CN115270503A - Method, device and equipment for adjusting air volume of mine ventilation system step by step - Google Patents
Method, device and equipment for adjusting air volume of mine ventilation system step by step Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F1/00—Ventilation of mines or tunnels; Distribution of ventilating currents
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Abstract
The application discloses a method, a device and equipment for adjusting air quantity of a mine ventilation system step by step. The method comprises the following steps: acquiring a ventilation network model of a mine ventilation system; performing air volume distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition; generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system; determining an optimal air volume adjusting scheme based on at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement; and setting an optimization model as an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming. The optimal air quantity adjusting scheme of the mine ventilation system can be obtained based on a two-step ventilation optimization method, the variable scale and the solving complexity of the model are greatly reduced, and the reliability of underground ventilation adjustment is further improved.
Description
Technical Field
The application relates to the field of ventilation control, in particular to a method, a device and equipment for adjusting air quantity of a mine ventilation system step by step.
Background
The aim of mine ventilation is to supply sufficient fresh air to a mining operation area, timely discharge underground dirty air out of the ground surface, improve the mine ventilation environment, strengthen the safety production standard and create a good and comfortable operation environment for underground workers.
On the premise of meeting dynamic air distribution according to needs at different underground periods, the ventilation optimization regulation and control requirement obtains a ventilation optimization regulation and control scheme which meets the actual safe production requirement of a mine, is reasonable and reliable in technology and optimal in economic performance by adopting a ventilation optimization theory based on a fluid network so as to adjust the air distribution and the air pressure distribution state of the ventilation network and ensure safe, reliable, stable and economic operation of a mine ventilation system.
The air volume regulation of the traditional mine ventilation system is large in variable scale and large in model solving complexity, and the ventilation regulation requirement is difficult to meet.
Disclosure of Invention
In view of this, the embodiment of the application provides a method, a device and equipment for adjusting air quantity of a mine ventilation system step by step, and aims to improve the reliability of underground ventilation adjustment of the mine ventilation system.
The technical scheme of the embodiment of the application is realized as follows:
the embodiment of the application provides a method for adjusting air quantity of a mine ventilation system step by step, which comprises the following steps:
acquiring a ventilation network model of a mine ventilation system;
performing air volume distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition;
generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system;
determining an optimal air volume adjusting scheme based on the at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement;
the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and comprises the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the decision variables for setting the optimization model comprise: adjusting wind pressure values of all branches; the air volume adjusting scheme comprises the following steps: provision of a ventilation structure, said ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
In some embodiments, the calculating method based on a ventilation network performs air volume distribution calculation on the ventilation network model, and adjusts the ventilation network model based on the air volume distribution calculation result until a simulated condition of a fan in the ventilation network model matches with an actual condition, including:
adopting a ventilation network resolving method based on loop air volume to perform air volume distribution calculation on the ventilation network model to obtain an air volume distribution calculation result;
adjusting the wind resistance parameters of the tunnel based on a resistance measuring mode, until the comparison error between the air volume distribution calculation result and the actually measured roadway air volume is within a set threshold value;
obtaining the simulation working condition of a fan in the ventilation network model based on the air volume distribution calculation result;
and judging whether the simulated working condition of the fan is matched with the actual working condition, if not, adjusting the model parameters of the ventilation network model until the simulated working condition of the fan in the ventilation network model is matched with the actual working condition.
In some embodiments, the set optimization model is as follows:
wherein Z is an optimization target, ω 1 Is a first weight coefficient, ω 2 Is the second weight coefficient, ω 3 Is a third weight coefficient, ω 4 Is a fourth weight coefficient, ω 5 Is a fifth weight coefficient, N is the number of branches of the ventilation network, q j The air quantity of the jth air-dividing branch according to needs, r j Wind resistance of jth branch,. DELTA.h j Is the wind pressure regulation value of the jth branch, h N,j Natural wind pressure of j-th branch, n j,a Indicating whether an adjustment is required for the jth branch, s j Number of adjustment stages for j-th branch, n j,c Indicating whether the jth branch needs to be subjected to energization regulation or resistance reduction regulation, n j,b Indicates whether the jth branch needs to be subjected to resistance increasing regulation, delta h' j Is Δ h j Absolute value of (a), b ij Denotes the relationship of the branch to the loop, h j Is algebraic sum of jth branch wind pressure, rho' j Indicating the number of energization or de-energization adjustments, h ", that the jth branch allows to be ignored j Representing the allowable negligible resistance increase adjustment, Δ h, of the jth branch j,min Adjustable lower wind pressure limit, Δ h, for jth branch j,max Adjustable Upper wind pressure Limit, N, for jth Branch a For allowing adjustment of the number of branches, p, in a ventilation network j Indicating the adjustment amount, n, allowed to be ignored for the jth branch max,a Is a first normal quantity, n max,b Is the second normal amount, n max,c Is the third normal amount.
In some embodiments, the generating at least one candidate air volume adjusting scheme based on the downhole ventilation air volume requirement and the corresponding set optimization model of the mine ventilation system includes:
determining an air volume distribution result meeting the air distribution requirement according to the underground ventilation air volume requirement;
setting each weight coefficient and decision variable of the set optimization model based on the component distribution result;
and solving at least one air volume adjusting scheme to be selected by utilizing the set optimization model based on the set weight coefficient and the decision variable.
