Underground cavern air conditioning equipment layout method and device, equipment and storage medium

Abstract

The invention discloses a layout method, device and equipment of underground cavern air conditioning equipment and a storage medium, and relates to the technical field of underground caverns, and the method comprises the steps that the wind speed node positions and the wind speed node number of all wind speed adding nodes are iterated through global optimization, so that the wind speed node number reaches the minimum value; all wind speed node positions corresponding to the minimum value of the number of the wind speed nodes are obtained and are the layout positions of all the ventilation devices. According to the method, the natural wind flowing condition of the underground cavern is considered, the wind speed probability distribution of the underground cavern is used as one parameter for dynamic simulation, and then the ventilation scheme of completely covering the underground cavern is achieved by searching the optimal placement position and the minimum use number of the ventilation equipment through global optimization. Compared with the prior art, the method has the advantages that subjective experience errors of manual arrangement are avoided, and the optimal layout scheme can be realized without manual participation in the whole calculation process.

Description

Technical Field

[0001] The present application relates to the technical field of underground caverns, and in particular to a layout method, device, equipment and storage medium for underground cavern air-conditioning equipment. Background Art

[0002] Underground caverns refer to structures artificially excavated or naturally existing in underground rock and soil for various purposes. They are divided according to their uses: mine shafts (vertical shafts, inclined shafts, tunnels), transportation tunnels, hydraulic tunnels, underground factories (warehouses), underground military projects, etc.

[0003] Especially during the excavation and construction of underground caverns in water conservancy and hydropower projects, blasting produces a large amount of dust and harmful gases. Due to the poor ventilation performance of the underground caverns themselves, the above-mentioned harmful substances can have a long-term impact on the life and health of construction workers. Construction ventilation is a necessary measure to reduce the concentration of blasting smoke and dust and ensure construction safety.

[0004] At present, most ventilation plans for underground cavern construction rely on subjective engineering experience to arrange equipment, with fans set up redundantly and run at full power. Due to the subjective experience errors in the manual arrangement of equipment, it is easy to not only fail to effectively reduce the ventilation and smoke dispersion time, but also easily cause energy waste. Summary of the invention

[0005] The main purpose of this application is to provide a layout method, device, equipment and storage medium for underground cavern air-conditioning equipment, so as to solve the problem in the prior art that due to the subjective experience error of manual arrangement of equipment, it is easy to not only fail to effectively reduce the ventilation and smoke dispersion time, but also easily cause energy waste.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A layout method for underground cavern air conditioning equipment, the layout method is applied to a plurality of ventilation equipment to be arranged in the underground cavern, the layout method comprising:

[0008] Step S1, obtaining all the connecting openings between the underground cavern and the outside air;

[0009] Step S2, obtaining a digital surface model of the underground cavern, wherein the digital surface model includes all the connecting ports;

[0010] Step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located;

[0011] Step S4, inputting the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model;

[0012] Step S5, obtaining all minimum values ​​of the air flow rate in the initial flow model;

[0013] Step S6, respectively obtaining the maximum output wind speed of each ventilation device;

[0014] Step S7, inputting all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes;

[0015] Step S8, on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, iterate the wind speed node positions and the number of wind speed nodes of all wind speed added nodes in the initial fluid flow model through global optimization, so that the number of wind speed nodes reaches a minimum value;

[0016] Step S9, obtaining all wind speed node positions corresponding to the minimum number of wind speed nodes, which are layout positions of all ventilation equipment.

[0017] As a further improvement of the present application, step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located, includes:

[0018] Step S31, collecting wind speed data of several random points in the area where the underground cavern is located;

[0019] Step S32, defining a probability distribution function and a probability density function according to a two-parameter Weibull distribution, wherein both the probability distribution function and the probability density function include a scale parameter and a shape parameter;

[0020] Step S33, substituting all random point wind speed data as known quantities into the probability distribution function and the probability density function respectively;

[0021] Step S34, defining a log-likelihood function of the scale parameter and the shape parameter;

[0022] Step S35, solving the scale parameter and the shape parameter based on the log-likelihood function;

[0023] Step S36, substituting the solved scale parameter and the solved shape parameter into the probability distribution function and the probability density function respectively to obtain the wind speed probability distribution.

[0024] As a further improvement of the present application, step S8, on the premise that all rate minima are greater than or equal to a preset rate threshold, iterates the wind speed node positions and the number of wind speed nodes of all wind speed added nodes in the initial fluid flow model by global optimization, so that the number of wind speed nodes reaches the minimum value, including:

[0025] Step S81, taking the initial fluid flow model as the iteration range, and defining a number of random solutions based on each wind speed adding node;

[0026] Step S82, defining the optimization result of all random solutions as that all rate minimum values ​​are greater than or equal to a preset rate threshold and the number of wind speed nodes reaches a minimum value;

[0027] Step S83, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively;

[0028] Step S84, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update;

[0029] Step S85, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S86;

[0030] Step S86, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S87;

[0031] Step S87, determining that the optimal solution for all wind speed added nodes has been obtained.

[0032] As a further improvement of the present application, in step S9, all wind speed node positions corresponding to the minimum number of wind speed nodes are obtained as the layout positions of all ventilation equipment, and then, the following steps are included:

[0033] Step S10: sending the layout positions of all ventilation equipment to an external visualization terminal.

