Building interior environment analysis method, device, equipment and medium

Abstract

The application relates to the technical field of building environment analysis, and discloses a building internal environment analysis method, device, equipment and medium, which comprises the following steps: constructing a three-dimensional geometric model of a target building interior; determining temperature-related data, speed-related data and material-related data of the target building interior; determining temperature boundary conditions according to the temperature-related data, determining speed boundary conditions according to the speed-related data, and determining material boundary conditions according to the material-related data; inputting the temperature boundary conditions, the speed boundary conditions and the material boundary conditions into a fluid simulation solver; and the fluid simulation solver couplingly calculates a preset iteration number of a temperature transport equation, a scalar transport equation of speed and a scalar transport equation of material to obtain temperature distribution data, airflow speed and material distribution data of the target building. The distribution data is real-time flow, and the application is more applicable.

Description

Technical Field

[0001] This invention relates to the field of building environment analysis technology, and in particular to a method, apparatus, equipment and medium for analyzing the internal environment of a building. Background Technology

[0002] Indoor environments mainly include residences, office buildings, and other similar spaces. Urban residents spend approximately 90% of their day working, studying, and living indoors. Therefore, indoor environmental comfort and quality are closely related to health. Factors influencing comfort include physical and personal factors. Physical factors include mean radiant temperature, air temperature, air velocity, and ambient humidity; personal factors include metabolism and individual psychological expectations. Visualizing mathematical models of people's subjective satisfaction with their surrounding thermal environment helps architects more intuitively see whether design parameters meet thermal comfort requirements. This helps architects research how to maximize resource conservation and provide healthy, comfortable, efficient, and harmonious green buildings that coexist with nature. Summary of the Invention

[0003] This invention provides a method, apparatus, computer equipment, and medium for analyzing the internal environment of a building.

[0004] Firstly, a method for analyzing the internal environment of a building is provided, including:

[0005] Construct a three-dimensional geometric model of the interior of the target building;

[0006] Determine the temperature-related data, velocity-related data, and material-related data inside the target building;

[0007] The temperature boundary conditions of the three-dimensional geometric model are determined based on the temperature-related data, the velocity boundary conditions of the three-dimensional geometric model are determined based on the velocity-related data, and the material boundary conditions of the three-dimensional geometric model are determined based on the material-related data.

[0008] The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the fluid simulation solver. The fluid simulation solver is then used to perform a coupled solution of the scalar transport equations for temperature, velocity, and material for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building.

[0009] In some embodiments, the coupled solution of the scalar transport equations for temperature, velocity, and matter using the fluid simulation solver for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and matter distribution data of the target building includes:

[0010] The three-dimensional geometric model is meshed to obtain the computational mesh of the three-dimensional geometric model;

[0011] Using the OpenFOAM model, based on the computational grid and the temperature, velocity, and mass boundary conditions, the scalar transport equations for temperature, velocity, and mass are solved in a coupled manner with a preset number of iterations to obtain the temperature distribution data, airflow velocity, and mass distribution data of the target building.

[0012] The scalar transport equation for velocity is as follows:

[0013]

[0014] In the formula, t is a calculation step time;

[0015] x1 is the x-coordinate of the calculated coordinate;

[0016] x2 is the ordinate of the calculated coordinate;

[0017] u is the velocity variable to be solved in the x1 direction;

[0018] v is the velocity variable to be solved in the x2 direction;

[0019] h1 is the matrix coefficient in the x1 direction;

[0020] h2 is the matrix coefficient in the x2 direction;

[0021] P is the real-time air pressure (Pa);

[0022] ρ is the air reference density;

[0023] σ 11 , is the air stress in the x1 direction;

[0024] σ 21 , is the air stress in the x2 direction in the x1 direction;

[0025] σ 12 , is the air stress in the x1 direction in the x2 direction;

[0026] σ 22 , is the air stress in the x2 direction;

[0027] g is the acceleration due to gravity;

[0028] β is the isothermal compressibility coefficient of the gas;

[0029] T is the temperature variable calculated at the previous time step;

[0030] T ref , is a reference temperature;

[0031] The scalar transport equation for temperature is as follows:

[0032]

[0033] In the formula, t is a calculation step time;

[0034] x1 is the x-coordinate of the calculated coordinate;

[0035] x2 is the ordinate of the calculated coordinate;

[0036] u is the velocity calculated in the x1 direction by the previous calculation step;

[0037] v is the velocity calculated in the x2 direction by the previous calculation step;

[0038] h1 is the matrix coefficient in the x1 direction;

[0039] h2 is the matrix coefficient in the x2 direction;

[0040] P is the real-time air pressure;

[0041] ρ is the air reference density;

[0042] σ 11 , is the air stress in the x1 direction;

[0043] σ 21 , is the air stress in the x2 direction in the x1 direction;

[0044] σ 12 , is the air stress in the x1 direction in the x2 direction;

[0045] σ 22 , is the air stress in the x2 direction;

[0046] g is the acceleration due to gravity;

[0047] β is the isothermal compressibility coefficient of the gas;

[0048] T is the temperature variable to be solved;

