Tunnel excavation face wet heat environment parameter prediction and optimization method, medium and equipment

Abstract

The present application relates to the technical field of tunnel and underground engineering, and particularly relates to a tunnel excavation face wet and hot environment parameter prediction and optimization method, comprising: collecting geological condition parameters, environment parameters and ventilation parameters of a tunnel excavation face; establishing an effective tunnel excavation three-dimensional numerical calculation model according to tunnel construction design data and the collected geological condition parameters, environment parameters and ventilation parameters; establishing a tunnel excavation face wet and hot environment parameter prediction model according to the tunnel excavation three-dimensional numerical calculation model and a response surface function based on a quadratic polynomial form; and performing parameter optimization based on a particle swarm algorithm according to the tunnel excavation face wet and hot environment parameter prediction model and outputting an optimal solution of the tunnel excavation face wet and hot environment parameters. The present application can predict the tunnel excavation face wet and hot environment and optimize the control parameters, can quickly control the excavation face wet and hot environment after a new tunnel face is exposed, and can enable personnel and machinery to quickly enter the working area for construction.

Description

Technical Field

[0001] This invention relates to the field of tunnel and underground engineering technology, specifically to a method, medium, and equipment for predicting and optimizing humid and hot environmental parameters at a tunnel excavation face. Background Technology

[0002] High-temperature (water-temperature) tunnels, being semi-enclosed underground spaces, are prone to developing high-temperature and high-humidity environments. The already harsh underground environment, coupled with extreme heat and humidity, poses a serious threat to the safety of construction workers. Therefore, predicting and controlling the heat and humidity environment at the tunnel excavation face during the construction of high-temperature (water-temperature) tunnels is a crucial requirement and a practical necessity for ensuring safety during construction.

[0003] Regarding the hot and humid environment at the excavation face during tunnel construction, relevant standards have imposed restrictions on factors such as air temperature, humidity, and equivalent temperature within the tunnel. Existing technologies have only proposed using liquid carbon dioxide for rapid cooling of the excavation face in high-temperature tunnels, and a stepped cooling device and method for ultra-long high-temperature tunnels. However, current standards only impose numerical restrictions on the hot and humid environment during tunnel construction, without proposing specific and feasible control measures. Furthermore, they focus solely on the design of cooling equipment based on a single temperature, without quantitatively predicting and developing control measures or equipment for the hot and humid environment in the tunnel construction area. Summary of the Invention

[0004] The purpose of this invention is to provide a method, medium, and equipment for predicting and optimizing the humid and hot environment parameters of a tunnel excavation face, which can rapidly control the humid and hot environment of the excavation face after a new tunnel face is exposed. The specific technical solution is as follows:

[0005] This invention provides a method, medium, and equipment for predicting and optimizing the humid and thermal environment parameters at a tunnel excavation face. The method for predicting and optimizing the humid and thermal environment parameters at a tunnel excavation face includes the following steps:

[0006] S1: Collect geological condition parameters, environmental parameters, and ventilation parameters of a tunnel excavation face, wherein: the geological condition parameters include the rock temperature and water seepage at the tunnel face; the environmental parameters include the air temperature and relative humidity inside the tunnel; and the ventilation parameters include the air temperature, relative humidity, and air volume delivered into the tunnel by the ventilation equipment.

[0007] S2: Based on the tunnel construction design data and the geological condition parameters, environmental parameters and ventilation parameters collected in S1, establish an effective three-dimensional numerical calculation model for tunnel excavation;

[0008] S3: Establish a prediction model for the damp and hot environment parameters of the tunnel excavation face based on the three-dimensional numerical calculation model of tunnel excavation and the response surface function based on the quadratic polynomial form;

[0009] S4: Based on the prediction model of the damp and hot environment parameters of the tunnel excavation face and the parameter optimization based on the particle swarm algorithm, the optimal solution of the damp and hot environment parameters of the tunnel excavation face is output.

[0010] Optionally, S3 includes:

[0011] S3.1 Determine the influencing factors and affected factors of the hot and humid environment during tunnel excavation. Among them, the influencing factors are: the rock temperature at the tunnel face, the seepage rate at the tunnel face, the temperature of the air delivered into the tunnel by the ventilation equipment, the relative humidity of the air delivered into the tunnel by the ventilation equipment, the air volume delivered into the tunnel by the ventilation equipment, and the duration of continuous ventilation; the affected factors are the air temperature and relative humidity in the working area of ​​the tunnel excavation face.

