Mine ventilation system air volume mixing adjustment method, device and equipment

Abstract

The present application discloses a method, device, and apparatus for mixed air volume regulation in a mine ventilation system. The method comprises: obtaining a ventilation network model of the mine ventilation system; performing air volume distribution calculations on the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; generating at least one candidate air volume regulation scheme based on the underground ventilation air volume demand and a set optimization model corresponding to the mine ventilation system; determining the optimal air volume regulation scheme based on the at least one candidate air volume regulation scheme and the underground ventilation air volume demand; and setting the optimization model as an air volume regulation mixed optimization model, wherein the air volume regulation mixed optimization model is a linear mixed integer programming model, which greatly reduces the model's variable scale and solution complexity, thereby improving the reliability of underground ventilation regulation.

Description

Technical Field

[0001] The present application relates to the field of ventilation control, and in particular to a method, device and equipment for adjusting air volume mixing in a mine ventilation system. Background Art

[0002] The purpose of mine ventilation is to supply sufficient fresh air to the mining area, discharge the polluted air underground to the surface in time, improve the mine ventilation environment, strengthen safety production standards, and create a good and comfortable working environment for underground workers.

[0003] Ventilation optimization and control requires that, under the premise of meeting the dynamic on-demand air distribution at different periods underground, ventilation optimization theory based on fluid network be used to obtain a ventilation optimization and control scheme that meets the actual safety production requirements of the mine, is technically reasonable and reliable, and has the best economic performance, so as to adjust the air volume distribution and air pressure distribution state of the ventilation network to ensure the safe, reliable, stable and economical operation of the mine ventilation system.

[0004] The air volume regulation of traditional mine ventilation systems often involves a large number of variables and the model solution is complex, which makes it difficult to meet the ventilation regulation needs. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method, device and equipment for mixing and adjusting the air volume of a mine ventilation system, aiming to improve the reliability of underground ventilation adjustment of the mine ventilation system.

[0006] The technical solution of the embodiment of the present application is implemented as follows:

[0007] The present application provides a method for adjusting the air volume of a mine ventilation system, comprising:

[0008] Obtain the ventilation network model of the mine ventilation system;

[0009] performing air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions;

[0010] generating at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and a setting optimization model corresponding to the mine ventilation system;

[0011] Determining an optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement;

[0012] Among them, the setting optimization model is a hybrid optimization model for air volume control, and the hybrid optimization model for air volume control is a linear mixed integer programming model, including the following optimization objectives: minimum ventilation energy consumption target, optimal adjustment position target, optimal adjustment method target and minimum adjustment number target; the decision variables of the setting optimization model include: the adjustment air pressure values ​​of all branches and a 0-1 integer variable representing the correspondence between the air volume of the on-demand air distribution branch and the multiple air volume values ​​of the branch; the air volume adjustment scheme includes: adjustment measures for the ventilation structure, and the ventilation structure includes at least one of the following: underground fans, dampers and windows.

[0013] In some embodiments, performing air volume distribution calculation on the ventilation network model based on the ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions, includes:

[0014] The ventilation network solution method based on the loop air volume is used to perform air volume distribution calculation on the ventilation network model to obtain an air volume distribution calculation result;

[0015] Adjusting the tunnel wind resistance parameters based on the resistance measurement method until the error between the calculated air volume distribution result and the measured tunnel air volume is within a set threshold;

[0016] Obtaining a simulated operating condition of a fan in the ventilation network model based on the air volume distribution calculation result;

[0017] It is determined whether the simulated operating condition of the fan matches the actual operating condition. If not, the model parameters of the ventilation network model are adjusted until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition.

[0018] In some embodiments, the configuration optimization model is as follows:

[0019]

[0020]

[0021] Where Z is the optimization target, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, ω5 is the fifth weight coefficient, F is the set of all wind turbine branches f, K f Indicates the number of air volume values ​​of fan branch f, q f,k To represent the k-th air volume value of fan branch f, n f,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch f is q f,k , h fis the fan pressure of fan branch f, N is the number of branches in the ventilation network, n j,a Indicates whether the j-th branch needs to be adjusted, s j is the adjustment level of the j-th branch, n j,c Indicates whether it is necessary to adjust the energy or reduce the resistance of the j-th branch, n j,b Indicates whether it is necessary to adjust the resistance of the j-th branch, Δh′ j is Δh j The absolute value of Δh j is the wind pressure adjustment value of the j-th branch, J is the number of nodes in the ventilation network, a ij Indicates the relationship between nodes and branches, K j Indicates the number of branch air volume values, q j,k To represent the k-th air volume value of fan branch j, n j,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch j is q j,k , b ij Indicates the relationship between branches and loops, h j is the algebraic sum of the wind pressure on the jth branch, ρ′ j represents the energy increase or resistance reduction adjustment amount that can be ignored in the j-th branch, ρ″ j Indicates the resistance increase adjustment amount that can be ignored in the j-th branch, Δh j,min is the lower limit of the adjustable wind pressure of the jth branch, Δh j,max is the upper limit of the adjustable wind pressure of the j-th branch, N a is the number of branches allowed to be adjusted in the ventilation network, ρ j Indicates the amount of regulation that can be ignored in the j-th branch, n max,a is the first normal quantity, n max,b is the second normal quantity, n max,c is the third normal quantity, q f is the fan air volume of fan branch f, a0, a1, a2 are the fan characteristic curve fitting coefficients.

[0022] In some embodiments, generating at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the setting optimization model corresponding to the mine ventilation system includes:

[0023] According to the underground ventilation air volume demand, the weight coefficients and decision variables of the optimization model are set;

[0024] Based on the set weight coefficients and decision variables, at least one air volume adjustment scheme to be selected is solved using the set optimization model.