[0034] As a further improvement of the present application, the underground cavern also has a plurality of heating devices to be arranged. In step S9, all node positions corresponding to the minimum number of wind speed nodes are obtained as the layout positions of all ventilation devices. After that, the following steps are included:

[0035] Step S100, adjusting the ventilation equipment at each layout position to an on state to obtain an intermediate fluid flow model;

[0036] Step S200, obtaining all temperature minimum values ​​of the air temperature in the intermediate fluid flow model through the preset fluid dynamic simulation software;

[0037] Step S300, obtaining the maximum output power of each heating device respectively;

[0038] Step S400, inputting all maximum output powers into the preset fluid dynamic simulation software as heat source adding nodes;

[0039] Step S500, on the premise that all temperature minimum values ​​are greater than or equal to a preset temperature threshold, iterate the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model through global optimization, so that the number of heat source nodes reaches a minimum value;

[0040] Step S600, obtaining the positions of all heat source nodes corresponding to the minimum number of the heat source nodes, which are the layout positions of all heating equipment.

[0041] As a further improvement of the present application, step S500, on the premise that all temperature minimum values ​​are greater than or equal to a preset temperature threshold, iterates the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model by global optimization, so that the number of heat source nodes reaches a minimum value, including:

[0042] Step S5001, taking the intermediate fluid flow model as the iteration range, and defining a number of random solutions based on adding nodes for each heat source;

[0043] Step S5002, defining the optimization result of all random solutions as that all temperature minimum values ​​are greater than or equal to a preset temperature threshold and the number of heat source nodes reaches a minimum value;

[0044] Step S5003, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively;

[0045] Step S5004, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update;

[0046] Step S5005, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S5006;

[0047] Step S5006, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S5007;

[0048] Step S5007, determining whether the optimal solution for all heat source addition nodes has been obtained.

[0049] As a further improvement of the present application, in step S600, all heat source node positions corresponding to the minimum number of heat source nodes are obtained as layout positions of all heating equipment, and then, the following steps are included:

[0050] Step S1000: sending the layout positions of all heating equipment to an external visualization terminal.

[0051] To achieve the above objectives, this application also provides the following technical solutions:

[0052] A layout device for underground cavern air conditioning equipment, the layout device is applied to the above-mentioned layout method, the layout device comprises:

[0053] An underground cavern communication port acquisition module, used to acquire all communication ports between the underground cavern and the outside air;

[0054] A digital surface model acquisition module, used to acquire a digital surface model of the underground cavern, wherein the digital surface model includes all the connecting ports;

[0055] A wind speed probability distribution acquisition module, used to obtain the wind speed probability distribution of the area where the underground cavern is located;

[0056] A fluid flow rate dynamic simulation module, used for inputting the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model;

[0057] A flow rate minimum value acquisition module, used for acquiring all minimum values ​​of the air flow rate in the initial flow model;

[0058] A maximum output wind speed acquisition module is used to respectively acquire the maximum output wind speed of each ventilation device;

[0059] A maximum output wind speed input module, used to input all maximum output wind speeds into the preset fluid dynamic simulation software as a wind speed addition node;

[0060] A wind speed node adding iteration module is used to iterate the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, so that the number of wind speed nodes reaches a minimum value;

[0061] The ventilation equipment layout position acquisition module is used to acquire all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation equipment.

[0062] To achieve the above objectives, this application also provides the following technical solutions:

[0063] An electronic device comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the above-mentioned layout method is implemented.

[0064] To achieve the above objectives, this application also provides the following technical solutions:

[0065] A storage medium stores program instructions, and when the program instructions are executed by a processor, the above-mentioned layout method can be implemented.

[0066] The present application obtains all the connecting openings between the underground cavern and the outside air; obtains a digital surface model of the underground cavern, the digital surface model including all the connecting openings; obtains the wind speed probability distribution in the area where the underground cavern is located; inputs the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model; obtains all rate minima of the air flow rate in the initial flow model; obtains the maximum output wind speed of each ventilation device respectively; inputs all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes; on the premise that all rate minima are greater than or equal to a preset rate threshold, iterates the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization to minimize the number of wind speed nodes; obtains all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation devices. The present application takes into account the natural wind flow in the underground caverns, uses the probability distribution of wind speed in the underground caverns as one of the parameters for dynamic simulation, and then uses global optimization to find the optimal placement of ventilation equipment and the minimum number of ventilation equipment to achieve a ventilation solution that completely covers the underground caverns. Compared with the prior art, the present application avoids the subjective experience errors of manual arrangement, and the entire calculation process does not require human participation to achieve the optimal layout solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of the steps of an embodiment of the method for laying out the underground cavern air conditioning equipment of the present application;

[0068] Figure 2 This is a functional module diagram of an embodiment of a layout device for underground cavern air conditioning equipment of the present application;

[0069] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0070] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0072] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0073] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0074] like Figure 1 As shown, this embodiment provides an embodiment of a layout method for underground cavern air-conditioning equipment. In this embodiment, the layout method is applied to several ventilation equipment to be laid out in the underground cavern.

