Mine airflow intelligent on-demand regulation and control method and device, electronic equipment and storage medium

Abstract

The invention provides a mine air flow intelligent on-demand regulation and control method and device, electronic equipment and a storage medium, and the method comprises the steps that an on-demand air control mathematical model is obtained, the model takes the air resistance of each branch of a mine as a decision variable, the conformity of the actual air volume of each branch and the needed air volume as a constraint condition, and the minimum difference between the target branch air volume and the ideal air volume as a target; obtaining related information of a ventilation network of a target mine area of which the air flow needs to be regulated and controlled and a weight coefficient value of each branch; based on the related information of the ventilation network and the weight coefficient value of each branch, solving the optimal solution of the on-demand wind control mathematical model through a search algorithm; and obtaining the target air volume and the target air resistance of each branch in the ventilation network of the target mine area based on the optimal solution, thereby obtaining an air window adjusting parameter, and adjusting the air window according to the air window adjusting parameter to realize air flow regulation and control. The air volume can be adjusted only by adjusting the roadway air window, and the target branch can accurately reach the expected air volume.

Description

Technical Field

[0001] The present application relates to the technical field of mine airflow control, and in particular to a method, device, electronic equipment and storage medium for intelligent on-demand control of mine airflow. Background Art

[0002] In the prior art, there are two main algorithms for controlling airflow in mines: (1) With the goal of minimizing the power consumption of the ventilator, a mathematical model is established, and then a meta-heuristic algorithm is used to solve it, in order to reduce the power consumption of the ventilator and achieve the goal of saving energy. (2) A tunnel that needs to increase airflow is defined as a target branch, and a mathematical model is established with the target branch air volume being maximized as the optimization goal. These mathematical models are solved by a meta-heuristic algorithm to obtain a control plan that maximizes the target branch air volume.

[0003] All of the above solutions require adjustment of the main ventilation fan in the mine, which is inconsistent with the actual situation of daily air adjustment in the mine. In the daily air adjustment of the mine, the air volume is generally adjusted by adjusting the area of ​​the wind window. At present, when modeling the mine airflow control algorithm, the penalty factor is used to convert the constraint equation into the penalty term of the objective function, and the mathematical model of air volume control is converted into an unconstrained optimization model. The penalty function method is used for solution. However, after the penalty function is introduced, the dimension of the original optimization problem will increase, which may lead to an increase in computational complexity. In addition, it will take a lot of time to solve the penalty factor. When the penalty factor is very large, the Hessen matrix condition number of the solution function may be too large and tend to be pathological. And because the goal is to maximize the air volume of the target branch, it cannot ensure that the target branch air volume reaches the ideal value. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of this application is to propose a method for intelligent on-demand control of mine airflow, so as to achieve air volume regulation by adjusting the tunnel wind windows, thereby solving the problem that the control scheme in the relevant technology is inconsistent with the actual situation of daily air regulation in the mine.

[0006] The second objective of the present application is to provide an intelligent on-demand control device for mine airflow.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for intelligent on-demand control of mine airflow, comprising:

[0011] Obtaining an on-demand wind control mathematical model, the on-demand wind control mathematical model comprising an objective function and constraint conditions, the objective function taking the wind resistance of each branch of the mine as a decision variable, the objective function taking the minimum difference between the wind volume of the target branch and the ideal wind volume value as a goal, the constraint condition comprising the wind volume of each branch being consistent with the required wind volume; the objective function comprising the weight of each branch, the weight being used to characterize the emphasis of each branch in the wind control decision;

[0012] Obtaining relevant information of the ventilation network of the target mine area where airflow needs to be regulated, the relevant information of the ventilation network including the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch;

[0013] Based on the weight corresponding to each branch type and the branch type of each branch, obtaining a weight coefficient value of each branch;

[0014] Based on the relevant information of the ventilation network and the weight coefficient values ​​of each branch, an optimal solution of the on-demand air control mathematical model is solved by a search algorithm;

[0015] Based on the optimal solution, the target air volume and target wind resistance of each branch in the ventilation network of the target mine area are obtained, and based on the target air volume and target wind resistance, the wind window adjustment parameters are obtained so that the wind windows can be adjusted according to the wind window adjustment parameters to achieve airflow control.

