An optimization method for indoor ventilation and heat preservation of Mongolian buildings based on an intelligent calculation model

Abstract

The present invention discloses an optimization method for indoor ventilation and heat preservation of Mongolian architecture based on an intelligent calculation model, which relates to the technical field of building environment applications. The key points of its technical solution are as follows: It includes the following steps: S1: Collection of the wind-heat environment and design scheme in alpine regions; S2: Calculation of the outdoor wind-heat environment index of buildings in alpine regions; S3: Calculation of the indoor wind-heat environment index of individual buildings in alpine regions; S4: Automatic adjustment of the layout of building groups and the envelope structure of individual buildings; S5: Iteration of the wind-heat environment based on the simulated annealing algorithm; S6: Model output and display. The present invention comprehensively considers different scales and multiple environmental factors, calculates and iteratively optimizes the ventilation and heat preservation performance of building groups through intelligent algorithms, greatly improving the accuracy and speed of calculation and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment applications, and more specifically, to an optimization method for indoor ventilation and heat preservation of Mongolian buildings based on an intelligent calculation model. Background Art

[0002] The architectural design in alpine regions faces special climatic conditions, such as extreme low temperatures and strong winds. These conditions pose higher requirements for the ventilation and heat preservation performance of buildings. On the one hand, the progress of environmental simulation software enables designers to more accurately simulate and analyze the performance of buildings in specific environments, thus making more effective decisions at the design stage. On the other hand, with the development of digital and intelligent technologies, more and more intelligent calculation methods (such as deep learning, reinforcement learning, etc.) are applied to the optimization of urban layout and architectural design. By processing complex environmental, model, and material data, the performance of the building environment is optimized. Currently, the research mainly focuses on the level of individual buildings or urban agglomerations, lacking cross-scale research. At the same time, it only targets single environmental elements such as wind, light, and heat, lacking a comprehensive evaluation of the elements. As a result, when solving single physical environment problems, the influence of other elements is ignored, making it difficult to achieve the global optimal solution for urban and building environments.

[0003] To solve the above problems, Chinese Patent (Patent Publication No.: CN115577658A) discloses an optimization simulation method for the indoor wind environment of buildings based on Fluent, including S1, sorting and analyzing meteorological data; S2, building a model to determine the building space model to be simulated; S3, performing grid division to obtain a grid model; S4, detecting the grid model; S5, specifying the direction of gravitational acceleration; S6, selecting a model that can effectively simulate the indoor wind environment of the building and selecting a standard turbulence model; S7, defining material properties; S8, setting boundary conditions; S9, iterative calculation; S10, post-processing the calculation results to optimize poorly ventilated points.

[0004] The above solution realizes the simulation of the indoor natural wind environment of buildings, analyzes and optimizes the current situation of the wind environment in buildings, improves the quality of the indoor natural wind environment of buildings, avoids generating too many ventilation dead corners and reduces the area ratio of areas with a large air age, effectively organizes the indoor air flow path, and reduces the excessive dependence on mechanical ventilation equipment; however, this optimization simulation method for the indoor wind environment of buildings based on Fluent still has some defects: First, the design cycle of the building group is long, the project progress is slow, and the iterative optimization efficiency is low; second, the energy efficiency of the building is low, the heating cost is high, the energy consumption is large, and at the same time, the indoor temperature comfort is difficult to guarantee; third, there is a lack of scientific data support, the measurement accuracy is not high, and the design rationality needs to be improved.

[0005] Therefore, the present invention comprehensively considers different scales and multiple environmental factors, calculates and iteratively optimizes the ventilation and heat preservation performance of building groups through intelligent algorithms, greatly improving the accuracy and speed of calculation and optimization. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimization method for indoor ventilation and heat preservation of Mongolian buildings based on an intelligent calculation model, so as to solve the problem that the calculation of the wind-heat environment of buildings in alpine regions in the prior art is time-consuming and laborious, and provide a solution for the intelligent optimization of building groups in alpine regions.

