Construction method and control method of highway tunnel fire non-variable frequency fan control model

Abstract

The invention discloses a highway tunnel fire non-variable frequency fan control model construction method and a control method, and the method comprises the steps: firstly, obtaining fan control parameters of a non-variable frequency fan under multiple groups of different fire characteristic parameters, tunnel structure facility parameters and initial environment parameters, and building a data set by taking the parameters as samples; secondly, building a control model by taking the tunnel fixed parameters, the environment initial parameters, the smoke spreading position and the target wind speed as input data and taking the fan control parameters as output data; and finally, training the control model through the constructed data set so as to obtain a non-variable-frequency fan control model capable of calculating fan control parameters of the non-variable-frequency fan in the tunnel according to the tunnel fixed parameters, the environment initial parameters, the smoke spreading position and the target wind speed. And fan control parameters in the tunnel are dynamically adjusted through the non-variable-frequency fan control model so as to adapt to the fire change condition, and longitudinal smoke exhaust intelligent control over the tunnel fire is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel ventilation control, and in particular to a control model building method and a control method for a non-variable frequency fan control machine in a highway tunnel fire. Background Art

[0002] The existing tunnel fire longitudinal smoke exhaust is mainly to start the jet fan in the tunnel to form a fixed critical wind speed or subcritical wind speed upstream of the fire source, and blow the fire smoke to the downstream of the fire source for discharge. When a fire occurs, the power of the fire source changes dynamically, and the fixed critical wind speed or subcritical wind speed cannot adapt to the change of the fire source, resulting in excessive wind speed in the early stage of the fire, causing the smoke stratification downstream of the fire source to be destroyed, which is not conducive to the evacuation of personnel downstream of the fire source. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a control model construction method and control method for non-variable frequency fans in highway tunnel fires, which can realize intelligent control of longitudinal smoke exhaust in highway tunnel fires through non-variable frequency fans. The specific technical solution is as follows: In a first aspect, a method for constructing a control model of a non-variable frequency fan in a highway tunnel fire is provided. In a first implementable manner of the first aspect, the method comprises: Acquire multiple sets of smoke exhaust data from non-variable frequency fans to build a data set. The smoke exhaust data includes tunnel fixed parameters, environmental initial parameters, smoke spreading location, target wind speed, and fan control parameters. A control model is constructed using the tunnel fixed parameters, environmental initial parameters, smoke spreading position, and target wind speed as input data and the fan control parameters as output data; The control model is trained using the data set to obtain a non-variable frequency fan control model.

[0004] In combination with the first implementable manner of the first aspect, in a second implementable manner of the first aspect, multiple sets of smoke exhaust data of non-variable frequency fans are acquired to construct a data set, including: A highway tunnel model was constructed using simulation software, and simulation experiments were carried out on the highway tunnel model to obtain smoke exhaust data of non-variable frequency fans under different smoke exhaust conditions.

[0005] In combination with the first implementable manner of the first aspect, in a third implementable manner of the first aspect, multiple sets of smoke exhaust data of non-variable frequency fans are acquired to construct a data set, including: The relative error between the target wind speed in each set of smoke exhaust data and the controlled wind speed in the fan control parameter is calculated respectively, and the smoke exhaust data with a relative error less than an error threshold are screened out to construct the data set.

[0006] In combination with the first implementable manner of the first aspect, in a fourth implementable manner of the first aspect, the fixed parameters of the tunnel include tunnel length, outlet wind speed, fan outlet area, number of fans and / or fan positions.

[0007] In combination with the first implementable manner of the first aspect, in a fifth implementable manner of the first aspect, training the control model by using the data set includes: Calculating a global error of the control model according to the control wind speed in the smoke exhaust data and the control wind speed output by the control model, and comparing the global error with a set accuracy value; In response to the global error being greater than the precision value, continuing training with the data set.

[0008] In combination with the fifth implementable manner of the first aspect, in a sixth implementable manner of the first aspect, training the control model by using the data set includes: In response to the global error being less than the accuracy value, comparing the smoke spreading position in the smoke exhaust data with the fan start position output by the model; If there is no intersection between the smoke spreading position and the fan start position, the control model training is completed. Otherwise, the control model training continues through the data set.