[0025] In some embodiments, determining the optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement includes:

[0026] Using a ventilation network solution method based on loop air volume, perform air volume distribution calculation on the at least one air volume adjustment scheme to be selected, and obtain air volume distribution calculation results corresponding to each air volume adjustment scheme;

[0027] The air volume distribution calculation results corresponding to each air volume adjustment scheme are compared with the distributed air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal air volume adjustment scheme.

[0028] In some embodiments, the method further comprises:

[0029] The mine ventilation system is adjusted based on the optimal air volume adjustment scheme.

[0030] In a second aspect, an embodiment of the present application provides an air volume mixing and regulating device for a mine ventilation system, comprising:

[0031] Ventilation network model acquisition module, used to obtain the ventilation network model of the mine ventilation system;

[0032] a ventilation network model optimization module, configured to perform air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjust the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions;

[0033] An air volume adjustment scheme generating module is used to generate at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the set optimization model corresponding to the mine ventilation system;

[0034] an air volume adjustment scheme selection module, configured to determine an optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement;

[0035] Among them, the setting optimization model is a hybrid optimization model for air volume control, and the hybrid optimization model for air volume control is a linear mixed integer programming model, including the following optimization objectives: minimum ventilation energy consumption target, optimal adjustment position target, optimal adjustment method target and minimum adjustment number target; the decision variables of the setting optimization model include: the adjustment air pressure values ​​of all branches and a 0-1 integer variable representing the correspondence between the air volume of the on-demand air distribution branch and the multiple air volume values ​​of the branch; the air volume adjustment scheme includes: adjustment measures for the ventilation structure, and the ventilation structure includes at least one of the following: underground fans, dampers and windows.

[0036] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor, when running the computer program, executes the steps of the method described in the first aspect of the embodiment of the present application.

[0037] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described in the first aspect of the embodiment of the present application are implemented.

[0038] The technical solution provided by the embodiment of the present application obtains a ventilation network model of a mine ventilation system; performs air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjusts the ventilation network model based on the air volume distribution calculation result until the simulated working condition of the fan in the ventilation network model matches the actual working condition; generates at least one to-be-selected air volume adjustment scheme based on the underground ventilation air volume demand and the set optimization model corresponding to the mine ventilation system; determines the optimal air volume adjustment scheme based on at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume demand; wherein the set optimization model is an air volume control hybrid optimization model, and the air volume control hybrid optimization model is a linear mixed integer programming model. In this way, the optimal air volume adjustment scheme can be obtained based on the linear mixed integer programming model, which greatly reduces the variable scale and solution complexity of the model, thereby improving the reliability of underground ventilation regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for adjusting the air volume of a mine ventilation system according to an embodiment of the present application;

[0040] Figure 2 This is a schematic structural diagram of an air volume mixing and regulating device for a mine ventilation system according to an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] The present application will be described in further detail below with reference to the accompanying drawings and embodiments.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0044] In order to meet the intelligent adjustment requirements of mine ventilation systems, an embodiment of the present application provides a method for mixed air volume adjustment of a mine ventilation system, which can be applied to electronic devices with data processing capabilities, such as laptops, desktop computers, or servers, to intelligently determine the air volume adjustment scheme of the mine ventilation system.

[0045] In related technologies, the nonlinearity, non-convexity, and large-scale decision variables of ventilation optimization and control mathematical models for complex ventilation networks greatly increase the complexity of solving the mathematical models, necessitating the development of efficient and reliable solution methods. Based on this, the mine ventilation system air volume hybrid regulation method of the present embodiment eliminates nonlinear decision variables by introducing 0-1 integer variables (also known as binary variables), significantly reducing the model's variable scale and solution complexity, thereby improving the reliability of underground ventilation regulation and meeting the underground ventilation needs of the mine ventilation system.

[0046] like Figure 1 As shown, the air volume mixing adjustment method of the mine ventilation system in the embodiment of the present application includes:

[0047] Step 101: Obtain a ventilation network model of a mine ventilation system.

[0048] It should be noted that the ventilation network model is established based on the underground measured data of the mine ventilation system, which is the data basis for constructing the setting optimization model of the embodiment of the present application.

[0049] Exemplarily, obtaining a ventilation network model of a mine ventilation system includes:

[0050] Step a: establishing a three-dimensional ventilation network diagram using the designed and measured horizontal cross-sections of each mining level;

[0051] Step b, collecting wind resistance parameters of all ventilation network lanes through ventilation resistance measurement;

[0052] Step c: Investigate the installation and layout of underground fans and structures to determine the initial state of the ventilation network model;

[0053] Step d, collecting the operating status of the fans at each level of the fan station, determining the fan station level of each fan and the current speed of the fan variable frequency operation;

[0054] Step e: For a non-variable frequency fan, it can be considered that the fan operating speed ratio is 100% and the fan speed cannot be adjusted.

[0055] Step 102 : performing air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions.

[0056] It can be understood that by comparing the simulated operating conditions of the fans in the ventilation network model with the actual operating conditions, the ventilation network model can accurately reflect the operating performance of the fans. The fan operating conditions can be understood as the air volume and air pressure corresponding to the fan's current speed.

