An energy-saving building design method based on digital simulation

Abstract

The present invention relates to the field of computer-aided design technology, specifically to an energy-saving building design method based on digital simulation, comprising the following steps: based on indoor aerodynamic parameters, analyzing air velocity, temperature gradient, density, and pressure distribution, identifying air vortex structure characteristics, and generating an energy-saving building environment data set. In the present invention, by accurately identifying the aerodynamic characteristics of the building interior, improving the vortex structure analysis capability, optimizing the air flow path, combining aerodynamic parameters, matching thermal resistance and thermal conductive materials, adjusting the envelope structure level, guiding heat flow transfer, reducing heat loss, and improving thermal energy utilization, adjusting the material spectral characteristics based on light redundancy, optimizing lighting efficiency, reducing heat load, combining airflow characteristics, optimizing exterior wall heat transfer, dynamically regulating ventilation rate, avoiding airflow short-circuiting, and integrating heat flow, light, and ventilation parameters, achieving precise optimization of building energy consumption and improving energy efficiency control level.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and in particular to an energy-saving building design method based on digital simulation. Background Art

[0002] The field of computer-aided design technology includes various methods and tools for design, analysis and optimization using computer technology. This technical field involves the use of computers for geometric modeling, graphics rendering, engineering analysis and data processing to improve design accuracy, reduce human errors and accelerate product development cycles. Computer-aided design is widely used in many fields such as mechanical manufacturing, construction engineering, and electronic circuit design. In the field of construction engineering, computer-aided design can be used for building structure modeling, construction planning, energy consumption simulation and optimization analysis. This technology relies on digital modeling, parametric design and computer simulation to make the design process more efficient and help achieve building energy conservation and environmental protection goals.

[0003] Among them, the energy-saving building design method based on digital simulation refers to a method of simulating, calculating and analyzing building energy consumption by establishing a digital model of the building and combining environmental parameters, material properties, climate data and other factors. This method covers key technical contents such as building geometric structure modeling, thermal performance calculation, lighting simulation, ventilation optimization, and relies on computer software for energy consumption calculation and analysis. This method uses building information modeling technology for space and structural modeling, analyzes the energy transfer characteristics of the enclosing structure through heat flow calculation methods, and combines the lighting calculation model to simulate the indoor light environment under different lighting conditions, and optimizes the building ventilation design based on aerodynamic simulation methods.

[0004] The existing technology has limited analysis accuracy of air flow states in building energy consumption simulation, and adopts a relatively simplified flow path calculation method, resulting in insufficient identification of local vortices and recirculation areas, making it difficult to achieve the expected optimization of ventilation and air exchange effects. Heat retention or ventilation short circuits are formed in some areas, reducing the building's energy-saving effect. In terms of heat flow analysis, the thermal resistance matching method of the envelope structure materials is relatively fixed, and the dynamic influence of aerodynamic factors is not fully considered. As a result, in the energy consumption optimization process, the heat flow guidance fails to achieve the optimal configuration, and some high heat loss areas cannot be effectively alleviated. At the lighting optimization level, the existing technology mainly relies on static parameters to adjust the building lighting design, and lacks the ability to dynamically adjust under different lighting conditions, resulting in redundant lighting in some areas and insufficient lighting in some areas, making the indoor light environment unbalanced, affecting comfort, and increasing unnecessary lighting energy consumption. In the energy consumption optimization process, the existing method fails to fully utilize the synergistic relationship between multiple key parameters, making it difficult to achieve refined dynamic regulation of the overall building environment, resulting in limited space for energy-saving optimization. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an energy-saving building design method based on digital simulation.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an energy-saving building design method based on digital simulation, comprising the following steps:

[0007] S1: Based on indoor aerodynamic parameters, analyze air velocity, temperature gradient, density, and pressure distribution, identify air vortex structure characteristics, and generate energy-saving building environment datasets;

[0008] S2: Based on the energy-saving building environment dataset, call indoor aerodynamic parameters, analyze air flow paths, screen areas with abnormal heat exchange rates, adjust the material layers of the envelope structure, optimize the heat flow direction, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, and obtain building layer modeling parameters;

[0009] S3: Based on the building layered modeling parameters, analyze the indoor and outdoor lighting redundancy, match the variable spectrum material parameters, adjust the spectral transmittance and reflectance, and obtain energy-saving building light environment control data;

[0010] S4: Based on the energy-saving building light environment control data, call the air vortex structure characteristics, analyze the airflow recirculation area, adjust the exterior wall heat transfer data, identify the ventilation rate and optimize the airflow path, and generate the building ventilation energy consumption assessment result;

[0011] S5: Based on the building ventilation energy consumption assessment results, call the energy-saving building layered modeling data, analyze the heat flow, light, and ventilation parameters, optimize the building energy consumption configuration, and obtain a dynamic simulation design plan for the energy-saving building.

[0012] As a further solution of the present invention, the energy-saving building environment data set includes air flow rate data, temperature gradient data, air density distribution, pressure distribution data, building lighting parameters, and air vortex structure characteristics; the building layered modeling parameters include air flow path data, heat exchange rate distribution, thermal resistance material parameters, thermal conductivity material parameters, envelope structure material layer parameters, heat flow conduction direction parameters, wall heat transfer coefficient parameters, and insulation layer thickness parameters; the energy-saving building light environment control data includes lighting redundancy data, variable spectrum material parameters, spectral transmittance parameters, and spectral reflectance parameters; the building ventilation energy consumption assessment results include exterior wall heat transfer data, airflow recirculation area data, ventilation rate parameters, and airflow path parameters; the energy-saving building dynamic simulation design scheme includes a heat flow parameter set, a lighting parameter set, a ventilation parameter set, and building energy consumption configuration parameters.