[0016] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a mine airflow intelligent on-demand control device, comprising:

[0017] A model determination module is used to obtain an on-demand wind control mathematical model, wherein the on-demand wind control mathematical model includes an objective function and constraint conditions, wherein the objective function takes the wind resistance of each branch of the mine as a decision variable, and the objective function takes the minimum difference between the wind volume of the target branch and the ideal wind volume value as a goal, and the objective function includes the weight of each branch; the constraint condition includes that the wind volume of each branch is consistent with the required wind volume; and the weight is used to characterize the emphasis of each branch in the wind control decision;

[0018] A data acquisition module is used to obtain relevant information of the ventilation network of the target mine area where airflow needs to be regulated, wherein the relevant information of the ventilation network includes the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch;

[0019] A weight acquisition module, used for acquiring a weight coefficient value of each branch based on the weight corresponding to each branch type and the branch type of each branch;

[0020] An optimization solution module, used for solving the optimal solution of the on-demand air control mathematical model through a search algorithm based on the relevant information of the ventilation network and the weight coefficient value of each branch;

[0021] The wind flow control module is used to obtain the target air volume and target wind resistance of each branch in the ventilation network of the target mine area based on the optimal solution, and to obtain the wind window adjustment parameters based on the target air volume and target wind resistance, so as to adjust the wind window according to the wind window adjustment parameters to achieve wind flow control.

[0022] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.

[0023] To achieve the above-mentioned purpose, the fourth aspect of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in the first aspect.

[0024] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0025] The intelligent on-demand control method, device, electronic device and storage medium of mine airflow provided in the present application construct an on-demand air control mathematical model, with the wind resistance of each branch of the mine as the decision variable, the actual air volume of each branch being consistent with the required air volume as the constraint condition, and the goal being to minimize the difference between the target branch air volume and the ideal air volume; the optimal solution of the on-demand air control mathematical model is solved by a search algorithm; thereby, the wind window adjustment parameters are obtained, and it is realized that the air volume can be adjusted only by adjusting the tunnel wind windows, so that the target branch can accurately reach the expected air volume; at the same time, compared with the existing technology, the meta-heuristic algorithm can be used to solve the problem without the need to solve the penalty factor, so the calculation amount is reduced and the solution efficiency is high.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A schematic diagram of a flow chart of a method for intelligent on-demand control of mine airflow provided in an embodiment of the present application;

[0029] Figure 2 A ventilation network diagram of the first panel area of ​​a mine provided as an example for this application;

[0030] Figure 3 A three-dimensional model diagram of the on-demand wind control mathematical model provided for the example of this application;

[0031] Figure 4 Convergence curves of various search algorithms provided for examples of this application;

[0032] Figure 5 A block diagram of an intelligent on-demand control device for mine airflow provided in an embodiment of the present application;

[0033] Figure 6 A block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following describes the method, device and equipment for intelligent on-demand control of mine airflow in embodiments of the present application with reference to the accompanying drawings.

[0036] Figure 1 A schematic flow chart of a method for intelligent on-demand control of mine airflow provided in an embodiment of the present application.

[0037] It should be noted that the executor of the method for intelligent on-demand control of mine airflow in the embodiment of the present application is the intelligent on-demand control device for mine airflow in the embodiment of the present application, and the intelligent on-demand control device for mine airflow can be configured in an electronic device so that the electronic device can perform the intelligent on-demand control function of mine airflow.

[0038] like Figure 1 As shown, the mine airflow intelligent on-demand control method includes the following steps:

[0039] Step 101, obtaining an on-demand wind control mathematical model, which includes an objective function and constraints. The objective function uses the wind resistance of each branch of the mine as a decision variable, and the objective function uses the minimum difference between the air volume of the target branch and the ideal air volume value as a goal. The constraints include that the air volume of each branch is consistent with the required air volume; the objective function includes the weight of each branch, and the weight is used to characterize the emphasis of each branch in the wind control decision.