[0057] Exemplarily, performing air volume distribution calculation on the ventilation network model based on the ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition, includes:

[0058] The ventilation network solution method based on the loop air volume is used to perform air volume distribution calculation on the ventilation network model to obtain an air volume distribution calculation result;

[0059] Adjusting the tunnel wind resistance parameters based on the resistance measurement method until the error between the calculated air volume distribution result and the measured tunnel air volume is within a set threshold;

[0060] Obtaining a simulated operating condition of a fan in the ventilation network model based on the air volume distribution calculation result;

[0061] It is determined whether the simulated operating condition of the fan matches the actual operating condition. If not, the model parameters of the ventilation network model are adjusted until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition.

[0062] It should be noted that the above-mentioned adjustment of the tunnel wind resistance parameters based on the resistance measurement method can make the air volume of each network branch of the ventilation network model as consistent as possible with the measured air volume, and the set threshold can be reasonably determined according to the design accuracy.

[0063] It should be noted that those skilled in the art can adjust the model parameters of the ventilation network model based on the comparison results between the simulated operating conditions and the actual operating conditions of the fan until the difference between the simulated operating conditions and the actual operating conditions is within a reasonable accuracy range. Preferably, the electronic device can also intelligently adjust the model parameters of the ventilation network model based on a model optimization algorithm until the difference between the simulated operating conditions and the actual operating conditions is within a reasonable accuracy range.

[0064] Step 103: generating at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the setting optimization model corresponding to the mine ventilation system.

[0065] Here, the setting optimization model is a hybrid optimization model for air volume control, and the hybrid optimization model for air volume control is a linear mixed integer programming model, including the following optimization objectives: minimum ventilation energy consumption target, optimal adjustment position target, optimal adjustment method target and minimum adjustment number target; the decision variables of the setting optimization model include: the adjustment air pressure values ​​of all branches and a 0-1 integer variable representing the correspondence between the air volume of the on-demand air distribution branch and the multiple air volume values ​​of the branch; the air volume adjustment scheme includes: adjustment measures for the ventilation structure, and the ventilation structure includes at least one of the following: underground fans, dampers and windows.

[0066] In some embodiments, generating at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the setting optimization model corresponding to the mine ventilation system includes:

[0067] According to the underground ventilation air volume demand, the weight coefficients and decision variables of the optimization model are set;

[0068] Based on the set weight coefficients and decision variables, at least one air volume adjustment scheme to be selected is solved using the set optimization model.

[0069] In an application example, generating at least one air volume adjustment scheme to be selected includes:

[0070] Step a: Calculate the required air volume at each operating point based on the underground ventilation air volume demand, so as to adjust the underground air volume according to demand using an air volume adjustment scheme;

[0071] Step b: Select corresponding objectives and constraints according to the actual needs of the mine and construct the setting optimization model of the embodiment of the present application;

[0072] Step c: according to the demand control requirements of underground air volume, set the weight coefficient of each optimization target, as well as parameters such as the on-demand ventilation demand branch air volume deviation range, operating condition air volume deviation range, operating condition air pressure deviation range, and fan operation range;

[0073] Step d: using the set optimization model to perform a solution operation to obtain at least one air volume adjustment scheme to be selected, the air volume adjustment scheme includes adjustment measures for the ventilation structure, and the ventilation structure includes at least one of the following: an underground fan, a damper, and a window.

[0074] Step 104: Determine an optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement.

[0075] Exemplarily, determining the optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement includes:

[0076] Using a ventilation network solution method based on loop air volume, perform air volume distribution calculation on the at least one air volume adjustment scheme to be selected, and obtain air volume distribution calculation results corresponding to each air volume adjustment scheme;

[0077] The air volume distribution calculation results corresponding to each air volume adjustment scheme are compared with the distributed air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal air volume adjustment scheme.

[0078] It should be noted that the underground ventilation air volume demand can be reasonably determined based on the number of underground workers, exhaust gas emission requirements, etc. The distributed air volume of the on-demand air branches can be obtained by converting the underground ventilation air volume demand and the calculation formula in the ventilation regulations. The relevant conversion processing belongs to the existing technology and will not be repeated here.

[0079] It is understood that the method of the embodiments of the present application can generate at least one air volume adjustment scheme based on the aforementioned multi-objective optimization model, and determine the optimal air volume adjustment scheme based on at least one candidate air volume adjustment scheme and the underground ventilation air volume demand, thereby enabling intelligent adjustment of the mine ventilation system based on this optimal air volume adjustment scheme. Because the optimal air volume adjustment scheme is derived based on a linear mixed integer programming model, the model's variable scale and solution complexity are greatly reduced, thereby improving the reliability of underground ventilation adjustment.

[0080] In some embodiments, the configuration optimization model is as follows:

[0081]

[0082]

[0083] Where Z is the optimization target, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, ω5 is the fifth weight coefficient, F is the set of all wind turbine branches f, K f Indicates the number of air volume values ​​of fan branch f, q f,k To represent the k-th air volume value of fan branch f, n f,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch f is q f,k , h f is the fan pressure of fan branch f, N is the number of branches in the ventilation network, n j,a Indicates whether the j-th branch needs to be adjusted, s j is the adjustment level of the j-th branch, n j,c Indicates whether it is necessary to adjust the energy or reduce the resistance of the j-th branch, n j,bIndicates whether it is necessary to adjust the resistance of the j-th branch, Δh′ j is Δh j The absolute value of Δh j is the wind pressure adjustment value of the j-th branch, J is the number of nodes in the ventilation network, a ij Indicates the relationship between nodes and branches, K j Indicates the number of branch air volume values, q j,k To represent the k-th air volume value of fan branch j, n j,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch j is q j,k , b ij Indicates the relationship between branches and loops, h j is the algebraic sum of the wind pressure on the jth branch, ρ′ j represents the energy increase or resistance reduction adjustment amount that can be ignored in the j-th branch, ρ″ j Indicates the resistance increase adjustment amount that can be ignored in the j-th branch, Δh j,min is the lower limit of the adjustable wind pressure of the jth branch, Δh j,max is the upper limit of the adjustable wind pressure of the j-th branch, N a is the number of branches allowed to be adjusted in the ventilation network, ρ j Indicates the amount of regulation that can be ignored in the j-th branch, n max,a is the first normal quantity, n max,b is the second normal quantity, n max,c is the third normal quantity, q f is the fan air volume of fan branch f, and a0, a1, and a2 are the fan characteristic curve fitting coefficients. It should be noted that st in the above formula is the abbreviation of subject to (such that), which means constrained.