In some embodiments, the determining an optimal air volume conditioning scheme based on the at least one candidate air volume conditioning scheme and the downhole ventilation air volume requirement includes:
performing air volume distribution calculation on the at least one air volume adjusting scheme to be selected by adopting a ventilation network resolving method based on loop air volume to obtain air volume distribution calculation results corresponding to the air volume adjusting schemes;
and comparing the air volume distribution calculation result corresponding to each air volume adjusting scheme with the distributed air volume of the air distribution branch according to the requirement determined based on the underground ventilation air volume requirement, and determining the optimal air volume adjusting scheme.
In some embodiments, the method further comprises:
adjusting the mine ventilation system based on the optimal air volume adjustment scheme.
In a second aspect, an embodiment of the present application provides a stepwise air volume adjusting device for a mine ventilation system, including:
the ventilation network model acquisition module is used for acquiring a ventilation network model of a mine ventilation system;
the ventilation network model optimization module is used for carrying out air volume distribution calculation on the ventilation network model based on a ventilation network calculation method and adjusting the ventilation network model based on an air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition;
the air volume adjusting scheme generating module is used for generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system;
the air volume adjusting scheme selecting module is used for determining an optimal air volume adjusting scheme based on the at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement;
the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and comprises the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the decision variables for setting the optimization model comprise: the air volume of all unknown air volume branches and the adjusted air pressure values of all branches; the air volume adjusting scheme comprises the following steps: a provision for adjusting a ventilation structure, the ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
In a third aspect, an embodiment of the present application provides 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, is configured to perform the steps of the method according to the first aspect of the embodiments of the present application.
In a fourth aspect, an embodiment of the present application provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method in the first aspect of the embodiment of the present application are implemented.
According to the technical scheme provided by the embodiment of the application, a ventilation network model of a mine ventilation system is obtained; performing air volume distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on the air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition; generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system; determining an optimal air volume adjusting scheme based on at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement; the optimization model is set to be an air quantity regulation and control step-by-step optimization model based on multi-objective mixed integer programming. Therefore, the optimal air volume adjusting scheme of the mine ventilation system can be obtained based on a two-step ventilation optimization method, the variable scale and the solving complexity of the model are greatly reduced, and the reliability of underground ventilation adjustment is further improved.
Drawings
FIG. 1 is a schematic flow chart of a method for adjusting the air quantity of a mine ventilation system step by step according to an embodiment of the application;
FIG. 2 is a schematic diagram of a network of mine ventilation systems in an embodiment of the application of the present application;
FIG. 3 is a schematic view of the air distribution of the ventilation network of the mine ventilation system in an embodiment of the application of the present application;
FIG. 4 is a schematic diagram of a list of alternative blowers in an application embodiment of the present application;
FIG. 5 is a schematic view of an alternative fan operating condition in an embodiment of the application of the present application;
FIG. 6 is a schematic structural diagram of an air quantity step-by-step adjusting device of a mine ventilation system in an embodiment of the application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples.
Unless defined otherwise, 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 in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In order to meet the requirement for intelligent adjustment of a mine ventilation system, the embodiment of the application provides a method for adjusting the air volume of the mine ventilation system step by step, which can be applied to electronic equipment with data processing capability, such as a notebook, a desktop computer or a server, and is used for intelligently determining an air volume adjustment scheme of the mine ventilation system. According to the method, the optimal air volume adjusting scheme of the mine ventilation system can be obtained based on a two-step ventilation optimization method, the variable scale and the solving complexity of the model are greatly reduced, the reliability of underground ventilation adjustment is further improved, and the underground ventilation requirement of the mine ventilation system is met.
As shown in fig. 1, the method for adjusting the air volume of the mine ventilation system step by step in the embodiment of the application includes:
It should be noted that the ventilation network model is established according to the downhole measured data of the mine ventilation system, which is a data base for constructing the setting optimization model of the embodiment of the present application.
Illustratively, obtaining a ventilation network model of a mine ventilation system includes:
step a, establishing a three-dimensional ventilation network diagram by utilizing various horizontal designs and actual measurement horizontal sectional views in mine mining;
b, measuring and collecting wind resistance parameters of all ventilation network roadways through ventilation resistance;
c, investigating the installation and arrangement conditions of the underground fan and the structure facilities, and determining the initial state of the ventilation network model;
d, acquiring the running states of the fans of all levels of fan stations, and determining the level of the fan station where each fan is located and the current rotating speed of the variable-frequency running of the fan;
and e, regarding the non-variable frequency fan, the running speed ratio of the fan is considered to be 100%, and the rotating speed of the fan is not allowed to be adjusted.
And 102, performing air volume distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air volume distribution calculation result until the simulated working condition of a fan in the ventilation network model is matched with the actual working condition.
It can be understood that the ventilation network model can accurately reflect the operation performance of the fan by comparing the simulation working condition and the actual working condition of the fan in the ventilation network model. The working condition of the fan can be understood as the corresponding air volume and air pressure of the fan at the current rotating speed.
Exemplarily, the calculating method based on the ventilation network performs air volume distribution calculation on the ventilation network model, 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 is matched with the actual working condition, including:
performing air volume distribution calculation on the ventilation network model by adopting a ventilation network calculation method based on loop air volume to obtain an air volume distribution calculation result;
adjusting the tunnel wind resistance parameters based on a resistance measurement mode until the comparison error between the wind volume distribution calculation result and the actually measured tunnel wind volume is within a set threshold value;
obtaining the simulation working condition of a fan in the ventilation network model based on the air volume distribution calculation result;
and judging whether the simulated working condition of the fan is matched with the actual working condition, if not, adjusting the model parameters of the ventilation network model until the simulated working condition of the fan in the ventilation network model is matched with the actual working condition.