[0007] The above technical purpose of the present invention is achieved through the following technical solutions: An optimization method for indoor ventilation and heat preservation of Mongolian buildings based on an intelligent calculation model, including the following steps:

[0008] S1: Collect the wind-heat environment data, design scheme model and construction material performance data of the site to be designed;

[0009] S2: Calculate the outdoor wind-heat environment index of buildings in alpine regions to obtain the outdoor wind-heat environment comfort index F 室外 ;

[0010] Input the wind-heat environment data and design scheme model collected in step S1 into the outdoor environment simulation software to obtain the wind and heat environment simulation color scale map of the building group. The simulation color scale map forms a condition threshold matrix through the hue mapping module and the condition threshold module, and the condition threshold matrix obtains the outdoor wind-heat environment comfort index F through the row matrix addition calculation and the element summation calculation 室外 ;

[0011] S3: Calculate the indoor wind-heat environment index of individual buildings in alpine regions to obtain the overall indoor ventilation and heat preservation index F 室内 ;

[0012] Obtain the wind speed and temperature data of the building monomer surrounding matrix, and input the construction material performance data described in step S1 into the indoor environment simulation software to obtain the wind and heat environment simulation color scale map of the monomer building. Calculate the wind speed and temperature data through the hue mapping module, and then obtain the monomer ventilation and heat preservation index F through the weighted integral algorithm 单体 , accumulate the ventilation and heat preservation index F of each building monomer 单体 , obtain the overall indoor ventilation and heat preservation index F 室内 ;

[0013] S4: Automatically adjust the layout of the building group and the individual building envelope of the site to be designed, integrate the adjusted model into the modeling software, and return to step S2 to recalculate the total score F of the wind-heat environment 室外 , if F 室外 increases in value, continue with step S3, otherwise jump to step S4;

[0014] S5: Set the iteration strategy based on the simulated annealing algorithm to obtain the optimal model of the indoor and outdoor environment of buildings in alpine regions;

[0015] S6: Interactively display the optimal model of the indoor and outdoor environment of the building obtained in step S5.

[0016] The present invention is further configured as follows: The hue mapping module is specifically: Divide the simulated color scale map into grids, extract the hues of the grid center points of the thermal environment and wind environment simulation maps respectively, calculate the temperature T and wind speed V respectively, and the changes in temperature and wind speed are linearly mapped to the hue display:

[0017] The temperature T calculation formula is as follows:

[0018]

[0019] Where x min and x max are the minimum and maximum values of the hue, T min and T max are the minimum and maximum values of the temperature, x is the given hue value, and T is the corresponding temperature value;

[0020] The wind speed V calculation formula is as follows:

[0021]

[0022] Where y min and y max are the minimum and maximum values of the hue, V min and V max are the minimum and maximum values of the wind speed, y is the given hue value, and V is the corresponding temperature value.

[0023] The present invention is further configured as follows: The condition threshold module described in step S2 is specifically: Traverse the magnitude relationship between each element in the temperature and wind speed matrices and the outdoor comfortable wind heat indexes T 舒适 and V 舒适 , and reassign values to the matrix.

[0024] The present invention is further configured as follows: The matrix addition measurement and element summation measurement described in step S2 are specifically: Add up each element in the temperature and wind speed matrices reassigned by the condition threshold module to obtain a new matrix with element values of [0, 2], and sum up each element of the matrix to obtain the outdoor wind heat environment discomfort index F 室外 .

[0025] The present invention is further configured as follows: The weighted integral algorithm described in step S3 is specifically: Calculate the single building thermal environment index F 热 and the single building wind environment index F 风 , and calculate F by adding weights单体 , the formula is as follows:

[0026] F 单体 = F 热 + cF 风

[0027]

[0028]

[0029] Wherein, a is the wind speed suitable for the indoor wind environment, b is the temperature suitable for the indoor thermal environment, and c is the exponential weight.

[0030] The present invention is further configured as: the specific method for automatically adjusting the layout of the building complex in the site to be designed in step S4 is: calling the intelligent adjustment module of the building complex layout, and intelligently adjusting the building spacing, height and orientation through the deep reinforcement learning model.

[0031] The present invention is further configured as: the deep reinforcement learning model is specifically: the model defines a reward function centered on sunshine duration, building spacing and height limit, and uses the deep reinforcement learning model to intelligently optimize the spacing, height and orientation of the building complex.

[0032] The present invention is further configured as: the iterative strategy in step S5 is an exponential cooling strategy, and the exponential cooling strategy specifically adopts the following formula:

[0033] T new = a·T old

[0034] Wherein, T new is the new annealing temperature, T old is the annealing temperature of the previous step, and a is the cooling factor.