[0084] In some embodiments, the method further comprises:

[0085] The mine ventilation system is adjusted based on the optimal air volume adjustment scheme.

[0086] It is understood that the above-mentioned optimal air volume adjustment scheme can be output to the terminal device and adjusted by manual control, for example, by manually adjusting and / or installing ventilation structures such as fans, dampers, and windows to achieve on-demand ventilation underground. Preferably, the electronic device can also control the mine ventilation system based on the optimal air volume adjustment scheme, for example, by remotely controlling each installed ventilation structure such as fans, dampers, and windows to achieve on-demand ventilation underground.

[0087] It should be noted that, considering that the air volume of each branch is within a specific range, for example, a minimum spanning tree can be constructed, a set of residual tree branches can be selected, and the interval of the residual tree branch air volume can be determined based on the air volume constraint. Then, an air volume interval is set according to the air volume control accuracy to determine all possible air volume values ​​for each branch. Secondly, a 0-1 integer variable is assigned to each possible air volume value of the branch to indicate whether the branch air volume takes a certain air volume value. Finally, a linear air volume objective function and linear air volume constraint are constructed based on the 0-1 integer variable.

[0088] Assume that the air volume control accuracy and the air volume range constraint of the j-th branch limit the branch air volume to be Among them, K j Indicates the number of air volume values ​​of the j-th branch.

[0089] Define a 0-1 integer variable n j,k Indicates whether the air volume value of the j-th branch is q j,k ,Right now

[0090]

[0091] Among them, q j is the air volume of the j-th branch; q j,k represents the kth possible air volume value of the jth branch, and is a constant.

[0092] In order to limit n j,k The value of n j,k Should meet

[0093]

[0094] Where N is the number of branches in the ventilation network.

[0095] There is an implicit condition in the above formula, K j n j,k The variable has only one value of 1, that is, the value of the air volume of the j-th branch must be A value in .

[0096] In particular, K of the j-th branch j A 0-1 integer variable n j,k Meet the following characteristics

[0097]

[0098] In order to eliminate the nonlinear variable q in the mathematical model j , need to study q j 、 and Through derivation and calculation, it is found that the nonlinear variable q can be replaced by the following formula j

[0099]

[0100] in,

[0101]

[0102]

[0103] The optimization objectives of the hybrid optimization model for air volume control in the embodiment of the present application are described as follows:

[0104] (1) Minimum ventilation energy consumption target

[0105] The minimum ventilation energy consumption target can be expressed as

[0106]

[0107] in,

[0108] z1 represents the minimum ventilation energy consumption target;

[0109] F is the set of all fan branches f, including main fans and auxiliary fans;

[0110] q f is the fan air volume of fan branch f;

[0111] h f is the fan wind pressure of fan branch f;

[0112] N is the number of branches of the ventilation network;

[0113] h′ r,j is the algebraic sum of the ventilation resistance of the j-th branch, h′ r,j =h r,j +Δh j -h N,j ;

[0114] h r,j is the ventilation resistance of the j-th branch,

[0115] r j is the wind resistance of the jth branch;

[0116] q j is the air volume of the j-th branch;

[0117] Δh j is the wind pressure adjustment value of the j-th branch;

[0118] h N,jis the natural wind pressure of the jth branch;

[0119] K f Indicates the number of air volume values ​​of fan branch f;

[0120] q f,k represents the kth possible air volume value of fan branch f, which is a constant;

[0121] 0-1 integer variable n f,k Indicates whether the air volume value of fan branch f is q f,k .

[0122] (2) Optimal adjustment position target

[0123] In order to quantify the adjustability of branches at specific adjustment positions in a ventilation network, a branch adjustment level (integer type value) can be defined to represent the adjustment position constraints. The branch adjustment level constructed in this application satisfies the following characteristics:

[0124] (a) The default value of the branch adjustment level is zero, indicating that the branch is an adjustable branch that allows any adjustment method;

[0125] (b) The larger the absolute value of the branch adjustment level, the less adjustable the branch is;

[0126] (c) The positive sign of the branch adjustment level and the larger the value, the less resistance-increasing adjustment the branch can achieve;

[0127] (d) The branch regulation level is negative and the smaller the value is, the less likely the branch is to be regulated by energy increase or resistance reduction;

[0128] (e) The absolute value of the adjustable branch adjustment level is close to zero, and the absolute value of the non-adjustable branch adjustment level tends to a large integer value;

[0129] (f) The sign of the adjustment level of the resistance-increasing adjustment branch is positive, and the sign of the adjustment level of the energy-increasing adjustment branch or the resistance-reducing adjustment branch is negative.

[0130] The optimal adjustment position target can be expressed as

[0131]

[0132] in,

[0133] z2 represents the optimal adjustment position target;

[0134] s j Indicates the adjustment level of the j-th branch, which is a constant set by the user;

[0135] n j,a Indicates whether the j-th branch needs to be adjusted;

[0136] n j,a satisfy

[0137] Δh j is the wind pressure adjustment value of the j-th branch;

[0138] ρ j Indicates the adjustment amount (adjustment factor) that can be ignored in the j-th branch, satisfying ρ j >0.