[0012] S3.2 Set three level values ​​for each parameter in the influencing factors;

[0013] S3.3. A three-dimensional numerical calculation model for tunnel excavation is used to establish numerical calculation models for different combinations of influencing factors and different level values, and the air temperature and relative humidity of the working area of ​​the tunnel excavation face corresponding to each combination are calculated.

[0014] S3.4 Based on the calculation results in S3.3, a prediction model for the damp and hot environment parameters of the tunnel excavation face is established using a response surface function in the form of a quadratic polynomial.

[0015] Optionally, among the three level values ​​in S3.2, the maximum level value is greater than the upper limit of the influencing factor value, the minimum level value is less than the lower limit of the influencing factor level value, and the middle level value is the average of the maximum level value and the minimum level value.

[0016] In S3.3, the number of numerical calculation models with different combinations of influencing factors and different level values ​​is m, where: m≥(n+1)(n+2) / 2, and n is the total number of influencing factors;

[0017] In S3.4, the specific expression for the prediction model of the damp and hot environment parameters at the tunnel excavation face is as follows:

[0018]

[0019] Where: f1(x1,x2,···,x n f2(x1,x2,...,x) is a prediction model for the air temperature in the working area of ​​the tunnel excavation face. n (x) is a prediction model for the relative humidity of the working area at the tunnel excavation face; gFor the g-th influencing factor, x h Let h be the h-th influencing factor; a0 is a constant term, a g a is the coefficient of the linear term. gh is the undetermined coefficient of the quadratic term; n is the total number of influencing factors; g and h are the influencing factor numbers, where g and h are natural numbers from 1 to n respectively.

[0020] Optionally, S4 includes:

[0021] S4.1 Determine the objective function for optimizing the control parameters of the humid and hot environment, specifically:

[0022] S4.1.1 Substitute the tunnel face rock temperature collected in S1 into the air temperature prediction model of the tunnel excavation face working area to obtain the air temperature prediction function f1(x) for the tunnel excavation face working area. e Substituting the seepage volume collected in S1 at the tunnel face into the air relative humidity prediction model for the tunnel excavation face working area, the air relative humidity prediction function f2(x) for the tunnel excavation face working area is obtained. e );

[0023] S4.1.2 Determine the objective function Y for optimizing the control parameters of the humid and hot environment. The specific expression is as follows:

[0024]

[0025] Where: st represents the constraint condition, x e The parameters for controlling the humid and thermal environment to be designed include the air temperature, relative humidity, air volume, and continuous ventilation duration supplied by the ventilation equipment into the tunnel. The constraint on the continuous ventilation duration is that it approaches a specified value q. lb represents the lower limit level of the parameters for controlling the humid and thermal environment to be designed, ub represents the upper limit level of the parameters for controlling the humid and thermal environment to be designed, and RH represents the upper limit level of the parameters for controlling the humid and thermal environment to be designed. min RH is the lower limit level of relative humidity in the working area of ​​the excavation face. max The lower limit of relative humidity in the working area of ​​the excavation face is given by , and k is the specified value of air temperature in the working area of ​​the tunnel excavation face.

[0026] S4.2 The control parameters are designed using the particle swarm optimization algorithm, specifically:

[0027] S4.2.1 Initialize the particle swarm, specifically: Let the iteration number t = 1, construct the initial particle swarm, and randomly generate the initial position x of each particle. ij (1) and initial velocity v ij (1);

[0028] S4.2.2 Calculate the current position x ij(t) The objective function value corresponding to each particle;

[0029] S4.2.3 For each particle, if the objective function value corresponding to it in the current iteration is less than the objective function value in the previous iteration, then the objective function corresponding to the current iteration is taken as the optimal solution for that particle, and this optimal solution and its corresponding position x are shared with other particles. ij (t); For a particle swarm, if the objective function value of a certain particle in the current iteration is the minimum value among all particles, then the objective value is the optimal solution of the particle swarm in the current iteration;

[0030] S4.2.4 If t reaches the set number of iterations, then stop the iteration. The position x corresponding to the optimal solution of the particle swarm in the current iteration is determined. ij (t) is the optimal solution, that is, the optimal solution of the control parameters of the humid and hot environment to be designed; otherwise, let t = t + 1, use the update formula to adjust the current position and velocity, return to S4.2.2 to update the optimal solution of the objective function value, until t reaches the set number of iterations.

