Building curtain wall BIM forward design method

Abstract

The invention discloses a building curtain wall BIM forward design method, and relates to the technical field of building curtain wall design, and the method specifically comprises the following steps: obtaining environment dynamic information of each region in real time, and respectively generating an air flow equilibrium coefficient and a thermal effect index of each region; and constructing a heat effect risk model for the generated air flow equilibrium coefficient and heat effect index of each region, generating a heat effect risk coefficient of each region, and predicting a risk level of a heat effect caused by unbalanced air flow in each region of the air layer in the double-layer curtain wall. Dividing each area of the air layer in the double-layer curtain wall into a high-risk area, a medium-risk area and a low-risk area according to the prediction result; and taking corresponding air flow optimization and heat effect control measures according to the division result of each area of the air layer in the double-layer curtain wall. The heat effect problem caused by unbalanced air flow of the double-layer curtain wall is solved, and the effects of optimizing the heat preservation performance and reducing energy consumption are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of building curtain wall design, and in particular to a BIM forward design method for a building curtain wall. Background Art

[0002] The building curtain wall refers to the lightweight wall structure covering the exterior of the building. It not only beautifies the appearance of the building, but also has the functions of windproof, rainproof, heat insulation, sound insulation, etc., and can bear the weight of the building and various loads from the external environment, such as wind load, earthquake effect, etc. Traditional curtain wall design often relies on the reverse process of designing first and then simulating. The building curtain wall BIM forward design is a modern design method based on building information modeling (BIM). It integrates the geometric form, material properties, mechanical parameters and other information of the building into a three-dimensional digital model, and conducts comprehensive analysis and optimization in the initial stage of design to ensure the rationality, feasibility and economy of the design. This forward design method can analyze the stress condition, cost-effectiveness and construction feasibility of the curtain wall in real time during the design process, avoid the tedious steps of modifying the design later, and improve the design efficiency and accuracy. In addition, BIM forward design can also perform construction simulation and collision detection before construction, discover potential problems in advance, reduce rework and resource waste during construction, and ensure seamless connection between design and construction. Therefore, the BIM forward design of building curtain walls not only improves design accuracy and construction efficiency, but also plays an important role in reducing costs, optimizing construction management and improving building quality.

[0003] The existing BIM forward design technology for building curtain walls first collects the shape, structure and environmental parameters of the building, and uses BIM software to build an initial three-dimensional model of the curtain wall. The model contains information such as the geometric dimensions, materials, and structural framework of the curtain wall. Then, the designer inputs key design parameters such as wind load, seismic action, temperature change, and installation conditions. The system uses mechanical analysis tools to perform force analysis on these parameters and generate a force report on the curtain wall under different environmental factors. On this basis, the technology will further optimize the design parameters such as the material, thickness, and connection method of the curtain wall to ensure the best balance between structural stability and cost-effectiveness. During the design process, BIM software will also perform construction simulation, predict possible problems during the installation of the curtain wall through collision detection, and propose adjustment plans. Finally, the design refinement generates accurate construction drawings and material lists to support the smooth progress of construction, and dynamically adjusts the design through the construction monitoring and feedback system to ensure that the final curtain wall design accurately matches the actual construction. This design process makes the design of the building curtain wall more refined, systematic, and visual, thereby reducing errors and rework in construction.

[0004] The prior art has the following deficiencies:

[0005] In the double-skin curtain wall design of high-rise buildings, due to the orientation of the building, the difference in wind speed at different heights, and the change in external ambient temperature, the air flow speed and temperature distribution in the curtain wall are often uneven, especially the flow in different areas of the air layer may fluctuate greatly. Due to the uneven air flow, the temperature of some local areas may rise or fall significantly, resulting in serious thermal effects, which in turn affect the overall insulation effect of the curtain wall. However, the existing BIM forward design technology for building curtain walls, especially in terms of fluid simulation, cannot accurately simulate the temperature changes and air flow distribution in these local areas in the air layer, so it is difficult to predict the thermal effect problems caused by uneven air flow. This lack of predictive ability will lead to the failure to discover and optimize the air flow plan in advance during the design process. In actual applications, excessively high or low local temperatures may increase the energy consumption of the building, reduce the efficiency of indoor temperature regulation, and may accelerate the aging or deformation of curtain wall materials, ultimately shortening the service life of the curtain wall and increasing operating and maintenance costs.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a BIM forward design method for building curtain walls to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a BIM forward design method for a building curtain wall, specifically comprising the following steps:

[0009] The air layer inside the double-layer curtain wall is evenly divided into several areas according to the curtain wall height, wind speed difference and air flow characteristics, and a sensor network is deployed in each area;

[0010] Acquire the environmental dynamic information of each area in real time, analyze it after acquisition, and generate the air flow balance coefficient and thermal effect index of each area;

[0011] A thermal effect risk model is constructed based on the air flow balance coefficient and thermal effect index of each area, and the thermal effect risk coefficient of each area is generated. After the generation, the thermal effect risk coefficient is analyzed to predict the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-layer curtain wall, and the air layer inside the double-layer curtain wall is divided into high-risk area, medium-risk area and low-risk area according to the prediction results;

[0012] According to the division results of each area of ​​the air layer inside the double-skin curtain wall, corresponding air flow optimization and thermal effect control measures are taken;

[0013] Continuously monitor the dynamic environmental information of each area, dynamically adjust the air flow path, regularly update the analysis model, and optimize the air flow design and thermal effect control strategy of the double-skin curtain wall.

[0014] Preferably, the environmental dynamic information of each area is acquired in real time, and analyzed after acquisition to generate the air flow balance coefficient and thermal effect index of each area respectively, which specifically includes the following steps:

[0015] Acquire environmental dynamic information of each area in real time and perform preprocessing after acquisition;

[0016] Extracting air flow information and temperature conduction information from the pre-processed environmental dynamic information of each area;

[0017] The air flow information and temperature conduction information in the extracted environmental dynamic information of each area are analyzed to generate the air flow balance coefficient and thermal effect index of each area respectively.

[0018] Preferably, the logic for obtaining the air flow balance coefficient of each area is as follows:

[0019] The air flow information is extracted from the preprocessed environmental dynamic information of each area, including the actual wind speed at the entrance of each area at different times within a period of time, the actual wind speed at the exit of each area, the longest path length and the shortest path length in the flow path of each area, and they are calibrated as and It represents the actual wind speed of the air at the entrance of the ith area at time m within a period of time. It represents the actual wind speed of the air at the outlet of the ith area at time m within a period of time. It represents the longest path length of the air flow path in the ith region at time m within a period of time. represents the shortest path length of the air flow path in the i-th region at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0020] Calculate the air flow balance coefficient of each area. The specific calculation formula is as follows:

[0021]

[0022] In the formula, ABC i is the air flow balance coefficient of the ith area.

[0023] Preferably, the logic for obtaining the thermal effect index of each area is as follows:

[0024] The temperature conduction information is extracted from the preprocessed environmental dynamic information of each area, including the average temperature of each area at different times within a period of time, the surface area of ​​air conduction, the average thickness of the air in each area at different times within a period of time, and the thermal conductivity of the air, and calibrated as and δ, represents the average temperature of the ith region at time m within a period of time, represents the surface area of ​​air conduction in the ith region at time m within a period of time, represents the average thickness of the air in the ith region at time m within a period of time, δ represents the thermal conductivity of the air, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0025] The average temperature of each area at different times over a period of time is constructed into a set, and the maximum and minimum values ​​in the set are marked as TAX respectively. i and TIN i ,TAX i Indicates the maximum value of the average temperature of the ith region over a period of time, TIN i It represents the minimum value of the average temperature of the ith region within a period of time;

[0026] Calculate the thermal effect index of each area. The specific calculation formula is as follows:

[0027]

[0028] Where, TEI i is the thermal effect index of the ith region.

[0029] Preferably, a thermal effect risk model is constructed based on the generated air flow balance coefficient and thermal effect index of each area to generate the thermal effect risk coefficient of each area, specifically including the following steps:

[0030] Collect the air flow balance coefficients, thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, and calibrate them as and x represents the numbers of several air flow balance coefficients, several thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set;

[0031] Select the multivariate regression model as the thermal effect risk model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0032]

[0033] In the formula, β 0 , β 1 and β 2 is the regression coefficient;

[0034] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the regression coefficient β is finally determined. 0 , β 1 and β 2 The value of

[0035] Using the final regression coefficient, the constructed thermal effect risk model is input with the air flow balance coefficient ABC for each area generated in real time. i and thermal effect index TEI i , generate the thermal effect risk coefficient ERC of each area in real time i .