[0040] It should be noted that, considering safety and economy, it is very difficult to control the air volume by changing the ventilation network structure of the mine. In the daily air volume control of the mine, the method of adjusting or installing wind windows is usually used to control the air volume by changing the wind resistance of the tunnel. Although this method will increase the wind resistance of some tunnels, the maximum resistance route of the mine is generally unchanged, and the total wind resistance of the mine will not change much. The air volume control can be completed without adjusting the main ventilation fan of the mine. Therefore, the on-demand wind control problem is transformed into an on-demand wind control optimization problem. After analysis, based on the classification of optimization problems, the on-demand wind control optimization problem can be summarized as a non-differentiable optimization problem with the wind resistance of each tunnel (branch) as the decision variable, the three laws of ventilation as the theoretical basis, the actual air volume of each branch and the required air volume as the constraint condition, and the air volume of the target branch reaching the ideal air volume as the goal. A mathematical model of the on-demand wind control optimization problem is constructed, and the model is solved to achieve on-demand wind control.

[0041] As an implementation method, a method for obtaining an on-demand wind control mathematical model includes:

[0042] Based on the functional relationship between the air volume of the target branch in the ventilation network under a certain set state and the wind resistance of each branch and the limit value of the air volume of the target branch, the preliminary objective function is determined; and according to the value range of the wind resistance and air volume of each branch, the preliminary constraint conditions are determined;

[0043] Based on Lagrangian relaxation theory, some constraints are moved to the preliminary objective function to obtain the relaxed objective function.

[0044] Based on the conflict number theory, the simplified objective function is obtained by simplifying the relaxed objective function; the conflict number theory includes recording the conflict number of the branch whose air volume exceeds the air volume range as 1, and recording the conflict number of the branch whose air volume does not exceed the air volume range as 0; when the conflict number of all branches is 0, it means that the air volume of each branch in the ventilation network is within its specified range, and the wind resistance corresponding to each branch is the optimal on-demand air control decision;

[0045] The weight of each branch is introduced into the simplified objective function to obtain the objective function, thereby obtaining the on-demand wind control mathematical model.

[0046] For example, assuming that the air volume of the j-th branch in a certain state varies with the wind resistance of each branch R = (r1, r2, ..., rn ) is as follows:

[0047] q j =f j (R) (1)

[0048] Where: q j is the air volume of the jth branch, m 3 / s; R=(r1,r2,…,r n ) is the wind resistance of each branch, N·s 2 / m 8 .

[0049] Then the objective function of the optimization problem can be expressed as:

[0050]

[0051] Where: t is the target branch number; b t ,a t are the upper and lower limits of the target branch air volume respectively.

[0052] The mathematical model of the optimization problem can be preliminarily established as follows:

[0053]

[0054] Where: n is the number of branches of the ventilation network; b s , a s are the upper and lower limits of the air volume of each branch except the target branch; l s (R) is -f s (R); c s -a s ; m is the number of nodes in the ventilation network; G is the set of wind resistance variation ranges of each branch; h Nj is the natural wind pressure;

[0055]

[0056] Next, we will optimize the mathematical model of the optimization problem that has been initially established.

[0057] When solving optimization problems, relaxation methods are often used to relax constraints to obtain a good approximate solution, such as linear relaxation, dual relaxation, Lagrangian relaxation, SDP relaxation, etc. Since the third and fourth constraints in equation (4) can be solved by the ventilation network solution method, and the last constraint on the value of the variable is a continuous variable, the last three constraints are regarded as "simple" constraints, and the first two constraints are regarded as "difficult" constraints. Using the idea of ​​Lagrangian relaxation to move the first two constraints to the objective function, the Lagrangian relaxation problem of equation (4) can be expressed as:

[0058]