[0035] By adopting the above technical solutions, the present invention ensures the data accuracy and integrity of simulation calculation and optimization adjustment by collecting wind and heat environment data and obtaining building parameters, including data such as wind speed, temperature, humidity, etc. around the building complex and parameters such as building material performance and building layout, providing sufficient support for the precision of the design scheme; the present invention uses simulation software to simulate the changes of wind and heat environment data under different environmental conditions in real time and quickly feedbacks them to the building complex layout and the single building envelope structure, realizing the rapid response and adjustment of the design scheme and ensuring the real-time and flexibility of the design; the present invention comprehensively applies a variety of algorithms, such as the hue mapping module, the weighted integral algorithm, etc., and comprehensively optimizes the building design scheme, including the building complex layout, the single building envelope structure, etc. on the basis of considering the indoor and outdoor wind and heat environment indexes, making the design scheme more comprehensive and effective, and improving the integrity and applicability of the design;

[0036] The present invention utilizes intelligent optimization algorithms such as the simulated annealing algorithm to search for the optimal solution in a complex design space according to the set iterative strategy, continuously optimize the design scheme, improve the intelligence level and scientific nature of the design, make the design scheme more in line with the actual requirements, and ensure the scientific nature and effectiveness of the design; through the interactive display function, the present invention intuitively displays the optimized design scheme to designers and decision-makers in the form of color scale diagrams, data diagrams, etc., more detailedly displays the improvement effect of the design scheme, and promotes the intuitive understanding of the design scheme and the achievement of decision-making.

[0037] In summary, the present invention can more comprehensively and accurately optimize the ventilation and heat preservation design of buildings in alpine regions, providing strong support and optimization solutions for building design.

[0038] In summary, the present invention has the following beneficial effects:

[0039] 1. Through intelligent measurement, intelligent adjustment, and intelligent iteration algorithms, the present invention realizes the rapid iterative optimization of the full scale and multi-environmental elements of building groups in alpine regions, significantly shortens the design cycle, speeds up the project progress, and significantly improves the comprehensive iterative optimization efficiency;

[0040] 2. By optimizing the indoor and outdoor ventilation and heat preservation performance, the present invention significantly improves the energy efficiency of buildings in alpine regions, reduces heating costs and energy consumption, and at the same time ensures the comfort of indoor temperature, significantly improving the energy-saving level and comfort of the overall building group;

[0041] 3. The present invention utilizes environmental simulation software and intelligent measurement models, and this method can provide more accurate and scientific data support to help architects and urban planners make more reasonable design decisions, significantly improving the global measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] The following further elaborates on the present invention Figure 1 with reference to the appended

[0044] Embodiment: An indoor ventilation and heat preservation optimization method for Mongolian buildings based on an intelligent measurement model, as Figure 1 shown, includes the following steps:

[0045] S1: Collection of the wind-heat environment and design scheme in alpine regions

[0046] Staff collect outdoor temperature data 3 times per quarter using an airfoil anemometer with an accuracy of not less than ±0.1 m, and collect outdoor wind speed data 3 times per quarter using an infrared temperature sensor with an accuracy of not less than ±0.5 °C. A linear scanning device samples extreme wind speed and temperature data under cold region climates; obtain the design scheme model and enclosure structure material types from the planning and architectural design units, and collect the thermal conductivity and insulation performance of the building's individual enclosure structures, including walls, doors and windows, and roof materials, using a thermal conductivity tester with an accuracy of not less than ±0.5%.

[0047] The linear scanning device mainly inputs temperature and wind speed measurement data through a data recording and storage device with a storage capacity of more than 1 TB, and uses a microprocessor with a memory of more than 8 GB and a processing speed of 2.5 GHz to traverse the temperature and wind speed measurement data sets 12 times a year [(a1, b1), (a2, b2), …, (a n , b n )], and selects the sample (a max , b n ) with the maximum wind speed as the extreme wind speed data, and selects the sample (a j , b min ) with the lowest temperature as the extreme temperature data.