[0009] In a second aspect, a method for controlling a non-variable frequency fan in a highway tunnel fire is provided. In a first achievable manner of the second aspect, the method includes: Using any one of the first to sixth possible implementations of the first aspect to construct a non-variable frequency fan control model; The fixed parameters of the controlled tunnel, the initial parameters of the environment, the smoke spreading position and the target wind speed are obtained, and the fan control parameters in the tunnel are determined through the trained non-variable frequency fan control model.

[0010] In combination with the first implementable manner of the second aspect, in a second implementable manner of the second aspect, determining the fan control parameter by using a non-variable frequency fan control model includes: The fan control parameters are verified through the smoke spreading position. If the verification fails, the fan control parameters are eliminated and the fan control parameters are re-determined through a non-variable frequency fan control model.

[0011] In combination with the second implementable manner of the second aspect, in a third implementable manner of the second aspect, verifying the fan control parameter includes: Comparing the smoke spreading position with the fan position in the tunnel fixed parameters to determine the fan position intersecting with the smoke spreading position; According to the fan start position and the number of fan starts in the fan control parameters, determining whether there is a fan start in the fan position intersecting with the smoke spreading position; In response to the presence of a fan activation in a fan position intersecting the smoke spread location, the verification fails.

[0012] In combination with the second implementable manner of the second aspect, in a third implementable manner of the second aspect, verifying the fan control parameter includes: If the verification is successful, the fan control parameter is used as an alternative control parameter, and the fan control parameter is continuously calculated by the non-variable frequency fan control model until the number of the alternative control parameters reaches a quantity threshold; The fan control parameter corresponding to the minimum control wind speed is selected from all the alternative control parameters.

[0013] Beneficial effect: The method for constructing a control model and control method for a non-variable frequency fan of a highway tunnel fire of the present invention can train a machine learning model with tunnel fixed parameters, environmental initial parameters, smoke spreading position, and target wind speed as input variables and fan control parameters of the non-variable frequency fan as output variables through a data set composed of smoke exhaust data of non-variable frequency fans under different tunnel fire situations. The trained non-variable frequency fan control model can dynamically adjust the start-up number, start-up position, and control wind speed of the fans in the tunnel according to the tunnel fixed parameters, the real-time smoke spreading position, and the target wind speed at the fire scene, so as to adapt to the changes in the fire in the tunnel and realize intelligent control of longitudinal smoke exhaust of highway tunnel fires. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific implementation of the present invention, the following will briefly introduce the drawings required for use in the specific implementation. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0015] Figure 1 A flowchart of a method for constructing a non-variable frequency fan control model for a highway tunnel fire provided by an embodiment of the present invention; Figure 2 A flow chart of a non-variable frequency fan control method for a highway tunnel fire provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the tunnel fan zoning. DETAILED DESCRIPTION

[0016] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0017] like Figure 1The flowchart of the method for constructing a non-variable frequency fan control model for a highway tunnel fire is shown, and the method comprises: Step 1, obtain multiple sets of smoke exhaust data of non-variable frequency fans to build a data set, the smoke exhaust data includes tunnel fixed parameters, environmental initial parameters, smoke spreading position, target wind speed and fan control parameters; Step 2: Using the tunnel fixed parameters, environmental initial parameters, smoke spreading position, and target wind speed as input data and the fan control parameters as output data, a control model is constructed; Step 3: training the control model using the data set to obtain a non-variable frequency fan control model.

[0018] Specifically, first, we can obtain multiple sets of fan control parameters and target wind speeds of non-variable frequency fans under different fire characteristic parameters, tunnel structure and facility parameters, and initial environmental parameters, and use these parameters as samples to build a data set. Then, we use tunnel fixed parameters, environmental initial parameters, smoke spread position, and target wind speed as input data, and fan control parameters as output data to build a control model. Finally, we train the control model with the constructed data set to obtain a non-variable frequency fan control model that can be used to calculate the fan control parameters of non-variable frequency fans in the tunnel according to tunnel fixed parameters, environmental initial parameters, smoke spread position, and target wind speed when a fire occurs. This allows us to adapt to the changes in the fire in the tunnel and realize adaptive control of longitudinal smoke exhaust in highway tunnel fires.

[0019] It should be understood that there are many indicators that affect the ventilation wind speed of highway tunnels, involving tunnel structure and facility parameters, tunnel fire characteristics, and tunnel initial environmental parameters.