[0059] Where: s and μ s is the Lagrange multiplier

[0060] On the basis of the above optimization, this application is inspired by the classic constrained optimization problem - the million queens problem, and introduces the concept of conflict number: if the air volume of a branch is not within the specified air volume range, the conflict number of this type of situation is recorded as 1, otherwise the conflict number is recorded as 0, then the conflict number function can be expressed as:

[0061]

[0062] It can be seen from the objective function of formula (5) that it describes the functional relationship between the air volume of each branch and the difference between its upper and lower limits (ranges) under different values. Therefore, the objective function in formula (5) can be simplified to:

[0063]

[0064] Further considering that the most important goal of the on-demand wind control decision is to ensure that the target branch reaches the ideal air volume, and because the functions of each tunnel in the mine are different, the strictness of the air volume range requirements is also different. If the conflict number theory is simply used for mathematical modeling, it cannot reflect the emphasis of the wind control decision. Therefore, this application divides all the tunnels in the mine into three categories: target branches, wind branches, and general branches. The target branch is the tunnel that needs to make its branch air volume reach the predetermined value through the on-demand wind control decision; the wind branch is the tunnel with strict air volume requirements such as the coal mining face, the excavation face, and the independent ventilation chamber; the general branch is the tunnel that does not require air volume, such as the pedestrian tunnel, the vehicle tunnel, and the auxiliary transport tunnel.

[0065] That is to say, branch types include target branches, air-using branches and general branches, among which target branches represent lanes that need to make on-demand wind control decisions to make their branch air volume reach the ideal air volume value; air-using branches represent lanes with strict air volume requirements; and general branches represent lanes with relatively loose air volume requirements compared to air-using branches.

[0066] In order to make the descent direction of the objective function change more rapidly in the target branch and the wind branch dimension, the weight coefficient p is introduced for different types of branches according to the size of their feasible wind volume range. j , in order to speed up the solution of the objective function. Therefore, the objective function of formula (7) can be improved as follows:

[0067]

[0068] According to equations (5), (6) and (8), the mathematical model of wind control on demand can be improved as follows:

[0069]

[0070] Since the functional relationship in equation (1) is hypothetical, there is currently no equation that can accurately describe the air volume q of each branch. j The relationship between the wind resistance R of the ventilation network is as follows:

[0071]

[0072] Therefore, formula (10) shows the final determined mathematical model of on-demand wind control.

[0073] Step 102, obtain relevant information of the ventilation network of the target mine area where airflow needs to be regulated, the relevant information of the ventilation network includes the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch.

[0074] Example 1: The ventilation network diagram of the first plate area of ​​a mine is as follows: Figure 2 As shown, the initial data of the ventilation network is shown in Table 1.

[0075] Table 1: Initial data of the ventilation network in the first plate area of ​​a mine

[0076]

[0077]

[0078] Step 103: Obtain a weight coefficient value of each branch based on the weight corresponding to each branch type and the branch type of each branch.

[0079] The corresponding weight is obtained according to the branch type of each branch, and the weight is dynamically adjustable.

[0080] Step 104, based on the relevant information of the ventilation network and the weight coefficient value of each branch, the optimal solution of the on-demand air control mathematical model is solved by a search algorithm.

[0081] As an implementation method, based on the relevant information of the ventilation network and the weight coefficient values ​​of each branch, the optimal solution of the on-demand air control mathematical model is solved by a search algorithm; including:

[0082] Iterate the following steps until the optimal value of the objective function reaches the target value;

[0083] The ventilation network solution method is used to solve the air volume value corresponding to the wind resistance variable of each branch;

[0084] Consider the air volume value as a variable and solve the objective function;

[0085] According to the changing trend of the objective function, the search direction and search step of the wind volume value are solved; the search direction and search step of the wind resistance variable are solved based on the changing trend of the wind volume value by using a random method.