[0036] Preferably, the thermal effect risk coefficient ERC of each region is generated i The pre-set thermal effect risk coefficient threshold interval [ERC min , ERC max ] and predicted the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-skin curtain wall according to the comparison results. The air layer inside the double-skin curtain wall was divided into high-risk area, medium-risk area and low-risk area according to the prediction results. The specific comparison analysis and division are as follows:

[0037] If ERC i <ERC min , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is low risk, so the area is classified as a low-risk area;

[0038] If ERC min ≤ERC i ≤ERC max , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is medium risk, so the area is classified as a medium risk area;

[0039] If ERC i >ERC max , the risk level of thermal effect caused by uneven air flow in this area of ​​the air layer inside the double-skin curtain wall is high, so this area is classified as a high-risk area.

[0040] Preferably, according to the division results of each area of ​​the air layer inside the double-layer curtain wall, corresponding air flow optimization and thermal effect control measures are taken, specifically:

[0041] For low-risk areas with a low risk of inducing thermal effects, the control measures taken are to maintain the current air flow design and only conduct routine monitoring without making additional adjustments;

[0042] For medium-risk areas with a medium risk of causing thermal effects, the optimization measures taken are to adjust the air flow path, increase local ventilation and flow stabilization devices, and monitor air flow changes in real time;

[0043] For high-risk areas with a high risk level of causing thermal effects, the control measures taken are to immediately carry out comprehensive optimization of air flow paths, introduce auxiliary ventilation systems and local cooling devices.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] 1. The present invention can significantly improve the monitoring and adjustment capabilities of the imbalanced air flow problem of the double-layer curtain wall. First, the air layer inside the curtain wall is divided into multiple areas and sensors are deployed to ensure that the air flow and temperature changes at different heights and positions can be monitored and analyzed in real time. Combined with the calculation of the air flow balance coefficient and the thermal effect index, the system can accurately capture the air flow fluctuations and temperature differences in local areas. This effectively quantifies the uneven air flow problem that is difficult to solve in traditional BIM design, improves the system's sensitivity to local problems, and thus provides a scientific basis for optimized design.

[0046] 2. The present invention solves the problem of the difficulty in discovering and optimizing air flow solutions in advance in the prior art by introducing a multiple regression model and thermal effect risk prediction. The multiple regression model analyzes the impact of the air flow balance coefficient and the thermal effect index in combination with historical data, and can accurately predict the thermal effect risk level of each area. By dividing the area into high, medium, and low risk areas and adopting corresponding adjustment measures, the system can effectively avoid the increase in energy consumption and aging of curtain wall materials caused by excessively high or low local temperatures. This strategy achieves precise management of different risk levels and ensures that the curtain wall system can maintain efficient operation under different environmental conditions.

[0047] 3. The present invention realizes dynamic optimization and adaptive adjustment by continuously monitoring and regularly updating the analysis model. As environmental conditions change, the system can dynamically adjust the air flow path according to real-time monitoring data to ensure that the thermal effect of each area always remains within a controllable range. This continuous optimization mechanism effectively reduces the negative impact of thermal effects on building energy efficiency and curtain wall material life, and improves the long-term stability and maintainability of the system. At the same time, this method reduces the need for manual intervention, reduces operating costs, and enhances the adaptability and reliability of the building curtain wall system under complex environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0049] Figure 1 The present invention is a flowchart of a BIM forward design method for a building curtain wall. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0051] The present invention provides Figure 1 A BIM forward design method for a building curtain wall is shown, which specifically includes the following steps:

[0052] The air layer inside the double-layer curtain wall is evenly divided into several areas according to the curtain wall height, wind speed difference and air flow characteristics, and a sensor network is deployed in each area;

[0053] BIM software can be used to accurately divide areas by collecting basic data such as the height of the building curtain wall, wind speed differences, and air flow characteristics. The specific division method can be combined with the geometric structure of the building and the external environmental conditions. Through complex fluid mechanics simulation algorithms, different heights, wind speeds, and air flow paths are analyzed to determine the nodes and transition areas where air flow changes significantly. Then, based on these nodes and transition areas, the air layer of the double-layer curtain wall is divided into several uniform areas to ensure that the air flow characteristics of each area remain balanced in the overall design. This area division can also be based on the geometric shape of the building facade, combined with the distribution trend of air flow, to ensure that each area in the complex building design can reflect the actual flow characteristics of the air.