[0020] The tunnel structural facility parameters that affect the longitudinal smoke exhaust of fire mainly include the length and fan-related parameters. The resistance along the tunnel length will reduce the tunnel wind speed. Parameters such as fan outlet wind speed, outlet area, number of fans and number of fans opened, and fan position are the basis for tunnel ventilation. The fan layout is generally divided into three parts: the tunnel entrance area, the middle area, and the exit area. When a tunnel fire occurs, if there is fire or smoke within 120m of the fan, the fan in this area cannot be turned on.

[0021] The characteristic parameters of tunnel fires that affect the longitudinal smoke exhaust of fires mainly include the fire location range and smoke spread range. The different locations of tunnel fires directly affect the opening position of the fans in the tunnel. The size of the fire is different, the amount of smoke generated and the speed of smoke spread are different. The smoke will spread along the top and side walls of the tunnel to the upstream and downstream of the tunnel, and the fans cannot be turned on at the covered positions. Therefore, when building a control model, the smoke spread position also needs to be considered.

[0022] The initial tunnel environmental parameters that affect the longitudinal smoke exhaust of fire mainly include the size and direction of wind speed. The greater the initial tunnel environmental wind speed, the smaller the wind speed generated by the required control fan; the smaller the environmental wind speed, the greater the wind speed generated by the required control fan. Therefore, when constructing the control model, the initial environmental wind speed also needs to be considered.

[0023] Based on the above analysis, information such as tunnel length, fan position, fan outlet wind speed, fan outlet area, fan number, fan opening number, ambient wind speed and direction, fire location range, and fire smoke spread range will affect the control of wind speed in the tunnel. Therefore, smoke exhaust data can include data corresponding to tunnel length, fan position, fan outlet wind speed, fan outlet area, fan number, fan opening number, ambient wind speed and direction, fire location range, fire smoke spread location, and target wind speed.

[0024] In this embodiment, optionally, multiple sets of smoke exhaust data of non-variable frequency fans are acquired to construct a data set, including: A highway tunnel model was constructed using simulation software, and simulation experiments were carried out on the highway tunnel model to obtain smoke exhaust data of non-variable frequency fans under different smoke exhaust conditions.

[0025] Specifically, it is difficult to conduct field test research on actual tunnel fan control. Therefore, Fluent software can be used to build a highway tunnel model, and the critical wind speed or subcritical wind speed required to ensure the safe evacuation of trapped personnel upstream and downstream of the tunnel can be used as the target wind speed for longitudinal smoke exhaust control of highway tunnel fires to simulate tunnel ventilation.

[0026] like Figure 3 As shown in the figure, considering that the location of the tunnel fire affects the starting location of the fan facilities, the tunnel is divided into 7 areas according to the location of the fans in the tunnel. In the inlet fan zone, intermediate fan zone or outlet fan zone, the fan in this zone cannot be turned on. , the middle fan area is , the outlet fan area is , , are the coordinates of the first and last fans at the inlet. , The coordinates of the first and last wind turbines in the middle position; and are the coordinates of the first and last fans at the outlet respectively. The location of the fire. , are the spreading lengths of the flame smoke in the upstream and downstream of the tunnel, Indicates the number of tunnel fires.

[0027] , , They are the number of fans in the inlet fan area, the middle fan area and the outlet fan area respectively.

[0028] Through simulation analysis of the constructed highway tunnel model, we can obtain the controlled wind speed in the highway tunnel under different tunnel lengths, fan positions, fan outlet wind speeds, fan outlet areas, number of fans, number of fans turned on, and environmental initial wind speed and direction parameters. We also construct a data set using tunnel length, fan position, fan outlet wind speed, fan outlet area, number of fans, number of fans turned on, environmental wind speed and direction, fire location range, and fire smoke spread location as samples, as shown in the following table:

[0029] Among them, the number of fans in the tunnel should be greater than or equal to the number of fans turned on, that is, ; Parameters The number of fans in the inlet, middle and outlet areas is respectively started to meet , and the number of fans in each area meets , , .

[0030] In this embodiment, optionally, multiple groups of smoke exhaust data of non-variable frequency fans are obtained to construct a data set, including: respectively calculating the relative error between the target wind speed in each group of smoke exhaust data and the control wind speed in the fan control parameters, and screening out the smoke exhaust data with a relative error screen less than an error threshold to construct the data set.