[0086] It should be noted that the search method is a common means of dealing with optimization problems, that is, first find an initial feasible solution on the feasible domain of the problem, then determine the search direction and search step length according to the function information of the problem to find the next feasible solution, and so on, until the optimal solution is found. Therefore, the solution to this optimization problem can be divided into the following four steps:

[0087] 1) Use the ventilation network solution method to solve the unknown quantity Q corresponding to the variable R.

[0088] 2) Consider Q as a variable and solve the objective function F.

[0089] 3) Analyze the changing trend of the objective function F, solve the search direction and search step of the wind volume Q, and finally solve the search direction and search step of the wind resistance R based on the changing trend of the wind volume Q.

[0090] 4) Return to step 1) until the optimal value 0 of the objective function is found.

[0091] Since the functional relationship in equation (1) is hypothetical, there is currently no equation that can accurately describe the air volume q of each branch. j The relationship between the wind resistance R of the ventilation network is such that, in step 3), the search direction and search step length of the wind resistance R are solved by using the change trend of the air volume Q. However, it is difficult to discuss the properties of the function, such as the concavity and convexity, derivative information, constraint specifications of the KKT conditions, and effective sets. Therefore, this scheme uses a meta-heuristic algorithm to determine the search direction and search step length in a random way to solve the above problem.

[0092] Furthermore, the three-dimensional image of the on-demand wind control mathematical model is as follows: Figure 3 As shown, it is concluded that the model has the characteristics of multiple peaks, so the improved artificial bee colony algorithm (NSABC) is used to solve the on-demand wind control mathematical model.

[0093] In some embodiments, when selecting a suitable search algorithm method, it also includes:

[0094] Obtain a three-dimensional image of the on-demand wind control mathematical model;

[0095] Based on the three-dimensional image, the image characteristics of the on-demand wind control mathematical model are obtained;

[0096] Select the target search algorithm based on the image characteristics of the on-demand wind control mathematical model;

[0097] Solve the mathematical model of on-demand wind control based on target search algorithm and other search algorithms;

[0098] The performance of the target search algorithm is verified based on the solution results of the target search algorithm and other search algorithms.

[0099] For example, while the improved artificial bee colony algorithm is used to solve the model, the other two algorithms are used to solve the model multiple times, so as to verify the efficiency and feasibility of the NSABC algorithm in dealing with the problem of on-demand wind distribution. The convergence effect diagram of the three methods is shown in Figure 4 Among them, the ABC / best / 1 method sometimes cannot converge to 0, that is, the optimal solution cannot be obtained, so the embodiment of the present application adopts an improved artificial bee colony algorithm to solve the model.

[0100] Step 105, based on the optimal solution, obtain the target air volume and target wind resistance of each branch in the ventilation network of the target mine area, and based on the target air volume and target wind resistance, obtain the wind window adjustment parameters, so as to adjust the wind window according to the wind window adjustment parameters to achieve wind flow control.

[0101] It can be understood that there is a fixed relationship between the air volume and wind resistance of each branch in the ventilation network. First, the adjustment range of the wind resistance is determined according to the actual problem, and the wind resistance data of each branch is randomly generated within the adjustment range; the air volume data of each branch is obtained by solving the on-demand wind control mathematical model; when the air volume data meets the requirements, it is considered that the wind resistance set that generates this group of air volume data is the optimal solution to the on-demand wind control problem; thus, the wind window adjustment parameters are obtained according to the wind resistance, and the alley wind windows are adjusted according to the wind window adjustment parameters, thereby realizing air volume regulation.

[0102] Taking Example 1 as an example, the data after solving the on-demand wind control algorithm is shown in Table 2.

[0103] Table 2: Data of the ventilation network in the first plate area of ​​a mine after solving the on-demand air control algorithm

[0104]

[0105] It can be seen from Table 2 that after adjustment, the air volume of each branch is within the required air volume range, and the air volume error of the target branch (the 10th branch) is ±0.5m 3 / s.

[0106] This solution can enable the target branch to accurately reach the desired air volume with an error range of ±0.5m 3 / s, the air volume of the target branch is truly regulated on demand, while ensuring that the air volume of the remaining branches meets the required air volume range. There is no need to adjust the fan, and the air volume can be adjusted by adjusting the alleyway window.

