoke concentration data of multiple smoke concentration collection points, and the positions of the temperature collection points and the smoke concentration collection points are fixed, which represent the environmental parameters within the collection points or collection point areas.

[0022] According to the real-time temperature data of the multiple temperature collection points, the real-time smoke density data of the multiple smoke density collection points and the preset fire warning environment threshold, the temperature dimension warning and the smoke density dimension warning are respectively carried out to determine the real-time temperature exceeding standard index and at least one risk smoke density collection point corresponding to each temperature collection point. First, according to the real-time temperature data of the multiple temperature collection points and the preset fire warning threshold temperature, the temperature dimension warning is carried out, and the real-time temperature exceeding standard index corresponding to each temperature collection point is determined by the difference between the real-time temperature data and the fire warning threshold temperature. The real-time temperature exceeding standard index here is a positive number greater than zero. When the real-time temperature data is not higher than the fire warning threshold temperature, the difference is a value not greater than 0, which is set to a unified initial value of 0. When the real-time temperature data is higher than the fire warning threshold temperature, the difference between the real-time temperature data and the fire warning threshold temperature is used as the real-time temperature exceeding standard index. The real-time temperature exceeding standard index is monitored, and the growth rate of the real-time temperature exceeding standard index is calculated. The growth rate here is calculated in the following way: the difference between the real-time temperature exceeding standard index and the previous real-time temperature exceeding standard index is calculated, and the ratio of this difference to the previous real-time temperature exceeding standard index is determined as the growth rate. Secondly, the smoke concentration dimension warning is performed through the difference between the real-time smoke concentration data of multiple smoke concentration collection points and the preset fire warning threshold concentration, and at least one risk smoke concentration collection point is determined among the multiple smoke concentration collection points, and the difference corresponding to the risk smoke concentration collection point is greater than the preset exceeding standard threshold. It should be noted that the multiple preset thresholds here can be obtained through empirical data, the historical fire data of this tunnel, and based on statistical analysis. For example, the smoke concentration corresponds to the preset fire warning threshold concentration, which can be obtained by counting the average value of the initial smoke concentration at each fire in multiple historical fire data. Due to differences in tunnel design standards, traffic flow, ventilation conditions, monitoring technology used, and related safety standards and specifications, the specific fire warning threshold concentration value varies from tunnel to tunnel.

[0023] When there is at least one temperature collection point whose real-time temperature exceeding standard index growth rate is greater than the preset threshold, and there is at least one risk smoke concentration collection point, it is determined that there is a fire in the highway tunnel. The growth rate of the real-time temperature exceeding standard index of the temperature collection point is greater than the preset threshold, and the preset threshold here is a positive number greater than 0. On the one hand, it indicates that this temperature collection point is a location where the temperature exceeds the fire warning threshold temperature. On the other hand, the growth rate is greater than the preset threshold, indicating that the temperature collection point has a faster growth rate than the previous temperature, that is, a sharp temperature rise has occurred. The fire condition of the temperature collection point is judged from two perspectives, and the accuracy and comprehensiveness of the fire monitoring is guaranteed in the temperature dimension. In addition, the existence of at least one risk smoke concentration collection point indicates that the smoke concentration of this collection point is large, and the judgment of the relationship between the smoke concentration dimension and the temperature dimension further ensures the accuracy of the fire judgment process. By real-time monitoring of the growth rate of the real-time temperature exceeding standard index of the temperature collection point and combining it with the monitoring of smoke concentration, an early warning signal can be issued at the early stage of the fire, thereby greatly shortening the time from the occurrence of the fire to its discovery; considering the growth rate of the temperature exceeding standard index and the monitoring results of the smoke concentration at the same time, the false alarm caused by a single factor can be effectively reduced, and the accuracy of the fire judgment can be improved.

[0024] When a fire is confirmed, the three-dimensional distribution information of the tunnel corresponding to the highway tunnel is obtained. Taking the three-dimensional laser scanning technology as an example, a three-dimensional laser scanner is used to scan the inside of the tunnel to obtain the point cloud data of the tunnel surface; the scanned point cloud data is processed by denoising, splicing, and registration to form a complete three-dimensional model of the tunnel, and a three-dimensional model of the tunnel is constructed based on the processed point cloud data using three-dimensional modeling software. The obtained three-dimensional model is stored to facilitate the subsequent call of the corresponding three-dimensional distribution information of the tunnel according to the tunnel logo.

[0025] The real-time temperature exceeding standard index corresponding to each temperature collection point is quantified, that is, the real-time temperature exceeding standard index is converted into the corresponding pixel value, and the pixel value of the position point corresponding to each temperature collection point is generated according to the three-dimensional distribution information of the tunnel, so as to construct the real-time tunnel temperature thermodynamic distribution map corresponding to the highway tunnel. For example, in the real-time tunnel temperature thermodynamic distribution map, the smaller the exceeding standard index, the lighter the corresponding color, and the larger the exceeding standard index, the darker the color. With each risk smoke concentration collection point as the center, at least one smoke gathering area is determined according to the real-time smoke concentration data of the multiple smoke concentration collection points. Based on the smoke gathering area and the real-time tunnel temperature thermodynamic distribution map, the initial location information of the fire is determined, and the area with the darkest color of the image, that is, the area with the highest exceeding standard index, is determined by the real-time tunnel temperature thermodynamic distribution map as the target fire source area, and the target fire source area is corrected by the smoke gathering area to determine the initial location information of the fire.