It should be noted that, by adjusting the tunnel wind resistance parameter based on the resistance measurement method, the wind volume of each network branch of the ventilation network model can be made to match the actually measured wind volume as much as possible, and the set threshold value can be reasonably determined according to the design accuracy.
It should be noted that, a person skilled in the art may adjust the model parameters of the ventilation network model based on the comparison result between the simulated condition and the actual condition of the fan until the difference between the simulated condition and the actual condition is within the reasonable precision range. Preferably, the electronic device can also intelligently adjust model parameters of the ventilation network model based on a model optimization algorithm until the difference between the simulated working condition and the actual working condition is within a reasonable precision range.
And 103, generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system.
Here, the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and includes the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the setting of the decision variables of the optimization model comprises: adjusting wind pressure values of all branches; the air volume adjusting scheme comprises the following steps: a provision for adjusting a ventilation structure, the ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
In some embodiments, the generating at least one candidate air volume adjusting scheme based on the downhole ventilation air volume requirement and the corresponding set optimization model of the mine ventilation system includes:
determining an air volume distribution result meeting the air distribution requirement according to the underground ventilation air volume requirement;
setting each weight coefficient and decision variable of the set optimization model based on the component distribution result;
and solving at least one air volume adjusting scheme to be selected by utilizing the set optimization model based on the set weight coefficient and the decision variable.
It should be noted that the method in the embodiment of the present application is based on a two-step ventilation optimization method, and the two-step ventilation optimization method refers to that air volume distribution optimization and air volume regulation and control optimization are respectively treated as two independent steps. The two-step ventilation optimization method comprises the steps of firstly resolving a natural wind distribution result of a ventilation network under a wind distribution condition according to needs, and then determining an optimization scheme of air volume regulation and control under the natural wind distribution condition. The method separates the air volume variable and the regulating variable from each other and processes the air volume variable and the regulating variable respectively according to two steps, thereby greatly reducing the variable scale and the solving complexity of the mathematical model. Specifically, in the embodiment of the present application, an air volume distribution result (i.e., a scheme of obtaining air volume distribution) meeting the demand air distribution requirement can be determined according to the demand of the downhole ventilation air volume, and the variable scale and the solving complexity of the setting optimization model in the embodiment of the present application can be effectively reduced.
In an application example, generating at least one candidate air volume adjusting scheme comprises:
step a, calculating the air quantity required by each operation point according to the underground ventilation air quantity requirement so as to conveniently regulate and control the underground air quantity according to the requirement by adopting an air quantity regulation scheme;
here, on the premise that the demand-based air volume distribution target can be achieved, different natural air distribution results can be obtained according to the upper and lower limits of the air volume distribution (the natural air volume distribution energy consumption is optimal). Wherein the air demand constraint should not be a fixed value but set to a maximum and minimum air volume range constraint. After the air distribution range is determined according to needs, the air quantity required by each operation point is obtained by respectively calculating the ventilation network at certain intervals (within the range of the upper limit and the lower limit of the air quantity), so that the air quantities of all branches of the ventilation network are known conditions, and the variable scale and the solving complexity of a subsequently constructed set optimization model can be effectively reduced.
B, selecting corresponding targets and constraint conditions according to actual mine requirements, and constructing a set optimization model of the embodiment of the application;
c, setting weight coefficients of all optimization targets according to the underground air quantity demand regulation and control demand, and parameters such as a branch air quantity deviation range, a working condition air pressure deviation range and a fan operation range according to the demand ventilation demand;
step d, performing solving operation by adopting a set optimization model to obtain at least one air volume adjusting scheme to be selected, wherein the air volume adjusting scheme comprises adjusting measures of a ventilation structure, and the ventilation structure comprises at least one of the following: the air conditioner comprises an underground fan, an air door and an air window.
And 104, determining an optimal air volume adjusting scheme based on the at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement.
Illustratively, the determining an optimal air volume adjusting scheme based on the at least one candidate air volume adjusting scheme and the downhole ventilation air volume requirement includes:
performing air volume distribution calculation on the at least one air volume adjusting scheme to be selected by adopting a ventilation network resolving method based on loop air volume to obtain air volume distribution calculation results corresponding to the air volume adjusting schemes;
and comparing the air volume distribution calculation result corresponding to each air volume adjusting scheme with the distributed air volume of the air distribution branch according to the requirement determined based on the underground ventilation air volume requirement, and determining the optimal air volume adjusting scheme.
It should be noted that the downhole ventilation air volume requirement can be reasonably determined according to the number of downhole operators, the exhaust emission requirement and the like, the distributed air volume of the air distribution branch according to needs can be obtained through conversion processing by the downhole ventilation air volume requirement and a calculation formula in a ventilation rule, and related conversion processing belongs to the prior art and is not described herein again.
It can be understood that the method of the embodiment of the application can generate at least one air volume adjusting scheme based on the multi-objective optimization setting optimization model, and determine the optimal air volume adjusting scheme based on at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement, so that the intelligent adjustment of the mine ventilation system can be realized based on the optimal air volume adjusting scheme.
