Method, device and equipment for frequency conversion optimization of fan of multi-stage machine station ventilation system

Abstract

This application discloses a method, apparatus, and equipment for optimizing the frequency conversion of fans in a multi-level station ventilation system. The method includes: acquiring a ventilation network model of the multi-level station ventilation system; calculating airflow distribution in the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the airflow 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 fan frequency conversion control scheme based on the underground ventilation airflow demand and the corresponding set optimization model of the multi-level station ventilation system; and determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the underground ventilation airflow demand; wherein the set optimization model is a multi-objective mixed integer linear programming model. This enables intelligent frequency conversion control of the multi-level station ventilation system and significantly improves the solution performance of the fan frequency conversion control scheme.

Description

Technical Field

[0001] This application relates to the field of ventilation control, and in particular to a method, apparatus and equipment for frequency conversion optimization of fans in a multi-stage station ventilation system. Background Technology

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

[0003] Compared to main fan ventilation systems, multi-fan station ventilation systems are more controllable and are widely used in metal mines. This type of system refers to an engineering facility system that uses multiple intake and return air stations to compress fresh air from the ground to the working area and expel polluted air from the mine. The series and parallel connection of multiple fans and the cascading of multiple stations make the ventilation system more adjustable and controllable, improving its efficiency and reducing energy consumption.

[0004] However, with the development of intelligent control technology, multi-level station ventilation systems urgently need to achieve intelligent frequency conversion control. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, apparatus and equipment for frequency conversion optimization of fans in a multi-level station ventilation system, which aims to realize intelligent frequency conversion control of the multi-level station ventilation system and meet the control requirements of unattended operation.

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

[0007] This application provides a method for optimizing the frequency conversion of fans in a multi-level station ventilation system, including:

[0008] Obtain the ventilation network model of a multi-level station ventilation system;

[0009] The ventilation network model is calculated based on the ventilation network solution method, and the ventilation network model is adjusted based on the calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions.

[0010] Based on the underground ventilation volume requirements and the corresponding optimization model of the multi-level station ventilation system, at least one candidate fan frequency conversion control scheme is generated.

[0011] Based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement, determine the optimal fan frequency conversion control scheme.

[0012] The optimization model is a multi-objective mixed-integer linear programming model, including the following optimization objectives: minimum ventilation fan power, optimal on-demand ventilation demand, optimal fan air volume, and optimal fan air pressure. The decision variables of the optimization model are 0-1 integer decision variables, including: a first variable representing the correspondence between the air volume of the on-demand ventilation branch and multiple air volume values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first variable and the second variable. The variable frequency control scheme for the fans includes: the target operating speed of each variable frequency fan.

[0013] In some embodiments, the step of calculating airflow distribution in the ventilation network model based on the ventilation network solution method, and adjusting the ventilation network model based on the airflow distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions, includes:

[0014] The ventilation network model is calculated to distribute airflow using a ventilation network solution method based on loop airflow, and the airflow distribution calculation results are obtained.

[0015] The roadway air resistance parameters are adjusted based on the resistance measurement method until the comparison error between the calculated air volume distribution result and the measured roadway air volume is within the set threshold.

[0016] Based on the air volume distribution calculation results, the simulated operating conditions of the fans in the ventilation network model are obtained;

[0017] Determine whether the simulated operating conditions of the fan match the actual operating conditions. If not, adjust the model parameters of the ventilation network model until the simulated operating conditions of the fan in the ventilation network model match the actual operating conditions.

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

[0019]

[0020]

[0021] Where Z is the optimization objective, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, F is the set of all wind turbine branches f, and q f,j Let h be the fan air volume of the j-th branch. f,j Let N be the fan pressure of the j-th branch. d This represents the set of all demand-based wind branches. Let j be the upper limit deviation of the air distribution range for the j-th branch under the on-demand air distribution scheme. q j Let $\frac{j}{j}$ be the lower limit deviation of the air distribution range for the on-demand air distribution branch. Let be the upper limit deviation of the optimal operating airflow range for the j-th branch. q f,j This represents the lower limit deviation of the optimal operating airflow range for the j-th branch. Let $\frac{j}{j}$ be the upper limit deviation of the optimal operating pressure range for the $j$-th branch. h f,j Let be the lower limit deviation of the optimal operating pressure range for the j-th branch, N be the number of branches in the ventilation network, J be the number of nodes in the ventilation network, and a ij To represent the relationship between nodes and branches, q j Let M be the air volume of the j-th branch for on-demand ventilation, M be the number of independent loops in the ventilation network, and h be the air volume of the branch. j Let b be the algebraic sum of the wind pressure of the j-th branch. ij For the relationship between branches and loops, v j,min S is the lower limit of the permissible wind speed for the j-th branch. j Let v be the cross-sectional area of ​​the j-th branch tunnel. j,max h is the maximum allowable wind speed for the j-th branch. f,j To adjust the fan pressure of the j-th branch after adjusting the rotation speed, a j,0 ,a j,1 ,a j,2 To adjust the fitting coefficient of the fan characteristic curve of the j-th branch before the rotational speed, n j q represents the speed ratio of the j-th branch after the fan speed adjustment compared to before the speed adjustment. f,j To adjust the fan airflow of the j-th branch after adjusting the rotation speed, h f,j,min h is the lower limit of the fan pressure for the j-th branch. f,j,max N represents the upper limit of the wind turbine pressure in the j-th branch. j N is the actual operating speed of the j-th branch. j,min N represents the lower limit of the adjustable fan speed for the j-th branch. j,max Let q be the upper limit of the adjustable fan speed for the j-th branch. f,j,min Let q be the lower limit of the allowable fan air volume for the j-th branch. f,j,max Let η be the maximum allowable fan airflow for the j-th branch. j Let C be the operating efficiency of the fan in the j-th branch. j q represents the minimum wind turbine operating efficiency required by the j-th branch. j,min q represents the lower limit of the permissible air volume for the j-th branch of the on-demand ventilation system. j,max Let be the upper limit of the allowable air volume for the j-th branch of the on-demand air distribution system.

[0022] In some embodiments, the generation of at least one candidate variable frequency drive (VFD) control scheme for the blower, based on the downhole ventilation volume demand and the set optimization model corresponding to the multi-stage station ventilation system, includes:

[0023] Based on the downhole ventilation volume requirements, 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 candidate wind turbine frequency conversion control scheme is obtained using the set optimization model.