[0048] S2: Calculation of the outdoor wind-thermal environment index for buildings in alpine regions

[0049] Staff input the extreme wind speed and temperature data collected in step S1 and the three-dimensional model of the building complex into outdoor environment simulation software, and output a wind and thermal environment simulation color scale map of the building complex range with a resolution of 1920x1080 pixels. Subsequently, divide the simulation color scale map into grids of 10*10 pixels, calculate the wind speed and temperature data at the center points of the grids through the hue mapping module and form two 192*108 pixel matrices. Perform numerical determination on the matrices through the conditional threshold module, re-label the comfortable values of the wind-thermal environment as 0, and re-label the uncomfortable values of the wind-thermal environment as 1 to form a conditional threshold matrix; and perform matrix addition measurement and element summation measurement on the two conditional threshold matrices to obtain the outdoor wind-thermal environment comfort index F 室外 ;

[0050] The hue mapping module mainly uses an NVIDIA GeForce RTX 3080 or higher-level GPU graphics processing device and uses processing software such as MATLAB or OpenCV for processing. Specifically: divide the simulation color scale map into grids of 100*100 pixels, and extract the hues (x1, x2, x3, …, x n ) and (y1, y2, y3, …, y n) Calculate the temperature T and wind speed V respectively. The changes in temperature and wind speed have a linear mapping relationship with the hue display. The minimum temperature of -40°C corresponds to the red hue of 0°, and the maximum temperature of 35°C corresponds to the blue hue of 240°; the minimum wind speed of 0 m / s corresponds to the red hue of 0°, and the maximum wind speed of 2 m / s corresponds to the blue hue of 240°:

[0051] The calculation formula for temperature T is as follows

[0052]

[0053] where x min and x max are the minimum and maximum values of the hue. In this embodiment, x min = 0, x max = 240, T min and T max are the minimum and maximum values of the temperature. In this embodiment, T min = -40, T max = 35, x is the given hue value, and T is the corresponding temperature value;

[0054] The calculation formula for wind speed V is as follows:

[0055]

[0056] where y min and y max are the minimum and maximum values of the hue. In this embodiment, y min = 0, y max = 240, V min and V max are the minimum and maximum values of the wind speed. In this embodiment, V min = 0, V max = 2, y is the given hue value, and V is the corresponding temperature value;

[0057] The condition threshold module mainly traverses the relationship between each element in the temperature and wind speed matrices and the outdoor comfortable hot wind index T 舒适 and V 舒适 through a microprocessor with a memory of more than 8 GB and a processing speed of 2.5 GHz, and reassigns values to the matrix. The matrix form is as follows:

[0058]

[0059] The calculation formula is as follows:

[0060] T n - T 舒适 > 0, T n = 0; T n - T 舒适 ≤ 0, Tn = 1

[0061] V n -V 舒适 ≥ 0, V n = 1; V n -V 舒适 ≤ 0, V n = 0;

[0062] The matrix addition measurement and the element summation measurement mainly add the elements in the temperature and wind speed matrices with the condition threshold module re-assigned to obtain a new matrix with element values in the range of [0, 2]. The matrix form is as follows:

[0063] <![CDATA[T1+V1]]> <![CDATA[T2+V2]]> <![CDATA[T3+V3]]> <![CDATA[T4+V4]]> <![CDATA[T5+V5]]> <![CDATA[T6+V6]]> <![CDATA[T7+V7]]> <![CDATA[T8+V8]]> <![CDATA[T9+V9]]> <![CDATA[T 10 +V 10 > <![CDATA[T 11 +V 11 > <![CDATA[T 12 +V 12 > … … … …

[0064] After summing up the elements of the above matrix, the outdoor wind-thermal environment discomfort index F is obtained 室外 .

[0065] S3: Calculation of the indoor wind-thermal environment index of a single building in alpine regions

[0066] The staff first obtain the wind speed and temperature data of the matrix around the single building, and together with the thermal insulation performance of the envelope structure and the three-dimensional model of the single building collected in step S1, input them into the indoor environment simulation software that can simulate air flow, temperature distribution, and heat conduction, and output a wind and thermal environment simulation color scale map of the single building range with a resolution of 1920x1080 pixels. Subsequently, the wind speed and temperature data of the grid center point are calculated through the hue mapping module, and then the single building ventilation and warmth preservation index F is obtained through the weighted integral algorithm 单体 , and the ventilation and warmth preservation indexes of each single building are accumulated to obtain the overall indoor ventilation and warmth preservation index F 室内 ;

[0067] The weighted integral algorithm mainly calculates the single building thermal environment index F 热 and the single building wind environment index F 风 , and calculates F 单体 by adding the weights. The formula is as follows:

[0068] F 单体 = F 热 + cF 风

[0069]

[0070] Among them, a is the wind speed suitable for the indoor wind environment, b is the temperature suitable for the indoor thermal environment, and c is the index weight; in this embodiment, a is set to 0.2 m / s, b is set to 22 °C, and c is set to 100.