In some embodiments, the set optimization model is as follows:
wherein Z is an optimization objective, ω 1 Is a first weight coefficient, ω 2 Is the second weight coefficient, ω 3 Is a third weight coefficient, ω 4 Is a fourth weight coefficient, ω 5 Is a fifth weight coefficient, N is the number of branches of the ventilation network, q j The air quantity of the jth air-dividing branch according to needs, r j Wind resistance of the jth branch,. DELTA.h j Is the wind pressure regulation value of the jth branch, h N,j Natural wind pressure of j-th branch, n j,a Indicating whether an adjustment is required for the jth branch, s j For the adjustment stage of the jth branch,n j,c indicating whether the jth branch needs to be subjected to energization regulation or resistance reduction regulation, n j,b Indicates whether the jth branch needs to be subjected to resistance increasing regulation, delta h' j Is Δ h j Absolute value of (a), b ij Denotes the relationship of the branch to the loop, h j Is algebraic sum of jth branch wind pressure, rho' j Indicating the number of energization or de-energization adjustments, h ", that the jth branch allows to be ignored j Representing the allowable negligible resistance increase adjustment, Δ h, of the jth branch j,min Adjustable lower wind pressure limit, Δ h, for jth branch j,max Adjustable upper limit of wind pressure for jth branch, N a For allowing adjustment of the number of branches, p, in a ventilation network j Indicating the adjustment amount, n, allowed to be ignored for the jth branch max,a Is a first normal quantity, n max,b Is the second normal amount, n max,c Is the third normal amount.
It should be noted that s.t. in the above formula is an abbreviation of subject to, i.e. a constrained meaning.
In some embodiments, the method further comprises:
adjusting the mine ventilation system based on the optimal air volume adjustment scheme.
It can be understood that the above-mentioned optimal air volume regulation scheme can be outputted to the terminal device, and regulated by means of manual control, for example, by adjusting and/or installing ventilation structures such as a fan, a damper and a window in a manual control manner, so as to realize the downhole ventilation according to requirements. Preferably, the electronic equipment can also regulate and control a mine ventilation system based on an optimal air volume regulation scheme, for example, the ventilation structures such as installation fans, air doors and air windows can be remotely regulated and controlled, so that the underground on-demand ventilation can be realized.
The optimization objective of the air volume control step-by-step optimization model based on multi-objective mixed integer programming in the embodiment of the present application is described as follows:
(1) Minimum ventilation energy consumption target
The minimum ventilation energy consumption target can be expressed as
Wherein the content of the first and second substances,
z 1 representing a minimum ventilation energy consumption target;
f is the set of all fan branches F, including the main fan and the auxiliary fan;
q f the fan air volume of the fan branch f;
h f the fan air pressure of the fan branch f;
n is the number of branches of the ventilation network;
h′ r,j is algebraic sum of j-th branch ventilation resistance, h' r,j ＝h r,j +Δh j -h N,j ；
h r,j Is the ventilation resistance of the jth branch,
r j the wind resistance of the jth branch;
q j the air quantity of the jth branch is;
Δh j the wind pressure adjusting value of the jth branch is obtained;
h N,j the natural wind pressure of the jth branch.
(2) Optimal adjustment position target
To quantify the adjustability of a particular tuning position branch in a ventilation network, a branch tuning series (integer type of value) may be defined to represent the tuning position constraint. The branch regulating series constructed by the application meets the following characteristics:
(a) The default value of the branch adjusting series is zero, which indicates that the branch is an adjustable branch allowing any adjusting mode;
(b) The larger the absolute value of the branch adjusting series is, the more irregulable the branch is;
(c) The branch regulation grade number is positive in sign and larger in numerical value, and the branch is less capable of increasing resistance and regulating;
(d) The branch regulating stage number is negative in sign and smaller in value, and the branch is less capable of increasing energy or reducing resistance;
(e) The absolute value of the adjustable branch adjusting series is close to zero, and the absolute value of the non-adjustable branch adjusting series tends to a larger integer value;
(f) The signs of the adjusting series of the resistance increasing adjusting branch are positive, and the signs of the adjusting series of the resistance increasing adjusting branch or the resistance reducing adjusting branch are negative.
The optimal adjustment position target can be expressed as
Wherein the content of the first and second substances,
z 3 representing an optimal adjustment position target;
s j the adjustment series of the jth branch is represented and is a constant set by a user;
n j,a indicating whether the adjustment of the jth branch is needed;
n j,a satisfy the requirement of
Δh j The wind pressure adjusting value of the jth branch is obtained;
ρ j an adjustment amount (adjustment factor) indicating that the jth branch is allowed to be ignored, satisfying ρ j >0。
To facilitate solving the mathematical model, a mixed integer programming method is introduced to n j,a A 0-1 integer variable is defined to indicate whether an adjustment is required for the jth branch. To represent n j,a In (1) | Δ h j L, < delta > h 'is introduced' j Represents Δ h j Absolute value of (a). Delta h' j Satisfies the following conditions
Δ h 'is present under the constraint of the above conditions' j An implicit constraint of 0 or more. To limit Δ h' j Is used for introducing a target constraint with the highest priority by adopting a priority method so as to ensure delta h' j ＝|Δh j |。
min z 0 ＝ω 0 Δh′ j j＝1,2,…,N (4)
Wherein the content of the first and second substances,
z 0 an additional target representing a variable limiting the wind pressure adjustment value;
ω 0 and a weight coefficient (taking a larger value) representing a variable limiting the wind pressure adjusting value.