[0031] Specifically, when constructing a data set, the target wind speed and the control wind speed in the smoke exhaust data obtained from each simulation analysis can be compared to calculate the relative error between the target wind speed and the control wind speed. If the relative error between the target wind speed and the control wind speed exceeds the error threshold, it indicates that this simulation does not achieve good smoke exhaust control. Therefore, the smoke exhaust data obtained from this simulation can be removed and not included in the data set to avoid affecting the model training accuracy. The specific calculation formula for the relative error is as follows: .

[0032] in, , are the target wind speed and controlled wind speed obtained from simulation analysis respectively.

[0033] In this embodiment, a BP neural network can be used as a control model, and the input parameters include tunnel length, outlet wind speed, fan outlet area, fan quantity, fan position, ambient initial wind speed, fire smoke spread position, and target wind speed. The output parameters include the control wind speed, fan start-up quantity, and fan start-up position.

[0034] During training, first initialize the weights of each node, the error function e, the calculation accuracy value and the maximum number of learning times M, and determine the number of hidden neurons , the specific calculation formula is as follows: ; in, is the number of neurons in the input layer, is the number of neurons in the output layer, is a constant between 0 and 10. Then the BP neural network is trained using the data set, and the output of the hidden layer and the output layer are calculated in turn. and , its calculation expression is: , ; , ; in, is the input data in the sample data, is the input layer weight, For the The input data in the group sample data, is the input layer bias, is the hidden layer weight, is the hidden layer input, is the hidden layer bias. The output parameters of the control model include the control wind speed, the number of fan starts and the fan start position.

[0035] In this embodiment, optionally, training the control model using the data set includes: Calculating a global error of the control model according to the control wind speed in the smoke exhaust data and the control wind speed output by the control model, and comparing the global error with a set accuracy value; When the global error is greater than the precision value, the gradient descent method is used to update the weights and bias data in the neural network, and training is continued from the sample through the data set.

[0036] Specifically, during training, the actual controlled wind speed can be compared with the controlled wind speed output by the model, and the global error E of the error function can be calculated. The specific calculation formula is as follows: ; in, is the number of samples in the dataset, The number of output samples is a constant of 3 in this invention. is the actual value of the controlled wind speed in the sample, The control wind speed value output by the model.

[0037] like Figure 3 As shown, the fan layout is generally divided into three parts: the tunnel entrance area, the middle area, and the exit area. When a tunnel fire occurs, fire or smoke will appear within 120m of the fan, and the fan in this area cannot be turned on. Therefore, in order to prevent the smoke from spreading to these three fan position areas, when the global error is less than the accuracy value, it is also necessary to compare the relationship between the smoke spread position and the fan position in the sample. If the smoke spread position intersects with any of the three fan position areas, and there is a fan start-up in the intersecting fan position area, continue to extract samples from the data set for training. That is: ① When When , that is, the smoke spreading position input by the model intersects with the tunnel entrance area, and the fan start position output by the model includes the fans within the entrance area, then continue to extract samples for training; ② When When , that is, the smoke spreading position input by the model intersects with the middle area of ​​the tunnel, and the fan start position output by the model includes the fans in the middle area, then continue to extract samples for training; ③When When , that is, the smoke spreading position input by the model intersects with the tunnel exit area, and the fan start-up position output by the model includes the fans within the exit area, continue to extract samples for training.

[0038] On the contrary, if the smoke spreading position in the sample does not intersect with the three divided fan position areas, and no fan is started in the fan position area where the fire intersects, it indicates that the model training is completed and the non-variable frequency fan control model is obtained.

[0039] When a fire occurs in a tunnel, the sensors in the tunnel can obtain the initial wind speed of the environment and the position of the spread of fire smoke in real time. Based on the initial wind speed of the environment, the position of the spread of fire smoke and the fixed parameters of the tunnel, the target wind speed can be determined by comparing with a pre-built database.

[0040] Combined with the initial wind speed of the environment, the location of the spread of fire smoke, fixed parameters of the tunnel and the target wind speed, the number of fans that need to be started in the tunnel, the fan start-up location and the control wind speed are determined through the trained non-variable frequency fan control model. The fans at the corresponding positions in the tunnel are controlled to open to adapt to the changes in the fire in the tunnel, realize adaptive control of longitudinal smoke exhaust in highway tunnel fires, and avoid interaction between fans.

[0041] like Figure 2 The flowchart of the non-variable frequency fan control method for a highway tunnel fire is shown, and the control method includes: Step S1, constructing a non-variable frequency fan control model using the above-mentioned construction method; Step S2, obtaining the tunnel fixed parameters, environmental initial parameters, smoke spreading position and target wind speed of the controlled tunnel, and determining the fan control parameters in the tunnel through the trained non-variable frequency fan control model.