[0025] In some embodiments, determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement includes:

[0026] A ventilation network solution method based on loop air volume is used to calculate the air volume allocation for at least one candidate fan frequency conversion control scheme, and the air volume allocation calculation results corresponding to each fan frequency conversion control scheme are obtained.

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

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

[0029] The multi-stage station ventilation system is controlled based on the optimal fan frequency conversion control scheme.

[0030] Secondly, embodiments of this application provide a frequency conversion optimization device for a multi-level station ventilation system fan, comprising:

[0031] The ventilation network model acquisition module is used to acquire the ventilation network model of a multi-level station ventilation system;

[0032] The ventilation network model optimization module is used to calculate the air volume distribution of the ventilation network model based on the ventilation network solution method, and adjust 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.

[0033] The control scheme generation module is used to generate at least one candidate fan frequency conversion control scheme based on the underground ventilation air volume demand and the set optimization model corresponding to the multi-level station ventilation system.

[0034] The control scheme selection module is used to determine the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement.

[0035] The optimization model is a multi-objective mixed-integer linear programming model, including the following optimization objectives: minimum ventilation fan power, optimal on-demand ventilation demand, optimal fan air volume, and optimal fan air pressure. The decision variables of the optimization model are 0-1 integer decision variables, including: a first variable representing the correspondence between the air volume of the on-demand ventilation branch and multiple air volume values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first variable and the second variable. The variable frequency control scheme for the fans includes: the target operating speed of each variable frequency fan.

[0036] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, executes the steps of the method described in the first aspect of embodiments of this application.

[0037] Fourthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect of embodiments of this application.

[0038] The technical solution provided in this application involves obtaining a ventilation network model of a multi-level station ventilation system; calculating airflow distribution in the ventilation network model based on a ventilation network solution method; adjusting the ventilation network model based on the airflow 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 fan frequency conversion control scheme based on the underground ventilation airflow demand and the corresponding set optimization model of the multi-level station ventilation system; and determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the underground ventilation airflow demand. The set optimization model is a multi-objective mixed-integer linear programming model. This enables intelligent frequency conversion control of the multi-level station ventilation system, meeting the control requirements for unattended operation. Furthermore, since the set optimization model is a multi-objective mixed-integer linear programming model, the solution performance of the fan frequency conversion control scheme can be greatly improved. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the frequency conversion optimization method for a multi-level station ventilation system fan according to an embodiment of this application.

[0040] Figure 2 This is a schematic diagram of the structure of the variable frequency optimization device for the fan in the multi-stage station ventilation system according to an embodiment of this application;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

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

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0044] To meet the intelligent control requirements of multi-level station ventilation systems, this application provides a method for optimizing the frequency conversion of fans in such systems. This method can be applied to electronic devices with data processing capabilities, such as laptops, desktop computers, or servers, to intelligently determine the optimal frequency conversion control scheme for the fans in the multi-level station ventilation system. Based on a database of ventilation frequency conversion characteristic curves at different frequencies, this method uses mathematical optimization to determine the fan frequency conversion control scheme while meeting the requirement of on-demand ventilation. This can satisfy the unattended control requirements of multi-level station ventilation systems.

[0045] like Figure 1 As shown in the embodiments of this application, the frequency conversion optimization method for the fan of a multi-stage station ventilation system includes:

[0046] Step 101: Obtain the ventilation network model of the multi-level station ventilation system.

[0047] It should be noted that the ventilation network model is established based on the downhole measured data of the multi-level station ventilation system, and it serves as the data basis for constructing the setting optimization model of the embodiments of this application.

[0048] For example, obtaining a ventilation network model for a multi-level station ventilation system includes:

[0049] Step a: Establish a three-dimensional ventilation network diagram using the design and measured plan and profile drawings of each level of mine mining;

[0050] Step b: Collect air resistance parameters of all ventilation network roadways by measuring ventilation resistance;

[0051] Step c: Investigate the installation and layout of underground fans and structures to determine the initial state of the ventilation network model;

[0052] Step d: Collect the operating status of the fans at each level of the station, and determine the station level of each fan and the current speed of the fan in variable frequency operation;

[0053] Step e: For non-variable frequency fans, the fan operating speed ratio can be considered to be 100% and the fan speed cannot be adjusted.

[0054] Step 102: Calculate the air volume distribution of the ventilation network model based on the ventilation network solution method, and adjust the ventilation network model based on the air volume distribution calculation results until the simulated operating conditions of the fan in the ventilation network model match the actual operating conditions.

[0055] This can be understood as follows: 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's operating conditions can be understood as the airflow and air pressure corresponding to the fan at its current speed.

[0056] For example, the step of calculating airflow distribution in the ventilation network model based on the ventilation network solution method, and adjusting the ventilation network model based on the airflow distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions, includes:

[0057] The ventilation network model is calculated to distribute airflow using a ventilation network solution method based on loop airflow, and the airflow distribution calculation results are obtained.

[0058] The roadway air resistance parameters are adjusted based on the resistance measurement method until the comparison error between the calculated air volume distribution result and the measured roadway air volume is within the set threshold.

[0059] Based on the air volume distribution calculation results, the simulated operating conditions of the fans in the ventilation network model are obtained;

[0060] Determine whether the simulated operating conditions of the fan match the actual operating conditions. If not, adjust the model parameters of the ventilation network model until the simulated operating conditions of the fan in the ventilation network model match the actual operating conditions.

[0061] It should be noted that the above-mentioned adjustment of the roadway air resistance parameters based on the resistance measurement method can make the air volume of each network branch of the ventilation network model match the measured air volume as closely as possible. The set threshold can be reasonably determined according to the design accuracy.

[0062] 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 and actual operating conditions is within a reasonable accuracy range. Preferably, the electronic equipment can also intelligently adjust the model parameters of the ventilation network model based on the model optimization algorithm, until the difference between the simulated and actual operating conditions is within a reasonable accuracy range.

[0063] Step 103: Based on the underground ventilation volume requirements and the setting optimization model corresponding to the multi-level station ventilation system, generate at least one candidate fan frequency conversion control scheme.

[0064] Here, the optimization model is defined as a multi-objective mixed-integer linear programming model, which includes the following optimization objectives: minimum ventilation fan power, optimal on-demand ventilation demand, optimal fan airflow under optimal operating conditions, and optimal fan air pressure under optimal operating conditions. The decision variables of the multi-objective mixed-integer linear programming model include: a first variable representing the correspondence between the airflow of the on-demand ventilation branch and multiple airflow values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first and second variables. The variable frequency drive (VFD) control scheme for the fans includes: the target operating speed of each variable frequency fan.