The additional objective must be met preferentially or else the 0-1 integer variable n will be affected j,a Reliability of the value. 0-1 integer variable n j,a The following conditions need to be satisfied
Wherein the content of the first and second substances,
n max,a can be set to a larger normal amount to ensure Δ h' j -ρ j ≤n max,a 。
Under the constraint of the above conditions, the integer variable n is 0-1 j,a Satisfy the requirement of
Where p is required j >0。
(3) Best mode objective
The adjustment stage number takes the adjustment mode corresponding to the branch into consideration, and the optimal adjustment position target does not take the adjustment mode of the adjustment point position into consideration. Therefore, it is necessary to further construct an optimal adjustment mode target.
The optimal adjustment mode target can be expressed as
Wherein, the first and the second end of the pipe are connected with each other,
z 3 representing an optimal adjustment mode target;
s j the adjustment series of the jth branch is represented and is a constant set by a user;
n j,b indicating whether the resistance increasing adjustment is needed to be carried out on the jth branch;
n j,b satisfy the requirement of
n j,c Indicating whether the j branch needs to be subjected to energy increasing adjustment or resistance reducing adjustment;
n j,c satisfy the requirement of
Δh j The wind pressure adjusting value of the jth branch is obtained;
ρ j an adjustment amount (adjustment factor) indicating that the jth branch is allowed to be ignored, satisfying ρ j >0。
To facilitate solving the mathematical model, a mixed integer programming method is introduced to n j,b Defining the integer variable as 0-1, and indicating whether the jth branch needs to be subjected to resistance increasing regulation or not; n is to be j,c And a 0-1 integer variable is defined to indicate whether the j branch needs to be subjected to energization regulation or resistance reduction regulation.
0-1 integer variable n j,b The following conditions need to be satisfied
Wherein the content of the first and second substances,
n max,b can be set to a larger normal amount to ensure Δ h j -ρ j ≤n max,b 。
0-1 integer variable n j,c The following conditions need to be satisfied
Wherein the content of the first and second substances,
n max,c can be set to a larger normal amount to ensure- (Δ h) j +ρ j )≤n max,c 。
(4) Minimum number of targets to adjust
When the sum of the absolute values of the adjustment stages is close, the minimum number of adjustment points should be ensured as much as possible. In addition to the optimal adjusting position, the optimal adjusting scheme also requires that an air volume adjusting and controlling optimization model should reduce the number of adjusting points as much as possible and adopt a resistance-increasing adjusting mode as much as possible so as to reduce the adjusting and controlling cost of a ventilation system and simplify the management process of ventilation adjusting and controlling facilities.
When the underground ventilation system is actually optimized and controlled, corresponding adjusting facilities are installed or adjusting operation is carried out at corresponding positions only when the adjusting quantity of a certain roadway reaches a certain value. To avoid the effect of smaller adjustments on the constraint on the number of adjustments, adjustment factors may be set for which a particular branch does not need to be adjusted.
The minimum adjustment number target can be expressed as
Wherein the content of the first and second substances,
z 4 representing a minimum adjustment number target;
n j,a indicating whether the adjustment of the jth branch is needed;
n j,a satisfy the requirement of
Δh j The wind pressure adjusting value of the jth branch is obtained;
ρ j an adjustment amount (adjustment factor) indicating that the jth branch is allowed to be ignored, satisfying ρ j >0。
The following description of the constraint conditions of the air volume regulation step-by-step optimization model based on the multi-objective mixed integer programming is as follows:
(1) Constraint condition of wind pressure balance
The ventilation network air quantity regulation scheme must meet the loop air pressure balance condition, i.e. the algebraic sum of the air pressures of all branches in any loop in the ventilation network is zero.
Wherein, the first and the second end of the pipe are connected with each other,
m is the number of independent loops of the ventilation network, and M = N-J + 1;
h j is the algebraic sum of the wind pressure of the jth branch,
r j the wind resistance of the jth branch;
Δh j the wind pressure adjusting value of the jth branch is obtained;
h f,j the wind pressure of the fan of the jth branch is set;
h N,j the natural wind pressure of the jth branch is obtained;
b ij representing the relationship of the branch and the loop;
b ij satisfy the requirement of
(2) Adjusting position constraints
When the jth branch does not allow the installation of a regulating facility (non-adjustable branch), then
Δh j ＝0 (11)
It is noted that a negligible adjustment tolerance range may be set for the non-adjustable branch without affecting the adjustment effect, and therefore the no-adjustment-facility-installation constraint for the jth branch may be expressed as
-ρ′ j ≤Δh j ≤ρ″ j (12)
Wherein the content of the first and second substances,
ρ′ j indicating the amount of boost or buck regulation permitted to be ignored for the jth branchSegment factor) satisfying ρ' j >0；
ρ″ j The resistance increase adjustment amount (adjustment factor) indicating that the jth branch is allowed to be ignored satisfies rho ″) j >0。
(3) Constraint condition of regulation mode
Constraint condition of adjustment amount of j-th branch
Δh j,min ≤Δh j ≤Δh j,max (13)
Wherein the content of the first and second substances,
Δh j,min the lower limit of the wind pressure can be adjusted for the jth branch;
Δh j,max the upper limit of the wind pressure can be adjusted for the jth branch.
When the jth branch only allows the resistance-increasing regulation constraint, then
Δh j ≥0 (14)
When the jth branch only allows the constraint of energy increasing adjustment (or resistance reducing adjustment), then
Δh j ≤0 (15)
When the constraint conditions of the adjustment modes are actually constructed, the constraint conditions for limiting a specific adjustment mode are not suitable for a large number of branches, otherwise, the optimization model can be caused to have no solution.