[0042] Specifically, first, the above-mentioned construction method can be used to construct a non-variable frequency fan control model. Then, when a fire occurs in the tunnel, the initial wind speed of the environment and the fire smoke spread position can be obtained in real time through the sensors in the tunnel, and the target wind speed can be determined by comparing the pre-constructed database according to the initial wind speed of the environment, the fire smoke spread position and the fixed parameters of the tunnel. Then, the initial wind speed of the environment, the fire smoke spread position, the fixed parameters of the tunnel and the target wind speed are combined, and the initial wind speed of the environment, the fire smoke spread position, the fixed parameters of the tunnel and the target wind speed are input into the trained non-variable frequency fan control model. The number of fans that need to be started in the tunnel, the fan start position and the control wind speed are determined by the trained non-variable frequency fan control model, so as to control the fans at the corresponding positions in the tunnel to adapt to the changes in the fire in the tunnel, realize the adaptive control of the longitudinal smoke exhaust of the highway tunnel fire, and avoid the interaction between the fans.

[0043] In this embodiment, optionally, the fan control parameters are determined by a non-variable frequency fan control model, including: The fan control parameters are verified through the smoke spreading position. If the verification fails, the fan control parameters are eliminated and the fan control parameters are re-determined through a non-variable frequency fan control model.

[0044] Specifically, when a tunnel fire occurs, fire or smoke appears within 120m of the fan, and the fan in this area cannot be turned on. Therefore, after calculating the number of fans that need to be started, the fan start position, and the controlled wind speed through the non-variable frequency fan control model, it is also necessary to compare the current smoke spread position with the positions of different fans to determine whether the smoke spread position intersects with the fan start position. If the smoke spread position intersects with the fan start position, the fan control parameters output by the model cannot be used to control the non-variable frequency fan in the tunnel to avoid erroneous operation.

[0045] In this embodiment, optionally, verifying the fan control parameter includes: Comparing the smoke spreading position with the fan position in the tunnel fixed parameters to determine the fan position intersecting with the smoke spreading position; According to the fan start position and the number of fan starts in the fan control parameters, determining whether there is a fan start in the fan position intersecting with the smoke spreading position; In response to the presence of a fan activation in a fan position intersecting the smoke spread location, the verification fails.

[0046] Specifically, when verifying the fan control parameters, first compare the smoke spreading position with the fan position in the tunnel fixed parameters to determine the fan position that intersects with the smoke spreading position. Then, based on the fan start position and fan start number in the fan control parameters, determine whether the fan start number at the fan position that intersects with the smoke spreading position is 0. If so, the fan control parameter is available, otherwise it is unavailable.

[0047] For example, when When the smoke spreading position intersects with the tunnel entrance area, if the fan start position output by the model Blower start position If the number of fans started is 0, the control wind speed, the number of fans started and the fan start position are taken as a set of alternative smoke exhaust data and stored in the alternative database; otherwise, the initial environmental parameters, smoke spread position and target wind speed are re-obtained to continue the calculation.

[0048] when When the smoke spreading position intersects the middle area, if the model outputs the fan start position Blower start position If the number of fans started is 0, the control wind speed, the number of fans started and the fan start position are taken as a set of alternative smoke exhaust data and stored in the alternative database; otherwise, the initial environmental parameters, smoke spread position and target wind speed are re-obtained to continue the calculation.

[0049] when When the smoke spreading position intersects with the tunnel exit area, if the output fan start position Blower start position If the number of fans started is 0, the control wind speed, the number of fans started and the fan start position are taken as a set of alternative smoke exhaust data and stored in the alternative database; otherwise, the initial environmental parameters, smoke spread position and target wind speed are re-obtained to continue the calculation.

[0050] In this embodiment, optionally, verifying the fan control parameter includes: If the verification is successful, the fan control parameter is used as an alternative control parameter, and the fan control parameter is continuously calculated by the non-variable frequency fan control model until the number of the alternative control parameters reaches a quantity threshold; The fan control parameter corresponding to the minimum control wind speed is selected from all the alternative control parameters.