[0065] In some embodiments, the generation of at least one candidate variable frequency drive (VFD) control scheme for the blower, based on the downhole ventilation volume demand and the set optimization model corresponding to the multi-stage station ventilation system, includes:

[0066] Based on the downhole ventilation volume requirements, the weight coefficients and decision variables of the optimization model are set.

[0067] Based on the set weight coefficients and decision variables, at least one candidate wind turbine frequency conversion control scheme is obtained using the set optimization model.

[0068] In one application example, at least one candidate wind turbine frequency conversion control scheme is generated, including:

[0069] Step a: Calculate the required air volume for each work point based on the underground ventilation air volume demand, so as to adjust the underground air volume as needed by using the frequency converter control of the fan;

[0070] Step b: Based on the actual needs of the mine, select the appropriate objectives and constraints, and construct the optimization model of this application embodiment;

[0071] Step c: Based on the demand for adjusting the downhole ventilation volume, set the weight coefficients of each target of the frequency converter control of the fan, as well as parameters such as the deviation range of the branch ventilation volume, the deviation range of the working condition ventilation volume, the deviation range of the working condition air pressure, and the operating range of the fan.

[0072] Step d involves using a set optimization model to perform calculations and obtain at least one candidate variable frequency control scheme for the wind turbine. This variable frequency control scheme includes the target operating speed of each variable frequency wind turbine.

[0073] Step 104: Based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement, determine the optimal fan frequency conversion control scheme.

[0074] For example, determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement includes:

[0075] A ventilation network solution method based on loop air volume is used to calculate the air volume allocation for at least one candidate fan frequency conversion control scheme, and the air volume allocation calculation results corresponding to each fan frequency conversion control scheme are obtained.

[0076] The calculation results of air volume distribution corresponding to each fan frequency conversion control scheme are compared with the distribution air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal fan frequency conversion control scheme.

[0077] It should be noted that the underground ventilation volume requirement can be reasonably determined based on the number of underground workers and the exhaust emission requirements. The distribution volume of the ventilation branches can be obtained by converting the underground ventilation volume requirement and the calculation formula in the ventilation procedure. The relevant conversion process is existing technology and will not be elaborated here.

[0078] It is understood that the method in this application embodiment can generate at least one fan frequency conversion control scheme based on the above-mentioned multi-objective optimization setting optimization model, and determine the optimal fan frequency conversion control scheme based on at least one candidate fan frequency conversion control scheme and the underground ventilation air volume demand, thereby realizing intelligent frequency conversion control of multi-level station ventilation system and meeting the control requirements of unattended operation; in addition, since the setting optimization model is a multi-objective mixed integer linear programming model, it can greatly improve the solution performance of fan frequency conversion control scheme, thereby quickly determining the optimal speed of variable frequency fan operation under on-demand ventilation conditions based on mathematical optimization methods, thereby realizing optimized control of frequency conversion regulation.

[0079] The method described in this application can be integrated into an online monitoring system for ventilation fans, enabling remote automatic control of large-scale underground ventilation fans and achieving intelligent on-demand frequency conversion for the fans, thus saving ventilation energy consumption. Furthermore, while ensuring the safe and stable operation of the ventilation system, it can be further combined with an airflow status monitoring system, a fan status monitoring system, and a regulation and control feedback system to obtain the optimal fan speed with better feedback effect through multiple frequency conversion speed adjustments.

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

[0081]

[0082]

[0083] Where Z is the optimization objective, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, F is the set of all wind turbine branches f, and q f,j Let h be the fan air volume of the j-th branch. f,j Let N be the fan pressure of the j-th branch. d This represents the set of all demand-based wind branches. Let j be the upper limit deviation of the air distribution range for the j-th branch under the on-demand air distribution scheme. q j Let $\frac{j}{j}$ be the lower limit deviation of the air distribution range for the on-demand air distribution branch. Let be the upper limit deviation of the optimal operating airflow range for the j-th branch. q f,j This represents the lower limit deviation of the optimal operating airflow range for the j-th branch. Let $\frac{j}{j}$ be the upper limit deviation of the optimal operating pressure range for the $j$-th branch. h f,j Let be the lower limit deviation of the optimal operating pressure range for the j-th branch, N be the number of branches in the ventilation network, J be the number of nodes in the ventilation network, and a ij To represent the relationship between nodes and branches, q j Let M be the air volume of the j-th branch for on-demand ventilation, M be the number of independent loops in the ventilation network, and h be the air volume of the branch. j Let b be the algebraic sum of the wind pressure of the j-th branch. ij For the relationship between branches and loops, v j,min S is the lower limit of the permissible wind speed for the j-th branch. j Let v be the cross-sectional area of ​​the j-th branch tunnel. j,max h is the maximum allowable wind speed for the j-th branch. f,j To adjust the fan pressure of the j-th branch after adjusting the rotation speed, a j,0 ,a j,1 ,a j,2 To adjust the fitting coefficient of the fan characteristic curve of the j-th branch before the rotational speed, n j q represents the speed ratio of the j-th branch after the fan speed adjustment compared to before the speed adjustment. f,j To adjust the fan airflow of the j-th branch after adjusting the rotation speed, h f,j,min h is the lower limit of the fan pressure for the j-th branch. f,j,max N represents the upper limit of the wind turbine pressure in the j-th branch. j N is the actual operating speed of the j-th branch. j,min N represents the lower limit of the adjustable fan speed for the j-th branch. j,max Let q be the upper limit of the adjustable fan speed for the j-th branch. f,j,min Let q be the lower limit of the allowable fan air volume for the j-th branch. f,j,max Let η be the maximum allowable fan airflow for the j-th branch. j Let C be the operating efficiency of the fan in the j-th branch. jq represents the minimum wind turbine operating efficiency required by the j-th branch. j,min q represents the lower limit of the permissible air volume for the j-th branch of the on-demand ventilation system. j,max Let be the upper limit of the allowable air volume for the j-th branch of the on-demand air distribution system.

[0084] It should be noted that in the above formula, "st" is an abbreviation for "subject to (such that)," meaning subject to constraints.

[0085] In some embodiments, the method of this application further includes:

[0086] The multi-stage station ventilation system is controlled based on the optimal fan frequency conversion control scheme.