[0107] The intelligent on-demand control method for mine airflow in the embodiment of the present application constructs an on-demand air control mathematical model, with the wind resistance of each branch of the mine as the decision variable, the actual air volume of each branch being consistent with the required air volume as the constraint condition, and the goal being to minimize the difference between the target branch air volume and the ideal air volume; the optimal solution of the on-demand air control mathematical model is solved by a search algorithm; thereby, the wind window adjustment parameters are obtained, and it is realized that the air volume can be adjusted only by adjusting the tunnel wind windows, and the target branch can accurately reach the expected air volume; at the same time, compared with the existing technology, the feasible domain is accurately characterized by combining the Scott-Hinsley method, and the meta-heuristic algorithm can be used for solution without the need to solve the penalty factor, thereby reducing the amount of calculation and improving the solution efficiency.

[0108] In order to implement the above-mentioned embodiments, the present application also proposes an intelligent on-demand control device for mine airflow. Figure 5 This is a schematic diagram of the structure of a mine wind flow intelligent on-demand control device provided in an embodiment of the present application. Figure 5 As shown, the intelligent on-demand control device for mine airflow may include: a model determination module 501 , a data acquisition module 502 , a weight acquisition module 503 , an optimization solution module 504 and an airflow control module 505 .

[0109] Among them, the model determination module 501 is used to obtain the mathematical model of wind control on demand. The mathematical model of wind control on demand includes an objective function and constraint conditions. The objective function takes the wind resistance of each branch of the mine as a decision variable, and takes the gap between the wind volume of the target branch and the ideal wind volume value as the minimum. The constraint condition includes that the wind volume of each branch is consistent with the required wind volume; the objective function includes the weight of each branch, and the weight is used to characterize the emphasis of each branch in the wind control decision;

[0110] The data acquisition module 502 is used to obtain relevant information of the ventilation network of the target mine area where the airflow needs to be regulated, and the relevant information of the ventilation network includes the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch;

[0111] A weight acquisition module 503, used to acquire a weight coefficient value of each branch based on the weight corresponding to each branch type and the branch type of each branch;

[0112] The optimization solution module 504 is used to solve the optimal solution of the on-demand air control mathematical model through a search algorithm based on the relevant information of the ventilation network and the weight coefficient value of each branch;

[0113] The wind flow control module 505 is used to obtain the target air volume and target wind resistance of each branch in the ventilation network of the target mine area based on the optimal solution, and obtain the wind window adjustment parameters based on the target air volume and target wind resistance, so as to adjust the wind window according to the wind window adjustment parameters to achieve wind flow control.

[0114] Furthermore, in a possible implementation of the embodiment of the present application, the optimization solution module 504 is specifically used to:

[0115] Iterate the following steps until the optimal value of the objective function reaches the target value;

[0116] The ventilation network solution method is used to solve the air volume value corresponding to the wind resistance variable of each branch;

[0117] Consider the air volume value as a variable and solve the objective function;

[0118] According to the changing trend of the objective function, the search direction and search step of the wind volume value are solved; the search direction and search step of the wind resistance variable are solved based on the changing trend of the wind volume value by using a random method.

[0119] Furthermore, in a possible implementation of the embodiment of the present application, the optimization solution module 504 is further configured to:

[0120] Obtain a three-dimensional image of the on-demand wind control mathematical model;

[0121] Based on the three-dimensional image, the image characteristics of the on-demand wind control mathematical model are obtained;

[0122] Select the target search algorithm based on the image characteristics of the on-demand wind control mathematical model;

[0123] Solve the mathematical model of on-demand wind control based on target search algorithm and other search algorithms;

[0124] The performance of the target search algorithm is verified based on the solution results of the target search algorithm and other search algorithms.