[0139] In order to solve the mathematical model, the mixed integer programming method is introduced to convert n j,a Defined as a 0-1 integer variable, indicating whether the j-th branch needs to be adjusted. j,a |Δh in j |, introduce Δh′ j Denotes Δh j The absolute value of Δh′ j Meet the following conditions

[0140]

[0141] Under the constraints of the above conditions, there exists Δh′ j ≥0. In order to limit Δh′ j The size of Δh′ is introduced by using the priority method to introduce a target constraint with the highest priority to ensure j =|Δh j |.

[0142] min z0=ω0Δh′ j j=1,2,…,N (8)

[0143] in,

[0144] z0 represents the additional target for limiting the wind pressure adjustment value variable;

[0145] ω0 represents the weight coefficient of the variable that limits the wind pressure adjustment value (a larger value is taken).

[0146] The above additional goals must be met first, otherwise it will affect the 0-1 integer variable n j,a Reliability of the value. 0-1 integer variable n j,a The following conditions must be met

[0147]

[0148] in,

[0149] n max,a It can be set to a larger positive constant to ensure that Δh′j -ρ j ≤n max,a .

[0150] Under the above conditions, the 0-1 integer variable n j,a satisfy Here we require ρ j >0.

[0151] (3) Optimal adjustment method target

[0152] The adjustment level considers the adjustment method corresponding to the branch, while the optimal adjustment position target does not consider the adjustment method of the adjustment point position. Therefore, it is necessary to further construct the optimal adjustment method target.

[0153] The optimal adjustment position target can be expressed as

[0154]

[0155] in,

[0156] z3 represents the target of the best adjustment method;

[0157] ω3 represents the weight coefficient of the optimal adjustment method target;

[0158] s j Indicates the adjustment level of the j-th branch, which is a constant set by the user;

[0159] n j,b Indicates whether it is necessary to increase the resistance of the j-th branch;

[0160] n j,b satisfy

[0161] n j,c Indicates whether the j-th branch needs to be adjusted for energy enhancement or resistance reduction;

[0162] n j,c satisfy

[0163] Δh j is the wind pressure adjustment value of the j-th branch;

[0164] ρ j Indicates the adjustment amount (adjustment factor) that can be ignored in the j-th branch, satisfying ρ j >0.

[0165] In order to solve the mathematical model, the mixed integer programming method is introduced to convert n j,b Defined as a 0-1 integer variable, indicating whether it is necessary to adjust the resistance of the j-th branch; j,cDefined as a 0-1 integer variable, indicating whether energy enhancement or resistance reduction adjustment is required for the j-th branch.

[0166] 0-1 integer variable n j,b The following conditions must be met

[0167]

[0168] in,

[0169] n max,b Can be set to a larger normal value to ensure that Δh j -ρ j ≤n max,b .

[0170] 0-1 integer variable n j,c The following conditions must be met

[0171]

[0172] in,

[0173] n max,c Can be set to a larger normal value to ensure -(Δh j +ρ j )≤n max,c .

[0174] (4) Minimum number of adjustments

[0175] When the absolute value of the total number of adjustment levels is close, the number of adjustment points should be minimized. In addition to the optimal adjustment position, the optimal adjustment solution also requires that the air volume control optimization model minimize the number of adjustment points and adopt a resistance-increasing adjustment method as much as possible to reduce ventilation system control costs and simplify the management process of ventilation control facilities.

[0176] When optimizing and controlling underground ventilation systems, only when the adjustment amount reaches a certain threshold in a particular tunnel will the corresponding adjustment facilities be installed or adjustments be performed. To avoid the impact of smaller adjustment amounts on the number of adjustments, adjustment factors can be set to eliminate the need for adjustment in specific branches.

[0177] The minimum number of adjustments can be expressed as

[0178]

[0179] in,

[0180] z4 represents the minimum number of adjustments;

[0181] ω4 represents the weight coefficient of the minimum number of adjustments;

[0182] n j,a Indicates whether the j-th branch needs to be adjusted;

[0183] n j,a satisfy

[0184] Δh j is the wind pressure adjustment value of the j-th branch;

[0185] ρ j Indicates the adjustment amount (adjustment factor) that can be ignored in the j-th branch, satisfying ρ j >0.

[0186] The constraints of the hybrid optimization model for air volume control are described as follows:

[0187] (1) Air volume balance constraints

[0188] The air volume regulation scheme of the ventilation network must meet the node air volume balance condition, that is, the algebraic sum of the air volumes of each branch flowing into and out of any node in the ventilation network is zero.

[0189]

[0190] in,

[0191] N is the number of branches of the ventilation network;

[0192] J is the number of nodes in the ventilation network;

[0193] a ij Indicates the relationship between nodes and branches;

[0194] a ij satisfy

[0195] (2) Wind pressure balance constraints

[0196] The air volume regulation scheme of the ventilation network must meet the circuit wind pressure balance condition, that is, the algebraic sum of the wind pressures of each branch in any circuit in the ventilation network is zero.