[0075] Preferably, an underground cavern refers to a structure artificially excavated or naturally existing in an underground rock mass for various purposes, and is divided into: mine shafts (vertical shafts, inclined shafts, tunnels), traffic tunnels, hydraulic tunnels, underground factories (warehouses), underground military projects, etc. An underground cavern generally requires at least two vents to ensure air circulation and humidity discharge. If the cavern area is large or there are many people, more vents may be required. It is usually recommended to have one vent at each high and low place to ensure ventilation.

[0076] Preferably, under normal circumstances, the basement needs to maintain at least two vents. If the basement space is large, the vents can be repeatedly opened to ensure air circulation and achieve good ventilation. Open a vent at each high and low point in the room to ensure ventilation.

[0077] Preferably, natural ventilation and mechanical ventilation are usually used to ventilate the underground cavern. The natural ventilation method uses vents and exhaust pipes to discharge the humid air inside the basement, while allowing fresh air to enter the basement to improve the air quality. The appropriate number of vents can be determined according to the size and height of the basement; the mechanical ventilation method uses exhaust fans and supply fans to discharge the air inside the basement through pipes, while allowing fresh air to enter the basement to improve the air quality.

[0078] Specifically, the layout method includes the following steps:

[0079] Step S1, obtaining all the connecting openings between the underground cavern and the outside air.

[0080] Step S2, obtaining a digital surface model of the underground cavern, wherein the digital surface model includes all connecting openings.

[0081] Preferably, since the digital surface model (DSM) of the underground cavern cannot be directly obtained through satellite remote sensing images, it is necessary to rely on other data sources, such as lidar scanning, drone oblique photography, ground laser scanning, etc. A lidar scanner can be used to scan the underground cavern to obtain point cloud data; or a drone equipped with an oblique photography camera can be used to photograph the underground cavern at a suitable angle and lighting conditions to obtain image data; ground laser scanning is also a commonly used method, which sets up a scanning station on the ground to perform a full-scale scan of the interior of the cavern.

[0082] Preferably, the collected point cloud data or image data is preprocessed, including denoising, registration, stitching and other steps to ensure the accuracy and integrity of the data. Professional software tools such as IMAGINE Photogrammetry, Inpho, Match-T, etc. can be used to perform aerial triangulation, DSM extraction and other processing processes on the preprocessed data. After extracting the original DSM, filtering may be required to eliminate noise and improve data accuracy.

[0083] Step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located.

[0084] Preferably, in order to obtain the probability distribution of wind speed in the underground cavern, it is necessary to conduct field measurement of wind speed. This usually involves setting up wind speed measuring equipment, such as an anemometer, in the underground cavern to record wind speed data at different times and locations. These data should cover wind speed information under multiple wind direction angles to ensure the comprehensiveness and accuracy of the data. Secondly, after collecting enough wind speed data, data analysis is required. This includes counting the frequency of wind speed occurrence in each wind speed interval, that is, the frequency distribution of wind speed. In this way, the probability of different wind speeds occurring in the underground cavern can be understood. In addition, local meteorological data, such as the joint probability distribution data of wind speed and wind direction, can be combined to further analyze and infer the probability distribution of wind speed in the underground cavern.

[0085] Step S4, inputting the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model.

[0086] Preferably, in order to realize the air flow dynamic simulation and the air heating dynamic simulation described below, the preset fluid dynamic simulation software of this embodiment can be set to one of ANSYS Fluent, CFX, COMSOL Multiphysics, and OpenFOAM.

[0087] Among them, ANSYS Fluent is a powerful commercial CFD software that is widely used in the simulation of various fluid dynamics problems, including air flow and air heating. It supports a variety of solvers and models, and can simulate complex phenomena such as turbulent flow, heat transfer, and chemical reactions. Fluent is suitable for aerospace, automotive, energy and other fields, and provides rich post-processing functions, which can visualize and analyze flow field data.

[0088] CFX is also a CFD software developed by ANSYS, which is suitable for complex multi-physics coupling problems. CFX uses the finite volume method for calculations. Although it has a large memory requirement, it is better than Fluent in terms of convergence speed. CFX can simulate a variety of physical processes, including fluid dynamics, heat transfer, chemical reactions, etc.

[0089] COMSOL Multiphysics is a commercial software for multi-physics coupling, which can simulate a variety of physical processes, including fluid-solid coupling, thermal fluid-solid coupling, etc. COMSOL is based on the finite element method and is suitable for complex models that require highly customized equations and boundary conditions. It is suitable for problems with multi-physics coupling effects, such as fluid-solid coupling, thermal fluid-solid coupling, etc.

[0090] OpenFOAM is an open source CFD software that is highly customizable and flexible. OpenFOAM is based on the finite volume method and supports parallel computing and custom programming. It is suitable for users who have a certain understanding of CFD software and programming skills, and is suitable for a variety of complex fluid flow and heat transfer problems.

[0091] Step S5, obtaining all minimum values ​​of the air flow rate in the initial flow model.

[0092] Step S6, respectively obtaining the maximum output wind speed of each ventilation device.

[0093] Preferably, the maximum output wind speed of different models of ventilation equipment can be directly obtained from the manufacturer or measured by oneself.

[0094] Step S7, input all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes.