In order to set the adjusting mode constraint condition, the properties of an allowable branch adjusting mode, a branch adjustable wind pressure upper limit and a branch adjustable wind pressure lower limit can be added to the branches of the ventilation network.
(4) Adjusting number constraints
In order to reduce the regulation and control cost of the ventilation system and simplify the management process of ventilation regulation and control facilities, the regulation scheme of the optimization model should reduce the number of regulation points as much as possible.
Wherein the content of the first and second substances,
N a adjusting the number of branches allowed in the ventilation network;
n j,a indicating whether the adjustment of the jth branch is needed;
n j,a satisfy the requirement of
Δh j The wind pressure adjusting value of the jth branch is obtained;
ρ j an adjustment amount (adjustment factor) indicating that the jth branch is allowed to be ignored, satisfying ρ j >0。
It should be noted that, in the air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming according to the embodiment of the present application, the air volumes of all branches are known, and the decision variable is the adjusted air pressure value Δ h of all branches j And an auxiliary decision variable Δ h' j 、n j,a 、n j,b And n j,c . Since the known air volume can be regarded as a constant, the objective function and the constraint condition are both linear functions, and the corresponding mathematical model is a linear mixed integer programming model. Because the adjusting model is established on the basis of the calculation of the ventilation network, the on-demand wind division calculation needs to be carried out on the ventilation network in advance so as to obtain the wind volume distribution result of the on-demand wind division.
The method for regulating the air quantity of the mine ventilation system in steps is exemplarily described in the following with reference to an application example.
Fig. 2 shows a network schematic diagram of the mine ventilation system in the embodiment of the application, which is used for verifying the reliability of the ventilation network air quantity regulation and optimization method, and the figure in the diagram is the number of the ventilation network branch. The ventilation network is provided with two wind distribution points according to requirements, and comprises 21 branches and 14 nodes.
Fig. 3 and table 1 show the distribution result of the air volume of the ventilation network obtained by the fixed air volume method on the premise of the demand of air distribution. On the basis of air volume distribution calculation, an air volume regulation multi-objective optimization mathematical model can be adopted to determine an air volume distribution-as-needed optimization regulation scheme.
TABLE 1
Table 2 shows the ventilation network air volume adjustment optimization scheme, as follows:
TABLE 2
Wherein, the first scheme is an adjustment scheme that is not adjustable by setting branch # 19. Scheme two improves the target weight of the number of the adjusting points on the basis of setting the nonadjustable branch # 19. And the third scheme further improves the target weight of the minimum regulating number on the basis of the second scheme. And on the basis that the branch # 19 is not adjustable, the optimal adjustment mode target weight is improved. From the perspective of the adjustment method, the adjustment schemes of scheme two and scheme three are similar. And the air volume regulation scheme is relatively weak in restriction due to the fact that no regulation stage number is set. When the adjustability of setting branch # 20 is lower than branch # 21, the optimally solved adjustment scheme will be more inclined to adjust branch # 21.
It should be noted that after the adjusted wind pressure distribution scheme is obtained by solving according to the wind volume control multi-objective optimization model, the final wind volume control scheme should be determined according to the adjusted wind pressure in combination with the actual underground conditions. And finally, simulating the actual regulation and control effect of the regulation scheme by using an air volume distribution method. FIG. 4 shows a determination of the on-demand split branch # 21 energization regulation candidate fan list using a fan optimization method, and FIG. 5 shows simulated operating conditions of the candidate fans.
In order to implement the method according to the embodiment of the present application, an air volume step-by-step adjusting device of a mine ventilation system is further provided in an electronic device, as shown in fig. 6, the air volume step-by-step adjusting device of the mine ventilation system includes: the system comprises a ventilation network model obtaining module 601, a ventilation network model optimizing module 602, an air volume adjusting scheme generating module 603 and an air volume adjusting scheme selecting module 604.
The ventilation network model acquisition module 601 is used for acquiring a ventilation network model of a mine ventilation system; the ventilation network model optimization module 602 is 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 an air volume distribution calculation result until a simulated condition of a fan in the ventilation network model matches with an actual condition; the air volume adjusting scheme generating module 603 is configured to generate at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system; the air volume adjusting scheme selecting module 604 is configured to determine an optimal air volume adjusting scheme based on the at least one to-be-selected air volume adjusting scheme and the downhole ventilation air volume requirement; the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and comprises the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the setting of the decision variables of the optimization model comprises: adjusting wind pressure values of all branches; the air volume adjusting scheme comprises the following steps: provision of a ventilation structure, said ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
In some embodiments, the ventilation network model optimization module 602 is specifically configured to:
performing air volume distribution calculation on the ventilation network model by adopting a ventilation network calculation method based on loop air volume to obtain an air volume distribution calculation result;
adjusting the tunnel wind resistance parameters based on a resistance measurement mode until the comparison error between the wind volume distribution calculation result and the actually measured tunnel wind volume is within a set threshold value;
obtaining the simulation working condition of a fan in the ventilation network model based on the air volume distribution calculation result;
and judging whether the simulated working condition of the fan is matched with the actual working condition, if not, adjusting the model parameters of the ventilation network model until the simulated working condition of the fan in the ventilation network model is matched with the actual working condition.