[0197]

[0198] in,

[0199] M is the number of independent circuits in the ventilation network, M = N-J+1;

[0200] h j is the algebraic sum of the wind pressure on the j-th branch,

[0201] q j is the air volume of the j-th branch;

[0202] r j is the wind resistance of the jth branch;

[0203] Δh j is the wind pressure adjustment value of the j-th branch;

[0204] h f,j is the wind pressure of the fan in the j-th branch;

[0205] h N,j is the natural wind pressure of the jth branch;

[0206] b ij Indicates the relationship between branches and loops;

[0207] b ij satisfy

[0208] (3) Adjusting position constraints

[0209] When the jth branch does not allow the installation of regulating facilities (non-regulatory branch), then

[0210] Δh j =0 (16)

[0211] It is worth noting that, without affecting the regulation effect, for the non-adjustable branches, a negligible regulation tolerance range can be set. Therefore, the constraint that the j-th branch is not allowed to install regulation facilities can be expressed as

[0212] -ρ′ j ≤Δh j ≤ρ″ j (17)

[0213] in,

[0214] ρ′ j Indicates the energy increase or resistance reduction adjustment (adjustment factor) that can be ignored in the j-th branch, satisfying ρ′ j >0;

[0215] ρ″ j Indicates the energy adjustment amount (adjustment factor) that can be ignored in the j-th branch, satisfying ρ″ j >0.

[0216] (4) Constraints on the adjustment method

[0217] The j-th branch regulation constraint

[0218] Δh j,min ≤Δh j ≤Δh j,max (18)

[0219] in,

[0220] Δh j,min is the lower limit of the adjustable wind pressure of the j-th branch;

[0221] Δh j,max The upper limit of the adjustable wind pressure of the j-th branch.

[0222] When the jth branch only allows resistance-increasing regulation constraints, then

[0223] Δh j ≥0 (19)

[0224] When the jth branch only allows energy-increasing regulation (or resistance-reducing regulation) constraints, then

[0225] Δh j ≤0 (20)

[0226] When constructing the actual adjustment mode constraint conditions, it is not advisable to set constraints that restrict specific adjustment modes for a large number of branches, otherwise the optimization model may have no solution.

[0227] In order to set the regulation mode constraint conditions, the properties of the allowed branch regulation mode, the branch adjustable wind pressure upper limit and the branch adjustable wind pressure lower limit can be added to the branches of the ventilation network.

[0228] (5) Adjust the number constraints

[0229] In order to reduce the cost of ventilation system control and simplify the management process of ventilation control facilities, the control scheme of the optimization model should minimize the number of control points.

[0230]

[0231] in,

[0232] N a The number of branches allowed to be adjusted in the ventilation network;

[0233] n j,a Indicates whether the j-th branch needs to be adjusted;

[0234] n j,a satisfy

[0235] Δh j is the wind pressure adjustment value of the j-th branch;

[0236] ρ j Indicates the adjustment amount (adjustment factor) that can be ignored in the j-th branch, satisfying ρ j >0.

[0237] (6) Fan operation constraints

[0238] When the fan type has been determined and the fan cannot be changed, the simulation of the fan operating conditions can be determined by the fan characteristic curve, and the fan characteristic curve can be integrated into the mathematical model constraints.

[0239] The fan operating condition constraints can be expressed as

[0240]

[0241] in,

[0242] a0, a1, a2 are the fan characteristic curve fitting coefficients;

[0243] q f is the fan air volume of fan branch f;

[0244] h f is the fan pressure of fan branch f.

[0245] It should be noted that the decision variable of the hybrid optimization model for air volume control is the regulated air pressure value Δh of all branches. j And air volume value n j,k , and auxiliary decision variables Δh′ j 、n j,a 、n j,b and n j,c Since the objective function and constraints are both linear functions, the corresponding mathematical model is a linear mixed integer programming model.

[0246] In order to implement the method of the embodiment of the present application, the embodiment of the present application also provides a mine ventilation system air volume mixing adjustment device, which is set on an electronic device, such as Figure 2 As shown, the air volume mixing and regulating device for the mine ventilation system includes: a ventilation network model acquisition module 201, a ventilation network model optimization module 202, an air volume regulation scheme generation module 203 and an air volume regulation scheme selection module 204.

[0247] The ventilation network model acquisition module 201 is used to obtain the ventilation network model of the mine ventilation system; the ventilation network model optimization module 202 is used to perform air volume distribution calculation on the ventilation network model based on the ventilation network solution method, and adjust the ventilation network model based on the air volume distribution calculation result until the simulated working condition of the fan in the ventilation network model matches the actual working condition; the air volume adjustment scheme generation module 203 is used to generate at least one to-be-selected air volume adjustment scheme based on the underground ventilation air volume demand and the set optimization model corresponding to the mine ventilation system; the air volume adjustment scheme selection module 204 is used to select the air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation system. Air volume demand, determine the best air volume adjustment scheme; wherein, the setting optimization model is an air volume control hybrid optimization model, and the air volume control hybrid optimization model is a linear mixed integer programming model, including the following optimization objectives: minimum ventilation energy consumption target, optimal adjustment position target, optimal adjustment method target and minimum adjustment number target; the decision variables of the setting optimization model include: the adjustment air pressure value of all branches and a 0-1 integer variable that characterizes the correspondence between the air volume of the on-demand air distribution branch and the multiple air volume values ​​of the branch; the air volume adjustment scheme includes: adjustment measures for the ventilation structure, and the ventilation structure includes at least one of the following: underground fans, dampers and windows.

[0248] In some embodiments, the ventilation network model optimization module 202 is specifically configured to:

[0249] The ventilation network solution method based on the loop air volume is used to perform air volume distribution calculation on the ventilation network model to obtain an air volume distribution calculation result;

[0250] Adjusting the tunnel wind resistance parameters based on the resistance measurement method until the error between the calculated air volume distribution result and the measured tunnel air volume is within a set threshold;

[0251] Obtaining a simulated operating condition of a fan in the ventilation network model based on the air volume distribution calculation result;

[0252] It is determined whether the simulated operating condition of the fan matches the actual operating condition. If not, the model parameters of the ventilation network model are adjusted until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition.