[0095] Step S8, on the premise that all rate minima are greater than or equal to a preset rate threshold, the wind speed node positions and the number of wind speed nodes of all wind speed added nodes are iterated through global optimization in the initial fluid flow model to minimize the number of wind speed nodes.

[0096] Step S9, obtaining all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation equipment.

[0097] Further, step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located, specifically includes the following steps:

[0098] Step S31, collecting wind speed data at a number of random points in the area where the underground cavern is located.

[0099] Step S32, defining a probability distribution function and a probability density function according to the two-parameter Weibull distribution, wherein the probability distribution function and the probability density function both include a scale parameter and a shape parameter.

[0100] Preferably, the probability distribution function is as follows:

[0101]

[0102] Among them, F(V) is the probability distribution function, and the value of the probability distribution function is in the interval [0,1]; c is the scale parameter of the Weibull distribution; k is the shape parameter of the Weibull distribution; V is the wind speed data of the current random point.

[0103] Preferably, the probability density function is as follows:

[0104]

[0105] Where f(V) is the probability density function.

[0106] Step S33, substituting all random point wind speed data as known quantities into the probability distribution function and the probability density function respectively.

[0107] Step S34, defining the log-likelihood function of the scale parameter and the shape parameter.

[0108] Preferably, the log-likelihood function is as follows:

[0109]

[0110] Among them, L(k,c) is the log-likelihood function.

[0111] Step S35, solving the scale parameter and the shape parameter based on the log-likelihood function.

[0112] Preferably, this embodiment provides a process for solving a log-likelihood function:

[0113] set up: as well as but:

[0114]

[0115] Modify the above formula to get the matrix equation:

[0116]

[0117] The above matrix equation is iterated by Jacobi iteration method until the spectral radius ρ(G) of the matrix equation is less than 1, which means convergence.

[0118] After convergence, the scale parameter and shape parameter of the Weibull distribution can be obtained.

[0119] It should be noted that the formulas in the above additional content are for principle explanation only, and the symbolic meanings of the formulas are not interchangeable with other formulas.

[0120] Step S36, substituting the solved scale parameter and the solved shape parameter into the probability distribution function and the probability density function respectively to obtain the wind speed probability distribution.

[0121] Further, step S8, on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, the wind speed node positions and the number of wind speed nodes of all wind speed added nodes are iterated by global optimization in the initial fluid flow model so that the number of wind speed nodes reaches the minimum value, specifically comprising the following steps:

[0122] Step S81, taking the initial fluid flow model as the iteration range, and defining several random solutions based on each wind speed adding node.

[0123] Preferably, all random solutions can be defined according to the following formula:

[0124]

[0125] Among them, P i is the set of all random solutions, p 1 ,p 2 ,...,p i ,…,p N-1 ,p N are each random solution, i is the number of the random solution, and N is the number of all random solutions; V i is the set of velocities of all random solutions, v 1 ,v 2 ,...,v i ,…,v N-1 ,v N are the speeds of each random solution respectively.

[0126] Step S82, defining the optimization result of all random solutions as that all rate minimum values ​​are greater than or equal to a preset rate threshold and the number of wind speed nodes reaches a minimum value.

[0127] Step S83, initialize the position of each random solution, and update the current position and current speed of each random solution respectively.

[0128] Preferably, the current position and current speed can be updated respectively based on the same random solution according to the following formula:

[0129]

[0130] Among them, v id is the speed of the ith random solution at step d, ω·v id-1 is the velocity inertia of the ith random solution at step d-1, ω is the inertia coefficient, c 1 ·rand·(P best,i -p i ) is the self-perception representation of the i-th random solution, c 2 ·rand·(G best,i -p i ) is the social cognitive representation of the i-th random solution; c 1 With c 2 are all learning factors, rand is a random number in [0,1], P best,i is the individual optimal solution obtained by the i-th random solution, G best,i is the global optimal solution obtained by the i-th random solution, p id is the ith random solution at step d, p id-1 is the i-th random solution at step d-1.

[0131] Preferably, c 1 The value range is [0,0.5], preferably 0.4; c 2 The value range is [0.5,1], with 0.8 being the preferred value.

[0132] Step S84, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update.

[0133] Preferably, the inertia coefficient may be linearly decreased once for each update step according to the following formula:

[0134]

[0135] Among them, ω id is the inertia coefficient of the i-th random solution after optimization in the d-th step, ω ini is the initial inertia coefficient, Pace current is the current update step number, Pace max is the maximum number of update steps.

[0136] Preferably, the initial inertia coefficient is generally set to 0.5, and the maximum update step number is generally set according to actual needs, and in this embodiment can be set to 10,000 times.

[0137] Step S85, respectively determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold. If both are less than, execute step S86.

[0138] Step S86, respectively determine whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold. If both are less than, execute step S87.

[0139] Preferably, the values ​​of the first preset adaptation threshold and the second preset adaptation threshold need to be adjusted according to the specific problem, and generally need to be adjusted according to the calculation results. If the adaptation threshold is set too small, the algorithm may stop prematurely and fail to obtain the optimal solution; if the adaptation threshold is set too large, the algorithm may be over-updated, wasting computing resources.