[0031] Optionally, the update formula in S4.2.4 is as follows:

[0032] v ij (t)=wv ij (t-1)+c1r 1j [p ij -x ij [(t-1)]+c2r 2j [p gj -x ij (t-1)];

[0033] x ij (t)=x ij (t-1)+v ij (t-1);

[0034] Where: i is the particle number; j is the spatial dimension of the particle; v ij (t-1) represents the particle velocity in the (t-1)th iteration; x ij (t-1) represents the particle position in the (t-1)th iteration; p ij Let p be the optimal position of particle i in the j-th dimension; gj Let be the optimal position of all particles in the j-th dimension; w is the inertia weight; c1 is the learning factor for an individual particle, and c2 is the learning factor for the particle swarm; r 1j and r 2j These are independent random numbers that take values ​​between 0 and 1.

[0035] Optionally, the particle swarm optimization algorithm employs dynamically decreasing inertia weights and a dynamic learning factor. The dynamic decreasing expression for the inertia weights, w(t), is:

[0036] w(t) = w ini (w ini -w fin (T) max -t) / T max ;

[0037] Where: w ini w is the initial value of the inertia weight. fin T represents the final value of the inertia weight. max This represents the total number of iterations.

[0038] The dynamic expression for the learning factor of an individual particle, c1(t), is:

[0039] c1(t)=c 1ini -(c 1ini -c 1fin )×(t / T max );

[0040] Where: c 1ini c is the initial value of the volume learning factor for each particle. 1fin This represents the final value of the volume learning factor for each individual particle.

[0041] The dynamic expression for the learning factor of the particle swarm optimization, c2(t), is:

[0042] c2(t)=c 2ini +(c 2ini -c 2fin )×(t / T max );

[0043] Where: c 2ini c is the initial value of the volume learning factor for the particle swarm. 2fin This represents the final value of the volume learning factor for the particle swarm.

[0044] Optionally, the method for obtaining the geological condition parameters, environmental parameters, and ventilation parameters in S1 includes:

[0045] Geological condition parameter monitoring equipment, environmental parameter monitoring equipment, and ventilation parameter monitoring equipment were installed in the working area of ​​the tunnel excavation face.

[0046] During the tunnel excavation process, monitoring equipment begins acquiring data as soon as a new working face is exposed, collecting data every 10 minutes for 5 hours.

[0047] Optionally, S2 includes:

[0048] S2.1. Assume the number of cycles P = 1. Based on the tunnel geometry, ventilation facilities, and drainage facility layout in the tunnel construction design data, establish a three-dimensional numerical calculation model O1.

[0049] S2.2. Based on the tunnel geological survey report and the geological condition parameters, environmental parameters and ventilation parameters collected in S1, set the initial values ​​of the material and physical field of the three-dimensional numerical calculation model O1;

[0050] S2.3, Run the three-dimensional numerical calculation model in the current loop. p Among them: three-dimensional numerical calculation model O p The runtime is consistent with the continuous data acquisition duration of each monitoring device in S1;

[0051] S2.4, If running the three-dimensional numerical calculation model O p If the difference between the air temperature and relative humidity of the working area at the tunnel excavation face and the air temperature and relative humidity of the environmental parameters collected in S1 is no greater than 5%, then the three-dimensional numerical calculation model O is considered to be... p Effective, O p That is, an effective three-dimensional numerical calculation model for tunnel excavation; otherwise, it is in O p O is obtained by adjusting the material parameters based on the initial material values. p+1 , making O p =O p+1 Return to S2.3.

[0052] The present invention also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the method for predicting and optimizing the damp and hot environment parameters of the tunnel excavation face as described above.

[0053] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the method for predicting and optimizing the wet and hot environment parameters of the tunnel excavation face as described above.

[0054] This invention collects geological, environmental, and ventilation parameters of a tunnel excavation face to establish a three-dimensional numerical calculation model for tunnel excavation. Based on this model, a prediction model for the damp and heat environment parameters of the tunnel excavation face is established using a quadratic polynomial response surface function. Then, based on this prediction model, a particle swarm optimization algorithm is used to optimize the damp and heat environment parameters of the tunnel excavation face. This allows for the prediction and optimization of control parameters for the damp and heat environment of the tunnel excavation face, enabling rapid control of the damp and heat environment after the tunnel face is exposed. This allows personnel and machinery to quickly enter the work area for construction, reducing construction waiting time and saving costs associated with damp and heat environment control measures. This provides a safety guarantee for the construction of high-altitude, high-rock-temperature (water-temperature) tunnels.