[0026] Image recognition is performed on the real-time tunnel traffic data to determine the target information of at least one tunnel target in the highway tunnel, wherein the tunnel targets include passing vehicles and tunnel power equipment, the target information of each passing vehicle includes the vehicle type and the real-time position of the vehicle, and the target information of the tunnel power equipment includes the position information of the tunnel power equipment. Based on the initial fire position information and the target information of at least one tunnel target, the risk fire source corresponding to the initial fire position is judged to determine the initial fire source information of the fire, wherein the risk fire source includes any one or more of the passing vehicle fire source and the tunnel power equipment fire source. Position matching is performed through the initial fire position information and the target information of each tunnel target, and the tunnel target with a position overlap higher than the preset matching error range is determined as the risk fire source corresponding to the initial fire position, and the initial fire source information of the fire is determined. According to the initial fire position information, the initial fire source information of the fire, and the time when the fire was first detected, a fire warning is issued to the highway tunnel to generate initial fire alarm information.

[0027] Through the initial fire alarm information, the tunnel fire is predicted and the predicted fire information is determined, specifically including: obtaining the tunnel distribution information and historical fire data of the highway tunnel, wherein the tunnel distribution information includes the tunnel structure data, the tunnel structure data includes the tunnel length, width, height, ventilation system layout, etc., and the historical fire data is the record of previous tunnel fires, including the type of fire source, spread speed, changes in environmental parameters, etc.

[0028] Through comprehensive prediction technology, the tunnel fire is predicted according to the tunnel distribution information, historical fire data and the initial fire alarm information of the highway tunnel, and the initial predicted fire information corresponding to the tunnel fire is determined, wherein the initial predicted fire information includes initial predicted spread area information and initial fire environment parameters, specifically including: first, using fluid dynamics, according to the tunnel distribution data of the highway tunnel, a three-dimensional simulation physical model corresponding to the highway tunnel is constructed, and the three-dimensional simulation physical model is used to simulate the fluid flow process, heat transfer process and combustion process. Based on the principles of fluid mechanics, combustion science, etc., a physical model of tunnel fire spread is established, for example, based on computational fluid dynamics (CFD) software (such as ANSYSFluent, Fire Dynamics Simulator, etc.), a three-dimensional physical model of tunnel fire spread is constructed, which can simulate fluid flow, heat transfer and combustion processes. According to the collected data and actual conditions, the boundary conditions of the model are set, including inlet wind speed, outlet pressure, wall thermal boundary conditions and position, etc., the boundary conditions corresponding to the inlet wind speed in the model are set according to the actual tunnel inlet wind speed value of the current highway tunnel, and the boundary conditions corresponding to the outlet pressure in the model are set according to the actual tunnel outlet pressure value of the current highway tunnel. According to the initial fire alarm information, the initial conditions are set for the three-dimensional simulation physical model, and the initial conditions for the occurrence of the fire are set, such as the initial fire source size, location, type of combustible material, etc. The CFD software is run to perform a numerical simulation of the spread of the fire. During the simulation process, the software will automatically calculate the changes in environmental parameters such as the temperature around the fire source, smoke concentration, gas flow rate, etc., predict the spread trend of the fire, and generate a first set of predicted environmental parameters corresponding to multiple prediction timestamps.

[0029] Secondly, through the historical fire data and the tunnel distribution information, the machine learning model is trained to predict the fire spread trend and obtain multiple second prediction environmental parameter sets corresponding to the prediction timestamps. This can be achieved in the following ways. Since the spread of fire is related to time (such as the fire grows over time), a time series prediction model, such as a long short-term memory network (LSTM), can be used to collect historical fire data, including the time, location, type of fire source, initial fire size, temperature, smoke concentration, etc., as well as the physical distribution information of the tunnel (such as length, width, ventilation conditions, emergency exit location, etc.). Clean the data, handle missing values, standardize or normalize numerical features such as temperature and smoke concentration, and encode categorical features (such as fire source type, tunnel area, etc.). Convert the time series data into a format acceptable to the model, use the data at continuous time points as the input sequence, and use the data at future time points as the target output. LSTM is used as the core network structure. The input layer receives time series data, and each time step contains multiple features (such as the temperature, smoke concentration, wind speed, etc. at the previous moment); multiple LSTM layers are set to capture long-term and short-term dependencies in the data. Each layer can be configured with different numbers of units (such as 100, 200, etc.), and whether to use dropout to prevent overfitting; after the LSTM layer, a fully connected layer (Dense layer) is added for feature combination and output prediction. Depending on the needs, the output layer may contain multiple neurons, each of which corresponds to an environmental parameter such as temperature and smoke concentration at a predicted timestamp.

[0030] The following is a code example of a model parameter setting provided in an embodiment of this specification:

[0031] from keras.models import Sequential

[0032] from keras.layers import LSTM,Dense,Dropout

[0033] #Assume that the number of features per time step is num_features and the number of predicted time steps is num_timesteps

[0034] num_features=5# For example: temperature, smoke concentration, wind speed, humidity, oxygen concentration

[0035] num_timesteps = 10 # predict data for the next 10 time points

[0036] model=Sequential()

[0037] model.add(LSTM(100,input_shape=(None,num_features),return_sequences=True))

[0038] model.add(Dropout(0.2))

[0039] model.add(LSTM(50))

[0040] model.add(Dense(num_timesteps*2))#Assume that temperature and smoke concentration are predicted at the same time, two values ​​at each time point

[0041] model.add(Reshape((-1,2)))#Reshape output to (num_timesteps,2)