[0049] T ref , is a reference temperature;

[0050] The scalar transport equation for the substance is as follows:

[0051]

[0052] In the formula, t is a calculation step time;

[0053] ρ is the air reference density;

[0054] C is the concentration of the substance to be determined;

[0055] h1 is the matrix coefficient in the x1 direction;

[0056] h2 is the matrix coefficient in the x2 direction;

[0057] x1 is the x-coordinate of the calculated coordinate;

[0058] x2 is the ordinate of the calculated coordinate;

[0059] u is the velocity calculated in the x1 direction by the previous calculation step;

[0060] v is the velocity calculated in the x2 direction by the previous calculation step;

[0061] k is the thermal conductivity of air;

[0062] T is the temperature output at the previous calculation step;

[0063] T ref , is the reference temperature.

[0064] In some embodiments, the method further includes one or a combination of the following steps:

[0065] The step of determining the preset number of iterations;

[0066] The step of determining the grid density of the computational grid;

[0067] The steps for determining the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building;

[0068] The step of determining the preset number of iterations includes:

[0069] Based on the preset initial density, the three-dimensional geometric model is meshed to obtain the initial computational mesh inside the building;

[0070] The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the OpenFOAM model. The OpenFOAM model iteratively solves the scalar transport equations for temperature, velocity, and material based on the initial computational grid and the temperature, velocity, and material boundary conditions until the solution meets a first preset condition. A reference iteration number is obtained, and it is determined that the preset iteration number is greater than the reference iteration number. The first preset condition includes that the residual of the solution result is less than a preset residual value.

[0071] The step of determining the grid density of the computational grid includes:

[0072] Calculate the wall shear stress of the initial computational mesh;

[0073] The initial computational mesh density is reduced according to a preset reduction step size, and the influence of the mesh change rate on the wall shear stress is calculated after each mesh density reduction, until the influence value meets the second preset condition, and the mesh after the mesh density reduction is determined as the computational mesh.

[0074] The step of determining the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building includes:

[0075] The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the OpenFOAM model. The OpenFOAM model performs a coupled solution of the scalar transport equations for temperature, velocity, and material for a preset number of iterations to obtain the temperature calculation data, airflow velocity calculation data, and / or material calculation data of the target building at the preset test points.

[0076] The calculated and measured data of the preset test points are compared for error, and the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building is determined based on the comparison results.

[0077] In some embodiments, determining the temperature-related data inside the target building includes one or a combination of the following:

[0078] The geographic information data of the target building is obtained, the illumination data of the target building is determined based on the geographic information data, and the solar thermal gain and thermal energy distribution of the curtain wall of the target building are determined based on the illumination data.

[0079] Obtain the product parameters of the heat energy consumption products related to the internal temperature of the target building, and determine the heat dissipation data of the heat energy products based on the product parameters;

[0080] Collect temperature data from preset temperature collection points inside the building;

[0081] Based on the population distribution data inside the target building, determine the population thermal distribution data inside the building.

[0082] In some embodiments, determining the speed-related data inside the target building includes:

[0083] The wind direction and wind speed are collected at preset speed collection points. The layout data of the wind power equipment related to gas flow inside the building is determined based on the three-dimensional geometric model, and the speed collection points are determined based on the layout data.

[0084] In some embodiments, the substance is carbon dioxide in the air, and determining the substance-related data inside the target building includes:

[0085] Carbon dioxide concentration data at preset material collection points are collected using a carbon dioxide sensor.

[0086] In some embodiments, determining the temperature boundary conditions of the three-dimensional geometric model based on the temperature-related data, determining the velocity boundary conditions of the three-dimensional geometric model based on the velocity-related data, and determining the material boundary conditions of the three-dimensional geometric model based on the material-related data include:

[0087] Based on the three-dimensional geometric model, construct the position coordinates inside the target building;

[0088] The temperature-related data is processed to obtain temperature-related data corresponding to the preset location coordinates;

[0089] The velocity-related data is processed to obtain velocity-related data corresponding to the preset position coordinates;

[0090] The material-related data is processed to obtain the material-related data corresponding to the preset location coordinates.

[0091] Secondly, a building interior environment analysis device is provided, comprising:

[0092] The modeling module is used to construct a three-dimensional geometric model of the interior of the target building;

[0093] The data processing module is used to determine temperature-related data, velocity-related data, and material-related data inside the target building;

[0094] A boundary condition module is used to determine the temperature boundary conditions of the three-dimensional geometric model based on the temperature-related data, the velocity boundary conditions of the three-dimensional geometric model based on the velocity-related data, and the material boundary conditions of the three-dimensional geometric model based on the material-related data.

[0095] The result output module is used to input the temperature boundary conditions, the velocity boundary conditions, and the material boundary conditions into the fluid simulation solver. The fluid simulation solver is used to perform a preset number of iterations of coupled solution on the scalar transport equations for temperature, velocity, and material to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building.

[0096] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described building interior environment analysis method.

[0097] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described building interior environment analysis method.