[0087] Understandably, the aforementioned optimal fan frequency conversion control scheme can be output to terminal equipment, allowing manual adjustment of the fan speed. Preferably, the electronic equipment can also control the multi-stage station ventilation system based on the optimal fan frequency conversion control scheme, for example, remotely controlling each frequency conversion fan to operate at the target speed, thereby achieving on-demand fan frequency conversion and saving ventilation energy consumption.

[0088] In one application example, the optimization model can be defined as follows:

[0089] minZ=ω1z1+ω2z2+ω3z3+ω4z4 (1)

[0090] Where z1 represents the minimum target power of the ventilation fan;

[0091] z2 represents the optimal on-demand ventilation requirement target;

[0092] z3 represents the target air volume for the fan under optimal operating conditions;

[0093] z4 represents the optimal operating condition wind pressure target for the fan;

[0094] ω1 represents the weighting coefficient for the minimum power target of the ventilation fan;

[0095] ω2 represents the weighting coefficient of the negative pressure balancing target for stations of the same level;

[0096] ω3 represents the weighting coefficient for the optimal fan airflow target under optimal operating conditions;

[0097] ω4 represents the weighting coefficient of the optimal operating condition wind turbine wind pressure target.

[0098] The optimization objectives mentioned above are explained below:

[0099] (1) Minimum power target for ventilation fans

[0100] The minimum power target for ventilation fans can be expressed as:

[0101]

[0102] in,

[0103] F is the set of all wind turbine branches;

[0104] q f,j Let J be the fan air volume of the j-th branch (fan branch);

[0105] h f,j Let be the wind pressure of the j-th branch (wind turbine branch).

[0106] (2) Optimal on-demand ventilation demand target

[0107] The optimal on-demand ventilation target can be expressed as:

[0108]

[0109] in,

[0110] N d This represents the set of all on-demand wind distribution branches;

[0111] For the j-th branch (on-demand air distribution branch), the upper limit deviation of the on-demand air distribution range is;

[0112] q j This is the lower limit deviation of the air distribution range for the j-th branch (the on-demand air distribution branch).

[0113] The following conditions must be met

[0114]

[0115] q j The following conditions must be met

[0116]

[0117] q j,min The lower limit of the allowable air volume for the j-th branch (on-demand air distribution branch) satisfies q. j,min >0;

[0118] q j,max The maximum allowable airflow for the j-th branch (on-demand air distribution branch) satisfies q. j,max ≥q j,min >0.

[0119] Under the constraints of the above conditions, there exists and q j There must be an implicit constraint condition that is zero. When the branch's airflow allocation value is within the on-demand airflow range, the upper limit deviation of the on-demand airflow range is... Deviation of the lower limit of the on-demand air distribution range q j All are zero.

[0120] (3) Target air volume of the fan under optimal operating conditions

[0121] The optimal fan airflow target under optimal operating conditions can be expressed as:

[0122]

[0123] Where F is the set of all wind turbine branches;

[0124] This represents the upper limit deviation of the optimal operating airflow range for the j-th branch (fan branch);

[0125] q f,j This is the lower limit deviation of the optimal operating air volume range for the j-th branch (fan branch).

[0126] The following conditions must be met

[0127]

[0128] q f,j The following conditions must be met

[0129]

[0130] q f,j,min The lower limit of the optimal operating airflow range for the j-th branch (fan branch) satisfies q. f,j,min >0;

[0131] q f,j,max The upper limit of the optimal operating air volume range for the j-th branch (fan branch) satisfies q. f,j,max ≥q f,j,min >0.

[0132] Under the constraints of the above conditions, there exists and q f,j There must be an implicit constraint condition with a value of zero. When the airflow of this branch fan is within the optimal operating airflow range, the upper limit deviation of the optimal operating airflow range is... Deviation from the lower limit of the optimal operating air volume range q f,j All are zero.

[0133] (4) Target wind pressure of the fan under optimal operating conditions

[0134] The optimal operating condition for the fan's air pressure target can be expressed as:

[0135]

[0136] Where F is the set of all wind turbine branches;

[0137] This represents the upper limit deviation of the optimal operating pressure range for the j-th branch (fan branch);

[0138] h f,j This represents the lower limit deviation of the optimal operating pressure range for the j-th branch (fan branch).

[0139] The following conditions must be met

[0140]

[0141] h f,j The following conditions must be met

[0142]

[0143] h f,j,min The lower limit of the optimal operating pressure range for the j-th branch (wind turbine branch) satisfies h. f,j,min >0;

[0144] h f,j,max The upper limit of the optimal operating pressure range for the j-th branch (wind turbine branch) satisfies h. f,j,max ≥h f,j,min >0.

[0145] Under the constraints of the above conditions, there exists and h f,j There must be an implicit constraint condition with a value of zero. When the wind pressure of this branch fan is within the optimal operating pressure range, the upper limit deviation of the optimal operating pressure range is... Deviation from the lower limit of the optimal operating condition wind pressure range h f,j All are zero.

[0146] The constraints of the optimization model of the above formula (1) are explained below:

[0147] (1) Air volume balance constraint

[0148] The ventilation network airflow regulation scheme must meet the node airflow balance condition, that is, the algebraic sum of the airflow of each branch flowing into and out of any node in the ventilation network is zero.

[0149]

[0150] in,

[0151] N is the number of branches in the ventilation network;

[0152] J represents the number of nodes in the ventilation network;

[0153] q j Let J be the air volume of the j-th branch;

[0154] a ij Indicates the relationship between nodes and branches;

[0155] a ij satisfy

[0156] (2) Wind pressure balance constraint conditions

[0157] The ventilation network airflow regulation scheme must meet the loop air pressure balance condition, that is, the algebraic sum of the air pressure of each branch in any loop of the ventilation network is zero.

[0158]

[0159] in,

[0160] M is the number of independent loops in the ventilation network, M = N - J + 1;

[0161] h j Let the sum of the wind pressures of the j-th branch be an algebraic sum.

[0162] r j Let the drag of the j-th branch be denoted as .

[0163] h f,j Let J be the fan pressure of the j-th branch;

[0164] h N,j Let J be the natural wind pressure of the j-th branch;

[0165] b ij Indicate the relationship between branches and loops;

[0166] b ij satisfy

[0167] (3) Wind speed range constraints

[0168] The wind speed range constraint conditions should be met.