[0071] S4: Automatic adjustment of the layout of the building complex and the envelope structure of a single building

[0072] Automatically adjust the layout of the building complex and the single building envelope structure of the site to be designed, integrate the adjusted model into the modeling software, and return to step S2 to recalculate the total score F of the wind-heat environment. 室外 , if the value of F 室外 increases, continue to step S3, otherwise jump to step S4;

[0073] The method for automatically adjusting the layout of the building complex is specifically as follows: Call the intelligent adjustment module for the layout of the building complex, define the reward function according to the local building code, set three optimization objectives of sunshine duration, building spacing, and height limit, and intelligently adjust the building spacing, height, and orientation through the deep reinforcement learning model;

[0074] The deep reinforcement learning model mainly defines a reward function centered on sunshine duration, building spacing, and height limit, and uses the deep reinforcement learning model to intelligently optimize the spacing, height, and orientation of the building complex. This process combines the pattern recognition ability of deep learning and the decision-making mechanism of reinforcement learning to optimize the building layout, improve the sunshine efficiency and space utilization efficiency while complying with the local building code;

[0075] The method for automatically adjusting the single building envelope structure is specifically as follows: Call the intelligent adjustment module for the doors and windows of the building monomer, and automatically change the position and size of the doors and windows through the random parameters of the doors and windows. Among them, the random parameter for the position movement of the doors and windows is (-1m, 1m), and the random parameter for the size adjustment is (-50cm, 50cm). Use the open-source 3D model library of doors and windows to match the doors and windows models of corresponding sizes through the Bayesian algorithm.

[0076] S5: Iteration of the wind-heat environment based on the simulated annealing algorithm

[0077] The present invention sets the iteration strategy through the simulated annealing algorithm to output the optimal model of the indoor and outdoor environment of buildings in alpine regions. First, set the exponential cooling strategy, and then determine whether to adopt the new model based on the difference between F before and after iteration 室内 and the annealing strategy; if the total score of the new model n is better, accept it as the current solution; if the total score of the new model n is worse, accept the worse solution with a probability of m. As the annealing temperature decreases, the acceptance probability gradually decreases, and the iteration stops when the termination annealing temperature is reached; the exponential cooling strategy specifically adopts the following formula:

[0078] T new = a·T old

[0079] where, T new is the new annealing temperature, which is set to 10 in this embodiment, T new is the annealing temperature of the previous step, which is set to 10 in this embodiment, T old is the annealing temperature of the previous step, which is set to 10 in this embodiment, T oldSet to 10 -5 , where a is the cooling factor, and in this embodiment, a is set to 0.5.

[0080] S6: Model output and display

[0081] Output the three-dimensional models of the building complex and individual buildings to a cave-style virtual reality interaction device for interactive display and modification. The device should be equipped with a digital screen with a size of more than 8 feet and a pixel count of more than 3840x2160.

[0082] Working principle: First, the staff needs to collect the wind speed, wind direction, temperature and other thermal environment data of the site to be designed, and at the same time collect the performance data of the design scheme model and building materials. These data are the basis for subsequent calculations and simulations; then calculate the thermal environment index outside the buildings in alpine regions to obtain the outdoor thermal environment comfort index F 室外 , calculate the outdoor thermal environment comfort index by inputting the wind and thermal environment simulation color scale map of the building complex range, and then measure the thermal environment index inside the individual buildings in alpine regions to obtain the overall indoor ventilation and warmth preservation index F 室内 , input the wind speed, temperature data around the individual building matrix and the performance data of building materials into the indoor environment simulation software to obtain the wind and thermal environment simulation color scale map of the individual building range, calculate the individual ventilation and warmth preservation index, and finally accumulate the ventilation and warmth preservation indexes of each building monomer to obtain the overall indoor ventilation and warmth preservation index;

[0083] The present invention automatically adjusts the layout of the building complex and the enclosure structure of the individual buildings, recalculates the thermal environment index. If the index is improved, continue with the optimization and adjustment. If not, end the optimization. Set the iteration strategy based on the simulated annealing algorithm, continuously optimize the design scheme, obtain the optimal model of the indoor and outdoor environment of the buildings in alpine regions, and finally perform interactive display on the obtained optimal model of the indoor and outdoor environment of the buildings, so that designers and relevant personnel can evaluate and make decisions on the design scheme.