[0125] Furthermore, in a possible implementation of the embodiment of the present application, the model determination module 501 is specifically configured to:

[0126] Based on the functional relationship between the air volume of the target branch in the ventilation network under a certain set state and the wind resistance of each branch and the limit value of the air volume of the target branch, the preliminary objective function is determined; and according to the value range of the wind resistance and air volume of each branch, the preliminary constraint conditions are determined;

[0127] Based on Lagrangian relaxation theory, some constraints are moved to the preliminary objective function to obtain the relaxed objective function.

[0128] Based on the conflict number theory, the simplified objective function is obtained by simplifying the relaxed objective function; the conflict number theory includes recording the conflict number of the branch whose air volume exceeds the air volume range as 1, and recording the conflict number of the branch whose air volume does not exceed the air volume range as 0; when the conflict number of all branches is 0, it means that the air volume of each branch in the ventilation network is within its specified range, and the wind resistance corresponding to each branch is the optimal on-demand air control decision;

[0129] The weight of each branch is introduced into the simplified objective function to obtain the objective function, thereby obtaining the on-demand wind control mathematical model.

[0130] Furthermore, in a possible implementation of an embodiment of the present application, branch types include target branches, wind-using branches and general branches, wherein target branches represent lanes whose branch air volume needs to reach an ideal air volume value through on-demand wind control decisions; wind-using branches represent lanes with strict air volume requirements; and general branches represent lanes with relatively loose air volume requirements compared to wind-using branches.

[0131] Furthermore, in a possible implementation of the embodiment of the present application, the on-demand wind control mathematical model is expressed as follows:

[0132]

[0133]

[0134] Where R = (r1, r2, ..., r n ) is the set of resistances of each branch, n is the number of branches, m is the number of nodes, p j is the weight coefficient, q j is the air volume of the jth branch; b j ,a j are the upper and lower limits of the air volume of each branch, h Nj is the natural wind pressure, and G is the set of wind resistance variation ranges of each branch.

[0135] It should be noted that the aforementioned explanation of the embodiment of the method for intelligent on-demand control of mine airflow is also applicable to the device for intelligent on-demand control of mine airflow of this embodiment, and will not be repeated here.

[0136] In order to implement the above embodiment, the present application also proposes an electronic device. Figure 6 , Figure 6 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device 600 includes: a processor 601, and a memory 602 connected to the processor 601; the memory 602 stores computer-executable instructions; the processor 601 executes the computer-executable instructions stored in the memory to implement the method provided in the above embodiment.

[0137] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0138] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0139] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0140] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0141] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0142] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0143] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for intelligent on-demand control of mine airflow, characterized in that: The following steps are involved: Obtaining an on-demand wind control mathematical model, the on-demand wind control mathematical model comprising an objective function and constraint conditions, the objective function taking the wind resistance of each branch of the mine as a decision variable, the objective function taking the minimum difference between the wind volume of the target branch and the ideal wind volume value as a goal, the constraint condition comprising the wind volume of each branch being consistent with the required wind volume; the objective function comprising the weight of each branch, the weight being used to characterize the emphasis of each branch in the wind control decision; Obtaining relevant information of the ventilation network of the target mine area where airflow needs to be regulated, the relevant information of the ventilation network including the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch; Based on the weight corresponding to each branch type and the branch type of each branch, obtaining a weight coefficient value of each branch; Based on the relevant information of the ventilation network and the weight coefficient values ​​of each branch, an optimal solution of the on-demand air control mathematical model is solved by a search algorithm; Based on the optimal solution, the target air volume and target wind resistance of each branch in the ventilation network of the target mine area are obtained, and based on the target air volume and target wind resistance, the wind window adjustment parameters are obtained so that the wind windows can be adjusted according to the wind window adjustment parameters to achieve airflow control.

2. The method according to claim 1, characterized in that The method of solving the optimal solution of the on-demand air control mathematical model by a search algorithm based on the relevant information of the ventilation network and the weight coefficient values ​​of each branch includes: Iteratively perform the following steps until the optimal value of the objective function reaches the target value; The ventilation network solution method is used to solve the air volume value corresponding to the wind resistance variable of each branch; The air volume value is regarded as a variable to solve the objective function; According to the changing trend of the objective function, the search direction and the search step of the wind volume value are solved; and a random method is used to solve the search direction and the search step of the wind resistance variable based on the changing trend of the wind volume value.