[0054] The deployment of sensor networks in each divided area can be designed by combining the type of sensors and the structural characteristics of the double-layer curtain wall. The specific method is to arrange temperature, wind speed, and wind direction sensors at key points in each area, such as areas with drastic changes in air flow or parts with significant changes in height. The deployment of sensors can be simulated in advance through BIM software to ensure that the sensor position can effectively cover the air flow conditions of the entire area. Each sensor transmits real-time data to the central control system or monitoring platform through wireless communication or wired connection. The layout of sensors must also take into account factors such as obstructions in the area and resistance to air flow to ensure the accuracy of data collection. Through reasonable network layout, sensors form a local adaptive network that can quickly reflect the dynamic changes of air in different areas.

[0055] By dividing the air layer inside the double-layer curtain wall into several areas and deploying a sensor network, the air flow and temperature changes in each area can be accurately captured, solving the "unbalanced air flow" problem in the technical problem. The changes in air flow at different heights and areas are very complex. Evenly dividing the areas and deploying a sensor network can help obtain dynamic environmental data in each area, facilitating the refined management of air flow and thermal effects. This process can ensure that in actual design and application, through real-time monitoring and analysis of the air flow balance coefficient and thermal effect index of each area, potential high thermal effect risk areas can be identified in a timely manner, thereby optimizing the air flow path, reducing energy consumption, improving the thermal insulation effect of the curtain wall, and extending the service life of the curtain wall. This is a key step in solving the thermal effect problem caused by unbalanced air flow, and provides a data basis for subsequent analysis, adjustment and optimization.

[0056] Acquire the environmental dynamic information of each area in real time, analyze it after acquisition, and generate the air flow balance coefficient and thermal effect index of each area;

[0057] In this embodiment, the environmental dynamic information of each area is obtained in real time, and analyzed after acquisition to generate the air flow balance coefficient and thermal effect index of each area respectively, which specifically includes the following steps:

[0058] Acquire environmental dynamic information of each area in real time and perform preprocessing after acquisition;

[0059] The air flow information and temperature conduction information of each area can be collected in real time through the sensor network deployed in each divided area. Sensors can include devices such as wind speed, temperature and wind direction, and transmit data to the central processing system in real time through wireless transmission technology (such as Wi-Fi, LoRa or Zigbee). The central processing system receives the data of each area on the same platform and integrates all information using the data integration module in the BIM software. In this process, the sensors in each area continuously update the data according to the set time interval and collection strategy to ensure that the dynamic environmental information can be accurately and real-time fed back to the system for subsequent analysis.

[0060] The purpose of preprocessing is to ensure the stability, reliability and availability of the collected data, so as to provide a high-quality data foundation for subsequent analysis and calculation. First, it is necessary to detect and remove outliers from the collected environmental dynamic information, such as extreme values ​​or unreasonable data caused by equipment failure or data transmission problems. Secondly, data smoothing is performed to reduce noise interference caused by short-term fluctuations through algorithms such as moving average to ensure the accuracy of data trends. Finally, the data is standardized according to the time series to ensure that data in different regions have a consistent time base and unit comparability. These preprocessing operations can be automatically completed at the software level through data cleaning and correction algorithms to ensure the accuracy and effectiveness of subsequent analysis.

[0061] Extracting air flow information and temperature conduction information from the pre-processed environmental dynamic information of each area;

[0062] Extracting air flow information and temperature conduction information from preprocessed environmental dynamic information can be done through feature recognition and data classification algorithms. First, the software system classifies the preprocessed data into parameters related to air flow (such as wind speed and wind direction) and parameters related to temperature (such as temperature difference in the area and external ambient temperature). Through data labels and rule matching, the system can identify wind speed and wind direction information representing air flow characteristics, and mark data related to temperature conduction. Next, the extracted data is further sorted using time series analysis to extract the velocity change trend and direction distribution reflecting the air flow path, as well as the temperature change data reflecting the temperature gradient. The data processing module in the software system will automatically classify this information into the corresponding data set to ensure that air flow information and temperature conduction information can be accurately extracted for subsequent analysis and calculation.