[0055] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0056] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0057] Figure 1 This is a flowchart illustrating the method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face in an embodiment of the present invention. Detailed Implementation

[0058] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways, which may be limited to or covered by the present invention.

[0059] In one embodiment, a method, medium, and equipment for predicting and optimizing humid and hot environmental parameters at a tunnel excavation face include the following steps:

[0060] S1: Collect geological condition parameters, environmental parameters, and ventilation parameters of a tunnel excavation face, wherein: the geological condition parameters include the rock temperature and water seepage at the tunnel face; the environmental parameters include the air temperature and relative humidity inside the tunnel; and the ventilation parameters include the air temperature, relative humidity, and air volume delivered into the tunnel by the ventilation equipment.

[0061] Specifically, the tunnel face rock temperature is the temperature of the exposed face during tunnel excavation, measured using an infrared thermometer; the tunnel face water seepage is the volume of water flowing through the ditch in front of the tunnel face per unit time, measured using a fluid flow meter; the tunnel interior air temperature and relative humidity are the air temperature and relative humidity of the working area at the tunnel excavation face, measured using an air thermometer and an air hygrometer, respectively; the air temperature and relative humidity of the air delivered into the tunnel by the ventilation equipment are the air temperature and relative humidity of the air at the outlet of the ventilation ducts inside the tunnel, measured using an air thermometer and an air hygrometer, respectively; and the air volume delivered into the tunnel by the ventilation equipment is the volume of air delivered into the tunnel by the ventilation equipment per unit time, measured using a fluid flow meter.

[0062] The methods for obtaining geological condition parameters, environmental parameters, and ventilation parameters in S1 include:

[0063] Geological condition parameter monitoring equipment, environmental parameter monitoring equipment, and ventilation parameter monitoring equipment were installed in the working area of ​​the tunnel excavation face.

[0064] During the tunnel excavation process, monitoring equipment begins acquiring data as soon as a new working face is exposed, collecting data every 10 minutes for 5 hours.

[0065] S2: Based on the tunnel construction design data and the geological condition parameters, environmental parameters and ventilation parameters collected in S1, establish an effective three-dimensional numerical calculation model for tunnel excavation;

[0066] S2 includes:

[0067] S2.1. Assume the number of cycles P = 1. Based on the tunnel geometry, ventilation facilities, and drainage facility layout in the tunnel construction design data, establish a three-dimensional numerical calculation model O1.

[0068] S2.2. Based on the tunnel geological survey report and the geological condition parameters, environmental parameters and ventilation parameters collected in S1, set the initial values ​​of the material and physical field of the three-dimensional numerical calculation model O1;

[0069] S2.3, Run the three-dimensional numerical calculation model in the current loop. p Among them: three-dimensional numerical calculation model O p The runtime is consistent with the continuous data acquisition duration of each monitoring device in S1;

[0070] S2.4, If running the three-dimensional numerical calculation model O p If the difference between the air temperature and relative humidity of the working area at the tunnel excavation face and the air temperature and relative humidity of the environmental parameters collected in S1 is no greater than 5%, then the three-dimensional numerical calculation model O is considered to be... p Effective, O p That is, an effective three-dimensional numerical calculation model for tunnel excavation; otherwise, it is in O p O is obtained by adjusting the material parameters based on the initial material values. p+1 , making O p =O p+1 Return to S2.3.

[0071] S3: Establish a prediction model for the damp and hot environment parameters of the tunnel excavation face based on the three-dimensional numerical calculation model of tunnel excavation and the response surface function based on the quadratic polynomial form;

[0072] S3 includes:

[0073] S3.1 Determine the influencing factors and affected factors of the hot and humid environment during tunnel excavation. Among them, the influencing factors are: the rock temperature at the tunnel face, the seepage rate at the tunnel face, the temperature of the air delivered into the tunnel by the ventilation equipment, the relative humidity of the air delivered into the tunnel by the ventilation equipment, the air volume delivered into the tunnel by the ventilation equipment, and the duration of continuous ventilation; the affected factors are the air temperature and relative humidity in the working area of ​​the tunnel excavation face.