[0098] The aforementioned method, apparatus, computer equipment, and storage medium for analyzing the internal environment of a building first construct a three-dimensional geometric model of the target building's interior. Second, it determines temperature-related, velocity-related, and material-related data within the target building. Third, it determines the temperature boundary conditions of the three-dimensional geometric model based on the temperature-related data, the velocity boundary conditions based on the velocity-related data, and the material boundary conditions based on the material-related data. Finally, it inputs the temperature, velocity, and material boundary conditions into a fluid simulation solver, which performs a pre-defined number of iterations to solve the scalar transport equations for temperature, velocity, and material, thereby obtaining the temperature distribution data, airflow velocity, and material distribution data of the target building. This invention constructs and solves the scalar transport equations for each environmental scalar, providing real-time, flowing distribution data of these environmental scalars within the target building. An interactive visualization interface for predictive data and the construction of building comfort-related information is also provided. Intuitive feedback predicts factors related to building space comfort, making it easier for architects and managers to control the building's air conditioning and heating systems, facilitating the development of energy-saving solutions, and enhancing applicability. Attached Figure Description

[0099] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, they can obtain [further details] based on these drawings without creative effort.

[0100] Figure 1 This is a flowchart illustrating a method for analyzing the internal environment of a building according to an embodiment of the present invention;

[0101] Figure 2 This is a flowchart illustrating the determination of the reference iteration number in a building interior environment analysis method according to an embodiment of the present invention;

[0102] Figure 3 This is a flowchart illustrating the process of determining the density of the computational grid in a method for analyzing the interior environment of a building according to an embodiment of the present invention.

[0103] Figure 4 This is a flowchart illustrating the process of determining temperature-related data collection points in a building interior environment analysis method according to an embodiment of the present invention.

[0104] Figure 5 This is a schematic diagram of a building interior environment analysis device according to an embodiment of the present invention;

[0105] Figure 6 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0106] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0107] Explanation of technical terms:

[0108] Building Information Modeling (BIM) is a digital electronic model that contains all information about a facility. It is a virtual representation of the facility, a knowledge resource for sharing facility information, and provides a reliable basis for decision-making.

[0109] Boundary conditions refer to the variation of the variables or their derivatives at the boundaries of the solution domain with time and location. Boundary conditions are a prerequisite for the governing equations to have definite solutions; they are necessary for any problem. The handling of boundary conditions directly affects the accuracy of the calculation results. Solving differential equations requires the introduction of conditions; these additional conditions are called boundary condition conditions.

[0110] Coupled computation, or the coupled solution of multiple equations, refers to a process where two or more independent equations require each other to provide partial information during the iterative solution process. For example, equation A has the equation f(x) = 0, and equation B has the equation g(y) = 0. Neither equation can be solved independently because equation A needs information about x from equation B, while equation B needs information about y from equation A. Therefore, data exchange is necessary during the iterative solution process.

[0111] This invention discloses a data processing-based method for monitoring and predicting building environmental comfort. It can be applied to evaluate the building comfort environment in multiple areas of a typical office building. Based on the building's energy system, structure, and environmental characteristics, key factors influencing the building are selected as input parameters for constructing a building comfort model. Energy data of different types of energy are acquired through pre-defined data interfaces, categorized, and statistically analyzed to enable configurable energy data acquisition. Using energy and mass transport models and steady-state computational fluid dynamics simulation, the accuracy of predictions is improved. The method outputs relevant factors for building environmental comfort (airflow velocity, temperature distribution, carbon dioxide concentration). Combined with the dependent variable, an optimized model is selected, and the optimized model is used to predict building environmental comfort. An interactive interface for visualizing building comfort is constructed based on the output parameters and the building's structure and environmental characteristics. The method also utilizes a visual interactive interface for building energy consumption information to visualize, optimize, and predict environmental maintenance of building comfort. To improve the accuracy of temperature simulation, the building's geometry, orientation, materials, and location are extracted using a BIM model, providing geometric and material information for computational fluid dynamics simulation. Solar energy analysis based on climate and weather is conducted using a GIS geographic information system to estimate solar thermal gain and the distribution of heat energy on the curtain wall.

[0112] Please see Figure 1 As shown, Figure 1 A flowchart illustrating the building interior environment analysis method provided in this embodiment of the invention includes the following steps:

[0113] Step 101: Construct a three-dimensional geometric model of the interior of the target building;

[0114] In some implementations, creating a three-dimensional geometric model of the interior space of the target building can include:

[0115] Obtain the floor plan of the target building; input the floor plan into the Building Information Model (BIM) software tool to obtain the three-dimensional geometric model of the target building.

[0116] Step 102: Determine the temperature-related data, velocity-related data, and material-related data inside the target building.

[0117] In some implementations, the temperature-related data determined in step 102 above for the interior of the target building may include one or a combination of the following:

[0118] Step 102a: Obtain geographic information data of the target building, determine the illumination data of the target building based on the geographic information data, and determine the solar thermal gain and thermal energy distribution of the curtain wall of the target building based on the illumination data.

[0119] In some application scenarios, geographic information data of building locations, including location, building height, surrounding obstructions, and lighting information, are extracted from GIS geographic information systems and urban building planning.