[0063] The air flow information and temperature conduction information in the extracted environmental dynamic information of each area are analyzed to generate the air flow balance coefficient and thermal effect index of each area respectively.

[0064] In this embodiment, the logic for obtaining the air flow balance coefficient of each area is as follows:

[0065] The air flow information is extracted from the preprocessed environmental dynamic information of each area, including the actual wind speed at the entrance of each area at different times within a period of time, the actual wind speed at the exit of each area, the longest path length and the shortest path length in the flow path of each area, and they are calibrated as and It represents the actual wind speed of the air at the entrance of the ith area at time m within a period of time. It represents the actual wind speed of the air at the outlet of the ith area at time m within a period of time. It represents the longest path length of the air flow path in the ith region at time m within a period of time. represents the shortest path length of the air flow path in the i-th region at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0066] To extract the air flow information of each area after preprocessing, it can be achieved by combining sensor networks and software algorithms in a variety of ways. First, in the sensor network deployed in each area, the wind speed sensor will monitor the wind speed of the air at the inlet and outlet in real time. These sensors obtain wind speed change data over a period of time through a timed sampling mechanism. Specifically, the sensor will record the air flow data at different times and upload it to the central database in the BIM system through the data processing module. The longest and shortest paths in each area can be pre-calculated by fluid mechanics simulation software. The BIM software will simulate the flow path of air in different areas according to the geometric structure and air flow characteristics of the building, mark all possible paths of air from the inlet to the outlet, and automatically identify the longest and shortest paths through path length calculation. This simulation process combines the speed of air flow and the structural layout of the building, simulates it through complex fluid mechanics analysis algorithms, and records the longest and shortest path lengths of each area as quantitative data in the system. All wind speed data and path length data will be collected by sensors and analyzed by fluid simulation, and finally transmitted to the data processing module in the software system for classification and further calculation. This process is completed entirely by the collaborative software and hardware systems, ensuring that air flow information can be accurately extracted for subsequent analysis and calculations.

[0067] Calculate the air flow balance coefficient of each area. The specific calculation formula is as follows:

[0068]

[0069] In the formula, ABCi is the air flow balance coefficient of the ith area.

[0070] This calculation formula comprehensively evaluates the balance of air flow by combining multiple factors such as wind speed and path differences. The first part of the formula Represents the difference in wind speed between the inlet and outlet of area i during the time period. This part is used to reflect the degree of change in air velocity at different time points. The greater the difference in wind speed, the more significant the imbalance of air flow. It represents the difference between the longest and shortest paths in the air flow path of the area, and is used to measure the volatility of the air flow path. If the difference between the longest path and the shortest path is large, it indicates that the air may flow slowly in some paths, or accelerate in some paths, which further reflects the fluctuation of the flow. Multiplying these two factors can comprehensively evaluate the flow velocity and path changes of the air at a certain moment and space, and form a balance evaluation value at a time point. The formula averages these values ​​for the time period g. This is to eliminate the instability of the air flow at individual moments and obtain an overall air flow balance coefficient ABC. i , which reflects the overall balance of air flow in area i over a period of time. This calculation method ensures a comprehensive evaluation of wind speed and path differences, and can more accurately reflect the imbalance of air flow.

[0071] Air flow balance coefficient ABC in the i-th area i The larger the coefficient, the more unbalanced the air flow in the area, the greater the wind speed difference and path fluctuation. This unbalanced air flow will weaken the air convection and heat transfer effect, and further aggravate the temperature difference and thermal effect in the area. Therefore, the higher the air flow balance coefficient, the higher the risk level of thermal effect caused by unbalanced air flow in the area inside the double-layer curtain wall, which may cause the temperature in the local area to rise or fall sharply, thus affecting the overall thermal insulation performance and energy efficiency of the curtain wall. When ABC i When it is smaller, it means that the air flow in the area is more uniform and the risk of thermal effect is relatively low, which helps to maintain the temperature stability in the area.