[0074] S3.2 Set three level values ​​for each parameter in the influencing factors;

[0075] Of the three level values ​​in S3.2, the maximum level value is greater than the upper limit of the influencing factor value, the minimum level value is less than the lower limit of the influencing factor level value, and the middle level value is the average of the maximum and minimum level values.

[0076] Based on the identified six factors, three level values ​​were set for each factor, as detailed in Table 1. Using the three-dimensional numerical calculation model for tunnel excavation established in step S2, and employing the BBD (Box-Behnken Design) method built into the Design-Expert software, 54 numerical calculation groups with different combinations of factors and levels were designed, and corresponding three-dimensional numerical calculation models for tunnel excavation were established. The three-dimensional numerical calculation model for tunnel excavation was run to calculate the air temperature and relative humidity in the working area of ​​the tunnel excavation face for each combination.

[0077] Table 1. Level values ​​for different factors.

[0078]

[0079]

[0080] S3.3. A three-dimensional numerical calculation model for tunnel excavation is used to establish numerical calculation models for different combinations of influencing factors and different level values, and the air temperature and relative humidity of the working area of ​​the tunnel excavation face corresponding to each combination are calculated.

[0081] In S3.3, the number of numerical calculation models with different combinations of influencing factors and different level values ​​is m, where: m≥(n+1)(n+2) / 2, and n is the total number of influencing factors;

[0082] S3.4 Based on the calculation results in S3.3, a prediction model for the damp and hot environment parameters of the tunnel excavation face is established using a response surface function in the form of a quadratic polynomial.

[0083] The specific expression for the prediction model of the humid and hot environment parameters at the tunnel excavation face is as follows:

[0084]

[0085] Where: f1(x1,x2,···,x n f2(x1,x2,...,x) is a prediction model for the air temperature in the working area of ​​the tunnel excavation face. n (x) is a prediction model for the relative humidity of the working area at the tunnel excavation face; g For the g-th influencing factor, x hLet h be the h-th influencing factor; a0 is a constant term, a g a is the coefficient of the linear term. gh The coefficients for the quadratic term are undetermined; n is the total number of influencing factors, which is 6; g and h are the influencing factor numbers, where g and h are natural numbers from 1 to n; the coefficients for the linear term are undetermined. g And the undetermined coefficient a of the quadratic term gh It is obtained by using the vector of undetermined coefficients.

[0086] S4: Based on the prediction model of the damp and hot environment parameters of the tunnel excavation face and the parameter optimization based on the particle swarm algorithm, the optimal solution of the damp and hot environment parameters of the tunnel excavation face is output.

[0087] S4 includes:

[0088] S4.1 Determine the objective function for optimizing the control parameters of the humid and hot environment, specifically:

[0089] S4.1.1 Substitute the tunnel face rock temperature collected in S1 into the air temperature prediction model of the tunnel excavation face working area to obtain the air temperature prediction function f1(x) for the tunnel excavation face working area. e Substituting the seepage volume collected in S1 at the tunnel face into the air relative humidity prediction model for the tunnel excavation face working area, the air relative humidity prediction function f2(x) for the tunnel excavation face working area is obtained. e );

[0090] S4.1.2 Determine the objective function Y for optimizing the control parameters of the humid and hot environment. The specific expression is as follows:

[0091]

[0092] Where: st represents the constraint condition, x e The parameters for controlling the humid and thermal environment to be designed include the air temperature, relative humidity, air volume, and continuous ventilation duration supplied by the ventilation equipment into the tunnel. The constraint on the continuous ventilation duration is that it approaches a specified value q. lb represents the lower limit level of the parameters for controlling the humid and thermal environment to be designed, ub represents the upper limit level of the parameters for controlling the humid and thermal environment to be designed, and RH represents the upper limit level of the parameters for controlling the humid and thermal environment to be designed. min RH is the lower limit level of relative humidity in the working area of ​​the excavation face. max The lower limit of relative humidity in the working area of ​​the excavation face is given by , and k is the specified value of air temperature in the working area of ​​the tunnel excavation face.