[0120] Step 102b: Obtain the product parameters of the heat energy consumption products related to the internal temperature of the target building, and determine the heat dissipation data of the heat energy products based on the product parameters;

[0121] In some application scenarios, data such as the number of light sources, power, material, and beam angle of the heat dissipation unit are obtained based on the product specifications (mainly lighting fixtures).

[0122] Step 102c: Collect temperature data from preset temperature collection points inside the building;

[0123] In some application scenarios, based on the temperature measurement equipment of the wireless temperature sensor, the wireless temperature sensor transmits the temperature signal to the wireless aggregation terminal wirelessly through electronic sensing technology and short-range wireless communication technology. The wireless aggregation terminal can receive data from multiple wireless sensors and forward all temperature data to the computer data terminal through wired communication.

[0124] Step 102d: Determine the thermal distribution data of the population inside the target building based on the population distribution data inside the building.

[0125] The population point data extraction process involves collecting population point data within a designated space using an infrared sensing detector. The data acquisition module collects data from the current entrance and exit points, and a microcontroller processes simple addition and subtraction operations. The real-time data is then transmitted to the data terminal via a wireless transmission unit using a wireless network or the Internet.

[0126] In some implementations, determining the speed-related data inside the target building in step 102 above may include:

[0127] The wind direction and wind speed are collected at preset speed collection points. The layout data of the wind power equipment related to gas flow inside the building is determined based on the three-dimensional geometric model, and the speed collection points are determined based on the layout data.

[0128] In some application scenarios, by analyzing the distribution of heating, ventilation, and air conditioning (HVAC), small ultrasonic wind speed and direction sensors are installed at fixed points. The wind speed sensor array uses the transmitted sound wave pulses to measure the phase difference at the receiving end to calculate the wind speed and direction. The wind speed of the supply air, return air, and exhaust air diffusers in the accessible area is measured, and the wind speed signal is sensed and transmitted to the computer data terminal.

[0129] In some implementations, the substance is carbon dioxide from the air. Step 102 above, which determines the substance-related data inside the target building, may include:

[0130] Carbon dioxide concentration data at preset material collection points are collected using a carbon dioxide sensor.

[0131] In some application scenarios, carbon dioxide concentration data collection and carbon dioxide concentration sensing steps are carried out by multiple carbon dioxide concentration sensor arrays sensing and transmitting carbon dioxide concentration signals to a computer data terminal.

[0132] Step 103: Determine the temperature boundary conditions of the three-dimensional geometric model based on temperature-related data, determine the velocity boundary conditions of the three-dimensional geometric model based on velocity-related data, and determine the material boundary conditions of the three-dimensional geometric model based on material-related data.

[0133] In some implementations, step 103 above may include:

[0134] Step 1031: Based on the three-dimensional geometric model, construct the position coordinates inside the target building;

[0135] Step 1032 performs data processing on the temperature-related data to obtain the temperature-related data corresponding to the preset location coordinates;

[0136] Step 1033 performs data processing on the velocity-related data to obtain the velocity-related data corresponding to the preset position coordinates;

[0137] Step 1034 processes the material-related data to obtain the material-related data corresponding to the preset location coordinates.

[0138] It is understandable that the location coordinates here refer to the division of the space inside the target building to obtain the coordinates of each location point.

[0139] Step 104: Input the temperature boundary conditions, velocity boundary conditions, and material boundary conditions into the fluid simulation solver. Use the fluid simulation solver to perform a coupled solution of the scalar transport equations for temperature, velocity, and material for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building.

[0140] In some implementations, step 104 above may include:

[0141] Step 1041: Mesh the 3D geometric model to obtain the computational mesh for the building's interior;

[0142] Step 1042: Input the temperature boundary conditions, velocity boundary conditions, and material boundary conditions into the OpenFOAM model. Using the OpenFOAM model, based on the computational grid and the temperature, velocity, and material boundary conditions, perform a coupled solution of the scalar transport equations for temperature, velocity, and material with a preset number of iterations to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building.

[0143] Considering temperature, airflow velocity, and the specific form of the scalar transport equation for matter, it is as follows:

[0144] (1) The scalar transport equation (momentum equation) for velocity is:

[0145]

[0146] In the formula, t is a calculation step time;

[0147] x1 is the x-coordinate of the calculated coordinate;

[0148] x2 is the ordinate of the calculated coordinate;

[0149] u is the velocity variable (m / s) to be solved in the x1 direction;

[0150] v is the velocity variable (m / s) to be solved in the x2 direction;

[0151] h1 is the matrix coefficient in the x1 direction;

[0152] h2 is the matrix coefficient in the x2 direction;

[0153] P is the real-time air pressure (Pa);

[0154] ρ is the air reference density (kg / m³) 3 );

[0155] σ 11 , is the air stress in the x1 direction;

[0156] σ 21 , is the air stress in the x2 direction in the x1 direction;

[0157] σ 12 , is the air stress in the x1 direction in the x2 direction;

[0158] σ 22 , is the air stress in the x2 direction;