[0051] Specifically, in order to improve the accuracy of non-variable frequency fan control, if the smoke spread position does not intersect with the above three fan areas, the fan control parameters output by the model can be directly used as an alternative and stored in the alternative database, and the initial environmental parameters, smoke spread position and target wind speed can be re-obtained, and the fan control parameters can continue to be calculated through the non-variable frequency fan control model until the number of fan control parameters stored in the alternative database exceeds the set quantity threshold.

[0052] Finally, the control wind speeds of all fan control parameters stored in the candidate database can be compared, and the fan control parameter with the smallest control wind speed can be selected as the optimal control parameter to control the non-variable frequency fan in the tunnel.

[0053] 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. A method for constructing a non-variable frequency fan control model for a highway tunnel fire, characterized in that: include: Acquire multiple sets of smoke exhaust data from non-variable frequency fans to build a data set. The smoke exhaust data includes tunnel fixed parameters, environmental initial parameters, smoke spreading location, target wind speed, and fan control parameters. A control model is constructed using the tunnel fixed parameters, environmental initial parameters, smoke spreading position, and target wind speed as input data and the fan control parameters as output data; The control model is trained using the data set to obtain a non-variable frequency fan control model.

2. The method for constructing a non-variable frequency fan control model for a highway tunnel fire according to claim 1 is characterized in that: Obtain multiple sets of smoke exhaust data from non-variable frequency fans to build a data set, including: A highway tunnel model was constructed using simulation software, and simulation experiments were carried out on the highway tunnel model to obtain smoke exhaust data of non-variable frequency fans under different smoke exhaust conditions.

3. The method for constructing a non-variable frequency fan control model for a highway tunnel fire according to claim 1, characterized in that: Obtain multiple sets of smoke exhaust data from non-variable frequency fans to build a data set, including: The relative error between the target wind speed in each set of smoke exhaust data and the controlled wind speed in the fan control parameter is calculated respectively, and the smoke exhaust data with a relative error less than an error threshold are screened out to construct the data set.

4. The method for constructing a non-variable frequency fan control model for a highway tunnel fire according to claim 1, characterized in that: The fixed parameters of the tunnel include tunnel length, outlet wind speed, fan outlet area, number of fans and / or fan positions.

5. The method for constructing a non-variable frequency fan control model for a highway tunnel fire according to claim 1, characterized in that: The control model is trained using the data set, including: Calculating a global error of the control model according to the control wind speed in the smoke exhaust data and the control wind speed output by the control model, and comparing the global error with a set accuracy value; In response to the global error being greater than the precision value, continuing training with the data set.

6. The method for constructing a non-variable frequency fan control model for a highway tunnel fire according to claim 5, characterized in that: The control model is trained using the data set, including: In response to the global error being less than the accuracy value, comparing the smoke spreading position in the smoke exhaust data with the fan start position output by the model; If there is no intersection between the smoke spreading position and the fan start position, the control model training is completed. Otherwise, the control model training continues through the data set.

7. A method for controlling a non-variable frequency fan in a highway tunnel fire, characterized in that: include: Using the construction method as described in any one of claims 1 to 6, a non-variable frequency fan control model is constructed; The fixed parameters of the controlled tunnel, the initial parameters of the environment, the smoke spreading position and the target wind speed are obtained, and the fan control parameters in the tunnel are determined through the trained non-variable frequency fan control model.

8. The method for controlling a non-variable frequency fan for a highway tunnel fire according to claim 7, characterized in that: The fan control parameters are determined by the non-variable frequency fan control model, including: The fan control parameters are verified through the smoke spreading position. If the verification fails, the fan control parameters are eliminated and the fan control parameters are re-determined through a non-variable frequency fan control model.

9. The method for controlling a non-variable frequency fan for a highway tunnel fire according to claim 8, characterized in that: Verifying the fan control parameters includes: Comparing the smoke spreading position with the fan position in the tunnel fixed parameters to determine the fan position intersecting with the smoke spreading position; According to the fan start position and the number of fan starts in the fan control parameters, determining whether there is a fan start in the fan position intersecting with the smoke spreading position; In response to the presence of a fan activation in a fan position intersecting the smoke spread location, the verification fails.

10. The method for controlling a non-variable frequency fan for a highway tunnel fire according to claim 8, characterized in that: Verifying the fan control parameters includes: If the verification is successful, the fan control parameter is used as an alternative control parameter, and the fan control parameter is continuously calculated by the non-variable frequency fan control model until the number of the alternative control parameters reaches a quantity threshold; The fan control parameter corresponding to the minimum control wind speed is selected from all the alternative control parameters.

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