[0093] In this embodiment, e is a natural number from 1 to 4; x1 is the temperature of the air delivered into the tunnel by the ventilation equipment, 0℃≤x1≤30℃; x2 is the relative humidity of the air delivered into the tunnel by the ventilation equipment, 0%≤x2≤100%; x3 is the air volume delivered into the tunnel by the ventilation equipment, 0.5m3 / s≤x3≤30m3 / s; x4 is the continuous ventilation duration, 0h≤x2≤5h; k is 28℃; q is 1h; RH min Take 30, RH max A value of 80 is adopted. Based on ensuring that the air temperature in the working area of ​​the tunnel excavation face is less than the specified value of 28℃ and the continuous ventilation time is less than and close to the specified value of 1 hour, and considering economic factors, the closer the air temperature in the working area of ​​the tunnel excavation face is to 28℃ and the closer the continuous ventilation time is to 1 hour, the faster the humid and hot environment of the excavation face can be controlled after the tunnel face is exposed. This allows personnel and machinery to quickly enter the working area for construction, thereby reducing construction waiting time and saving costs on humid and hot environment control measures at the tunnel excavation face. An additional constraint is set for the relative humidity of the air in the working area of ​​the tunnel excavation face to be between 30% and 80%.

[0094] S4.2 The control parameters are designed using the particle swarm optimization algorithm, specifically:

[0095] S4.2.1 Initialize the particle swarm, specifically: Let the iteration number t = 1, construct the initial particle swarm, and randomly generate the initial position x of each particle. ij (1) and initial velocity v ij (1);

[0096] S4.2.2 Calculate the current position x ij (t) The objective function value corresponding to each particle;

[0097] S4.2.3 For each particle, if the objective function value corresponding to it in the current iteration is less than the objective function value in the previous iteration, then the objective function corresponding to the current iteration is taken as the optimal solution for that particle, and this optimal solution and its corresponding position x are shared with other particles. ij (t); For a particle swarm, if the objective function value of a certain particle in the current iteration is the minimum value among all particles, then the objective value is the optimal solution of the particle swarm in the current iteration;

[0098] S4.2.4 If t reaches the set number of iterations, then stop the iteration. The position x corresponding to the optimal solution of the particle swarm in the current iteration is determined. ij (t) is the optimal solution, that is, the optimal solution of the control parameters of the humid and hot environment to be designed; otherwise, let t = t + 1, use the update formula to adjust the current position and velocity, return to S4.2.2 to update the optimal solution of the objective function value, until t reaches the set number of iterations.

[0099] The update formula in S4.2.4 is as follows:

[0100] v ij (t)=wv ij (t-1)+c1r 1j [p ij -x ij [(t-1)]+c2r 2j [p gj -x ij (t-1)];

[0101] x ij (t)=x ij (t-1)+v ij (t-1);

[0102] Where: i is the particle number; j is the spatial dimension of the particle; v ij (t-1) represents the particle velocity in the (t-1)th iteration; x ij (t-1) represents the particle position in the (t-1)th iteration; p ij Let p be the optimal position of particle i in the j-th dimension; gj Let be the optimal position of all particles in the j-th dimension; w is the inertia weight, which decreases; c1 is the learning factor for an individual particle, which decreases; c2 is the learning factor for the particle swarm, which increases; c1 and c2 range from 1 to 2.5; r 1j and r 2j These are independent random numbers that take values ​​between 0 and 1.

[0103] The particle swarm optimization algorithm employs dynamically decreasing inertia weights and a dynamic learning factor. The dynamic decreasing expression for the inertia weights, w(t), is:

[0104] w(t) = w ini (w ini -w fin (T) max -t) / T max ;

[0105] Where: w ini w is the initial value of the inertia weight. fin T represents the final value of the inertia weight. max This represents the total number of iterations.

[0106] The dynamic expression for the learning factor of an individual particle, c1(t), is:

[0107] c1(t)=c 1ini -(c 1ini -c 1fin )×(t / T max );

[0108] Where: c 1ini c is the initial value of the volume learning factor for each particle. 1fin This represents the final value of the volume learning factor for each individual particle.

[0109] The dynamic expression for the learning factor of the particle swarm optimization, c2(t), is:

[0110] c2(t)=c 2ini +(c 2ini -c 2fin )×(t / T max );

[0111] Where: c 2ini c is the initial value of the volume learning factor for the particle swarm. 2fin This represents the final value of the volume learning factor for the particle swarm.