[0013] As a further solution of the present invention, the steps for acquiring the energy-saving building environment dataset are specifically as follows:

[0014] S111: Based on indoor aerodynamic parameters, extract air velocity, temperature gradient, density, and pressure distribution data, analyze the air velocity variation, screen areas with air velocity within the critical velocity range, and identify the pressure gradient in the area to obtain an air velocity and pressure gradient dataset;

[0015] S112: Call the air velocity and pressure gradient dataset, identify the area affected by light intensity, analyze the air density and flow rate based on the light intensity change rate, and adjust the aerodynamic parameters using the formula:

[0016] ;

[0017] Calculate the air flow rate correction amount, adjust the flow rate parameters, and obtain the air flow rate correction data set;

[0018] in, Represents the air velocity correction, Representative Air velocity at the location, Representative Air velocity at the location, Representative Position pressure, Representative The air density at the location, Representative The rate of change of light intensity at a location, The total number of data points representing aerodynamic parameters;

[0019] S113: Calling the air velocity correction data set, identifying the air vortex structure characteristics, analyzing the coordinated changes in air velocity and pressure distribution, screening the vortex core area, extracting aerodynamic parameters, and forming an energy-saving building environment data set.

[0020] As a further solution of the present invention, the steps for obtaining the building layered modeling parameters are specifically as follows:

[0021] S211: Based on the energy-saving building environment dataset, call indoor aerodynamic parameters, analyze air flow paths, screen areas with abnormal heat exchange rates, and obtain air flow deviation intervals;

[0022] S212: Call the air flow offset interval, match the thermal resistance and thermal conductive material, analyze the thermal resistance value and thermal conductivity gradient change, and use the formula:

[0023] ;

[0024] Calculate the mean value of thermal resistance gradient, adjust the material hierarchy of the enclosure structure based on the mean value, and obtain the material hierarchy optimization data;

[0025] in, represents the mean thermal resistance gradient, represents the number of layers in the analysis area, Representative Thermal resistance of the layer material, and represents the thermal conductivity of the adjacent layers;

[0026] S213: Based on the material layer optimization data, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, identify the heat transfer coefficient of each optimized material layer, and obtain building layer modeling parameters.

[0027] As a further solution of the present invention, the steps for acquiring the energy-saving building light environment control data are specifically as follows:

[0028] S311: Based on the building layered modeling parameters, call indoor and outdoor lighting parameters, analyze the lighting distribution of each area, filter areas where the lighting intensity exceeds a set threshold, and obtain lighting redundancy intervals;

[0029] S312: Call the illumination redundancy interval, match the variable spectrum material parameters, and identify the spectral transmittance and reflectance of the differentiated material under the target illumination conditions using the formula:

[0030] ;

[0031] Calculate the spectral transmittance and reflectance matching values, adjust the material layer, and establish spectral matching data;

[0032] in, Represents the spectral transmittance and reflectance matching value, Represents the number of material layers, represents the spectral reflectance, Represents thickness, represents the spectral transmittance, represents the spectral refractive index, represents the spectral refractive index of the adjacent layer, represents accumulation;

[0033] S313: Based on the spectral matching data, adjust the spectral transmittance and reflectance, identify the overall light environment control parameters of the building, and obtain energy-saving building light environment control data.

[0034] As a further solution of the present invention, the steps for obtaining the building ventilation energy consumption evaluation results are specifically as follows:

[0035] S411: Based on the energy-saving building light environment control data, call the air vortex structure characteristics, analyze the flow velocity gradient and pressure distribution in the vortex core area, and obtain the airflow recirculation distribution parameters;

[0036] S412: Calling the airflow recirculation distribution parameters, adjusting the exterior wall heat transfer data, identifying the heat transfer gradient in the vortex core area, analyzing the energy exchange in the airflow circulation area, and obtaining the exterior wall heat transfer coefficient;

[0037] S413: Based on the exterior wall heat transfer coefficient, identify the ventilation rate and optimize the airflow path using the formula:

[0038] ;

[0039] Calculate the ventilation energy consumption per unit volume of building space and obtain the building ventilation energy consumption assessment results;

[0040] in, Represents the ventilation energy consumption per unit volume of building space. Representative The temperature gradient of each exterior wall unit, Representative The surface area of each exterior wall unit, Representative The gas density corresponds to the unit of the exterior wall. represents the specific heat capacity of air, Representative Airflow velocity at each exterior wall unit, Represents the total number of exterior wall units.

[0041] As a further solution of the present invention, the steps for obtaining the energy-saving building dynamic simulation design solution are specifically as follows:

[0042] S511: Based on the building ventilation energy consumption assessment result, call the hierarchical modeling data, extract the heat flow, light, and ventilation parameters, and obtain the regional heat energy accumulation parameters;

[0043] S512: Based on the regional heat energy accumulation parameter, identify the heat flow exchange situation of the building functional area using the formula:

[0044] ;

[0045] Calculate the heat flow balance coefficient, call the ventilation rate parameter, analyze the impact of air circulation, adjust the building energy consumption parameters based on the heat flow balance coefficient, and obtain the energy consumption optimization adjustment parameters;

[0046] in, represents the heat flow balance coefficient, Represents the temperature of the high temperature zone, Represents the low temperature zone temperature, represents the ventilation influencing factor, Represents the indoor heat loss, represents the window transmission heat loss, Represents the thermal resistance parameter of the building envelope;