[0253] In some embodiments, the configuration optimization model is as follows:

[0254]

[0255]

[0256] Where Z is the optimization target, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, ω5 is the fifth weight coefficient, F is the set of all wind turbine branches f, K f Indicates the number of air volume values ​​of fan branch f, q f,k To represent the k-th air volume value of fan branch f, n f,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch f is q f,k , h f is the fan pressure of fan branch f, N is the number of branches in the ventilation network, n j,a Indicates whether the j-th branch needs to be adjusted, s j is the adjustment level of the j-th branch, n j,c Indicates whether it is necessary to adjust the energy or reduce the resistance of the j-th branch, n j,b Indicates whether it is necessary to adjust the resistance of the j-th branch, Δh′ j is Δh j The absolute value of Δh j is the wind pressure adjustment value of the j-th branch, J is the number of nodes in the ventilation network, a ij Indicates the relationship between nodes and branches, K j Indicates the number of branch air volume values, q j,k To represent the k-th air volume value of fan branch j, n j,k It is a 0-1 integer variable and indicates whether the air volume value of fan branch j is q j,k , b ij Indicates the relationship between branches and loops, h j is the algebraic sum of the wind pressure on the jth branch, ρ′ j represents the energy increase or resistance reduction adjustment amount that can be ignored in the j-th branch, ρ″ j Indicates the resistance increase adjustment amount that can be ignored in the j-th branch, Δh j,min is the lower limit of the adjustable wind pressure of the jth branch, Δh j,max is the upper limit of the adjustable wind pressure of the j-th branch, N a is the number of branches allowed to be adjusted in the ventilation network, ρ j Indicates the amount of regulation that can be ignored in the j-th branch, n max,a is the first normal quantity, n max,b is the second normal quantity, n max,c is the third normal quantity, q f is the fan air volume of fan branch f, a0, a1, a2 are the fan characteristic curve fitting coefficients.

[0257] In some embodiments, the air volume adjustment scheme generating module 203 is specifically configured to:

[0258] According to the underground ventilation air volume demand, the weight coefficients and decision variables of the optimization model are set;

[0259] Based on the set weight coefficients and decision variables, at least one air volume adjustment scheme to be selected is solved using the set optimization model.

[0260] In some embodiments, the air volume adjustment scheme selection module 204 is specifically used to:

[0261] Using a ventilation network solution method based on loop air volume, perform air volume distribution calculation on the at least one air volume adjustment scheme to be selected, and obtain air volume distribution calculation results corresponding to each air volume adjustment scheme;

[0262] The air volume distribution calculation results corresponding to each air volume adjustment scheme are compared with the distributed air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal air volume adjustment scheme.

[0263] In some embodiments, the air volume mixing and regulating device for a mine ventilation system further includes: an regulating module 205 for regulating the mine ventilation system based on the optimal air volume regulating solution.

[0264] In practical applications, the ventilation network model acquisition module 201, ventilation network model optimization module 202, air volume adjustment solution generation module 203, air volume adjustment solution selection module 204, and adjustment module 205 can be implemented by a processor in an electronic device. Of course, the processor needs to run a computer program in memory to implement its functions.

[0265] It should be noted that: the mine ventilation system air volume mixing and adjustment device provided in the above embodiment only uses the division of the above program modules as an example to illustrate when performing the mine ventilation system air volume mixing and adjustment. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the mine ventilation system air volume mixing and adjustment device provided in the above embodiment and the mine ventilation system air volume mixing and adjustment method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0266] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an electronic device. Figure 3 Only the exemplary structure of the device is shown, not all structures, and can be implemented as needed. Figure 3 Partial or complete structure shown.

[0267] like Figure 3As shown, the device 300 provided in the embodiment of the present application includes: at least one processor 301, a memory 302, a user interface 303 and at least one network interface 304. The various components in the electronic device 300 are coupled together through a bus system 305. It can be understood that the bus system 305 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 305 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 3 Various buses are labeled as bus system 305 .

[0268] The user interface 303 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0269] The memory 302 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program used to operate on the electronic device.

[0270] The method for adjusting the air volume mixing of a mine ventilation system disclosed in the embodiments of this application can be applied to or implemented by processor 301. Processor 301 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the method for adjusting the air volume mixing of a mine ventilation system can be completed by hardware integrated logic circuits or software instructions in processor 301. The processor 301 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. Processor 301 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in memory 302. Processor 301 reads information from memory 302 and, in conjunction with its hardware, completes the steps of the method for adjusting the air volume mixing of a mine ventilation system provided in the embodiments of this application.

[0271] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0272] It is understood that memory 302 can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0273] In an exemplary embodiment, the present application also provides a storage medium, namely, a computer storage medium, which may be a computer-readable storage medium. For example, the storage medium 302 may store a computer program. The computer program may be executed by the processor 301 of the electronic device to complete the steps of the method described in the embodiment of the present application. The computer-readable storage medium may be a memory such as a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0274] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0275] In addition, the technical solutions described in the embodiments of the present application can be arbitrarily combined without conflict.