[0072] In this embodiment, the logic for obtaining the thermal effect index of each area is as follows:

[0073] The temperature conduction information is extracted from the preprocessed environmental dynamic information of each area, including the average temperature of each area at different times within a period of time, the surface area of ​​air conduction, the average thickness of the air in each area at different times within a period of time, and the thermal conductivity of the air, and calibrated as and δ, represents the average temperature of the ith region at time m within a period of time, represents the surface area of ​​air conduction in the ith region at time m within a period of time, represents the average thickness of the air in the ith region at time m within a period of time, δ represents the thermal conductivity of the air, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers;

[0074] In order to extract the temperature conduction information of each area at different times within a period of time, it is mainly carried out by combining sensor networks with BIM software. First, the temperature sensors deployed in each area will record the average temperature data of the air in the area in real time. These sensors can automatically collect temperature at a predetermined time interval and transmit the data to the central system through a wireless network (such as Wi-Fi or LoRa technology). In the central processing system, these temperature data will be classified and sorted according to the timestamp to calculate the average temperature of each area at different time points. At the same time, the surface area of ​​air conduction is determined by modeling the building geometry and air flow path. Through the geometric model analysis in the BIM software, the air flow path and contact surface in each area will be accurately calculated, and combined with the environmental dynamic data obtained by the sensor, the air conduction surface area in different time periods will be determined. Next, the average thickness of the air in the area can be obtained by fluid mechanics modeling simulation. The BIM system uses factors such as air pressure difference, flow rate, and temperature distribution inside and outside the building to automatically simulate the thickness distribution of air in each area. Finally, the thermal conductivity of the air can be obtained through a preset physical model. The thermal conductivity of the air is usually relatively stable within a known standard range, so it is only necessary to call the corresponding thermal conductivity data in the system according to the regional conditions. These quantitative data are obtained through sensors and combined with BIM modeling to ensure that all information can be transmitted to the system in real time and accurately for subsequent calculation and analysis of thermal effect index.

[0075] The average temperature of each area at different times over a period of time is constructed into a set, and the maximum and minimum values ​​in the set are marked as TAX respectively. i and TIN i ,TAX i Indicates the maximum value of the average temperature of the ith region over a period of time, TIN i It represents the minimum value of the average temperature of the ith region within a period of time;

[0076] Calculate the thermal effect index of each area. The specific calculation formula is as follows:

[0077]

[0078] Where, TEI i is the thermal effect index of the ith region.

[0079] The calculation formula of the thermal effect index combines multiple variables to accurately evaluate the thermal effect caused by uneven air flow in each area. First, Partly reflects the highest temperature in the region TAX i With the minimum temperature TIN i and compare it with the air thickness Normalization is performed. This is to assess the intensity of temperature fluctuations within the region, as greater temperature differences and less air thickness mean stronger thermal effects. The thermal conductivity δ reflects the ability of air to transfer heat under different conditions, while the surface area Indicates the heat transfer area where air contacts the curtain wall or other materials, and the temperature difference changes It reflects the rate of change of temperature over time. This part is used to evaluate the heat conduction effect between air and materials. The summation part of the formula takes into account the data at multiple time points, eliminates short-term fluctuations, and obtains a more representative thermal effect index. By combining these factors, the formula can accurately evaluate the thermal effect intensity of each area, thus providing a scientific basis for subsequent optimization and adjustment.

[0080] Thermal effect index TEI of the i-th region i The larger it is, the stronger the thermal effect caused by the uneven air flow in the area, and the more significant the temperature difference and heat conduction efficiency. The thermal effect index reflects factors such as the fluctuation amplitude of temperature in the area, the thickness of air conduction, the thermal conductivity, and the contact area between air and the conduction surface. If the thermal effect index is high, it means that the temperature difference in the area is large, the air flow and heat transfer effects are poor, resulting in drastic temperature changes in the local area. Such drastic temperature changes will increase thermal stress and energy consumption. Therefore, the higher the thermal effect index, the higher the risk level of thermal effects caused by uneven air flow in the area, which may lead to a decrease in the overall insulation effect of the curtain wall, increased energy consumption, and accelerated material aging.