[0042] model.compile(optimizer='adam',loss='mean_squared_error')

[0043] It should be noted that the use of the Reshape layer depends on the format of the model output data. In the above example, it is assumed that the model predicts both temperature and smoke density, so the output layer has num_timesteps*2 neurons, and is reshaped into a shape of (num_timesteps,2) through the Reshape layer. The loss function in the example uses mean_squared_error as the loss function for the regression problem. Use historical fire data to train the model, adjust the hyperparameters of the LSTM layer such as the number of layers, number of units, and dropout rate to determine the trained machine learning model. Match the corresponding machine learning model with the tunnel structure data of the current tunnel, input the initial fire alarm information and the predicted timestamp into the trained machine learning model, and predict the fire spread trend. The model will output a second set of predicted environmental parameters corresponding to multiple predicted timestamps, including temperature, smoke density, etc.

[0044] The first prediction environment parameter set and the second prediction environment parameter set are data aligned according to the prediction timestamp, and the average value of the first prediction environment parameter and the second preset environment parameter under the same data dimension is used, for example, the average value of the temperature data obtained by two prediction methods at the same prediction timestamp is taken to determine the corrected prediction environment parameter set corresponding to the tunnel fire condition, and the initial prediction fire information is determined through the corrected prediction environment parameter set.

[0045] Through the above technical scheme, by combining the prediction results of fluid dynamics simulation and machine learning model, they can verify and complement each other, thereby improving the accuracy of prediction. Fluid dynamics simulation can simulate the spread of fire in detail based on physical principles, while machine learning model can learn the complex patterns and laws of fire spread from historical data. The combination of the two can more comprehensively reflect the actual development of fire. Using the initial fire alarm information as input, the prediction process can be quickly started to generate a set of environmental parameters under multiple prediction timestamps, providing timely data support for emergency response. The prediction results include not only the spread area of ​​the fire, but also detailed environmental parameters (such as temperature, smoke concentration, etc.), which is conducive to assessing the degree of fire hazard, selecting appropriate fire extinguishing strategies and evacuation routes, etc.; because the prediction results have high accuracy and real-time performance, the time of emergency response can be greatly shortened and the efficiency of emergency response can be improved. At the same time, by understanding the development trend of the fire in advance, rescue resources can be more effectively deployed to reduce casualties and property losses.

[0046] In the application scenario of highway tunnels, the difference from fire in other scenarios is that the tunnel is a relatively closed environment, and there are changing passing vehicles, fixed power equipment, etc. in the tunnel. The changing passing vehicles and power equipment are both fire-sensitive equipment, which can easily expand the scale of the fire. That is, the spread of tunnel fire is easily affected by the tunnel target.

[0047] The target information of at least one tunnel target in the highway tunnel is determined through the real-time tunnel traffic data, so as to evaluate each tunnel target according to the target information and the initial predicted spread area, and to judge whether there is a spread risk target through the position relationship between the position of the tunnel target and the initial predicted spread area. It should be noted that the spread risk target is a tunnel target in the initial predicted spread area, and there may be multiple spread risk targets. When the spread risk target exists, the fire condition of the spread risk target is simulated through the target information of the spread risk target and the initial fire environment parameters, that is, in the state of the initial predicted fire condition, the initial predicted fire condition is used as the initial condition of the three-dimensional simulation physical model, and the target parameters of the spread risk target, such as the position in the tunnel, are added to simulate the fire spread process in the presence of combustibles to obtain the final predicted fire condition information.

[0048] By determining target information in the tunnel through real-time tunnel traffic data, especially for passing vehicles and power equipment that are considered to be fire-sensitive equipment, fire prediction can be more accurately focused on these high-risk areas, which helps to identify and evaluate potential spread risk targets in advance, so as to more effectively formulate prevention and response measures; by judging the positional relationship between the tunnel target and the initial predicted spread area, the target at risk of fire spread can be quickly identified. According to the development characteristics of the fire in the tunnel scenario, not only the natural spread trend of the fire is considered, but also the existence and attributes of specific targets in the tunnel are combined, making the fire spread risk assessment more comprehensive and accurate; for the identified spread risk targets, by simulating their fire spread process under the initial predicted fire state, we can further understand the specific path and speed of fire development, as well as the performance of these targets in the fire.

[0049] Step S102, based on the predicted fire information and the tunnel information of the highway tunnel acquired in advance, determine the fire initial node control scheme corresponding to the tunnel fire situation to perform remote control of fire fighting equipment.

[0050] In one embodiment of the present specification, a fire early stage node control scheme corresponding to the tunnel fire is determined based on the predicted fire information and the tunnel information of the highway tunnel obtained in advance, so as to remotely control the fire fighting equipment through the fire early stage node control scheme. The tunnel information includes fire fighting equipment distribution data and tunnel distribution information, and the fire early stage node control scheme includes at least one remote direct start fire fighting equipment in the highway tunnel and the initial control parameters of each remote direct start fire fighting equipment. By predicting fire information, potential threat areas can be identified at the early stage of the fire, and a node control plan for the early stage of the fire can be formulated accordingly. The formulation process of the node control plan for the early stage of the fire fully considers the combined impact of the predicted fire information and tunnel information. Preemptive control measures can greatly shorten the response time, so that fire-fighting equipment can be quickly activated before the fire spreads on a large scale, thereby effectively controlling the development of the fire; the node control plan for the early stage of the fire clearly defines the fire-fighting equipment that needs to be remotely activated in the highway tunnel and its initial control parameters. Accurate positioning not only ensures the effective use of fire-fighting resources, but also avoids unnecessary waste of resources. At the same time, by setting the initial control parameters, it can ensure that the fire-fighting equipment can work in a predetermined mode after startup to achieve the best fire-fighting effect. By timely and accurately controlling the development of the early stage of the fire, the harm of the fire to personnel, vehicles and facilities in the tunnel can be significantly reduced.