[0112] In this embodiment, the number of particles is 100, and the initial value of the individual particle learning factor is c. 1ini and the final value c 1fin The initial value of the particle swarm learning factor c is 0.8 and 0.2 respectively. 2ini and the final value c 2fin The initial values ​​for the inertia weight are 0.2 and 0.9, respectively. ini With the final value w fin The values ​​are 0.8 and 0.4 respectively, and the total number of iterations T max The value is 500. The optimal solution for the control parameters of the humid and hot environment to be designed in this embodiment is shown in Table 2.

[0113] Table 2 Optimal Solution of Control Parameters for the Designed Humid and Thermal Environment

[0114]

[0115] This embodiment also includes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for predicting and optimizing the damp and hot environment parameters of the tunnel excavation face as described above.

[0116] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0117] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the method for predicting and optimizing the wet and hot environment parameters of the tunnel excavation face as described above.

[0118] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0119] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, a processor and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc. In this embodiment, the electronic device has at least one cooling / heating device, at least one humidifying / dehumidifying device, and at least one ventilation / airflow control device.

[0120] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0121] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0122] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting and optimizing humid and thermal environmental parameters at a tunnel excavation face, characterized in that, Includes the following steps: S1. Determine the influencing factors and affected factors of the hot and humid environment during tunnel excavation. Among them, the influencing factors are: rock temperature at the tunnel face, water seepage at the tunnel face, air temperature delivered into the tunnel by ventilation equipment, relative humidity of air delivered into the tunnel by ventilation equipment, air volume delivered into the tunnel by ventilation equipment, and duration of continuous ventilation; the affected factors are: air temperature and relative humidity in the working area of ​​the tunnel excavation face. S2. Set three level values ​​for each parameter in the influencing factors; S3. A three-dimensional numerical calculation model for tunnel excavation is used to establish numerical calculation models for different combinations of influencing factors and different level values, and the air temperature and relative humidity of the working area of ​​the tunnel excavation face corresponding to each combination are calculated. S4. Based on the calculation results in S3, a prediction model for the damp and hot environment parameters of the tunnel excavation face is established using a response surface function in the form of a quadratic polynomial. This prediction model includes a prediction model for the air temperature and a prediction model for the relative humidity of the working area at the tunnel excavation face. Specifically: The collected rock temperature data from the tunnel face is substituted into the air temperature prediction model for the working area of ​​the tunnel excavation face to obtain the air temperature prediction function for the working area of ​​the tunnel excavation face. The collected seepage data from the tunnel face is substituted into the air relative humidity prediction model for the tunnel excavation face working area to obtain the air relative humidity prediction function for the tunnel excavation face working area. ; Determine the objective function for optimizing control parameters in a humid and hot environment. The specific expression is: ； in: As constraints, The parameters for controlling the humid and hot environment to be designed include the air temperature, relative humidity, air volume, and continuous ventilation duration supplied by the ventilation equipment into the tunnel. The constraint on the continuous ventilation duration is that it approaches a specified value. ; This represents the lower limit level of the control parameters for the humid and hot environment to be designed. This represents the upper limit level of the control parameters for the humid and hot environment to be designed. This represents the lower limit of relative humidity in the working area of ​​the excavation face. This represents the lower limit of relative humidity in the working area of ​​the excavation face. The specified value for the air temperature in the working area of ​​the tunnel excavation face; S5: Optimize parameters based on particle swarm optimization algorithm and output the optimal solution for the humid and hot environment parameters of the tunnel excavation face.

2. The method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face according to claim 1, characterized in that, Of the three levels of S2, the maximum level value is greater than the upper limit of the influencing factor value, the minimum level value is less than the lower limit of the influencing factor level value, and the middle level value is the average of the maximum and minimum level values. In S3, the number of numerical calculation models established for different combinations of influencing factors and different level values ​​is: ,in: , The total number of influencing factors; In S4, the specific expression for the prediction model of the damp and hot environment parameters at the tunnel excavation face is as follows: ； in: This is a model for predicting air temperature in the working area of ​​the tunnel excavation face. A model for predicting the relative humidity of the air in the working area of ​​the tunnel excavation face; For the first One influencing factor, For the first One influencing factor; For constant terms, The coefficients of the linear term are undetermined. The coefficients of the quadratic term are undetermined. The total number of influencing factors; and Number the influencing factors. and Take respectively The natural number.