[0081] A thermal effect risk model is constructed based on the air flow balance coefficient and thermal effect index of each area, and the thermal effect risk coefficient of each area is generated. After the generation, the thermal effect risk coefficient is analyzed to predict the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-layer curtain wall, and the air layer inside the double-layer curtain wall is divided into high-risk area, medium-risk area and low-risk area according to the prediction results;

[0082] In this embodiment, a thermal effect risk model is constructed based on the generated air flow balance coefficient and thermal effect index of each area to generate the thermal effect risk coefficient of each area, which specifically includes the following steps:

[0083] Collect the air flow balance coefficients, thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, and calibrate them as and x represents the numbers of several air flow balance coefficients, several thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set;

[0084] In order to collect the air flow balance coefficient, thermal effect index and corresponding thermal effect risk coefficient of each area generated in the past period of time, this can be achieved through the database management system of the BIM software and the data recording module of the sensor network. First, the sensors deployed in each area will monitor the air flow information and temperature conduction information in real time, and regularly upload the data to the central database, which will store and archive the data. Through the preset data collection cycle, the software system will automatically calculate the air flow balance coefficient in each time period. Thermal effect index The results are saved in the historical data set. At the same time, the data analysis module in the BIM software will generate and Combined with the thermal effect risk model, the corresponding thermal effect risk coefficient is automatically generated The software system will mark and store these historical data in time series, and call these data for subsequent regression analysis and model optimization as needed. In this way, historical data can be effectively collected and archived to ensure data integrity and traceability.

[0085] It is reasonable and necessary to limit x to a positive integer greater than or equal to 3, because in regression analysis, there is only one target equation and three variables. In order to calculate the three regression coefficients β 0 , β 1 and β 2 The value of x must be sufficient to form three independent equations. This means that we need at least three different historical data points to construct these three equations in order to calculate the specific value of each regression coefficient through multiple regression. If x is less than 3, there are not enough equations to form a complete set of equations, and it is impossible to calculate the three regression coefficients. Therefore, limiting x to 3 is to ensure that there is enough historical data to accurately perform regression analysis and ultimately generate a reliable thermal effect risk model.

[0086] Select the multivariate regression model as the thermal effect risk model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0087]

[0088] In the formula, β 0 , β 1 and β 2 is the regression coefficient;

[0089] The reason for choosing the multiple regression model as the heat effect risk model is that the multiple regression model can handle the relationship between multiple independent variables and predict the results through these variables. Thermal effect index As two independent predictors, the thermal effect risk coefficient Through multiple regression analysis, the contribution of each variable to the risk of thermal effect can be accurately quantified. Specifically, the regression coefficient β 0 is the intercept term, representing the and When both are zero, the basic value of the thermal effect risk coefficient; β 1 yes The regression coefficient of β indicates the influence of air flow balance on the risk of thermal effect; 2 yes The regression coefficients of the thermal effect index and the contribution of the thermal effect risk to the thermal effect risk are quantified. By training the model with historical data and determining the three regression coefficients, we can better predict and evaluate the thermal effect risk level of each area in the future, so as to make more scientific and accurate adjustments and optimizations.

[0090] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the regression coefficient β is finally determined. 0 , β 1 and β 2 The value of

[0091] This step can be automated in the software through regression analysis algorithms. First, the system uses the collected historical data to take the known air flow balance coefficient, thermal effect index and corresponding thermal effect risk coefficient as the training set and inputs them into the regression analysis module. The software will use optimization algorithms such as least squares method or gradient descent method to minimize the predicted value (calculated by the regression model). ) and the actual observed value (in historical data ). In specific implementation, the software will adjust the regression coefficient β through multiple iterations. 0 , β 1 and β 2, until the prediction error is minimized, and finally the best regression coefficient value is determined. Through this optimization process, the model can be made more accurate, ensuring that the generated thermal effect risk coefficient accurately reflects the impact of air flow and temperature changes on thermal effect risk. The purpose of this is to improve the prediction accuracy of the model, thereby ensuring that more reliable thermal effect risk prediction and management decisions can be made based on real-time data in actual applications.