[0051] According to the predicted fire information and the tunnel information, the fire initial node control scheme corresponding to the tunnel fire is determined, specifically including: first, obtaining the fire prediction spread area information in the predicted fire information, the fire prediction spread area information includes the fire initial location information and the predicted spread area information, it should be noted that the fire prediction spread area information here includes the fire initial location information of the actual fire and the predicted spread area information obtained by prediction. Based on the fire prediction spread area information and the fire equipment distribution data, the relative position relationship between each fire equipment and the fire prediction spread area information is determined. In a highway tunnel, the distribution of fire equipment is relatively fixed, so the actual position of each fire equipment is determined through the fire equipment distribution data; the relative position relationship between each fire equipment and the fire prediction spread area information (the complete prediction area composed of the fire initial location information and the predicted spread area information) is determined by matching the fire prediction spread area information with the fire equipment distribution data. The relative position relationship here can be divided into adjacent states and contradictory states. The radiation area of ​​each fire-fighting device is generated through the maximum control distance of each fire-fighting device. The radiation area here refers to the area where each fire-fighting device can perform fire control, that is, the fire extinguishing range. If there is an overlapping area between the radiation area and the complete prediction area, it is determined to be in an adjacent state. Through the fire prediction spread area information and the relative position relationship, multiple direct-start fire-fighting devices are determined. The multiple direct-start fire-fighting devices here refer to fire-fighting devices in an adjacent state, and the device control parameters of each direct-start fire-fighting device are determined, wherein the device control parameters include any one or more of the water spray volume, spray angle, foam concentration, and gas release volume.

[0052] In this process, since the multiple direct-start fire-fighting equipment obtained is set for the complete predicted area, which includes the initial location information of the actual fire and the predicted spread area information, the control parameters of the multiple direct-start fire-fighting equipment should be set according to the actual situation. The control parameters of the fire-fighting equipment are determined by the relationship between the fire-fighting equipment and the two types of areas, which can not only ensure effective control of the actual fire, but also carry out targeted control according to the predicted situation.

[0053] Through the fire prediction spread area information and the relative position relationship, multiple direct-start fire-fighting equipment are determined, and the equipment control parameters of each direct-start fire-fighting equipment are determined, specifically including: through the fire initial location information and the fire-fighting equipment distribution data, position matching is performed to determine the first-tier remote direct-start fire-fighting equipment, so as to generate the first-tier equipment control parameters of each first-tier remote direct-start fire-fighting equipment according to the predicted fire environment parameters and the relative position relationship. In other words, the first-tier remote direct-start fire-fighting equipment refers to the fire-fighting equipment with an overlapping area of ​​the fire initial location information corresponding to the actual fire situation. According to the predicted fire environment parameters and the relative position relationship, the first-tier equipment control parameters of each first-tier remote direct-start fire-fighting equipment are generated. In addition to the adjacent relationship and the contradictory relationship, the relative position relationship here can also set different adjacent relationship types according to the orientation, so as to set the control direction, angle, etc. according to this type, and set the parameter control amount in combination with the predicted fire environment parameters to ensure that the control is carried out according to the predicted fire environment, so as to achieve the effect of suppressing the development of the fire.

[0054] According to the predicted spread area information and the fire equipment distribution data, the second-tier remote direct start fire equipment is determined, so as to generate the second-tier equipment control parameters of each second-tier remote direct start fire equipment according to the predicted fire environment parameters corresponding to the next timestamp of the predicted spread area information and the relative position relationship. In other words, the second-tier remote direct start fire equipment is determined based on the predicted spread area information. The goal of this part of the equipment control is to predict the control of the spread of fire. The second-tier equipment control parameters are determined according to the predicted fire environment parameters corresponding to the next timestamp of the predicted spread area information, the predicted fire environment data and the relative position relationship, and the targeted control of fire equipment with different functions is achieved.

[0055] When the multiple direct-start fire-fighting equipment is remotely started, at least one linkage control device is determined through the predicted fire information, so that the linkage control device is linked and controlled under the trigger of the remote direct start of the direct-start fire-fighting equipment, wherein the linkage control device includes any one or more of the ventilation and smoke exhaust system, the emergency lighting system, the evacuation indication system and the communication system. When it is confirmed that the personnel have been safely evacuated or there is no need to worry about the exhaust of smoke causing harm to the personnel, the smoke exhaust fan in the tunnel is started to quickly discharge the smoke and toxic gas out of the tunnel to create favorable conditions for fire fighting and rescue. According to the fire and smoke exhaust conditions, the operating state of the air supply fan is adjusted in time to maintain air circulation and visibility in the tunnel. When a fire occurs, it automatically switches to the emergency lighting system to ensure that the tunnel is adequately illuminated to facilitate personnel evacuation and fire fighting and rescue operations, and starts evacuation indication signs, such as luminous floor stickers, indicator lights, etc., to provide personnel with clear evacuation directions and paths. Ensure that the fire telephone is unobstructed to facilitate communication between firefighters and personnel in the tunnel and the control center.