[0047] S513: Based on the energy consumption optimization adjustment parameters, the regional ventilation mode and lighting mode are adjusted, the optimal building energy consumption configuration is screened, and a dynamic simulation design scheme for an energy-saving building is obtained.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, by analyzing parameters such as air velocity, temperature gradient, density, and pressure distribution, the aerodynamic characteristics of the building can be accurately identified, so that the study of air flow state has a more detailed vortex structure analysis capability, which can effectively identify the airflow recirculation area and the heat exchange abnormal area, and improve the optimization accuracy of the air flow path. By screening aerodynamic parameters, the thermal resistance and thermal conductive materials are matched in the heat flow transfer process, and reasonable adjustments are made to the layers of the enclosure structure materials, so that the heat flow guidance of the building is more in line with energy-saving needs, unnecessary heat loss is reduced, and the overall thermal energy utilization rate is improved. Combined with indoor and outdoor light data, According to the redundancy of light, the spectral transmittance and reflectivity of materials are adjusted to further improve the lighting efficiency while reducing unnecessary heat load. The coordinated optimization of building heat flow and light can reduce energy consumption while maintaining comfort. Combined with the airflow characteristics, the heat transfer parameters of the exterior wall are optimized to achieve dynamic regulation of ventilation rate, avoid the occurrence of airflow short circuits and stagnation areas, and make the ventilation path more in line with the energy consumption optimization goal. Based on the coordinated analysis of the overall energy consumption parameters, the three major factors of heat flow, light and ventilation are integrated to accurately optimize the building energy consumption configuration, realize the accuracy of dynamic simulation of energy-saving buildings, and improve the overall energy efficiency control level of the building environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0051] Figure 2 This is a flow chart for obtaining an energy-saving building environment dataset in the present invention;

[0052] Figure 3 This is a flow chart for obtaining building layered modeling parameters in the present invention;

[0053] Figure 4 This is a flow chart for obtaining light environment control data for energy-saving buildings in the present invention;

[0054] Figure 5 This is a flow chart for obtaining building ventilation energy consumption evaluation results in the present invention;

[0055] Figure 6 This is a flow chart for obtaining the dynamic simulation design scheme of energy-saving buildings in the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0058] Example 1

[0059] See also Figure 1 The present invention provides a technical solution: an energy-saving building design method based on digital simulation, comprising the following steps:

[0060] S1: Based on indoor aerodynamic parameters, analyze air velocity, temperature gradient, density, and pressure distribution, extract building lighting parameters, identify air vortex structure characteristics, and generate energy-saving building environment datasets;

[0061] S2: Based on the energy-saving building environment dataset, call indoor aerodynamic parameters, analyze air flow paths, screen areas with abnormal heat exchange rates, match thermal resistance and thermal conductivity materials, adjust the material layers of the envelope structure, optimize the heat flow conduction direction, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, and obtain building layer modeling parameters;

[0062] S3: Based on the building layered modeling parameters, call the indoor and outdoor lighting parameters, analyze the lighting redundancy, match the variable spectrum material parameters, adjust the spectral transmittance and reflectance, and obtain the energy-saving building light environment control data;

[0063] S4: Based on the light environment control data of energy-saving buildings, the air vortex structure characteristics are called, the air recirculation area is analyzed, the exterior wall heat transfer data is adjusted, the ventilation rate is identified and the airflow path is optimized to generate the building ventilation energy consumption assessment results;

[0064] S5: Based on the building ventilation energy consumption assessment results, call the energy-saving building layered modeling data, analyze the heat flow, lighting, and ventilation parameters, optimize the building energy consumption configuration, and obtain the energy-saving building dynamic simulation design plan.

[0065] The energy-saving building environment data set includes air flow rate data, temperature gradient data, air density distribution, pressure distribution data, building lighting parameters, and air vortex structure characteristics. The building layered modeling parameters include air flow path data, heat exchange rate distribution, thermal resistance material parameters, thermal conductivity material parameters, envelope structure material layer parameters, heat flow conduction direction parameters, wall heat transfer coefficient parameters, and insulation layer thickness parameters. The energy-saving building light environment control data includes lighting redundancy data, variable spectrum material parameters, spectral transmittance parameters, and spectral reflectance parameters. The building ventilation energy consumption assessment results include exterior wall heat transfer data, airflow recirculation area data, ventilation rate parameters, and airflow path parameters. The energy-saving building dynamic simulation design scheme includes heat flow parameter set, lighting parameter set, ventilation parameter set, and building energy consumption configuration parameters.

[0066] See also Figure 2 ,The steps for obtaining the energy-saving building environment dataset are as follows:

[0067] S111: Based on indoor aerodynamic parameters, extract air velocity, temperature gradient, density, and pressure distribution data, analyze the air velocity variation, screen areas with air velocity within the critical velocity range, and identify the pressure gradient in the area to obtain an air velocity and pressure gradient dataset;

[0068] In indoor environments, real-time monitoring of air flow rate, temperature gradient, density and pressure distribution can effectively control and optimize the energy consumption of air conditioning systems. For example, in a typical office, sensors are used to obtain air flow rate and temperature gradient data, thereby adjusting the air conditioning output to reduce energy consumption and improve comfort. By calculating the amplitude of air flow rate changes in different spatial locations, efficient air flow areas and areas that need improvement can be identified. For example, the air flow rate in the corner of the conference room is low, indicating that the air flow path needs to be optimized or auxiliary ventilation equipment needs to be added, and areas with flow rates within the critical flow rate range need to be screened. By monitoring the pressure gradient of the area, the ventilation strategy can be further adjusted to achieve optimal energy efficiency. By calculating the density change rate corresponding to the temperature gradient and combining the pressure distribution data to calculate the impact of the density gradient on the pressure gradient, the response sensitivity and accuracy of the air conditioning system are improved, and an air flow rate and pressure gradient data set is obtained. The data provides an experimental basis and theoretical support for the energy-saving design of buildings.