3. The method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face according to claim 2, characterized in that, The control parameters are designed using the particle swarm optimization algorithm, specifically: ① Initialize the particle swarm, specifically: set the number of iterations. =1, construct the initial particle swarm, and randomly generate the initial position of each particle. and initial velocity ; ② Calculate the current position The objective function value corresponding to each particle; ③ For each particle, if the objective function value in the current iteration is less than the objective function value in the previous iteration, then the objective function value in the current iteration is taken as the optimal solution for that particle, and this optimal solution and its corresponding position are shared with other particles. For a particle swarm, if the objective function value of a certain particle in the current iteration is the minimum value among all particles, then the optimal solution for the particle swarm in the current iteration is obtained. ④ If If the set number of iterations is reached, the iteration stops, and the position corresponding to the optimal solution of the particle swarm in the current iteration is determined. The optimal solution is the optimal solution for the control parameters of the humid and hot environment to be designed. Otherwise = +1, adjust the current position and velocity using the update formula, return to the optimal solution of updating the objective function value in step ②, until... The set number of iterations has been reached.

4. The method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face according to claim 3, characterized in that, The update formula in step ④ is as follows: ； ； in: Number the particles; The dimension of the space in which the particle resides; For the first Particle velocity during iteration; For the first Particle positions during iteration; For particles In the The optimal position of the dimension; For all particles in the first The optimal position in 3D space; Inertial weight; For individual particles, The learning factor for particle swarm optimization; and These are independent random numbers that take values ​​between 0 and 1.

5. The method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face according to claim 4, characterized in that, The particle swarm optimization algorithm employs dynamically decreasing inertia weights and a dynamic learning factor. The dynamic decreasing expression for the inertia weights is... for: ； in: This is the initial value for the inertia weight. This is the final value of the inertia weight. This represents the total number of iterations. The dynamic expression of the learning factor of an individual particle for: ； in: This represents the initial value of the volume learning factor for each individual particle. This represents the final value of the volume learning factor for each individual particle. Dynamic expression of the learning factor of particle swarm optimization for: ； in: The initial value of the volume learning factor for the particle swarm. ， This represents the final value of the volume learning factor for the particle swarm.

6. The method for predicting and optimizing the humid and hot environment parameters of a tunnel excavation face according to any one of claims 1-5, characterized in that, Geological condition parameters, environmental parameters, and ventilation parameters of a tunnel excavation face are collected. The geological condition parameters include the rock temperature and water seepage at the tunnel face. The environmental parameters include the air temperature and relative humidity inside the tunnel. The ventilation parameters include the air temperature, relative humidity, and air volume delivered into the tunnel by the ventilation equipment. The methods for obtaining the geological condition parameters, environmental parameters, and ventilation parameters include: Geological condition parameter monitoring equipment, environmental parameter monitoring equipment, and ventilation parameter monitoring equipment were installed in the working area of ​​the tunnel excavation face. During the tunnel excavation process, monitoring equipment begins acquiring data as soon as a new working face is exposed, collecting data every 10 minutes for 5 hours.

7. The method for predicting and optimizing the humid and hot environment parameters of the tunnel excavation face according to claim 6, characterized in that, The process of establishing a three-dimensional numerical calculation model for tunnel excavation is as follows: S2.1, Let the number of iterations be... A three-dimensional numerical model was established based on the tunnel's geometric dimensions, ventilation facilities, and drainage facility layout as described in the tunnel construction design data. ; S2.

2. Based on the tunnel geological survey report and the collected geological condition parameters, environmental parameters, and ventilation parameters, a three-dimensional numerical calculation model is set up. Initial values ​​of materials and initial values ​​of physical fields; S2.3, Run the three-dimensional numerical calculation model in the current loop. Among them: three-dimensional numerical calculation model The runtime is consistent with the continuous data collection duration of each monitoring device; S2.4 If running a three-dimensional numerical calculation model If the difference between the obtained air temperature and relative humidity in the working area of ​​the tunnel excavation face and the air temperature and relative humidity in the collected environmental parameters is no greater than 5%, then the three-dimensional numerical calculation model is considered to be... efficient, That is, an effective three-dimensional numerical calculation model for tunnel excavation; otherwise, and The material parameters are adjusted based on the initial material values ​​to obtain the desired result. ,make Return to S2.

3.

8. A computer-readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement the method for predicting and optimizing the damp and hot environment parameters of the tunnel excavation face as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: The method for predicting and optimizing humid and hot environmental parameters at a tunnel excavation face as described in any one of claims 1 to 7 includes at least one processor, at least one memory, and computer program instructions stored in the memory.

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