[0140] Preferably, the adaptation threshold may also be evaluated by one of the Griewank function, the Rastrigin function, the Schaffer function, the Ackley function, and the Rosenbrock function.

[0141] It should be noted that the formulas in the above additional content are for principle explanation only, and the symbolic meanings of the formulas are not interchangeable with other formulas.

[0142] Step S87, determining that the optimal solution for all wind speed added nodes has been obtained.

[0143] Further, in step S9, all wind speed node positions corresponding to the minimum number of wind speed nodes are obtained, which are the layout positions of all ventilation equipment. After that, the following steps are also included:

[0144] Step S10: sending the layout positions of all ventilation equipment to an external visualization terminal.

[0145] Furthermore, there are several heating devices to be arranged in the underground cavern. In step S9, all node positions corresponding to the minimum number of wind speed nodes are obtained as the layout positions of all ventilation devices. After that, the following steps are also included:

[0146] Step S100, adjusting the ventilation equipment at each layout position to an open state to obtain an intermediate fluid flow model.

[0147] Preferably, the above software is designed based on the principle of computational fluid dynamics (CFD) and can simulate complex physical phenomena such as fluid flow and heat transfer. These software usually provide a wealth of physical models and boundary condition settings. When simulating the fluid flow and heat transfer process of blowers and heaters, a geometric model of the heater can be established in the software, and corresponding boundary conditions and physical parameters such as inlet velocity, temperature, pressure, etc. can be set. Then, the software will use the CFD algorithm to solve the model and calculate the flow of the fluid inside the heater and the temperature distribution and other results.

[0148] Step S200, obtaining all temperature minima of the air temperature in the intermediate fluid flow model through a preset fluid dynamic simulation software.

[0149] Step S300, obtaining the maximum output power of each heating device respectively.

[0150] Preferably, the maximum output power of the heating equipment can be directly queried or measured.

[0151] Step S400, input all maximum output powers into a preset fluid dynamic simulation software as a heat source adding node.

[0152] Step S500, on the premise that all temperature minima are greater than or equal to a preset temperature threshold, the heat source node positions and the number of heat source nodes of all heat source addition nodes are iterated through global optimization in the intermediate fluid flow model to minimize the number of heat source nodes.

[0153] Step S600, obtaining the positions of all heat source nodes corresponding to the minimum number of heat source nodes, which are the layout positions of all heating equipment.

[0154] Further, step S500, on the premise that all temperature minimum values ​​are greater than or equal to a preset temperature threshold, iterates the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model by global optimization, so that the number of heat source nodes reaches the minimum value, including:

[0155] Step S5001, taking the intermediate fluid flow model as the iteration scope, and defining several random solutions based on adding nodes for each heat source.

[0156] Step S5002, defining the optimization results of all random solutions as all temperature minima are greater than or equal to a preset temperature threshold and the number of heat source nodes reaches a minimum value.

[0157] Step S5003, initialize the position of each random solution, and update the current position and current speed of each random solution respectively.

[0158] Step S5004, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update.

[0159] Step S5005, respectively determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold. If both are less than, execute step S5006.

[0160] Step S5006, determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold. If both are less than, executing step S5007.

[0161] Step S5007, determining whether the optimal solution for all heat source addition nodes has been obtained.

[0162] Preferably, the optimization principle of step S5001 to step S5007 is the same as the optimization principle of the above-mentioned steps S81 to S87, and the formula principle is not repeated in this embodiment.

[0163] Further, in step S600, all heat source node positions corresponding to the minimum number of heat source nodes are obtained, that is, the layout positions of all heating equipment, and then, the following steps are included:

[0164] Step S1000: sending the layout positions of all heating equipment to an external visualization terminal.

[0165] This embodiment obtains all the connecting openings between the underground cavern and the outside air; obtains a digital surface model of the underground cavern, the digital surface model including all the connecting openings; obtains the wind speed probability distribution in the area where the underground cavern is located; inputs the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model; obtains all rate minima of the air flow rate in the initial flow model; obtains the maximum output wind speed of each ventilation device respectively; inputs all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes; on the premise that all rate minima are greater than or equal to a preset rate threshold, iterates the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization to minimize the number of wind speed nodes; obtains all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation devices. This embodiment takes into account the natural wind flow in the underground cavern, takes the wind speed probability distribution of the underground cavern as one of the parameters for dynamic simulation, and then uses global optimization to find the optimal placement position and the minimum number of ventilation equipment to achieve a ventilation plan that completely covers the underground cavern. Compared with the prior art, this embodiment avoids the subjective experience error of manual arrangement, and the entire calculation process does not require human participation to achieve the preferred layout plan.

[0166] like Figure 2 As shown, this embodiment provides an embodiment of a layout device for underground cavern air-conditioning equipment. In this embodiment, the layout device is applied to the layout method as in the above-mentioned embodiment.

[0167] Specifically, the layout device includes an underground cavern connection port acquisition module 1, a digital surface model acquisition module 2, a wind speed probability distribution acquisition module 3, a fluid flow rate dynamic simulation module 4, a flow rate minimum value acquisition module 5, a maximum output wind speed acquisition module 6, a maximum output wind speed input module 7, a wind speed adding node iteration module 8, and a ventilation equipment layout position acquisition module 9, which are electrically connected in sequence.