[0159] g is the acceleration due to gravity (m / s²). 2 );

[0160] β is the isothermal compressibility coefficient of the gas;

[0161] T is the temperature variable calculated at the previous time step;

[0162] T ref , is a reference temperature;

[0163] It is understood that in this invention, the gravitational acceleration g and the isothermal compressibility coefficient β of the gas are constants (the other two equations are the same, and of course, the meanings of other identical letter terms are also the same, which will not be repeated here). The stress σ of the air in the x1 direction is... 11 The air stress σ in the x1 direction is located in the x2 direction. 21 σ is the air stress in the x1 direction and the stress in the x2 direction. 12 The air stress σ in the x2 direction and x2 direction 22 It is a specific physical concept, referring to the air stress σ in the x1 direction. 11 For example, it refers to the distance of the calculated coordinate x1 from the origin in the x1 direction, where... During the calculation process, the calculated coordinates in the x1 and x2 directions form a matrix, where h1 is the coefficient corresponding to the calculated coordinate in the x1 direction and h2 is the coefficient corresponding to the calculated coordinate in the x2 direction. Each calculated coordinate (x1, x2) has a different coefficient; however, for the same calculated coordinate, different calculation step sizes correspond to the same coefficient. In principle, air density and pressure change with air temperature. In this invention, at the start of the calculation, the initial air density is collected as the reference density ρ (that is, the reference density ρ remains unchanged during subsequent calculations); the initial air temperature is collected as the reference temperature T. refIn the subsequent calculations of the momentum transport equation, the real-time air pressure P represents the real-time air pressure. It is evident that as the air temperature changes, the velocity variables u in the x1 direction and v in the x2 direction, as solved by the momentum equation, are affected by the air temperature. Specifically, the effect of velocity on temperature is mainly through the buoyancy term ρgβ(TT) in the momentum equation. ref )accomplish.

[0164] Additionally, as the temperature increases from T ref The density of air after the change to T is ρ(TT). ref This means that the real-time density of air differs depending on the temperature. Air density affects the real-time air pressure, meaning that the real-time air pressure P is also affected by air temperature.

[0165] The scalar transport equation for temperature is as follows:

[0166]

[0167] In the formula, t is a calculation step time;

[0168] x1 is the x-coordinate of the calculated coordinate;

[0169] x2 is the ordinate of the calculated coordinate;

[0170] u is the velocity (m / s) calculated in the x1 direction by the previous calculation step;

[0171] v is the velocity (m / s) calculated in the x2 direction by the previous calculation step;

[0172] h1 is the matrix coefficient in the x1 direction;

[0173] h2 is the matrix coefficient in the x2 direction;

[0174] P is the real-time air pressure (Pa);

[0175] ρ is the air reference density (kg / m³) 3 );

[0176] σ 11 , is the air stress in the x1 direction;

[0177] σ 21 , is the air stress in the x2 direction in the x1 direction;

[0178] σ 12 , is the air stress in the x1 direction in the x2 direction;

[0179] σ 22 , is the air stress in the x2 direction;

[0180] g is the acceleration due to gravity (m / s²). 2 );

[0181] β is the isothermal compressibility coefficient of the gas;

[0182] k is the thermal conductivity of air, W / (m·K);

[0183] T is the temperature variable (K) to be solved;

[0184] T ref , is the reference temperature (K);

[0185] It is understood that in this invention, when calculating the scalar transport equation for temperature, temperature T is the value to be calculated, where u is the velocity obtained from the previous calculation step in the x1 direction, and v is the velocity obtained from the previous calculation step in the x2 direction. It is evident that the temperature T obtained from the scalar transport equation for temperature is affected by the velocity variables u in the x1 direction and v in the x2 direction of the air. Specifically, the influence of velocity on temperature is mainly through the convection term. and diffusion terms accomplish.

[0186] The equation for the scalar transport of matter is:

[0187]

[0188] In the formula, t is a calculation step time;

[0189] ρ is the reference density of air (kg / m³). 3 );

[0190] C is the concentration of the substance to be solved (mol / m³). 3 );

[0191] h1 is the matrix coefficient in the x1 direction;

[0192] h2 is the matrix coefficient in the x2 direction;

[0193] x1 is the x-coordinate of the calculated coordinate;

[0194] x2 is the ordinate of the calculated coordinate;

[0195] u is the velocity (m / s) calculated in the x1 direction by the previous calculation step;

[0196] v is the velocity (m / s) calculated in the x2 direction by the previous calculation step;

[0197] k is the thermal conductivity of air, W / (m·K);

[0198] T is the temperature (K) output by the previous calculation step;

[0199] T ref , is the reference temperature (K).

[0200] Understandably, when calculating the scalar transport equation for matter, the concentration C is the value to be calculated, and the thermal conductivity k of air is a constant. Temperature T is the calculated value obtained from the previous calculation step, u is the velocity obtained from the previous calculation step in the x1 direction, and v is the velocity obtained from the previous calculation step in the x2 direction. Therefore, the concentration C obtained from the scalar transport equation is affected by the air velocity variables u in the x1 direction and v in the x2 direction, as well as the temperature T. Specifically, the influence of velocity on matter transport is mainly through the convection term. and diffusion terms In practice, the effect of temperature on mass transport is mainly achieved through the effect of temperature on fluid density, that is...