[0169] v j,min ×S j ≤q j ≤v j,max ×S j (14)

[0170] in,

[0171] v j,min For the j-th branch, the lower limit of the permissible wind speed satisfies v j,min ≥0;

[0172] v j,max Let v be the upper limit of the allowed wind speed for the j-th branch. j,max ≥v j,min≥0;

[0173] S j Let be the cross-sectional area of ​​the j-th branch roadway.

[0174] (4) Constraints of variable frequency operation of wind turbine

[0175] For the same fan, as the fan speed changes from N1 to N2, the characteristic curve of the variable frequency fan is simulated according to the proportional law. The formulas for converting the fan's air pressure and air volume satisfy the following conditions:

[0176]

[0177] in,

[0178] H1 and Q1 represent the fan pressure and fan air volume when the fan speed is N1;

[0179] H2 and Q2 represent the fan pressure and fan air volume when the fan speed is N2.

[0180] The constraints of variable frequency operation of the fan should meet

[0181]

[0182] in,

[0183] a j,0 ,a j,1 ,a j,2 To adjust the fitting coefficient of the wind turbine characteristic curve of the j-th branch (installed branch) before the rotational speed;

[0184] q′ f,j and h′ f,j To adjust the fan air volume and fan pressure of the j-th branch (installed branch) before the rotation speed;

[0185] q f,j and h f,j To adjust the fan air volume and fan pressure of the j-th branch (installed branch) after adjusting the speed;

[0186] n j This represents the speed ratio of the j-th branch (installation branch) after the fan speed is adjusted to before the speed adjustment.

[0187] By deriving the above formula, the characteristic curve of the variable frequency fan can be further expressed as follows:

[0188]

[0189] (5) Fan operating pressure constraint

[0190] The pressure regulation range constraint of the installed branch fan should meet the following requirements.

[0191] h f,j,min≤h f,j ≤h f,j,max ,j∈F (17)

[0192] in,

[0193] h f,j,min This is the lower limit of the wind pressure of the fan in the j-th branch (installed branch);

[0194] h f,j,max This represents the upper limit of the wind pressure of the fan in the j-th branch (installed branch).

[0195] (6) Fan speed range constraints

[0196] The pressure regulation range constraint of the installed branch fan should meet the following requirements.

[0197] N j,min ≤N j ≤N j,max ,j∈F (18)

[0198] in,

[0199] N j The actual operating speed of the j-th branch (installation branch);

[0200] N j,min The lower limit of the fan speed can be adjusted for the j-th branch (installation branch);

[0201] N j,max The upper limit of the fan speed can be adjusted for the j-th branch (installation branch).

[0202] (7) Fan operating air volume constraint

[0203] The air volume constraint of the fan operation should meet the following requirements.

[0204] q f,j,max ≥q f,j ≥q f,j,min ,j∈F (19)

[0205] in,

[0206] q f,j The air volume (installed air volume) of the j-th branch (installed branch);

[0207] q f,j,min The minimum allowable fan air volume for the j-th branch (installed branch) satisfies q. f,j,min ≥0;

[0208] q f,j,max The maximum allowable fan air volume for the j-th branch (installation branch) satisfies q. f,j,max ≥q f,j,min ≥0.

[0209] (8) Fan operating efficiency constraints

[0210] The operating efficiency constraints of the fan should be met.

[0211] η j ≥C j ,j∈F (20)

[0212] in,

[0213] η j This represents the operating efficiency of the fan in the j-th branch (installed branch);

[0214] C j Let be a constant, representing the minimum wind turbine operating efficiency required for the j-th branch (installation branch).

[0215] It should be noted that the objective function and constraints mentioned above are all nonlinear functions, and the corresponding mathematical model is a nonlinear programming model. To convert this mathematical model into a linear model, thereby transforming the nonlinear multi-stage turbine frequency conversion control problem into a linear problem and improving the solution performance of turbine frequency conversion control, this embodiment introduces 0-1 integer decision variables as the decision variables for setting the optimization model, and performs linearization processing on the mathematical model to obtain a Mixed Integer Linear Programming (MILP) model. These 0-1 integer decision variables are binary variables that only take the value 0 or 1.

[0216] For example, the introduced 0-1 integer decision variables are defined as follows:

[0217] (1) The first variable characterizing the correspondence between the air volume of the on-demand air distribution branch and the multiple air volume values ​​of that branch.

[0218] Assuming that the airflow control precision and the airflow range constraints of the j-th branch are considered, the airflow value of this branch is {q}. j,1 ,q j,2 ,…,q j,k ,…,q j,Kj}, where K j This indicates the number of air volume values ​​for the j-th branch.

[0219] Define a 0-1 integer variable n j,k (corresponding to the first variable mentioned above) indicates whether the air volume value of the j-th branch is q. j,k ,Right now

[0220]

[0221] Where, q j Let q be the air volume of the j-th branch; j,kLet $\mathbf{k}$ be a constant to represent the $\mathbf{k}$ possible airflow value for the $\mathbf{j}$ branch.

[0222] To limit n j,k The value of n j,k Should meet

[0223]

[0224] Where N is the number of branches in the ventilation network.

[0225] The above equation contains an implicit condition, K j n j,k The variable has exactly one value of 1, meaning the airflow value of the j-th branch must be {q}. j,1 ,q j,2 ,…,q j,k ,…,q j,Kj A value in}.

[0226] Specifically, K of the j-th branch j n integer variables of type 0-1 j,k Satisfy the following characteristics

[0227]

[0228] To eliminate the nonlinear variable q in the mathematical model j q needs to be studied j , and The linear expression for q. Through derivation and calculation, it was found that the nonlinear variable q can be replaced by the following formula. j

[0229]

[0230] in,

[0231]

[0232]

[0233] (2) The second variable characterizing the relationship between the speed ratio of the fan branch before and after adjustment and the multiple speed ratios of that branch.

[0234] Assuming that the fan speed control accuracy and the speed ratio range of the j-th branch (fan branch) are constrained, the fan speed ratio of this branch is limited to {N}. j,1 N j,2 ,…,N j,t ,…,N j,Tj}, where T j This indicates the number of values ​​for the fan speed ratio in the j-th branch (fan branch).

[0235] Define a 0-1 integer variable n j,t (Corresponding to the second variable mentioned above) indicates whether the fan speed ratio of the j-th branch (fan branch) is N. j,t ,Right now

[0236]

[0237] Where, N j N represents the fan speed ratio of the j-th branch (fan branch); j,t Let t be a constant to represent the t-th possible wind turbine speed ratio of the j-th branch (wind turbine branch).