[0168] Among them, the underground cavern connection port acquisition module 1 is used to obtain all the connection ports between the underground cavern and the external air; the digital surface model acquisition module 2 is used to obtain the digital surface model of the underground cavern, and the digital surface model includes all the connection ports; the wind speed probability distribution acquisition module 3 is used to obtain the wind speed probability distribution of the area where the underground cavern is located; the fluid flow rate dynamic simulation module 4 is used to input the digital surface model and the wind speed probability distribution into the preset fluid dynamic simulation software for dynamic simulation to obtain the initial fluid flow model; the flow rate minimum value acquisition module 5 is used to obtain all the minimum values ​​of the air flow rate in the initial flow model; the maximum output wind speed acquisition module The acquisition module 6 is used to obtain the maximum output wind speed of each ventilation device respectively; the maximum output wind speed input module 7 is used to input all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes; the wind speed adding node iteration module 8 is used to iterate the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization on the premise that all rate minima are greater than or equal to the preset rate threshold, so that the number of wind speed nodes reaches the minimum value; the ventilation equipment layout position acquisition module 9 is used to obtain all wind speed node positions corresponding to the minimum number of wind speed nodes, which is the layout position of all ventilation equipment.

[0169] Furthermore, the wind speed probability distribution acquisition module 3 specifically includes a first wind speed probability distribution acquisition unit, a second wind speed probability distribution acquisition unit, a third wind speed probability distribution acquisition unit, a fourth wind speed probability distribution acquisition unit, a fifth wind speed probability distribution acquisition unit, and a sixth wind speed probability distribution acquisition unit, which are electrically connected in sequence; the first wind speed probability distribution acquisition unit is electrically connected to the digital surface model acquisition module 2, and the sixth wind speed probability distribution acquisition unit is electrically connected to the fluid flow rate dynamic simulation module 4.

[0170] Among them, the first wind speed probability distribution acquisition unit is used to collect wind speed data of several random points in the area where the underground cavern is located; the second wind speed probability distribution acquisition unit is used to define a probability distribution function and a probability density function according to the two-parameter Weibull distribution, and the probability distribution function and the probability density function both include a scale parameter and a shape parameter; the third wind speed probability distribution acquisition unit is used to substitute all random point wind speed data as known quantities into the probability distribution function and the probability density function respectively; the fourth wind speed probability distribution acquisition unit is used to define the log-likelihood function of the scale parameter and the shape parameter; the fifth wind speed probability distribution acquisition unit is used to solve the scale parameter and the shape parameter based on the log-likelihood function; the sixth wind speed probability distribution acquisition unit is used to substitute the solved scale parameter and the solved shape parameter into the probability distribution function and the probability density function respectively to obtain the wind speed probability distribution.

[0171] Furthermore, the wind speed adding node iteration module 8 specifically includes a first wind speed adding node iteration unit, a second wind speed adding node iteration unit, a third wind speed adding node iteration unit, a fourth wind speed adding node iteration unit, a fifth wind speed adding node iteration unit, a sixth wind speed adding node iteration unit, and a seventh wind speed adding node iteration unit, which are electrically connected in sequence; the connected first wind speed adding node iteration unit is electrically connected to the maximum output wind speed input module 7, and the seventh wind speed adding node iteration unit is electrically connected to the ventilation equipment layout position acquisition module 9.

[0172] Among them, the first wind speed adding node iteration unit is used to define several random solutions based on each wind speed adding node with the initial fluid flow model as the iteration range; the second wind speed adding node iteration unit is used to define the optimization result of all random solutions as all rate minima are greater than or equal to the preset rate threshold and the number of wind speed nodes reaches the minimum value; the third wind speed adding node iteration unit is used to initialize the position of each random solution, and update the current position and current speed of each random solution respectively; the fourth wind speed adding node iteration unit is used to obtain the individual optimal solution and the global optimal solution of each random solution based on each update respectively; the fifth wind speed adding node iteration unit is used to respectively determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to the first preset adaptation threshold; the sixth wind speed adding node iteration unit is used to respectively determine whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to the second preset adaptation threshold if both are less than; the seventh wind speed adding node iteration unit is used to determine that the optimal solutions of all wind speed adding nodes have been obtained if both are less than.

[0173] Furthermore, the layout device also includes a ventilation equipment layout position sending module electrically connected to the ventilation equipment layout position acquisition module 9, and the module is used to send the layout positions of all ventilation equipment to an external visualization terminal.

[0174] Furthermore, the layout device also includes an intermediate fluid flow model acquisition module, a fluid temperature minimum acquisition module, a heating equipment maximum output power acquisition module, a maximum output power input module, a heat source adding node iteration module, and a heating equipment layout position acquisition module, which are electrically connected in sequence; the intermediate fluid flow model acquisition module is electrically connected to the ventilation equipment layout position acquisition module 9.