3. The method according to claim 1, characterized in that The method further comprises: Acquiring a three-dimensional image of the on-demand wind control mathematical model; Based on the three-dimensional image, obtaining image characteristics of the on-demand wind control mathematical model; Selecting a target search algorithm based on the image characteristics of the on-demand wind control mathematical model; Solving the on-demand wind control mathematical model based on the target search algorithm and other search algorithms; The performance of the target search algorithm is verified based on the solution results of the target search algorithm and the other search algorithms.

4. The method according to claim 1, characterized in that: The method for obtaining the on-demand wind control mathematical model includes: Based on the functional relationship between the air volume of the target branch in the ventilation network under a certain set state and the wind resistance of each branch and the limit value of the air volume of the target branch, the preliminary objective function is determined; and according to the value range of the wind resistance and air volume of each branch, the preliminary constraint conditions are determined; Based on Lagrangian relaxation theory, part of the constraint conditions are moved to the preliminary objective function to obtain a relaxed objective function; Based on the conflict number theory, the relaxed objective function is simplified to obtain a simplified objective function; the conflict number theory includes recording the conflict number of the branch whose air volume exceeds the air volume range as 1, and recording the conflict number of the branch whose air volume does not exceed the air volume range as 0. When the conflict number of all branches is 0, it means that the air volume of each branch in the ventilation network is within its specified range. At this time, the wind resistance corresponding to each branch is the optimal on-demand wind control decision; The weight of each branch is introduced into the simplified objective function to obtain the objective function, thereby obtaining an on-demand wind control mathematical model.

5. The method according to claim 1, characterized in that The branch types include target branches, air-using branches and general branches, wherein the target branches represent lanes that need to achieve ideal air volume values ​​through on-demand air control decisions; the air-using branches represent lanes with strict air volume requirements; and the general branches represent lanes with looser air volume requirements than the air-using branches.

6. The method according to claim 1, characterized in that The on-demand wind control mathematical model is expressed as follows: Where R = (r1, r2, ..., r n ) is the set of resistances of each branch, n is the number of branches, m is the number of nodes, p j is the weight coefficient, q j is the air volume of the jth branch; b j ,a j are the upper and lower limits of the air volume of each branch, h Nj is the natural wind pressure, and G is the set of wind resistance variation ranges of each branch.

7. An intelligent on-demand control device for mine airflow, characterized in that: include: A model determination module is used to obtain an on-demand wind control mathematical model, wherein the on-demand wind control mathematical model includes an objective function and constraints, wherein the objective function uses the wind resistance of each branch of the mine as a decision variable, and the objective function uses the minimum difference between the wind volume of the target branch and the ideal wind volume value as a goal, and the constraints include that the wind volume of each branch is consistent with the required wind volume; the objective function includes the weight of each branch, and the weight is used to characterize the emphasis of each branch in the wind control decision; A data acquisition module is used to obtain relevant information of the ventilation network of the target mine area where airflow needs to be regulated, wherein the relevant information of the ventilation network includes the number of branches and nodes of the ventilation network, the wind resistance variation range of each branch, the branch type of each branch, the target branch and the ideal air volume value of the target branch; A weight acquisition module, used for acquiring a weight coefficient value of each branch based on the weight corresponding to each branch type and the branch type of each branch; An optimization solution module, used for solving the optimal solution of the on-demand air control mathematical model through a search algorithm based on the relevant information of the ventilation network and the weight coefficient value of each branch; The wind flow control module is used to obtain the target air volume and target wind resistance of each branch in the ventilation network of the target mine area based on the optimal solution, and to obtain the wind window adjustment parameters based on the target air volume and target wind resistance, so as to adjust the wind window according to the wind window adjustment parameters to achieve wind flow control.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

Images