[0238] To limit n j,t The value of n j,t Should meet

[0239]

[0240] Where F is the set of all wind turbine branches f.

[0241] To eliminate the nonlinear variable N in the mathematical model j N needs to be studied j , and The linear expression for N is derived and calculated. It is found that the nonlinear variable N can be replaced by the following formula. j

[0242]

[0243] (3) Third variable

[0244] To avoid nonlinear terms in the mathematical model, an integer variable n of type 0-1 is introduced. j,k,t (corresponding to the third variable mentioned above), such that

[0245]

[0246] in,

[0247] n j,k,t =n j,k n j,t (29)

[0248] To satisfy the above constraints, the 0-1 integer variable n j,k,t Should meet

[0249]

[0250] When introducing n j,t n j,k and n j,k,tBased on three types of 0-1 integer variables, all nonlinear terms in the setting optimization model of a multi-stage station ventilation system can be transformed into linear terms, as follows:

[0251] (1) Linearization of wind pressure balance constraint conditions

[0252]

[0253] in,

[0254] q j Let J be the air volume of the j-th branch;

[0255] h j Let be the algebraic sum of the wind pressure of the j-th branch;

[0256] r j Let the drag of the j-th branch be denoted as .

[0257] h f,j Let J be the fan pressure of the j-th branch;

[0258] h N,j Let J be the natural wind pressure of the j-th branch;

[0259] (2) Linearization of constraints for variable frequency operation of wind turbine

[0260]

[0261] in,

[0262] a j,0 ,a j,1 ,a j,2 To adjust the fitting coefficient of the wind turbine characteristic curve of the j-th branch (installed branch) before the rotational speed; q f,j and h f,j To adjust the fan airflow and fan pressure of the j-th branch (installed branch) after adjusting the rotation speed; N j This represents the speed ratio of the j-th branch (installed branch) after the fan speed adjustment and before the speed adjustment; q f,j,k This represents the k-th possible airflow value for the j-th branch (installation branch).

[0263] (3) Linearization of minimum power target for ventilation fans

[0264]

[0265] in,

[0266] q f,j Let J be the fan air volume of the j-th branch (fan branch);

[0267] h f,j Let be the wind pressure of the j-th branch (wind turbine branch).

[0268] Understandably, after the above linearization process, the decision variable in the mathematical model becomes n. j,k,t n j,k and n j,t and auxiliary decision variables q j , and h j The objective function and constraints are both linear functions, and the corresponding mathematical model is a mixed-integer linear programming model.

[0269] To implement the method of this application embodiment, this application embodiment also provides a multi-level station ventilation system fan frequency conversion optimization device, which is installed in electronic equipment, such as... Figure 2 As shown, the multi-level station ventilation system fan frequency conversion optimization device includes: ventilation network model acquisition module 201, ventilation network model optimization module 202, control scheme generation module 203 and control scheme selection module 204.

[0270] The ventilation network model acquisition module 201 is used to acquire the ventilation network model of the multi-level station ventilation system; the ventilation network model optimization module 202 is used to perform air volume allocation calculation on the ventilation network model based on the ventilation network solution method, and adjust the ventilation network model based on the air volume allocation calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; the control scheme generation module 203 is used to generate at least one candidate fan frequency conversion control scheme based on the underground ventilation air volume demand and the set optimization model corresponding to the multi-level station ventilation system; the control scheme selection module 204 is used to determine the optimal control scheme based on the at least one candidate fan frequency conversion control scheme and the underground ventilation air volume demand. An optimal variable frequency drive (VFD) control scheme for fans is proposed. The optimization model is a multi-objective mixed-integer linear programming model, including the following optimization objectives: minimum fan power, optimal on-demand ventilation demand, optimal fan airflow under optimal operating conditions, and optimal fan air pressure under optimal operating conditions. The decision variables of the optimization model are 0-1 integer decision variables, including: a first variable representing the correspondence between the airflow of the on-demand ventilation branch and multiple airflow values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first and second variables. The VFD control scheme includes the target operating speed of each variable frequency fan.

[0271] In some embodiments, the ventilation network model optimization module 202 is specifically used for:

[0272] The ventilation network model is calculated to distribute airflow using a ventilation network solution method based on loop airflow, and the airflow distribution calculation results are obtained.

[0273] The roadway air resistance parameters are adjusted based on the resistance measurement method until the comparison error between the calculated air volume distribution result and the measured roadway air volume is within the set threshold.

[0274] Based on the air volume distribution calculation results, the simulated operating conditions of the fans in the ventilation network model are obtained;

[0275] Determine whether the simulated operating conditions of the fan match the actual operating conditions. If not, adjust the model parameters of the ventilation network model until the simulated operating conditions of the fan in the ventilation network model match the actual operating conditions.

[0276] In some embodiments, the optimization model is set as follows:

[0277]

[0278]

[0279] Where Z is the optimization objective, ω1 is the first weight coefficient, ω2 is the second weight coefficient, ω3 is the third weight coefficient, ω4 is the fourth weight coefficient, F is the set of all wind turbine branches f, and q f,j Let h be the fan air volume of the j-th branch. f,j Let N be the fan pressure of the j-th branch. d This represents the set of all demand-based wind branches. Let j be the upper limit deviation of the air distribution range for the j-th branch under the on-demand air distribution scheme. q j Let $\frac{j}{j}$ be the lower limit deviation of the air distribution range for the on-demand air distribution branch. Let be the upper limit deviation of the optimal operating airflow range for the j-th branch. q f,j This represents the lower limit deviation of the optimal operating airflow range for the j-th branch. Let $\frac{j}{j}$ be the upper limit deviation of the optimal operating pressure range for the $j$-th branch. h f,j Let be the lower limit deviation of the optimal operating pressure range for the j-th branch, N be the number of branches in the ventilation network, J be the number of nodes in the ventilation network, and a ij To represent the relationship between nodes and branches, q j Let M be the air volume of the j-th branch for on-demand ventilation, M be the number of independent loops in the ventilation network, and h be the air volume of the branch. j Let b be the algebraic sum of the wind pressure of the j-th branch. ij For the relationship between branches and loops, v j,min S is the lower limit of the permissible wind speed for the j-th branch. j Let v be the cross-sectional area of ​​the j-th branch tunnel. j,max h is the maximum allowable wind speed for the j-th branch. f,j To adjust the fan pressure of the j-th branch after adjusting the rotation speed, a j,0 ,a j,1 ,aj,2 To adjust the fitting coefficient of the fan characteristic curve of the j-th branch before the rotational speed, n j q represents the speed ratio of the j-th branch after the fan speed adjustment compared to before the speed adjustment. f,j To adjust the fan airflow of the j-th branch after adjusting the rotation speed, h f,j,min h is the lower limit of the fan pressure for the j-th branch. f,j,max N represents the upper limit of the wind turbine pressure in the j-th branch. j N is the actual operating speed of the j-th branch. j,min N represents the lower limit of the adjustable fan speed for the j-th branch. j,max Let q be the upper limit of the adjustable fan speed for the j-th branch. f,j,min Let q be the lower limit of the allowable fan air volume for the j-th branch. f,j,max Let η be the maximum allowable fan airflow for the j-th branch. j Let C be the operating efficiency of the fan in the j-th branch. j q represents the minimum wind turbine operating efficiency required by the j-th branch. j,min q represents the lower limit of the permissible air volume for the j-th branch of the on-demand ventilation system. j,max Let be the upper limit of the allowable air volume for the j-th branch of the on-demand air distribution system.