[0084] This specific embodiment is only an explanation of the present invention and is not a limitation of the present invention. Those skilled in the art can make modifications without creative contributions to this embodiment according to needs after reading this specification, but as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A Monte building indoor ventilation and heating optimization method based on an intelligent measurement model, characterized by: The following steps are involved: S1: Collect the wind and heat environment data of the site to be designed, the design model and the performance data of the construction materials; S2: Calculate the outdoor wind and heat environment index of buildings in high-cold areas and obtain the outdoor wind and heat environment comfort index F 室外 ; Input the wind and heat environment data and design scheme model collected in step S1 into the outdoor environment simulation software to obtain the wind and heat environment simulation color code map of the building complex; divide the simulation color code map into grids, extract the hue of the center point of the grid of the thermal environment and wind environment simulation map respectively, calculate the temperature T and wind speed V respectively, and the changes of temperature and wind speed are linearly mapped with the hue display: The temperature T calculation formula is as follows: Among them, x min and x max is the minimum and maximum value of hue, T min and T max is the minimum and maximum temperature, x is the given hue value, and T is the corresponding temperature value; The calculation formula of wind speed V is as follows: Among them, y min and max is the minimum and maximum value of hue, V min and V max is the minimum and maximum value of wind speed, y is the given hue value, and V is the corresponding temperature value; Traverse the elements in the temperature and wind speed matrix and the outdoor comfortable wind and heat index T 舒适 and V 舒适 The size relationship is calculated, and the matrix is ​​reassigned to form a conditional threshold matrix. The elements in the temperature and wind speed matrices reassigned by the conditional threshold matrix are added to obtain a new matrix with element values ​​of [0, 2]. The elements of the matrix are summed to obtain the outdoor wind-heat environment discomfort index F. 室外 ; S3: Calculate the indoor wind and heat environment index of single buildings in high-cold areas to obtain the overall indoor ventilation and warmth index F 室内 ; The wind speed and temperature data of the matrix around the building unit are obtained, and input into the indoor environment simulation software together with the building material performance data described in step S1, to obtain the wind and thermal environment simulation color code diagram of the building unit range, calculate the wind speed and temperature data through the hue mapping module, and then obtain the ventilation and thermal insulation index F of the building unit through the weighted integral algorithm. 单体 , accumulate the ventilation and thermal insulation index F of each building unit 单体 , and obtain the overall indoor ventilation and warmth index F 室内 ; The weighted integral algorithm is specifically as follows: the thermal environment index F of the individual buildings is calculated respectively. 热 And the wind environment index F of a single building 风 , and calculate F by weighted addition 单体 , the formula is as follows: F 单体 =F 热 +cF 风 Among them, a is the suitable wind speed for the indoor wind environment, b is the suitable temperature for the indoor thermal environment, and c is the index weight; S4: Automatically adjust the building complex layout and the single enclosure structure of the site to be designed, integrate the adjusted model into the modeling software, and return to step S2 to recalculate the total wind and heat environment score F 室外 , if F 室外 If the value increases, continue to step S3, otherwise jump to step S4; The specific method of automatically adjusting the layout of the building complex of the site to be designed is: calling the intelligent adjustment module of the building complex layout, and intelligently adjusting the building spacing, height and orientation through the deep reinforcement learning model; S5: The iterative strategy is set based on the simulated annealing algorithm to obtain the optimal model of indoor and outdoor environment of buildings in high-cold areas; The iterative strategy is an exponential cooling strategy, which specifically adopts the following formula: T new =a·T old Among them, T new is the new annealing temperature, T old is the annealing temperature of the previous step, a is the cooling factor; S6: interactively display the optimal model of the building's indoor and outdoor environment obtained in step S5.

2. According to claim 1, a Monte building indoor ventilation and heating optimization method based on an intelligent measurement model is characterized by: The deep reinforcement learning model is specifically as follows: the model defines a reward function with sunshine time, building spacing and height limit as the core, and uses the deep reinforcement learning model to intelligently optimize the spacing, height and orientation of the building complex.

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