[0069] S112: Call the air velocity and pressure gradient data set to identify the area affected by light intensity. Based on the rate of change of light intensity, analyze the air density and flow rate, and adjust the aerodynamic parameters using the formula:

[0070] ;

[0071] Calculate the air flow rate correction amount, adjust the flow rate parameters, and obtain the air flow rate correction data set;

[0072] in, Represents the air velocity correction, Representative Air velocity at the location, Representative Air velocity at the location, Representative Position pressure, Representative The air density at the location, Representative The rate of change of light intensity at a location, The total number of data points representing aerodynamic parameters;

[0073] In the lighting optimization design of office buildings, the influence of light intensity on air flow rate and temperature is taken into account. By calculating the change rate of light intensity and its correction value to aerodynamic parameters, it is assumed that the air flow rate in the five different monitored areas is They are 0.5m / s, 0.7m / s, 0.8m / s, 0.65m / s and 0.6m / s respectively;

[0074] The corresponding pressure 101kPa, 102kPa, 100kPa, 101.5kPa, 101kPa;

[0075] The air density A common value is around 1.225 ;

[0076] Light intensity change rate Assume that in the region they are 20%, 25%, 22%, 18%, and 21% respectively;

[0077] Using the data, calculate the air velocity correction rate ;

[0078] The specific calculation steps are as follows: The default is 0.5m / s (initial flow velocity). , m / s, m / s, m / s, m / s;

[0079] Calculate the correction rate for each term using the formula:

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] Total correction rate ;

[0086] The results show that, considering the illumination change rate and aerodynamic parameter changes in each area, the overall air flow rate correction rate is 0.1072. This indicates that after considering the influence of light intensity, the adjustment range of air flow rate needs to be increased by about 10.72% to adapt to the impact of illumination changes, thereby optimizing the environmental quality of the office.

[0087] S113: Calling the air velocity correction data set, identifying the air vortex structure characteristics, analyzing the coordinated changes in air velocity and pressure distribution, screening the vortex core area, extracting aerodynamic parameters, and forming an energy-saving building environment data set;

[0088] Through real-time data monitoring and control systems, key areas that affect air quality and energy efficiency can be identified. For example, by identifying vortex structure characteristics, the output of the HVAC system can be precisely adjusted to match actual needs, reduce energy waste, and calculate the coordinated variation coefficient of air flow rate and pressure distribution. This helps engineers optimize the layout and angle of air conditioning outlets, screen out areas that meet the conditions for vortex formation, and select areas that require special attention to avoid local overheating or overcooling. The spatial distribution of vortex structures in this area is calculated to provide a basis for optimizing the HVAC system. The vortex core area is further screened, and the relevant aerodynamic parameters are extracted to form an energy-saving building environment data set. The data set provides building managers with a basis for real-time adjustment and optimization of the building environment to achieve energy conservation and emission reduction goals.

[0089] See also Figure 3 , the steps for obtaining building layered modeling parameters are as follows:

[0090] S211: Based on the energy-saving building environment dataset, call the indoor aerodynamic parameters, analyze the air flow path, screen the areas with abnormal heat exchange rates, and obtain the air flow deviation interval;

[0091] First, the indoor aerodynamic parameters are called up. This operation involves extracting measured air speed and direction data from a database. For example, in a specific energy-saving building, data is collected by sensors installed in different room corners. Then, an analysis of the air flow path is performed. This analysis not only focuses on the path itself, but also monitors any deviations in the path in more depth. For example, by comparing data at consecutive time points, areas with sudden changes in flow rate are identified. These areas cause flow anomalies due to obstacles or design defects. Then, using advanced data processing software, a spatial distribution map of the heat exchange rate is drawn. By setting a heat exchange rate threshold, for example, setting the threshold to 0.5 kilowatts per second, areas exceeding this value are marked as anomalies. The threshold is based on a data set previously collected in similar buildings, resulting in an air flow deviation range. The result provides key data for subsequent steps. For example, when selecting wall materials or making design adjustments, the specific location of the deviation range can be used to indicate the areas that need to be focused on and addressed to ensure the optimization of air flow inside the building.

[0092] S212: Call the air flow offset interval, match the thermal resistance and thermal conductive material, analyze the thermal resistance value and thermal conductivity gradient change, and use the formula:

[0093] ;

[0094] Calculate the mean value of thermal resistance gradient, adjust the material hierarchy of the enclosure structure based on the mean value, and obtain the material hierarchy optimization data;

[0095] in, represents the mean thermal resistance gradient, represents the number of layers in the analysis area, Representative Thermal resistance of the layer material, and represents the thermal conductivity of the adjacent layers;

[0096] After obtaining the air flow deviation range, this data is used as the basis for material matching operations. Appropriate thermal resistance and thermal conductivity materials are selected to optimize the thermal conductivity performance in areas with large air flow deviations. Detailed calculations of the thermal resistance and thermal conductivity of each area are performed to identify changes in the thermal properties of the materials within the area.

[0097] Suppose the exterior wall of a building is composed of three layers of different materials:

[0098] First layer material (insulation layer): m²·K / W, W / mK;

[0099] Second layer material (brick wall): m²·K / W, W / mK;

[0100] The third layer of material (interior layer): m²·K / W, W / mK.