[0280] In some embodiments, the control scheme generation module 203 is specifically used for:

[0281] Based on the downhole ventilation volume requirements, the weight coefficients and decision variables of the optimization model are set.

[0282] Based on the set weight coefficients and decision variables, at least one candidate wind turbine frequency conversion control scheme is obtained using the set optimization model.

[0283] In some embodiments, the control scheme selection module 204 is specifically used for:

[0284] A ventilation network solution method based on loop air volume is used to calculate the air volume allocation for at least one candidate fan frequency conversion control scheme, and the air volume allocation calculation results corresponding to each fan frequency conversion control scheme are obtained.

[0285] The calculation results of air volume distribution corresponding to each fan frequency conversion control scheme are compared with the distribution air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal fan frequency conversion control scheme.

[0286] In some embodiments, the fan frequency conversion optimization device further includes a frequency conversion control module 205, used to control the multi-stage station ventilation system based on the optimal fan frequency conversion control scheme.

[0287] In practical applications, the ventilation network model acquisition module 201, the ventilation network model optimization module 202, the control scheme generation module 203, the control scheme selection module 204, and the frequency conversion control module 205 can be implemented by a processor in an electronic device. Of course, the processor needs to run a computer program in its memory to perform its functions.

[0288] It should be noted that the multi-stage station ventilation system fan frequency conversion optimization device provided in the above embodiments is only illustrated by the division of the above-described program modules when performing multi-stage station ventilation system fan frequency conversion optimization. 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 multi-stage station ventilation system fan frequency conversion optimization device and the multi-stage station ventilation system fan frequency conversion optimization method embodiment are based on the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0289] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide an electronic device for frequency conversion optimization of fans in multi-level station ventilation systems. Figure 3 This is only an exemplary structure of the device, not the entire structure; it can be implemented as needed. Figure 3 The structure shown may be part or all of the structure.

[0290] like Figure 3 As shown, the device 300 provided in this embodiment 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 via a bus system 305. It can be understood that the bus system 305 is used to implement communication between these components. In addition to a data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 3 The general designated all buses as Bus System 305.

[0291] The user interface 303 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0292] The memory 302 in this embodiment 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.

[0293] The frequency conversion optimization method for multi-stage station ventilation system fans disclosed in 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 frequency conversion optimization method for multi-stage station ventilation system fans can be completed through integrated logic circuits in the hardware or instructions in software form within processor 301. The processor 301 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 301 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in memory 302. The processor 301 reads the information in memory 302 and, in conjunction with its hardware, completes the steps of the multi-level station ventilation system fan frequency conversion optimization method provided in this application embodiment.

[0294] 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 methods.

[0295] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. 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 disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but 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), SyncLink 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.

[0296] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 302 that stores a computer program. The computer program can be executed by a processor 301 of an electronic device to complete the steps described in the method of this application embodiment. The computer-readable storage medium can be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.

[0297] It should be noted that terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0298] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0299] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for frequency conversion optimization of fans in a multi-stage station ventilation system, characterized in that, include: Obtain the ventilation network model of a multi-level station ventilation system; The ventilation network model is calculated based on the ventilation network solution method, and the ventilation network model is adjusted based on the calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions. Based on the underground ventilation volume requirements and the corresponding optimization model of the multi-level station ventilation system, at least one candidate fan frequency conversion control scheme is generated. Based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement, determine the optimal fan frequency conversion control scheme. The optimization model is a multi-objective mixed-integer linear programming model, including the following optimization objectives: minimum ventilation fan power, optimal on-demand ventilation demand, optimal fan air volume, and optimal fan air pressure. The decision variables of the optimization model are 0-1 integer decision variables, including: a first variable representing the correspondence between the air volume of the on-demand ventilation branch and multiple air volume values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first and second variables. The variable frequency drive (VFD) control scheme for the fans includes: the target operating speed of each variable frequency fan. The optimization model is set as follows: ； ； Where Z is the optimization objective. As the first weighting coefficient, This is the second weighting coefficient. The third weighting coefficient, The third weighting coefficient, For all wind turbine branches The set, Let J be the fan air volume of the j-th branch. Let J be the fan pressure of the j-th branch. This represents the set of all demand-based wind branches. Let j be the upper limit deviation of the air distribution range for the j-th branch under the on-demand air distribution scheme. Let $\frac{j}{j}$ be the lower limit deviation of the air distribution range for the on-demand air distribution branch. Let be the upper limit deviation of the optimal operating airflow range for the j-th branch. This represents the lower limit deviation of the optimal operating airflow range for the j-th branch. Let $\frac{j}{j}$ be the upper limit deviation of the optimal operating pressure range for the $j$-th branch. This represents the lower limit deviation of the optimal operating pressure range for the j-th branch. The number of branches in the ventilation network. This refers to the number of nodes in the ventilation network. This relates to the relationship between nodes and branches. For the first The air volume of each branch can be divided into different sections as needed. The number of independent loops in the ventilation network. For the first The algebraic sum of the wind pressures of the branches, The relationship between branches and loops, For the first The lower limit of permissible wind speed for each branch For the first The cross-sectional area of ​​the branch tunnels, For the first The maximum allowable wind speed for each branch is... After adjusting the speed, the first The wind pressure of the branch fan, Before adjusting the speed, the first The fitting coefficients of the characteristic curves of the branched wind turbines. Indicates the first The speed ratio of the branch after the fan speed is adjusted to that before the speed adjustment. After adjusting the speed, the first The air volume of the fan in each branch For the first The lower limit of the wind pressure of the branch fan, For the first The upper limit of the wind pressure of the branch fan. For the first The actual operating speed of the branch. For the first The lower limit of the adjustable fan speed for each branch. For the first The upper limit of the adjustable fan speed for each branch. For the first The lower limit of the permissible fan air volume for each branch. For the first The maximum allowable airflow of the fan in each branch. For the first The operating efficiency of the branch fan. For the first The minimum fan operating efficiency required for each branch. For the first The lower limit of the permissible air volume for branch ventilation as needed. For the first The upper limit of the allowable air volume for branch ventilation as needed.