[0056] Through the above technical scheme, through the real-time matching of the fire prediction spread area information and the fire-fighting equipment distribution data, the fire-fighting equipment in the adjacent state can be quickly determined as the direct-start fire-fighting equipment, and according to the different situations of the actual fire situation and the predicted fire situation, the appropriate control parameters can be set for these equipment. The rapid response and precise control mechanism can suppress the initial development of the fire to the greatest extent, reduce the spread and loss of the fire; the fire-fighting equipment is divided into the first echelon and the second echelon, and the actual fire initial location and the predicted spread area are controlled respectively. The first echelon fire-fighting equipment is responsible for immediately extinguishing the fire source, while the second echelon is deployed in advance according to the predicted information to prevent the fire from spreading further. The multi-level defense strategy not only improves the flexibility of responding to fire situations, but also enhances the overall safety assurance capability; through reasonable equipment selection and parameter setting, the waste of fire-fighting resources is avoided. At the same time, the synergy of linkage control equipment (such as ventilation and smoke exhaust systems, emergency lighting systems, etc.) is utilized to further improve the efficiency and safety of fire-fighting.

[0057] Step S103, collect the real-time fire control information corresponding to the fire initial node control plan, synchronize the real-time fire control information to the fire rescue terminal, and determine the rescue duration information and rescue entry route information corresponding to the fire rescue terminal.

[0058] In one embodiment of the present specification, in the process of remote control of fire-fighting equipment according to the fire early node control scheme, the real-time synchronous storage method is used to synchronize to the command center. In this process, if the user of the command center triggers a remote control instruction, such as adding a remote direct start control device or changing the control parameters of the device, the priority of the remote control instruction triggered by the user is set to the highest priority. In the process of remote control of fire-fighting equipment, real-time fire environment parameters in the highway tunnel are collected in real time, and the real-time control fire information corresponding to the fire early node control scheme is determined. The real-time control fire information here includes a mapping relationship reflecting the control effect of the control parameter, real-time fire spread area information, and the real-time fire environment parameters.

[0059] Collecting the real-time control fire information corresponding to the fire initial node control scheme specifically includes: monitoring the remote direct-start fire-fighting equipment and collecting the real-time control parameters of each remote direct-start fire-fighting equipment; after remotely controlling the fire-fighting equipment, collecting the real-time fire environment parameters of the tunnel fire, and determining the real-time fire spread area information through the real-time fire environment parameters. According to the real-time fire environment parameters and the predicted fire environment parameters at the corresponding timestamp, determine the fire extinguishing evaluation index, where the fire extinguishing evaluation index can be the actual controlled temperature drop data, for example, the water spraying amount and the temperature drop data corresponding to the water spraying amount. Establish a mapping relationship between the real-time control parameter and the fire extinguishing evaluation index, so as to generate the real-time control fire information based on the mapping relationship and the real-time fire environment parameters, wherein the real-time control fire information includes the mapping relationship, the real-time fire spread area information and the real-time fire environment parameters.

[0060] The real-time control fire information is synchronized to the fire rescue terminal involved in the fire rescue, so that the fire rescue terminal can grasp the on-site fire status in real time, so as to carry out targeted rescue after arriving at the scene. At the same time, the communication connection is maintained with the fire rescue terminal to receive the real-time location data of the fire rescue terminal. The road congestion is determined through the real-time tunnel traffic data, and the rescue route is determined by combining the fire prediction spread area information of the tunnel fire and the real-time location data of the fire rescue terminal, so as to determine the rescue duration information and rescue entry route information corresponding to the fire rescue terminal.

[0061] By synchronizing fire information in real time, fire rescue personnel can have a comprehensive understanding of the fire situation before arriving at the scene, so as to develop a more targeted rescue plan. The preparatory work can significantly shorten the decision-making time after arriving at the scene and improve the rescue efficiency. Mastering the real-time fire status helps fire rescue personnel assess the severity of the fire and possible risks, so as to take corresponding protective measures to ensure the safety of the rescue operation. By determining the real-time location data and rescue routes, rescue resources and forces can be deployed more reasonably to avoid waste and duplication of rescue resources and ensure effective rescue. Combined with the predicted spread area information of the tunnel fire and the real-time location data of the fire rescue terminal, the rescue time and rescue entry route can be calculated in real time, making the rescue operation faster and more accurate, controlling the fire in the shortest time, and reducing the damage to the tunnel structure and the surrounding environment.

[0062] Step S104, according to the rescue time information and the real-time fire control information, adjust the fire initial node control plan, determine the fire mid-term node control plan, and control the fire in the highway tunnel according to the fire mid-term node control plan and the rescue entry route information.

[0063] The development of the fire situation is dynamic, so the control scheme also needs to be dynamically adjusted according to the actual situation. By obtaining fire information and rescue time information in real time, it is possible to respond more flexibly to changes in the fire situation before the fire rescue arrives.