[0101] Calculated according to the formula:

[0102] ;

[0103] Calculate the thermal conductivity difference: , ;

[0104] Calculate the square root: , ;

[0105] Compute the product: , ;

[0106] Calculate the sum: ;

[0107] Calculate the final mean: ;

[0108] Calculate the mean thermal resistance gradient , this value is low, indicating that the thermal conductivity between different layers of the wall is quite different, resulting in a high thermal bridge effect. It is necessary to further optimize the material layer based on the calculation results, such as increasing the thickness of the insulation layer, improving In order to reduce heat flow loss, more uniform thermal conductive materials are selected to reduce the difference in thermal conductivity of adjacent materials. Thermal conductive filling materials are used to fill wall gaps to reduce thermal bridge phenomena. The material layer of the envelope structure is adjusted according to the calculated thermal resistance gradient mean, and material layer optimization data is established. This data includes the optimized material combination of each layer, the optimized thermal resistance value, and the adjusted thermal conductivity coefficient to ensure that the overall thermal performance of the building is improved.

[0109] S213: Based on the material layer optimization data, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, identify the heat transfer coefficient of each optimized material layer, and obtain the building layer modeling parameters;

[0110] First, the heat transfer coefficient of each layer of material is recalculated to ensure that each calculated value is accurate. For example, field tests are conducted using a thermal imager to obtain the thermal conductivity of the actual material. Combined with the optimized insulation layer thickness, the thermal conduction behavior of each layer is simulated through advanced simulation software. The software will automatically calculate the total heat transfer coefficient of the wall based on the input material properties and structural configuration. Based on the analysis of the coefficient and the direction of heat flow conduction, the thermal efficiency of the building is comprehensively evaluated. Through precise calculations, the building layer modeling parameters are obtained, which not only provides detailed guidance for construction, but also ensures the maximum energy efficiency of the building. The parameters include the total thermal resistance of the wall, the thermal conductivity of each layer of material, and the optimized insulation layer thickness, making the thermal environment control of the entire building more accurate and efficient, and providing the architectural design and construction team with a clear operational framework based on actual calculations.

[0111] See also Figure 4 ,The specific steps for obtaining energy-saving building light environment control data are:

[0112] S311: Based on the building layer modeling parameters, call the indoor and outdoor lighting parameters, analyze the lighting distribution of each area, filter out areas where the light intensity exceeds the set threshold, and obtain the lighting redundancy interval;

[0113] Obtain indoor and outdoor lighting parameters for each area inside the building. The parameters are measured by light sensors. For example, sensors installed at different locations in the building can measure the intensity of direct light, scattered light, and reflected light respectively. Layered data collection is performed based on the building's external obstructions, window angles, and glass materials. The light distribution in each area is then calculated, and time series analysis is performed on the light data to divide the light changes at different times of the day to obtain the light stability zone and light fluctuation zone for each time period. On this basis, the light intensity of each area is compared, and the maximum and minimum light values of each space inside the building are selected. The illumination value is calculated and its average illumination intensity is calculated. The set illumination threshold is used to filter out areas where the illumination intensity exceeds the set threshold. For example, if the illumination threshold is set to 800 lux, when the illumination intensity in a certain area reaches 1000 lux, the area is determined to be a light-redundant area. Combined with the surface light reflection characteristics of building walls, floors, and furniture, the illumination redundancy is further calculated. This calculation involves analysis of light reflectivity, surface absorptivity, and material transmittance. For example, for a wall with a light reflectivity of 0.6, its contribution to the illumination of the surrounding space is calculated, and the illumination redundancy interval is finally obtained.

[0114] S312: Call the illumination redundancy interval, match the variable spectrum material parameters, and identify the spectral transmittance and reflectance of the differentiated material under the target illumination conditions using the formula:

[0115] ;

[0116] Calculate the spectral transmittance and reflectance matching values, adjust the material layer, and establish spectral matching data;

[0117] in, Represents the spectral transmittance and reflectance matching value, Represents the number of material layers, represents the spectral reflectance, Represents thickness, represents the spectral transmittance, represents the spectral refractive index, represents the spectral refractive index of the adjacent layer, represents accumulation;

[0118] Identify the lighting environment in different areas and the degree of adaptability of spectral materials that affect the energy-saving effect of buildings. Therefore, it is necessary to calculate the spectral transmittance and reflectance of different materials under specific lighting conditions. Represents the spectral transmittance and reflectance matching value, represents the number of material layers to be calculated, Representative spectral reflectance of the layer material, Represents the thickness of the layer of material, represents the spectral transmittance of the material layer, represents the spectral refractive index of the layer material, represents the spectral refractive index of the adjacent layer material, represents accumulation;

[0119] Assume that the building walls in a certain area are composed of three layers of materials:

[0120] First layer material (exterior wall coating): , m, , ;

[0121] Second layer material (glass): , m, , ;

[0122] Third layer material (curtain): , m, , ;

[0123] Calculated according to the formula:

[0124] ;

[0125] Calculate the product of spectral reflectance and thickness: , , ;

[0126] Calculate the absolute value of the spectral refractive index difference: , ;

[0127] Calculate the spectral transmittance product: , ;

[0128] Calculate the final matching value: ;

[0129] Calculate the spectral transmittance and reflectance matching values This value reflects the ability of different spectral material combinations to regulate the lighting environment. If the value is too high, it means that the current material combination has a large reflectivity, resulting in light pollution. It is necessary to adjust the transmittance or add light-absorbing materials. According to the calculation results, the material layer is adjusted to optimize the spectral matching characteristics of the building surface, and finally the spectral matching data is obtained.