[0201] The coupling of velocity-temperature-mass transport is achieved by solving a system of equations (1)(2)(3) simultaneously, using the Newton iteration method for solving nonlinear equation systems in OpenFOAM. In the computational program, to ensure consistency in the order of the equation system and the sequence of unknowns (three unknowns per node: velocity, temperature, and mass concentration) to achieve the general form of the equation system, the order of the equation system is adjusted. The velocity equation system, temperature equation system, and mass concentration equation system are changed from sequential to alternating order. That is, after calculating and iterating the velocity equation system, temperature equation system, and mass concentration equation system within one time step, the program moves to the next time step.

[0202] The scalar transport equations were solved using OpenFOAM (a fluid simulation analysis software) to obtain the airflow velocity, temperature distribution, and carbon dioxide concentration distribution. The accuracy of the model was evaluated by comparing the measured data with data from specific geographical locations. To improve data processing capabilities and solution efficiency, cloud computing technology was used to synchronize the simulation system. The parameters in the transport model dynamically change over time, and the instantaneous values ​​of the sensitivity and relative sensitivity of the model parameters were calculated synchronously through continuous iterative calculations. The calculation accuracy was increased by setting the number of iterations.

[0203] The present invention may also include determining the deviation between the solved data points and the measured data points based on the fitting results, verifying the sensitivity of the model by increasing the density of the grid model and the number of points, evaluating the error of the model calculation results, and correcting the deviation by using an offline deviation correction method.

[0204] In some embodiments, the above method further includes one or a combination of the following steps:

[0205] The steps to determine the preset number of iterations;

[0206] The steps to determine the grid density of the computational grid;

[0207] The steps to determine the number of data collection points for temperature-related data, velocity-related data, and material-related data inside the target building;

[0208] The steps for determining the preset number of iterations include:

[0209] Based on the preset initial density, the three-dimensional geometric model is meshed to obtain the initial computational mesh for the building interior;

[0210] Temperature boundary conditions, velocity boundary conditions, and matter boundary conditions are input into the OpenFOAM model. The OpenFOAM model iteratively solves the scalar transport equations of temperature, velocity, and matter based on the initial computational grid and the temperature, velocity, and matter boundary conditions until the solution meets the first preset condition. The reference iteration number is obtained, and the preset iteration number is determined to be greater than the reference iteration number. The first preset condition includes that the residual of the solution result is less than the preset residual value.

[0211] In an application scenario, such as Figure 2 As shown, after determining the mesh size D of the initial geometry, the initial mesh model is adaptively generated. When the residual R < 10... -5 When the iteration stops, the residual is the difference between the solution values ​​of two adjacent step sizes.

[0212] The steps for determining the mesh density of the computational mesh include:

[0213] Calculate the wall shear stress of the initial computational mesh;

[0214] The initial computational mesh density is reduced according to the preset reduction step size, and the influence of the mesh change rate on the wall shear stress is calculated after each mesh density reduction until the influence value meets the second preset condition, and the mesh after the mesh density reduction is determined as the computational mesh.

[0215] In an application scenario, such as Figure 3 As shown, the shear stress at a wall distance of L is calculated. The shear stress τ on the wall when the mesh size is D is obtained. w (D). The corresponding wall shear stress τ is obtained by reducing the geometric mesh size to a D / 2 finer mesh. w(D / 2), when the effect of the mesh change rate on the wall shear stress is less than 10 -3 When the mesh data has a negligible effect on the wall stress, i.e., |τ w (D)-τ w (D / 2)| / τ w (D) < 10 -3 Otherwise, continue to reduce the mesh size and refine the mesh until the rate of change of shear stress is less than 10. -3 .

[0216] The steps for determining the number of data collection points for temperature-related data, velocity-related data, and material-related data inside the target building include:

[0217] Temperature boundary conditions, velocity boundary conditions, and mass boundary conditions are input into the OpenFOAM model. The OpenFOAM model performs a coupled solution of the scalar transport equations for temperature, velocity, and mass for a preset number of iterations to obtain the temperature calculation data, airflow velocity calculation data, and / or mass calculation data of the target building at the preset test point.

[0218] The calculated and measured data of the preset test points are compared for error, and the number of collection points for temperature-related data, velocity-related data and material-related data inside the target building is determined based on the comparison results.

[0219] In an application scenario, such as Figure 4 As shown, the temperature at m temperature acquisition points is calculated. Then compare the calculated temperatures from the m data points. Compared with actual temperature If the error range is less than 10%, then the velocity point used to set the boundary temperature boundary condition is considered sufficient, i.e., |T(x,y)-T(x,y) real | / T(x,y) real If the error is less than 0.1, then add one more data point until the error range is less than 10%.

[0220] The model was optimized based on sensitivity tests, and corresponding variables (such as the preset number of iterations, the density of the computational grid, and the collection points for temperature, velocity, and material-related data) were determined.