[0175] Among them, the intermediate fluid flow model acquisition module is used to adjust the ventilation equipment at each layout position to the open state to obtain the intermediate fluid flow model; the fluid temperature minimum acquisition module is used to obtain all temperature minimum values ​​of the air temperature in the intermediate fluid flow model through the preset fluid dynamic simulation software; the heating equipment maximum output power acquisition module is used to obtain the maximum output power of each heating equipment respectively; the maximum output power input module is used to input all maximum output powers into the preset fluid dynamic simulation software as a heat source addition node; the heat source addition node iteration module is used to iterate the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model through global optimization on the premise that all temperature minima are greater than or equal to the preset temperature threshold, so as to minimize the number of heat source nodes; the heating equipment layout position acquisition module is used to obtain all heat source node positions corresponding to the minimum number of heat source nodes, which is the layout position of all heating equipment.

[0176] Furthermore, the heat source adding node iteration module specifically includes a first heat source adding node iteration unit, a second heat source adding node iteration unit, a third heat source adding node iteration unit, a fourth heat source adding node iteration unit, a fifth heat source adding node iteration unit, a sixth heat source adding node iteration unit, and a seventh heat source adding node iteration unit, which are electrically connected in sequence; the first heat source adding node iteration unit is electrically connected to the maximum output power input module, and the seventh heat source adding node iteration unit is electrically connected to the heating equipment layout position acquisition module.

[0177] Among them, the first heat source adding node iteration unit is used to define several random solutions based on each heat source adding node with the intermediate fluid flow model as the iteration range; the second heat source adding node iteration unit is used to define the optimization result of all random solutions as all temperature minima are greater than or equal to the preset temperature threshold and the number of heat source nodes reaches the minimum value; the third heat source adding node iteration unit is used to initialize the position of each random solution, and update the current position and current speed of each random solution respectively; the fourth heat source adding node iteration unit is used to obtain the individual optimal solution and the global optimal solution of each random solution based on each update respectively; the fifth heat source adding node iteration unit is used to respectively determine whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to the first preset adaptation threshold; the sixth heat source adding node iteration unit is used to respectively determine whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to the second preset adaptation threshold if both are less than; the seventh heat source adding node iteration unit is used to determine that the optimal solutions of all heat source adding nodes have been obtained if both are less than.

[0178] Furthermore, the layout device also includes a heating equipment layout position sending module electrically connected to the heating equipment layout position acquisition module, and the module is used to send the layout positions of all heating equipment to an external visualization terminal.

[0179] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. The optimization, expansion, limitation, example, and principle description of this embodiment can be referred to the above embodiment, and this embodiment will not be repeated.

[0180] This embodiment obtains all the connecting openings between the underground cavern and the outside air; obtains a digital surface model of the underground cavern, the digital surface model including all the connecting openings; obtains the wind speed probability distribution in the area where the underground cavern is located; inputs the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model; obtains all rate minima of the air flow rate in the initial flow model; obtains the maximum output wind speed of each ventilation device respectively; inputs all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes; on the premise that all rate minima are greater than or equal to a preset rate threshold, iterates the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization to minimize the number of wind speed nodes; obtains all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation devices. This embodiment takes into account the natural wind flow in the underground cavern, takes the wind speed probability distribution of the underground cavern as one of the parameters for dynamic simulation, and then uses global optimization to find the optimal placement position and the minimum number of ventilation equipment to achieve a ventilation plan that completely covers the underground cavern. Compared with the prior art, this embodiment avoids the subjective experience error of manual arrangement, and the entire calculation process does not require human participation to achieve the preferred layout plan.

[0181] like Figure 3 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0182] The memory 102 stores program instructions for implementing the layout method of the underground cavern air conditioning equipment of any of the above-mentioned embodiments.

[0183] The processor 101 is used to execute the program instructions stored in the memory 102 to arrange the underground cavern air conditioning equipment.

[0184] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having signal processing capabilities. The processor 101 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0185] Further, Figure 4 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0186] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0187] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0188] The specific implementation methods of the present application are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A layout method for underground cavern air conditioning equipment, the layout method is applied to a plurality of ventilation equipment to be arranged in an underground cavern, characterized in that: The layout method comprises: Step S1, obtaining all the connecting openings between the underground cavern and the outside air; Step S2, obtaining a digital surface model of the underground cavern, wherein the digital surface model includes all the connecting ports; Step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located; Step S4, inputting the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model; Step S5, obtaining all minimum values ​​of the air flow rate in the initial flow model; Step S6, respectively obtaining the maximum output wind speed of each ventilation device; Step S7, inputting all maximum output wind speeds into the preset fluid dynamic simulation software as wind speed adding nodes; Step S8, on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, iterate the wind speed node positions and the number of wind speed nodes of all wind speed added nodes in the initial fluid flow model through global optimization, so that the number of wind speed nodes reaches a minimum value; Step S9, obtaining all wind speed node positions corresponding to the minimum number of wind speed nodes, which are layout positions of all ventilation equipment.

2. The layout method according to claim 1, characterized in that: Step S3, obtaining the wind speed probability distribution in the area where the underground cavern is located, including: Step S31, collecting wind speed data of several random points in the area where the underground cavern is located; Step S32, defining a probability distribution function and a probability density function according to a two-parameter Weibull distribution, wherein both the probability distribution function and the probability density function include a scale parameter and a shape parameter; Step S33, substituting all random point wind speed data as known quantities into the probability distribution function and the probability density function respectively; Step S34, defining a log-likelihood function of the scale parameter and the shape parameter; Step S35, solving the scale parameter and the shape parameter based on the log-likelihood function; Step S36, substituting the solved scale parameter and the solved shape parameter into the probability distribution function and the probability density function respectively to obtain the wind speed probability distribution.