[0130] S313: Based on the spectral matching data, adjust the spectral transmittance and reflectance, identify the overall building light environment control parameters, and obtain energy-saving building light environment control data;

[0131] First, further measurements are taken of the light environment in each area of the building. A spectrum analyzer is used to record the optical properties of the adjusted materials and verify the accuracy of the spectral matching data. During the adjustment process, the spectral adaptability parameters of the building materials are introduced. This parameter is determined by the material's angle of incidence dependence, surface microstructure, and coating reflectance. For example, in the glass curtain wall area, low-emissivity glass is introduced to reduce the amount of solar radiation transmitted, thereby improving the indoor light environment. The changes in light intensity in each area before and after the adjustment are compared, and the final light environment control parameters are calculated, including spectral reflectance distribution, transmittance balance value, and ambient brightness adjustment coefficient. Combined with the building layout and window orientation, the overall light environment is optimized, and ultimately the light environment control data for energy-saving buildings is obtained.

[0132] See also Figure 5 , the specific steps for obtaining the building ventilation energy consumption assessment results are as follows:

[0133] S411: Based on the light environment control data of the energy-saving building, the air vortex structure characteristics are called, the velocity gradient and pressure distribution in the vortex core area are analyzed, and the airflow recirculation distribution parameters are obtained;

[0134] First, it is necessary to obtain the light intensity distribution inside the building. This can be achieved by installing light sensors at different locations in the building, such as placing a sensor in the four corners and the center of the room to record the light intensity value at each point. Assume that at a certain moment, the sensor readings are 300 lux, 320 lux, 310 lux, 305 lux and 315 lux respectively. To call the air vortex structure characteristics, it is necessary to introduce aerodynamic analysis in the ventilation system in the building to determine the air flow pattern and vortex area. This can be achieved through computational fluid dynamics (CFD) simulation. For example, use CFD software to simulate the flow of air in the room, identify the existence of a counterclockwise vortex near the window, and calculate the flow velocity gradient in the core area of the vortex. and pressure distribution, velocity and pressure sensors can be arranged in the vortex core area to measure the flow rate and pressure at different positions. For example, at the four measuring points in the vortex core, the flow rates are 1.2m / s, 1.5m / s, 1.3m / s and 1.4m / s, and the pressures are 101325Pa, 101320Pa, 101330Pa and 101328Pa, respectively. Through the data, the flow velocity gradient and pressure distribution can be calculated, and their impact on the airflow recirculation area can be identified. This requires analyzing the interaction between the vortex area and the surrounding air flow to determine which areas of the air are affected by the vortex and recirculation is formed. For example, it is found that the vortex causes the air flow velocity in the corner of the room to decrease, forming an airflow recirculation area, and the airflow recirculation distribution parameters are obtained.

[0135] S412: Calling airflow recirculation distribution parameters, adjusting exterior wall heat transfer data, identifying the heat transfer gradient in the vortex core area, analyzing energy exchange in the airflow circulation area, and obtaining the exterior wall heat transfer coefficient;

[0136] First, it is necessary to analyze the impact of parameters on the heat transfer of the exterior wall. For example, the airflow recirculation area causes the heat conduction efficiency of certain parts of the exterior wall to decrease. The exterior wall heat transfer data can be adjusted by installing temperature sensors at different positions on the exterior wall to monitor the temperature difference between the inside and outside of the wall in real time. For example, at the upper, middle and lower positions of the exterior wall, the temperature differences measured by the temperature sensors were 5°C, 4°C and 6°C respectively. The heat transfer gradient of the air vortex core area was calculated. This requires combining the airflow recirculation distribution parameters and the exterior wall heat transfer data to determine the temperature change rate in the vortex area. For example, using the above temperature difference data and airflow velocity data, the heat transfer gradient of the vortex core area is calculated to be 0.5°C / m. The energy exchange characteristics in the airflow recirculation area are analyzed, which includes evaluating the impact of air flow on heat transfer. For example, it is found that airflow recirculation causes heat to be retained in the room, affecting the overall temperature distribution, and the exterior wall heat transfer coefficient is obtained.

[0137] S413: Based on the exterior wall heat transfer coefficient, identify the ventilation rate and optimize the airflow path using the formula:

[0138] ;

[0139] Calculate the ventilation energy consumption per unit volume of building space and obtain the building ventilation energy consumption assessment results;

[0140] in, Represents the ventilation energy consumption per unit volume of building space. Representative The temperature gradient of each exterior wall unit, Representative The surface area of each exterior wall unit, Representative The gas density corresponds to the unit of the exterior wall. represents the specific heat capacity of air, Representative Airflow velocity at each exterior wall unit, Represents the total number of exterior wall units;

[0141] First, it is necessary to measure the heat transfer of the building's exterior wall to calculate the building's ventilation rate. The temperature gradient is obtained through infrared thermal imaging, heat flow sensors, etc. For example, the measured temperature gradients of a building's exterior wall are K / m, K / m, K / m, K / m, the corresponding exterior wall unit areas are 、 、 、 , based on the data, the heat transfer contribution of the exterior wall unit during ventilation can be calculated, which requires measuring the density of the air and air flow rate To calculate the ventilation energy consumption of the building, set the air density 、 、 、 , specific heat capacity of air J / (kg·K), wind speeds are m / s, m / s, m / s, m / s;

[0142] Substitute the values for calculation:

[0143] ;

[0144] ;

[0145] ;

[0146] The calculation shows that the ventilation energy consumption value per unit volume of building space is 0.65, indicating that the energy consumption required for air exchange per unit volume during the building's ventilation process is low, which is conducive to energy-saving design and obtaining building ventilation energy consumption assessment results. The results can be used to evaluate the energy consumption performance of different ventilation paths and adjust the airflow path to optimize the overall ventilation energy consumption of the building.