[0221] In some embodiments, such as Figure 5 As shown, the building interior environment analysis device includes: a modeling module 511, a data processing module 512, a boundary condition module 513, and a result output module 514. Specifically,

[0222] Modeling module 511 is used to construct a three-dimensional geometric model of the interior of the target building;

[0223] Data processing module 512 is used to determine temperature-related data, velocity-related data, and material-related data inside the target building;

[0224] Boundary condition module 513 is used to determine the temperature boundary conditions of the three-dimensional geometric model based on temperature-related data, the velocity boundary conditions of the three-dimensional geometric model based on velocity-related data, and the material boundary conditions of the three-dimensional geometric model based on material-related data.

[0225] The output module 514 is used to input temperature boundary conditions, velocity boundary conditions, and material boundary conditions into the fluid simulation solver. The fluid simulation solver performs a coupled solution of the scalar transport equations for temperature, velocity, and material with a preset number of iterations to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building. The data acquisition module is used to acquire model boundary condition data, including geographic information data, such as location, building height, surrounding shading, and illumination time and intensity.

[0226] Databases, including geographic databases and building energy consumption and electricity unit data, are used to provide and store data information;

[0227] The data processing module preprocesses the measured and extracted data.

[0228] Cloud computing module, a model used for remote high-speed computing;

[0229] The model optimization module is used to adjust model parameters and select the optimal parameters for the model.

[0230] Visualization module: Connects to the main server, GIS system, and BIM service platform respectively, and is used to combine measurement data, calculation data, and geographic information data to build a visual interactive interface for building comfort information, thereby optimizing building comfort;

[0231] Network server: Used for data transmission between various modules.

[0232] The optimized comfort-related factors, including geographic information data, thermal energy consumption units (lighting fixtures), temperature data, population point data, heating, ventilation, and air conditioning (HVAC) distribution, and carbon dioxide concentration data, are uploaded to the GBS server in either GBXML or DXF format. The calculation results are displayed instantly and can be exported and compared. GBXML files are primarily used for analyzing building thermal, lighting, acoustic, resource consumption and environmental impact, and solar radiation analysis. DXF files are suitable for lighting analysis, shadow occlusion analysis, and visibility analysis.

[0233] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 6 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. When the computer program is executed by the processor, it implements the functions or steps of a building interior environment analysis method on the server side.

[0234] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0235] Construct a three-dimensional geometric model of the interior of the target building;

[0236] Determine the temperature-related data, velocity-related data, and material-related data inside the target building;

[0237] The temperature boundary conditions of the three-dimensional geometric model are determined based on temperature-related data, the velocity boundary conditions of the three-dimensional geometric model are determined based on velocity-related data, and the material boundary conditions of the three-dimensional geometric model are determined based on material-related data.

[0238] Temperature boundary conditions, velocity boundary conditions, and mass boundary conditions are input into the fluid simulation solver. The fluid simulation solver performs a coupled solution of the scalar transport equations for temperature, velocity, and mass for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and mass distribution data of the target building.

[0239] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0240] Construct a three-dimensional geometric model of the interior of the target building;

[0241] Determine the temperature-related data, velocity-related data, and material-related data inside the target building;

[0242] The temperature boundary conditions of the three-dimensional geometric model are determined based on temperature-related data, the velocity boundary conditions of the three-dimensional geometric model are determined based on velocity-related data, and the material boundary conditions of the three-dimensional geometric model are determined based on material-related data.

[0243] Temperature boundary conditions, velocity boundary conditions, and mass boundary conditions are input into the fluid simulation solver. The fluid simulation solver performs a coupled solution of the scalar transport equations for temperature, velocity, and mass for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and mass distribution data of the target building.

[0244] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0245] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0246] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0247] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the interior environment of a building, characterized in that, include: Construct a three-dimensional geometric model of the interior of the target building; Determine the temperature-related data, velocity-related data, and material-related data inside the target building; The temperature boundary conditions of the three-dimensional geometric model are determined based on the temperature-related data, the velocity boundary conditions of the three-dimensional geometric model are determined based on the velocity-related data, and the material boundary conditions of the three-dimensional geometric model are determined based on the material-related data. The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the fluid simulation solver. The fluid simulation solver is used to perform a coupled solution of the scalar transport equations for temperature, velocity, and material for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building. The fluid simulation solver is used to perform a coupled solution of the scalar transport equations for temperature, velocity, and mass for a preset number of iterations to obtain the temperature distribution data, airflow velocity, and mass distribution data of the target building, including: The three-dimensional geometric model is meshed to obtain the computational mesh of the three-dimensional geometric model; Using the OpenFOAM model, based on the computational grid and the temperature, velocity, and mass boundary conditions, the scalar transport equations for temperature, velocity, and mass are solved in a coupled manner with a preset number of iterations to obtain the temperature distribution data, airflow velocity, and mass distribution data of the target building. The scalar transport equation for velocity is as follows: In the formula, t is a calculation step time; , is the x-coordinate of the calculated coordinate; , is the ordinate of the calculated coordinate; ,yes The velocity variable to be solved in the direction; ,yes The velocity variable to be solved in the direction; ,yes Matrix coefficients in the direction; yes Matrix coefficients in the direction; , is the real-time air pressure (Pa); This is the air reference density; ,yes Air in the direction ; ,yes Air in the direction ; ,yes Air in the direction ; ,yes Air in the direction ; , is the acceleration due to gravity; , is the isothermal compressibility coefficient of a gas; , is the temperature variable calculated at the previous time step; , is a reference temperature; The scalar transport equation for temperature is as follows: In the formula, C P , is specific heat capacity; The scalar transport equation for the substance is as follows: In the formula, , is the concentration of the substance to be solved; , is the thermal conductivity coefficient of air.