[0064] In one embodiment of the present specification, the control scheme of the initial node of the fire is adjusted according to the rescue duration information and the real-time fire control information, and the control scheme of the mid-term node of the fire is determined. In combination with the real-time fire control information and the rescue duration information, a fire model or a machine learning algorithm is used to predict the development trend of the fire. According to the predicted fire spread area and fire intensity, it is decided whether the number of remote direct-start fire-fighting equipment needs to be increased or decreased. According to the real-time changes in fire environment parameters (such as temperature and smoke concentration), the control parameters of the fire-fighting equipment (such as spray intensity and smoke exhaust rate) are dynamically adjusted. It is ensured that the fire-fighting equipment can control the fire to the maximum extent before the fire rescue arrives, creating favorable conditions for the rescue operation. According to the mid-term node control scheme of the fire and the rescue entry route information, the fire is controlled in the highway tunnel. The adjusted mid-term node control scheme of the fire and the real-time fire information are synchronized to the fire rescue terminal to ensure that the rescue team understands the on-site situation, and plans the optimal entry route for the rescue team according to the tunnel structure and fire distribution to avoid dangerous areas. Through real-time data analysis and dynamic adjustment of control plans, the development trend of fire can be predicted more accurately, control measures can be taken in advance, and the spread of fire can be effectively curbed; according to the actual fire situation and rescue time information, fire-fighting equipment and other resources can be reasonably deployed to ensure maximum effectiveness at critical moments; through information sharing and real-time communication, close cooperation between the fire department and the tunnel management department can be achieved to improve the coordination and efficiency of rescue operations.

[0065] According to the rescue duration information and the real-time fire control information, the control plan for the initial fire node is adjusted to determine the control plan for the mid-term fire node, specifically including: determining the first predicted fire information at the corresponding predicted timestamp through the rescue duration information to obtain the first predicted fire environment parameter and the first predicted fire spread area information in the first predicted fire information. Determine the current timestamp of the real-time fire environment parameter of the tunnel fire, determine the current predicted fire information corresponding to the current timestamp in the predicted fire information, and obtain the predicted fire environment parameter and the predicted fire spread area information, where the predicted fire environment parameter and the predicted fire spread area information refer to the historical prediction information corresponding to the current timestamp.

[0066] The real-time fire spread area information in the real-time fire control information is obtained, and the regional prediction state is determined through the real-time fire spread area information and the predicted fire spread area information corresponding to the current timestamp, wherein the regional prediction state includes a forward prediction state and a reverse prediction state. When the real-time fire spread area information exceeds the predicted fire spread area information, it means that the fire spreads faster, which is called the reverse prediction state. When the real-time fire spread area information does not exceed the predicted fire spread area information, that is, the real-time fire spread area is smaller than the predicted fire spread area, it is called the forward prediction state, which is used to indicate that the current fire development is controllable and positive.

[0067] When the regional prediction state is a reverse prediction state, the corresponding multiple designated remote direct-start fire-fighting equipment is determined according to the determination method of the fire-fighting equipment in the fire initial node control scheme through the first fire prediction spread area information. The first fire prediction spread area information corresponds to the rescue duration. Through the first fire prediction spread area information, the corresponding multiple designated remote direct-start fire-fighting equipment is determined, and the designated remote direct-start fire-fighting equipment is matched according to the prediction information of the fire rescue arrival time to ensure that the tunnel fire is controlled before the fire rescue arrives. By combining rescue time information and real-time fire control information, the development trend of future fires can be predicted more accurately, so that responses can be made in advance, which helps to take effective control measures before the fire worsens further; real-time adjustment of control plans can ensure that the configuration of fire-fighting equipment and resources is always consistent with changes in the fire situation, improving the pertinence and timeliness of control; based on the comparison between the real-time fire spread area and the predicted spread area, the spread speed and direction of the fire can be judged, so as to more reasonably select and configure remote direct-start fire-fighting equipment; in the reverse prediction state, the designated remote direct-start fire-fighting equipment is determined by the first fire prediction spread area information, which can ensure that the tunnel fire is effectively controlled before the fire rescue arrives, creating a more favorable environment for rescue operations.

[0068] Through the difference between the predicted fire environment data and the real-time fire environment parameter, according to the data dimension of the environment data (for example, the temperature dimension, the smoke concentration dimension, etc.), the fire environment parameter deviation index under each data dimension is determined respectively; according to the fire environment parameter deviation index, the first predicted fire environment parameter is corrected to determine the corrected predicted fire environment parameter. The correction amount is determined by the result of the preset error correction factor and the fire environment parameter deviation index, and the correction amount is added to the first predicted fire environment parameter to obtain the corrected predicted fire environment parameter. Based on the difference between the corrected predicted fire environment parameter and the real-time fire environment parameter, the target control fire extinguishing index is determined, and the mid-term real-time control parameters of each remote direct-start fire fighting equipment are determined according to the mapping relationship in the real-time control fire situation information and the target control fire extinguishing index. By analyzing the difference between the predicted fire environment data and the real-time fire environment parameters and correcting the predicted fire environment parameters accordingly, more accurate corrected predicted fire environment parameters can be obtained. The dynamic adjustment method makes the control scheme more flexible and able to adapt to the complex changes in the fire situation. The target control extinguishing indicators determined based on the corrected predicted fire environment parameters can more accurately guide the operation of fire-fighting equipment and the setting of control parameters, thereby improving the precision and effectiveness of fire control. Real-time adjustment of the control scheme and maintaining communication connection with the fire rescue terminal can ensure the coordination and consistency of the rescue operation, improve the overall efficiency and effectiveness of the emergency response, and reduce the damage of the fire to the tunnel structure and the surrounding environment.