[0147] See also Figure 6 ,The specific steps for obtaining the dynamic simulation design scheme of energy-saving buildings are:

[0148] S511: Based on the building ventilation energy consumption assessment results, call the hierarchical modeling data, extract heat flow, light, and ventilation parameters, and obtain regional heat energy accumulation parameters;

[0149] Preliminary extraction of heat flow, lighting, and ventilation parameters is performed. In an office building embodiment, heat flow parameters include heat loss from exterior walls and windows, lighting parameters involve daylight duration and light intensity, and ventilation parameters are based on statistical data of air circulation on each floor and in each space. The parameters are used to perform a preliminary calculation of the office building's energy consumption, identify key areas of heat loss, and calculate regional heat accumulation parameters. The data used in this process are all based on actual measurement results from the previous year. By comparing current data with historical data, energy efficiency improvement points in each area can be determined, providing a basis for the next step of energy consumption optimization.

[0150] S512: Based on the regional heat energy accumulation parameters, identify the heat flow exchange of the building functional areas using the formula:

[0151] ;

[0152] Calculate the heat flow balance coefficient, call the ventilation rate parameter, analyze the impact of air circulation, adjust the building energy consumption parameters based on the heat flow balance coefficient, and obtain the energy consumption optimization adjustment parameters;

[0153] in, represents the heat flow balance coefficient, Represents the temperature of the high temperature zone, Represents the low temperature zone temperature, represents the ventilation influencing factor, Represents the indoor heat loss, represents the amount of heat loss through the window, Represents the thermal resistance parameter of the building envelope;

[0154] The heat exchange of each functional area in the building is calculated. A high-rise office building is selected as the implementation object. Assuming that the building is located in a hot summer and cold winter area, the external ambient temperature fluctuates greatly. In order to quantify the heat exchange, it is necessary to introduce the indoor and outdoor temperature difference, ventilation influencing factors, indoor heat loss, window transmission heat loss and thermal resistance parameters of the enclosure structure. The temperature data of the building at different time periods are collected. Assuming that the maximum indoor temperature in the high-rise office area of the building in summer is The lowest indoor temperature is 28℃ is 22℃, then the temperature difference between the two is , based on the building design standards, the ventilation factor of this floor is 0.8 (dimensionless parameter, calculated based on ventilation system design), indoor heat loss Detected by the measuring equipment, assuming 1500W, the window transmission heat loss Based on the glass material and orientation, it is assumed to be 1200W, and the thermal resistance parameter of the building envelope is Calculated from material thickness and thermal conductivity, assumed to be 2.5 , substitute the data into the formula:

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] Calculated heat flow balance coefficient The value indicates the degree of heat exchange inside the building, that is, the heat flow fluctuation caused by indoor temperature changes. The value means that the heat flow exchange of the building is strong, indicating that the demand for energy consumption optimization is large. Based on the calculation result, the ventilation rate parameter is further called to calculate the impact of building air circulation. Assuming that the ventilation rate of the office building is 5 times / hour, the air exchange volume directly affects the adjustment of the heat flow balance coefficient. Combined with the optimization calculation of the building energy consumption parameters, the energy consumption optimization adjustment parameter is finally obtained. This parameter will be used for dynamic simulation and configuration optimization of building energy consumption in subsequent steps.

[0160] S513: Based on energy consumption optimization, adjust parameters, adjust regional ventilation mode and lighting mode, select the optimal building energy consumption configuration, and obtain a dynamic simulation design plan for energy-saving buildings;

[0161] Energy consumption optimization and adjustment parameters were called up, and further adjustments were made to the heat flow exchange conditions of various functional areas in the building. A school teaching building was selected as an example. According to the energy consumption optimization and adjustment parameters, the ventilation mode and lighting adjustment method of each classroom and office area were adjusted. Through dynamic simulation tests, the optimal building energy consumption configuration plan was screened out. In particular, customized energy consumption management plans were implemented to meet the special needs of large lecture halls and laboratories. Finally, a series of dynamic simulations were carried out in combination with regional climatic conditions, and a dynamic simulation design plan for energy-saving buildings was successfully obtained.

[0162] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An energy-saving building design method based on digital simulation, characterized in that: The following steps are involved: S1: Based on indoor aerodynamic parameters, analyze air velocity, temperature gradient, density, and pressure distribution, identify air vortex structure characteristics, and generate energy-saving building environment datasets; S2: Based on the energy-saving building environment dataset, call indoor aerodynamic parameters, analyze air flow paths, screen areas with abnormal heat exchange rates, adjust the material layers of the envelope structure, optimize the heat flow direction, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, and obtain building layer modeling parameters; The steps for obtaining the building layered modeling parameters are specifically as follows: S211: Based on the energy-saving building environment dataset, call indoor aerodynamic parameters, analyze air flow paths, screen areas with abnormal heat exchange rates, and obtain air flow deviation intervals; S212: Call the air flow offset interval, match the thermal resistance and thermal conductive material, analyze the thermal resistance value and thermal conductivity gradient change, and use the formula: ； Calculate the mean value of thermal resistance gradient, adjust the material hierarchy of the enclosure structure based on the mean value, and obtain the material hierarchy optimization data; in, represents the mean thermal resistance gradient, represents the number of layers in the analysis area, Representative Thermal resistance of the layer material, and represents the thermal conductivity of the adjacent layers; S213: Based on the material layer optimization data, adjust the wall heat transfer coefficient, optimize the insulation layer thickness, identify the heat transfer coefficient of each optimized material layer, and obtain building layer modeling parameters; S3: Based on the building layered modeling parameters, analyze the indoor and outdoor lighting redundancy, match the variable spectrum material parameters, adjust the spectral transmittance and reflectance, and obtain energy-saving building light environment control data; S4: Based on the energy-saving building light environment control data, call the air vortex structure characteristics, analyze the airflow recirculation area, adjust the exterior wall heat transfer data, identify the ventilation rate and optimize the airflow path, and generate the building ventilation energy consumption assessment result; The steps for obtaining the building ventilation energy consumption assessment results are as follows: S411: Based on the energy-saving building light environment control data, call the air vortex structure characteristics, analyze the flow velocity gradient and pressure distribution in the vortex core area, and obtain the airflow recirculation distribution parameters; S412: Calling the airflow recirculation distribution parameters, adjusting the exterior wall heat transfer data, identifying the heat transfer gradient in the vortex core area, analyzing the energy exchange in the airflow circulation area, and obtaining the exterior wall heat transfer coefficient; S413: Based on the exterior wall heat transfer coefficient, identify the ventilation rate and optimize the airflow path using the formula: ； Calculate the ventilation energy consumption per unit volume of building space and obtain the building ventilation energy consumption assessment results; in, Represents the ventilation energy consumption per unit volume of building space. Representative The temperature gradient of each exterior wall unit, Representative The surface area of each exterior wall unit, Representative The gas density corresponds to the unit of the exterior wall. represents the specific heat capacity of air, Representative Airflow velocity at each exterior wall unit, represents the total number of exterior wall units; S5: Based on the building ventilation energy consumption assessment results, call the energy-saving building layered modeling data, analyze the heat flow, light, and ventilation parameters, optimize the building energy consumption configuration, and obtain a dynamic simulation design plan for the energy-saving building.