3. The layout method according to claim 1, characterized in that: Step S8, on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, iterate the wind speed node positions and the number of wind speed nodes of all wind speed added nodes in the initial fluid flow model through global optimization, so that the number of wind speed nodes reaches a minimum value, including: Step S81, taking the initial fluid flow model as the iteration range, and defining a number of random solutions based on each wind speed adding node; Step S82, defining the optimization result of all random solutions as that all rate minimum values ​​are greater than or equal to a preset rate threshold and the number of wind speed nodes reaches a minimum value; Step S83, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively; Step S84, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update; Step S85, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S86; Step S86, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S87; Step S87, determining that the optimal solution for all wind speed added nodes has been obtained.

4. The layout method according to claim 1, characterized in that: Step S9, obtaining all wind speed node positions corresponding to the minimum number of wind speed nodes, that is, the layout positions of all ventilation equipment, and then comprising: Step S10: sending the layout positions of all ventilation equipment to an external visualization terminal.

5. According to the layout method of claim 1, the underground cavern also has a plurality of heating equipment to be arranged, characterized in that: Step S9, obtaining all node positions corresponding to the minimum number of wind speed nodes, that is, the layout positions of all ventilation equipment, and then comprising: Step S100, adjusting the ventilation equipment at each layout position to an on state to obtain an intermediate fluid flow model; Step S200, obtaining all temperature minimum values ​​of the air temperature in the intermediate fluid flow model through the preset fluid dynamic simulation software; Step S300, obtaining the maximum output power of each heating device respectively; Step S400, inputting all maximum output powers into the preset fluid dynamic simulation software as heat source adding nodes; Step S500, on the premise that all temperature minimum values ​​are greater than or equal to a preset temperature threshold, iterate the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model through global optimization, so that the number of heat source nodes reaches a minimum value; Step S600, obtaining the positions of all heat source nodes corresponding to the minimum number of the heat source nodes, which are the layout positions of all heating equipment.

6. The layout method according to claim 5, characterized in that: Step S500, on the premise that all temperature minimum values ​​are greater than or equal to a preset temperature threshold, iterate the heat source node positions and the number of heat source nodes of all heat source addition nodes in the intermediate fluid flow model by global optimization, so that the number of heat source nodes reaches a minimum value, including: Step S5001, taking the intermediate fluid flow model as the iteration range, and defining a number of random solutions based on adding nodes for each heat source; Step S5002, defining the optimization result of all random solutions as that all temperature minimum values ​​are greater than or equal to a preset temperature threshold and the number of heat source nodes reaches a minimum value; Step S5003, initializing the position of each random solution, and updating the current position and current speed of each random solution respectively; Step S5004, obtaining the individual optimal solution and the global optimal solution of each random solution based on each update; Step S5005, respectively determining whether the difference between each individual optimal solution and each individual optimal solution updated last time is less than or equal to a first preset adaptation threshold, if both are less than, executing step S5006; Step S5006, respectively determining whether the difference between each global optimal solution and each global optimal solution updated last time is less than or equal to a second preset adaptation threshold, if both are less than, executing step S5007; Step S5007, determining whether the optimal solution for all heat source addition nodes has been obtained.

7. The layout method according to claim 5, characterized in that: Step S600, obtaining all heat source node positions corresponding to the minimum number of heat source nodes, that is, the layout positions of all heating equipment, and then including: Step S1000: sending the layout positions of all heating equipment to an external visualization terminal.

8. A layout device for underground cavern air conditioning equipment, the layout device being applied to the layout method according to any one of claims 1 to 7, characterized in that: The layout device comprises: An underground cavern communication port acquisition module, used to acquire all communication ports between the underground cavern and the outside air; A digital surface model acquisition module, used to acquire a digital surface model of the underground cavern, wherein the digital surface model includes all the connecting ports; A wind speed probability distribution acquisition module, used to obtain the wind speed probability distribution of the area where the underground cavern is located; A fluid flow rate dynamic simulation module, used for inputting the digital surface model and the wind speed probability distribution into a preset fluid dynamic simulation software for dynamic simulation to obtain an initial fluid flow model; A flow rate minimum value acquisition module, used for acquiring all minimum values ​​of the air flow rate in the initial flow model; A maximum output wind speed acquisition module is used to respectively acquire the maximum output wind speed of each ventilation device; A maximum output wind speed input module, used to input all maximum output wind speeds into the preset fluid dynamic simulation software as a wind speed addition node; A wind speed node adding iteration module is used to iterate the wind speed node positions and the number of wind speed nodes of all wind speed adding nodes in the initial fluid flow model through global optimization on the premise that all rate minimum values ​​are greater than or equal to a preset rate threshold, so that the number of wind speed nodes reaches a minimum value; The ventilation equipment layout position acquisition module is used to acquire all wind speed node positions corresponding to the minimum number of wind speed nodes, which are the layout positions of all ventilation equipment.

9. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the layout method as described in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by a processor, the layout method according to any one of claims 1 to 7 can be implemented.

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