2. The building interior environment analysis method as described in claim 1, characterized in that, The method further includes one or a combination of the following steps: The step of determining the preset number of iterations; The step of determining the grid density of the computational grid; The steps for determining the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building; The step of determining the preset number of iterations includes: Based on the preset initial density, the three-dimensional geometric model is meshed to obtain the initial computational mesh inside the building; The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the OpenFOAM model. The OpenFOAM model iteratively solves the scalar transport equations for temperature, velocity, and material based on the initial computational grid and the temperature, velocity, and material boundary conditions until the solution meets a first preset condition. A reference iteration number is obtained, and it is determined that the preset iteration number is greater than the reference iteration number. The first preset condition includes that the residual of the solution result is less than a preset residual value. The step of determining the grid density of the computational grid includes: Calculate the wall shear stress of the initial computational mesh; The initial computational mesh density is reduced according to a preset reduction step size, and the influence of the mesh change rate on the wall shear stress is calculated after each mesh density reduction, until the influence value meets the second preset condition, and the mesh after the mesh density reduction is determined as the computational mesh. The step of determining the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building includes: The temperature boundary conditions, velocity boundary conditions, and material boundary conditions are input into the OpenFOAM model. The OpenFOAM model performs a coupled solution of the scalar transport equations for temperature, velocity, and material for a preset number of iterations to obtain the temperature calculation data, airflow velocity calculation data, and / or material calculation data of the target building at the preset test points. The calculated and measured data of the preset test points are compared for error, and the number of collection points for temperature-related data, velocity-related data, and material-related data inside the target building is determined based on the comparison results.

3. The building interior environment analysis method as described in claim 1, characterized in that, The determination of temperature-related data inside the target building includes one or a combination of the following: The geographic information data of the target building is obtained, the illumination data of the target building is determined based on the geographic information data, and the solar thermal gain and thermal energy distribution of the curtain wall of the target building are determined based on the illumination data. Obtain the product parameters of the heat energy consumption product related to the internal temperature of the target building, and determine the heat dissipation data of the heat energy consumption product based on the product parameters; Collect temperature data from preset temperature collection points inside the building; Based on the population distribution data inside the target building, determine the population thermal distribution data inside the building.

4. The building interior environment analysis method as described in claim 1, characterized in that, The determination of speed-related data inside the target building includes: The wind direction and wind speed are collected at preset speed collection points. The layout data of the wind power equipment related to gas flow inside the building is determined based on the three-dimensional geometric model, and the speed collection points are determined based on the layout data.

5. The building interior environment analysis method as described in claim 1, characterized in that, The substance is carbon dioxide in the air, and the determination of relevant data regarding the substance inside the target building includes: Carbon dioxide concentration data at preset material collection points are collected using a carbon dioxide sensor.

6. The building interior environment analysis method as described in claim 1, characterized in that, The process of determining the temperature boundary conditions of the three-dimensional geometric model based on the temperature-related data, determining the velocity boundary conditions of the three-dimensional geometric model based on the velocity-related data, and determining the material boundary conditions of the three-dimensional geometric model based on the material-related data includes: Based on the three-dimensional geometric model, construct the position coordinates inside the target building; The temperature-related data is processed to obtain temperature-related data corresponding to the preset location coordinates; The velocity-related data is processed to obtain velocity-related data corresponding to the preset position coordinates; The material-related data is processed to obtain the material-related data corresponding to the preset location coordinates.

7. A building interior environment analysis device applied to the building interior environment analysis method of claim 1, characterized in that, include: The modeling module is used to construct a three-dimensional geometric model of the interior of the target building; The data processing module is used to determine temperature-related data, velocity-related data, and material-related data inside the target building; A boundary condition module is used to determine the temperature boundary conditions of the three-dimensional geometric model based on the temperature-related data, the velocity boundary conditions of the three-dimensional geometric model based on the velocity-related data, and the material boundary conditions of the three-dimensional geometric model based on the material-related data. The result output module is used to input the temperature boundary conditions, the velocity boundary conditions, and the material boundary conditions into the fluid simulation solver. The fluid simulation solver is used to perform a preset number of iterations of coupled solution on the scalar transport equations for temperature, velocity, and material to obtain the temperature distribution data, airflow velocity, and material distribution data of the target building.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the building interior environment analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the building interior environment analysis method as described in any one of claims 1 to 6.

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