[0069] Through the above technical solution, by collecting real-time environmental data and traffic data of highway tunnels, the system can detect fires early and generate initial fire alarm information, shortening the time from the occurrence of fire to its detection, and buying precious time for subsequent rapid response and effective control; based on the initial fire alarm information, the system can further predict the development trend of the fire, including the predicted spread area of ​​the fire and the predicted fire environmental parameters, providing a scientific basis for formulating the control plan for the initial node of the fire, making the control plan more accurate and effective; the control plan for the initial node of the fire includes the selection of remote direct-start fire-fighting equipment and the setting of initial control parameters. Through remote control, the fire can be started before the arrival of fire rescue personnel. The fire situation can be initially controlled to reduce the risk of fire spread. The automated management method improves the efficiency and reliability of fire prevention and control. The real-time fire control information is synchronized to the fire rescue terminal, and the rescue time and entry route information are determined, which helps fire rescue personnel to grasp the fire situation in real time and formulate a more reasonable rescue plan. According to the rescue time and real-time fire control information, the control plan for the initial node of the fire is dynamically adjusted, and the control plan for the mid-term node of the fire is determined. The dynamic adjustment mechanism can ensure that the control plan is always consistent with the changes in the fire situation, which improves the flexibility and effectiveness of fire control, and can ensure that the tunnel fire is effectively controlled before the fire rescue arrives, creating a more favorable environment for rescue operations.

[0070] The embodiment of this specification also provides a fire control device for a highway tunnel environment, such as Figure 2 As shown, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0071] The embodiments of the present specification also provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the above method.

[0072] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0073] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, commodity or device including the element.

[0074] The above are only one or more embodiments of this specification and are not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A fire control method for a highway tunnel environment, characterized in that: The method comprises: Collecting real-time tunnel environment data and real-time tunnel traffic data of the highway tunnel, providing fire warning for the highway tunnel, and generating initial fire alarm information, wherein the initial fire alarm information includes fire initial location information and fire initial source information; Predicting the tunnel fire condition through the initial fire alarm information and determining the predicted fire condition information specifically includes: Acquiring tunnel distribution information and historical fire data of the highway tunnel, wherein the tunnel distribution information includes tunnel structure data; By using a comprehensive prediction technology, the tunnel fire condition is predicted according to the tunnel distribution information of the highway tunnel, the historical fire condition data and the initial fire alarm information, and the initial predicted fire condition information corresponding to the tunnel fire condition is determined, wherein the initial predicted fire condition information includes initial predicted spread area information and initial fire environment parameters; Determine target information of at least one tunnel target in the highway tunnel through the real-time tunnel traffic data, evaluate the tunnel target according to the target information and the initial predicted spreading area, and judge whether there is a spreading risk target, wherein the spreading risk target is a tunnel target within the initial predicted spreading area; When the spread risk target exists, the fire condition of the spread risk target is simulated through the target information of the spread risk target and the initial fire environment parameters to determine the predicted fire condition information, wherein the predicted fire condition information includes the predicted fire spread area information and the predicted fire environment parameters; based on the predicted fire condition information and the tunnel information of the highway tunnel acquired in advance, the initial fire node control scheme corresponding to the tunnel fire condition is determined to perform remote control of fire-fighting equipment, wherein the tunnel information includes fire-fighting equipment distribution data and tunnel distribution information, and the initial fire node control scheme includes at least one remote direct-start fire-fighting equipment in the highway tunnel and the initial control parameters of each remote direct-start fire-fighting equipment; Collect the real-time fire control information corresponding to the fire early node control scheme, synchronize the real-time fire control information to the fire rescue terminal, and determine the rescue duration information and rescue entry route information corresponding to the fire rescue terminal; According to the rescue time information and the real-time fire control information, the fire initial node control plan is adjusted to determine the fire mid-term node control plan, specifically including: Determine the first predicted fire information at the corresponding predicted timestamp through the rescue duration information to obtain the first predicted fire environment parameter and the first predicted fire spread area information; Determine the current timestamp of the real-time fire environment parameters of the tunnel fire, and determine the current predicted fire information corresponding to the current timestamp in the predicted fire information to obtain the predicted fire environment parameters and the fire predicted spread area information; Acquire the real-time fire spread area information in the real-time fire control information, and determine the regional prediction state through the real-time fire spread area information and the fire prediction spread area information, wherein the regional prediction state includes a forward prediction state and a reverse prediction state; When the regional prediction state is a reverse prediction state, the corresponding plurality of designated remote direct-start fire-fighting equipment are determined through the first fire prediction spread area information; By using the difference between the predicted fire environment data and the real-time fire environment parameter, and according to the data dimension of the environment data, respectively determining the fire environment parameter deviation index under each data dimension; According to the fire environment parameter deviation index, the first predicted fire environment parameter is corrected to determine a corrected predicted fire environment parameter; Based on the difference between the corrected predicted fire environment parameter and the real-time fire environment parameter, a target control fire extinguishing index is determined, and the mid-term real-time control parameter of each remote direct-start fire fighting device is determined according to the target control fire extinguishing index based on the mapping relationship in the real-time fire control information; The fire situation in the highway tunnel is controlled according to the mid-term node control plan for the fire and the rescue entry route information.