[0276] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for adjusting the air volume of a mine ventilation system, characterized in that: include: Obtain the ventilation network model of the mine ventilation system; performing air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; generating at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and a setting optimization model corresponding to the mine ventilation system; Determining an optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement; The optimization model is set as follows: ； ； Among them, Z is the optimization target, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, is the fourth weight coefficient, is the fifth weight coefficient, For all wind turbine branches A collection of Indicates fan branch The number of air volume values, To represent the fan branch No. The air volume value, It is a 0-1 integer variable and represents the fan branch Is the air volume value , For fan branch The fan pressure, is the number of branches of the ventilation network, Indicates whether the The branches are adjusted. For the The adjustment level of the branch, Indicates whether the The branches are adjusted to increase energy or reduce resistance. Indicates whether the The branches are adjusted to increase resistance. for The absolute value of For the The wind pressure adjustment value of the branch, is the number of nodes in the ventilation network, Indicates the relationship between nodes and branches, Indicates the The number of branch air volume values, To represent the fan branch No. The air volume value, It is a 0-1 integer variable and represents the fan branch Is the air volume value , Indicates the relationship between branches and loops, For the The algebraic sum of the branch wind pressure, Indicates the The branch allows the neglected energy increase or resistance reduction adjustment amount, Indicates the The resistance increase adjustment amount allowed for the branch is negligible. For the The branches can adjust the lower limit of wind pressure, For the The branches can adjust the upper limit of wind pressure, The number of branches allowed to be adjusted in the ventilation network, Indicates the The branch allows for negligible adjustment. is the first normal quantity, is the second normal quantity, is the third normal quantity, For fan branch The fan air volume, is the fan characteristic curve fitting coefficient; The air volume adjustment scheme includes: adjustment measures for ventilation structures, and the ventilation structures include at least one of the following: an underground fan, a damper, and a wind window.

2. The method according to claim 1, characterized in that The method of performing air volume distribution calculation on the ventilation network model based on the ventilation network solution method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition, includes: The ventilation network solution method based on the loop air volume is used to perform air volume distribution calculation on the ventilation network model to obtain an air volume distribution calculation result; Adjusting the tunnel wind resistance parameters based on the resistance measurement method until the error between the calculated air volume distribution result and the measured tunnel air volume is within a set threshold; Obtaining a simulated operating condition of a fan in the ventilation network model based on the air volume distribution calculation result; It is determined whether the simulated operating condition of the fan matches the actual operating condition. If not, the model parameters of the ventilation network model are adjusted until the simulated operating condition of the fan in the ventilation network model matches the actual operating condition.

3. The method according to claim 1, characterized in that The generating of at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the setting optimization model corresponding to the mine ventilation system includes: According to the underground ventilation air volume demand, the weight coefficients and decision variables of the optimization model are set; Based on the set weight coefficients and decision variables, at least one air volume adjustment scheme to be selected is solved using the set optimization model.

4. The method according to claim 1, wherein The determining of the optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement includes: Using a ventilation network solution method based on loop air volume, perform air volume distribution calculation on the at least one air volume adjustment scheme to be selected, and obtain air volume distribution calculation results corresponding to each air volume adjustment scheme; The air volume distribution calculation results corresponding to each air volume adjustment scheme are compared with the distributed air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal air volume adjustment scheme.

5. The method according to claim 1, wherein The method further comprises: The mine ventilation system is adjusted based on the optimal air volume adjustment scheme.

6. A mine ventilation system air volume mixing and regulating device, characterized in that: include: Ventilation network model acquisition module, used to obtain the ventilation network model of the mine ventilation system; a ventilation network model optimization module, configured to perform air volume distribution calculation on the ventilation network model based on a ventilation network solution method, and adjust the ventilation network model based on the air volume distribution calculation result until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; An air volume adjustment scheme generating module is used to generate at least one air volume adjustment scheme to be selected based on the underground ventilation air volume demand and the set optimization model corresponding to the mine ventilation system; an air volume adjustment scheme selection module, configured to determine an optimal air volume adjustment scheme based on the at least one to-be-selected air volume adjustment scheme and the underground ventilation air volume requirement; The setting optimization model is as follows: ； ； Among them, Z is the optimization target, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, is the fourth weight coefficient, is the fifth weight coefficient, For all wind turbine branches A collection of Indicates fan branch The number of air volume values, To represent the fan branch No. The air volume value, It is a 0-1 integer variable and represents the fan branch Is the air volume value , For fan branch The fan pressure, is the number of branches of the ventilation network, Indicates whether the The branches are adjusted. For the The adjustment level of the branch, Indicates whether the The branches are adjusted to increase energy or reduce resistance. Indicates whether the The branches are adjusted to increase resistance. for The absolute value of For the The wind pressure adjustment value of the branch, is the number of nodes in the ventilation network, Indicates the relationship between nodes and branches, Indicates the The number of branch air volume values, To represent the fan branch No. The air volume value, It is a 0-1 integer variable and represents the fan branch Is the air volume value , Indicates the relationship between branches and loops, For the The algebraic sum of the branch wind pressure, Indicates the The branch allows the neglected energy increase or resistance reduction adjustment amount, Indicates the The resistance increase adjustment amount allowed for the branch is negligible. For the The branches can adjust the lower limit of wind pressure, For the The branches can adjust the upper limit of wind pressure, The number of branches allowed to be adjusted in the ventilation network, Indicates the The branch allows for negligible adjustment. is the first normal quantity, is the second normal quantity, is the third normal quantity, For fan branch The fan air volume, is the fan characteristic curve fitting coefficient; The air volume adjustment scheme includes: adjustment measures for ventilation structures, and the ventilation structures include at least one of the following: underground fans, dampers and windows.

7. An electronic device, characterized in that: include: A processor and a memory for storing a computer program capable of being executed on the processor, wherein The processor is configured to execute the steps of the method according to any one of claims 1 to 5 when running a computer program.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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