In some embodiments, the set optimization model is as follows:
wherein Z is an optimization target, ω 1 Is a first weight coefficient, ω 2 Is the second weight coefficient, ω 3 Is a third weight coefficient, ω 4 Is a fourth weight coefficient, ω 5 Is a fifth weight coefficient, N is the number of branches of the ventilation network, q j The air quantity of the jth air-dividing branch according to needs, r j Wind resistance of the jth branch,. DELTA.h j Is the wind pressure regulation value of the jth branch, h N,j Natural wind pressure of jth branch, n j,a Indicating whether an adjustment is required for the jth branch, s j Number of adjustment stages for j-th branch, n j,c Indicating whether the jth branch needs to be subjected to energization regulation or resistance reduction regulation, n j,b Indicates whether the jth branch needs to be subjected to resistance increasing regulation, delta h' j Is Δ h j Absolute value of (a), b ij Denotes the relationship of the branch to the loop, h j Is algebraic sum of jth branch wind pressure, rho' j Indicating the number of energization or de-energization adjustments, h ", that the jth branch allows to be ignored j Representing the allowable negligible resistance increase adjustment, Δ h, of the jth branch j,min Adjustable lower wind pressure limit, Δ h, for jth branch j,max Adjustable Upper wind pressure Limit, N, for jth Branch a For allowing adjustment of the number of branches, p, in a ventilation network j Indicating the adjustment amount, n, allowed to be ignored for the jth branch max,a Is a first normal quantity, n max,b Is the second normal amount, n max,c Is the third normal amount.
In some embodiments, the air volume adjusting scheme generating module 603 is specifically configured to:
determining an air volume distribution result meeting the air distribution requirement according to the underground ventilation air volume requirement;
setting each weight coefficient and decision variable of the set optimization model based on the component distribution result;
and solving at least one air volume adjusting scheme to be selected by utilizing the set optimization model based on the set weight coefficient and the decision variable.
In some embodiments, the airflow rate adjustment scheme selecting module 604 is specifically configured to:
performing air volume distribution calculation on the at least one air volume adjusting scheme to be selected by adopting a ventilation network resolving method based on loop air volume to obtain air volume distribution calculation results corresponding to the air volume adjusting schemes;
and comparing the air volume distribution calculation result corresponding to each air volume adjusting scheme with the distributed air volume of the air distribution branch according to the requirement determined based on the underground ventilation air volume requirement, and determining the optimal air volume adjusting scheme.
In some embodiments, the step-by-step airflow regulating device of the mine ventilation system further comprises: an adjusting module 605 for adjusting the mine ventilation system based on the optimal air volume adjustment scheme.
In practical application, the ventilation network model obtaining module 601, the ventilation network model optimizing module 602, the air volume adjusting scheme generating module 603, the air volume adjusting scheme selecting module 604 and the adjusting module 605 may 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.
It should be noted that: the air quantity step-by-step adjusting device of the mine ventilation system provided by the above embodiment is exemplified by the division of the above program modules only when the air quantity of the mine ventilation system is adjusted step-by-step, and in practical application, the processing distribution can be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the above-described processing. In addition, the air quantity step-by-step adjusting device of the mine ventilation system and the air quantity step-by-step adjusting method embodiment of the mine ventilation system provided by the embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment and is not repeated herein.
Based on the hardware implementation of the program module, in order to implement the method according to the embodiment of the present application, an electronic device is further provided in the embodiment of the present application. Fig. 7 shows only an exemplary structure of the apparatus, not a whole structure, and a part or the whole structure shown in fig. 7 may be implemented as necessary.
As shown in fig. 7, an apparatus 700 provided in the embodiment of the present application includes: at least one processor 701, memory 702, user interface 703, and at least one network interface 704. The various components in the electronic device 700 are coupled together by a bus system 705. It will be appreciated that the bus system 705 is used to enable communications among the components. The bus system 705 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various busses are labeled as the bus system 705 in figure 7.
The user interface 703 may include, among other things, a display, a keyboard, a mouse, a trackball, a click wheel, a key, a button, a touch pad, or a touch screen.
The memory 702 in the embodiments 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 for operating on an electronic device.
The method for adjusting the air quantity of the mine ventilation system step by step disclosed by the embodiment of the application can be applied to the processor 701 or realized by the processor 701. The processor 701 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the air quantity step-by-step adjusting method of the mine ventilation system can be completed through an integrated logic circuit of hardware in the processor 701 or an instruction in a software form. The Processor 701 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 701 may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the memory 702, and the processor 701 reads information in the memory 702, and completes the steps of the method for adjusting the air volume of the mine ventilation system step by step provided in the embodiment of the present application in combination with hardware thereof.
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, micro Controllers (MCUs), microprocessors (microprocessors), or other electronic elements for performing the aforementioned methods.
It will be appreciated that the memory 702 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a magnetic random access Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration 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), enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), synchronous Dynamic Random Access Memory (SLDRAM), direct Memory (DRmb Access), and Random Access Memory (DRAM). The memories described in the embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.
In an exemplary embodiment, the present application further provides a storage medium, that is, a computer storage medium, which may be specifically a computer readable storage medium, for example, including a memory 702 storing a computer program, where the computer program is executable by a processor 701 of an electronic device to perform the steps described in the method of the present application. The computer readable storage medium may be a ROM, PROM, EPROM, EEPROM, flash Memory, magnetic surface Memory, optical disk, or CD-ROM, among others.