2. A fire control method for a highway tunnel environment according to claim 1, characterized in that: Providing fire warning for the highway tunnel and generating initial fire alarm information, specifically including: Acquiring the real-time tunnel environment data, wherein the tunnel environment data includes real-time temperature data of multiple temperature collection points and real-time smoke concentration data of multiple smoke concentration collection points; According to the real-time temperature data of the multiple temperature collection points, the real-time smoke density data of the multiple smoke density collection points and the preset fire warning environment threshold, a temperature dimension warning and a smoke density dimension warning are respectively performed to determine the real-time temperature exceeding standard index corresponding to each temperature collection point and at least one risk smoke density collection point; When there is at least one temperature collection point whose real-time temperature exceeding standard index growth rate is greater than a preset threshold value and there is at least one risk smoke concentration collection point, it is determined that there is a fire in the highway tunnel, and the three-dimensional distribution information of the tunnel corresponding to the highway tunnel is obtained; Quantify the real-time temperature exceeding standard index corresponding to each temperature collection point, and generate the position point pixel value corresponding to each temperature collection point according to the three-dimensional distribution information of the tunnel, so as to construct the real-time tunnel temperature thermal distribution map corresponding to the highway tunnel; Taking each of the risk smoke concentration collection points as the center, determining at least one smoke gathering area according to the real-time smoke concentration data of the multiple smoke concentration collection points, and determining the fire initial location information based on the smoke gathering area and the real-time tunnel temperature thermal distribution map; Performing image recognition on the real-time tunnel traffic data to determine target information of at least one tunnel target in the highway tunnel, wherein the tunnel targets include passing vehicles and tunnel power equipment, the target information of each passing vehicle includes the vehicle type and the real-time position of the vehicle, and the target information of the tunnel power equipment includes the position information of the tunnel power equipment; Based on the fire initial location information and the target information of at least one tunnel target, the risk fire source corresponding to the fire initial location is judged to determine the fire initial source information, wherein the risk fire source includes any one or more of a passing vehicle fire source and a tunnel power equipment fire source.

3. A fire control method for a highway tunnel environment according to claim 1, characterized in that: By using comprehensive prediction technology, the tunnel fire condition is predicted according to the tunnel distribution information of the highway tunnel, the historical fire condition data and the initial fire alarm information, and the initial predicted fire condition information corresponding to the tunnel fire condition is determined, specifically including: Using fluid dynamics, according to the tunnel distribution data of the highway tunnel, construct a three-dimensional simulation physical model corresponding to the highway tunnel, wherein the three-dimensional simulation physical model is used to simulate the fluid flow process, heat transfer process and combustion process; According to the initial fire alarm information, initial conditions are set for the three-dimensional simulation physical model to perform fire spread simulation and generate a first prediction environment parameter set corresponding to a plurality of prediction timestamps; Training a machine learning model through historical fire data and the tunnel distribution information to predict the fire spread trend and obtain a second set of prediction environment parameters corresponding to a plurality of prediction timestamps; The first prediction environment parameter set and the second prediction environment parameter set are data aligned according to the prediction timestamp to determine a revised prediction environment parameter set corresponding to the tunnel fire condition, and initial prediction fire condition information is determined through the revised prediction environment parameter set.

4. A fire control method for a highway tunnel environment according to claim 1, characterized in that: Determining a fire early stage node control scheme corresponding to the tunnel fire according to the predicted fire information and the tunnel information, specifically comprising: Obtaining the fire prediction spread area information in the predicted fire information, wherein the fire prediction spread area information includes fire initial location information and predicted spread area information; Based on the fire prediction spread area information and the fire fighting equipment distribution data, determining the relative position relationship between each fire fighting equipment and the fire prediction spread area information; Determine a plurality of direct-start fire-fighting equipment through the fire prediction spread area information and the relative position relationship, and determine the equipment control parameters of each of the direct-start fire-fighting equipment, wherein the equipment control parameters include any one or more of the water spray volume, spray angle, foam concentration, and gas release volume; When the multiple direct-start fire-fighting equipment are remotely started, at least one linkage control device is determined through the predicted fire information, so that the linkage control device is linked and controlled when triggered by the remote direct start of the direct-start fire-fighting equipment, wherein the linkage control device includes any one or more of the ventilation and smoke exhaust system, the emergency lighting system, the evacuation indication system and the communication system.

5. A fire control method for a highway tunnel environment according to claim 4, characterized in that: Determine multiple direct-start fire fighting equipment based on the fire prediction spread area information and the relative position relationship, and determine the equipment control parameters of each direct-start fire fighting equipment, specifically including: By performing position matching through the fire initial location information and the fire-fighting equipment distribution data, the first-tier remote direct-start fire-fighting equipment is determined, so as to generate the first-tier equipment control parameters of each of the first-tier remote direct-start fire-fighting equipment according to the predicted fire environment parameters and the relative position relationship; According to the predicted spread area information and the fire-fighting equipment distribution data, the second-tier remote direct-start fire-fighting equipment is determined, so as to generate the second-tier equipment control parameters of each second-tier remote direct-start fire-fighting equipment according to the predicted fire environment parameters corresponding to the next timestamp of the timestamp corresponding to the predicted spread area information and the relative position relationship.

6. A fire control method for a highway tunnel environment according to claim 1, characterized in that: Collect the real-time fire control information corresponding to the fire early stage node control scheme, specifically including: Performing equipment monitoring on the remote direct-start fire-fighting equipment and collecting real-time control parameters of each remote direct-start fire-fighting equipment; After the fire-fighting equipment is remotely controlled, real-time fire environment parameters of the tunnel fire are collected to determine the real-time fire spreading area information based on the real-time fire environment parameters; Determining a fire extinguishing evaluation index according to the real-time fire environment parameters and the predicted fire environment parameters at the corresponding timestamp; A mapping relationship between the real-time control parameters and the fire extinguishing assessment index is established to generate the real-time fire control information based on the mapping relationship and the real-time fire environment parameters, wherein the real-time fire control information includes the mapping relationship, the real-time fire spread area information and the real-time fire environment parameters.

7. A fire control device for a highway tunnel environment, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 6.

8. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to execute the method according to any one of claims 1 to 6.

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