2. The method according to claim 1, characterized in that, The method based on ventilation network calculation performs airflow distribution calculation on the ventilation network model, and adjusts the ventilation network model based on the airflow distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions, including: The ventilation network model is calculated to distribute airflow using a ventilation network solution method based on loop airflow, and the airflow distribution calculation results are obtained. The roadway air resistance parameters are adjusted based on the resistance measurement method until the comparison error between the calculated air volume distribution result and the measured roadway air volume is within the set threshold. Based on the air volume distribution calculation results, the simulated operating conditions of the fans in the ventilation network model are obtained; Determine whether the simulated operating conditions of the fan match the actual operating conditions. If not, adjust the model parameters of the ventilation network model until the simulated operating conditions of the fan in the ventilation network model match the actual operating conditions.

3. The method according to claim 1, characterized in that, The optimization model based on the downhole ventilation volume demand and the corresponding settings of the multi-level station ventilation system generates at least one candidate fan frequency conversion control scheme, including: Based on the downhole ventilation volume requirements, the weight coefficients and decision variables of the optimization model are set. Based on the set weight coefficients and decision variables, at least one candidate wind turbine frequency conversion control scheme is obtained using the set optimization model.

4. The method according to claim 1, characterized in that, The process of determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement includes: A ventilation network solution method based on loop air volume is used to calculate the air volume allocation for at least one candidate fan frequency conversion control scheme, and the air volume allocation calculation results corresponding to each fan frequency conversion control scheme are obtained. The calculation results of air volume distribution corresponding to each fan frequency conversion control scheme are compared with the distribution air volume of the on-demand air distribution branch determined based on the underground ventilation air volume demand to determine the optimal fan frequency conversion control scheme.

5. The method according to claim 1, characterized in that, The method further includes: The multi-stage station ventilation system is controlled based on the optimal fan frequency conversion control scheme.

6. A frequency conversion optimization device for a multi-stage station ventilation system fan, characterized in that, include: The ventilation network model acquisition module is used to acquire the ventilation network model of a multi-level station ventilation system; The ventilation network model optimization module is used to calculate the air volume distribution of the ventilation network model based on the ventilation network solution method, and adjust 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. The control scheme generation module is used to generate at least one candidate fan frequency conversion control scheme based on the underground ventilation air volume demand and the set optimization model corresponding to the multi-level station ventilation system. The control scheme selection module is used to determine the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the downhole ventilation air volume requirement. The optimization model is a multi-objective mixed-integer linear programming model, including the following optimization objectives: minimum ventilation fan power, optimal on-demand ventilation demand, optimal fan air volume, and optimal fan air pressure. The decision variables of the optimization model are 0-1 integer decision variables, including: a first variable representing the correspondence between the air volume of the on-demand ventilation branch and multiple air volume values ​​of that branch; a second variable representing the correspondence between the speed ratio of the fan branch before and after adjustment and multiple speed ratios of that branch; and a third variable representing the product of the first and second variables. The variable frequency drive (VFD) control scheme for the fans includes: the target operating speed of each variable frequency fan. The optimization model is set as follows: ； ； Where Z is the optimization objective. As the first weighting coefficient, This is the second weighting coefficient. The third weighting coefficient, The third weighting coefficient, For all wind turbine branches The set, Let J be the fan air volume of the j-th branch. Let J be the fan pressure of the j-th branch. This represents the set of all demand-based wind branches. Let j be the upper limit deviation of the air distribution range for the j-th branch under the on-demand air distribution scheme. Let $\frac{j}{j}$ be the lower limit deviation of the air distribution range for the on-demand air distribution branch. Let be the upper limit deviation of the optimal operating airflow range for the j-th branch. This represents the lower limit deviation of the optimal operating airflow range for the j-th branch. Let $\frac{j}{j}$ be the upper limit deviation of the optimal operating pressure range for the $j$-th branch. This represents the lower limit deviation of the optimal operating pressure range for the j-th branch. The number of branches in the ventilation network. This refers to the number of nodes in the ventilation network. This relates to the relationship between nodes and branches. For the first The air volume of each branch can be divided into different sections as needed. The number of independent loops in the ventilation network. For the first The algebraic sum of the wind pressures of the branches, The relationship between branches and loops, For the first The lower limit of permissible wind speed for each branch For the first The cross-sectional area of ​​the branch tunnels, For the first The maximum allowable wind speed for each branch is... After adjusting the speed, the first The wind pressure of the branch fan, Before adjusting the speed, the first The fitting coefficients of the characteristic curves of the branched wind turbines. Indicates the first The speed ratio of the branch after the fan speed is adjusted to that before the speed adjustment. After adjusting the speed, the first The air volume of the fan in each branch For the first The lower limit of the wind pressure of the branch fan, For the first The upper limit of the wind pressure of the branch fan. For the first The actual operating speed of the branch. For the first The lower limit of the adjustable fan speed for each branch. For the first The upper limit of the adjustable fan speed for each branch. For the first The lower limit of the permissible fan air volume for each branch. For the first The maximum allowable airflow of the fan in each branch. For the first The operating efficiency of the branch fan. For the first The minimum fan operating efficiency required for each branch. For the first The lower limit of the permissible air volume for branch ventilation as needed. For the first The upper limit of the allowable air volume for branch ventilation as needed.

7. An electronic device, characterized in that, include: A processor and memory for storing computer programs that can run on the processor, wherein, The processor, when running a computer program, performs the steps of the method according to any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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