It should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The technical means described in the embodiments of the present application may be arbitrarily combined without conflict.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for adjusting air quantity of a mine ventilation system step by step is characterized by comprising the following steps:
acquiring a ventilation network model of a mine ventilation system;
performing air volume distribution calculation on the ventilation network model based on a ventilation network calculation method, and adjusting the ventilation network model based on an air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition;
generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system;
determining an optimal air volume adjusting scheme based on the at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement;
the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and comprises the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the setting of the decision variables of the optimization model comprises: adjusting wind pressure values of all branches; the air volume adjusting scheme comprises the following steps: a provision for adjusting a ventilation structure, the ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
2. The method according to claim 1, wherein the calculating method based on the ventilation network performs air volume distribution calculation on the ventilation network model, 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 is matched with the actual working condition, and the method comprises the following steps:
performing air volume distribution calculation on the ventilation network model by adopting a ventilation network calculation method based on loop air volume to obtain an air volume distribution calculation result;
adjusting the tunnel wind resistance parameters based on a resistance measurement mode until the comparison error between the wind volume distribution calculation result and the actually measured tunnel wind volume is within a set threshold value;
obtaining the simulation working condition of a fan in the ventilation network model based on the air volume distribution calculation result;
and judging whether the simulated working condition of the fan is matched with the actual working condition, if not, adjusting the model parameters of the ventilation network model until the simulated working condition of the fan in the ventilation network model is matched with the actual working condition.
3. The method of claim 1, wherein the set optimization model is as follows:
wherein Z is an optimization objective, ω 1 Is a first weight coefficient, ω 2 Is a second weight coefficient, ω 3 Is a third weight coefficient, ω 4 Is a fourth weight coefficient, ω 5 Is a fifth weight coefficient, N is the number of branches of the ventilation network, q j The air quantity of the jth air-dividing branch according to needs, r j Wind resistance of the jth branch,. DELTA.h j Is the wind pressure regulation value of the jth branch, h N,j Natural wind pressure of j-th branch, n j,a Indicating whether an adjustment is required for the jth branch, s j Number of adjustment stages for jth branch, n j,c Indicating whether the jth branch needs to be subjected to energization regulation or resistance reduction regulation, n j,b Denotes whether the jth branch needs to be subjected to resistance increasing regulation, delta h' j Is Δ h j Absolute value of (a), b ij Denotes the relationship of the branch to the loop, h j Is algebraic sum of jth branch wind pressure, rho' j Indicates the amount of energization or de-energization adjustment, ρ ″, allowed to be ignored in the jth branch j Denotes the allowable negligible resistance increase adjustment of the jth branch,. DELTA.h j,min The lower limit of wind pressure, Δ h, can be adjusted for the jth branch j,max Adjustable upper limit of wind pressure for jth branch, N a For allowing adjustment of the number of branches, p, in a ventilation network j Indicating the adjustment amount, n, allowed to be ignored for the jth branch max,a Is the first normalAmount, n max,b Is the second normal amount, n max,c Is the third normal amount.
4. The method of claim 3, wherein generating at least one candidate air volume conditioning solution based on the downhole ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system comprises:
determining an air volume distribution result meeting the air distribution requirement according to the underground ventilation air volume requirement;
setting each weight coefficient and decision variable of the set optimization model based on the component distribution result;
and solving at least one air volume adjusting scheme to be selected by utilizing the set optimization model based on the set weight coefficient and the decision variable.
5. The method of claim 1, wherein determining an optimal air volume conditioning profile based on the at least one candidate air volume conditioning profile and the downhole ventilation air volume requirement comprises:
performing air volume distribution calculation on the at least one air volume adjusting scheme to be selected by adopting a ventilation network resolving method based on loop air volume to obtain air volume distribution calculation results corresponding to the air volume adjusting schemes;
and comparing the air volume distribution calculation result corresponding to each air volume adjusting scheme with the distributed air volume of the air distribution branch according to the requirement determined based on the underground ventilation air volume requirement, and determining the optimal air volume adjusting scheme.
6. The method of claim 1, further comprising:
adjusting the mine ventilation system based on the optimal air volume adjustment scheme.
7. The air quantity stepped adjusting device of the mine ventilation system is characterized by comprising:
the ventilation network model acquisition module is used for acquiring a ventilation network model of a mine ventilation system;
the ventilation network model optimization module is used for carrying out air volume distribution calculation on the ventilation network model based on a ventilation network calculation method and adjusting the ventilation network model based on an air volume distribution calculation result until the simulation working condition of a fan in the ventilation network model is matched with the actual working condition;
the air volume adjusting scheme generating module is used for generating at least one air volume adjusting scheme to be selected based on the underground ventilation air volume requirement and a set optimization model corresponding to the mine ventilation system;
the air volume adjusting scheme selecting module is used for determining an optimal air volume adjusting scheme based on the at least one air volume adjusting scheme to be selected and the underground ventilation air volume requirement;
the set optimization model is an air volume regulation and control step-by-step optimization model based on multi-objective mixed integer programming, and comprises the following optimization objectives: a minimum ventilation energy consumption target, an optimal adjustment position target, an optimal adjustment mode target and a minimum adjustment number target; the setting of the decision variables of the optimization model comprises: adjusting wind pressure values of all branches; the air volume adjusting scheme comprises the following steps: provision of a ventilation structure, said ventilation structure comprising at least one of: the air conditioner comprises an underground fan, an air door and an air window.
8. An electronic device, comprising: a processor and a memory for storing a computer program capable of running on the processor, wherein,
the processor, when executing the computer program, is adapted to perform the steps of the method of any of claims 1 to 6.
9. A storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the method of any one of claims 1 to 6.
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