2. The energy-saving building design method based on digital simulation according to claim 1 is characterized in that: The energy-saving building environment data set includes air flow rate data, temperature gradient data, air density distribution, pressure distribution data, building lighting parameters, and air vortex structure characteristics. The building layered modeling parameters include air flow path data, heat exchange rate distribution, thermal resistance material parameters, thermal conductivity material parameters, envelope structure material layer parameters, heat flow conduction direction parameters, wall heat transfer coefficient parameters, and insulation layer thickness parameters. The energy-saving building light environment control data includes lighting redundancy data, variable spectrum material parameters, spectral transmittance parameters, and spectral reflectance parameters. The building ventilation energy consumption assessment results include exterior wall heat transfer data, airflow recirculation area data, ventilation rate parameters, and airflow path parameters. The energy-saving building dynamic simulation design scheme includes a heat flow parameter set, a lighting parameter set, a ventilation parameter set, and building energy consumption configuration parameters.

3. The energy-saving building design method based on digital simulation according to claim 1 is characterized in that: The steps for obtaining the energy-saving building environment dataset are specifically as follows: S111: Based on indoor aerodynamic parameters, extract air velocity, temperature gradient, density, and pressure distribution data, analyze the air velocity variation, screen areas with air velocity within the critical velocity range, and identify the pressure gradient in the area to obtain an air velocity and pressure gradient dataset; S112: Call the air velocity and pressure gradient dataset, identify the area affected by light intensity, analyze the air density and flow rate based on the light intensity change rate, and adjust the aerodynamic parameters using the formula: ； Calculate the air flow rate correction amount, adjust the flow rate parameters, and obtain the air flow rate correction data set; in, Represents the air velocity correction, Representative Air velocity at the location, Representative Air velocity at the location, Representative Position pressure, Representative The air density at the location, Representative The rate of change of light intensity at a location, The total number of data points representing aerodynamic parameters; S113: Calling the air velocity correction data set, identifying the air vortex structure characteristics, analyzing the coordinated changes in air velocity and pressure distribution, screening the vortex core area, extracting aerodynamic parameters, and forming an energy-saving building environment data set.

4. The energy-saving building design method based on digital simulation according to claim 1 is characterized in that: The steps for obtaining the energy-saving building light environment control data are specifically as follows: S311: Based on the building layered modeling parameters, call indoor and outdoor lighting parameters, analyze the lighting distribution of each area, filter areas where the lighting intensity exceeds a set threshold, and obtain lighting redundancy intervals; S312: Call the illumination redundancy interval, match the variable spectrum material parameters, and identify the spectral transmittance and reflectance of the differentiated material under the target illumination conditions using the formula: ； Calculate the spectral transmittance and reflectance matching values, adjust the material layer, and establish spectral matching data; in, Represents the spectral transmittance and reflectance matching value, Represents the number of material layers, represents the spectral reflectance, Represents thickness, represents the spectral transmittance, represents the spectral refractive index, represents the spectral refractive index of the adjacent layer, represents accumulation; S313: Based on the spectral matching data, adjust the spectral transmittance and reflectance, identify the overall light environment control parameters of the building, and obtain energy-saving building light environment control data.

5. The energy-saving building design method based on digital simulation according to claim 1 is characterized in that: The steps for obtaining the energy-saving building dynamic simulation design scheme are specifically as follows: S511: Based on the building ventilation energy consumption assessment result, call the hierarchical modeling data, extract the heat flow, light, and ventilation parameters, and obtain the regional heat energy accumulation parameters; S512: Based on the regional heat energy accumulation parameter, identify the heat flow exchange situation of the building functional area using the formula: ； Calculate the heat flow balance coefficient, call the ventilation rate parameter, analyze the impact of air circulation, adjust the building energy consumption parameters based on the heat flow balance coefficient, and obtain the energy consumption optimization adjustment parameters; in, represents the heat flow balance coefficient, Represents the temperature of the high temperature zone, Represents the low temperature zone temperature, represents the ventilation influencing factor, Represents the indoor heat loss, represents the amount of heat loss through the window, Represents the thermal resistance parameter of the building envelope; S513: Based on the energy consumption optimization adjustment parameters, the regional ventilation mode and lighting mode are adjusted, the optimal building energy consumption configuration is screened, and a dynamic simulation design scheme for an energy-saving building is obtained.

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