[0092] Using the final regression coefficient, the constructed thermal effect risk model is input with the air flow balance coefficient ABC for each area generated in real time. i and thermal effect index TEI i , generate the thermal effect risk coefficient ERC of each area in real time i .

[0093] In this embodiment, the thermal effect risk coefficient ERC of each region is generated i The pre-set thermal effect risk coefficient threshold interval [ERC min , ERC max ] and predicted the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-skin curtain wall according to the comparison results. The air layer inside the double-skin curtain wall was divided into high-risk area, medium-risk area and low-risk area according to the prediction results. The specific comparison analysis and division are as follows:

[0094] If ERC i <ERC min , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is low risk, so the area is classified as a low-risk area;

[0095] This means that the air flow in this area is relatively balanced and the thermal effect is low. In this case, the convection and heat conduction effects of the air are relatively stable and will not have a significant impact on the thermal insulation performance or structural materials of the curtain wall. The temperature difference is small and the energy consumption is relatively small. In terms of impact, such low-risk areas basically do not need to be adjusted and can maintain the existing design and operation status. When dividing specifically, the software system will automatically divide all ERC-compliant areas into i <ERC min The areas with the lowest conditions are marked as low-risk areas, suggesting that these areas are controllable in terms of thermal effects and do not require further optimization.

[0096] If ERC min ≤ERC i ≤ERC max , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is medium risk, so the area is classified as a medium risk area;

[0097] This indicates that there is a certain degree of air flow imbalance and thermal effect in the area, but it is still within the controllable range. In this case, the temperature difference in the area is more obvious, and the air flow is not completely uniform, which may have some negative impact on the thermal insulation performance and energy consumption of the curtain wall. If proper optimization is not performed, it may cause increased energy consumption or gradual aging of local materials after long-term operation. When dividing, the system will divide ERC min ≤ERC i ≤ERC max The areas with high heat load are marked as medium risk areas, and appropriate optimization is recommended, such as adjusting ventilation paths or improving air flow to balance the thermal effects.

[0098] If ERC i >ERC max , the risk level of thermal effect caused by uneven air flow in this area of ​​the air layer inside the double-skin curtain wall is high, so this area is classified as a high-risk area.

[0099] This situation means that the air flow in this area is seriously unbalanced and the risk of thermal effect is high. This indicates that there may be serious temperature differences and heat conduction problems in this area, resulting in overheating or overcooling in some areas. This will have a significant negative impact on the thermal insulation performance, energy efficiency and material life of the curtain wall, and may accelerate the aging of the curtain wall material, increase energy consumption, and even affect the overall performance of the building. The system will automatically i >ERC max The area is classified as a high-risk area and an immediate warning is triggered, indicating that drastic adjustments are needed, such as increasing auxiliary ventilation or optimizing air flow design to mitigate the negative impact of heat effects.

[0100] The "pre-set thermal effect risk factor threshold interval" can be determined by a combination of historical data analysis and model simulation. First, the software system will generate a series of corresponding thermal effect risk coefficients (ERCERCERC) based on the long-term monitored data of the air flow balance coefficient (ABCABCABC) and thermal effect index (TEITEITEI) of each region. By analyzing these historical data, the changing trends and risk levels of thermal effects in each region under different conditions can be identified. Then, the system can apply statistical analysis methods, such as cluster analysis or percentile analysis, to classify the thermal effect risk coefficients in the historical data according to high and low distribution. Through this classification, the system can derive the typical thermal effect risk coefficient ranges for low-risk, medium-risk and high-risk areas. Then, based on the results of these historical data analysis, the software will automatically calculate and set the corresponding risk factor threshold ERC min and ERC max, ensuring that these values ​​accurately reflect the critical points of different risk levels. Through this process, the software system can generate a suitable thermal effect risk factor threshold range for each project for real-time monitoring and risk assessment.

[0101] According to the division results of each area of ​​the air layer inside the double-skin curtain wall, corresponding air flow optimization and thermal effect control measures are taken;

[0102] In this embodiment, according to the division results of each area of ​​the air layer inside the double-layer curtain wall, corresponding air flow optimization and thermal effect control measures are taken, specifically:

[0103] For low-risk areas with a low risk of inducing thermal effects, the control measures taken are to maintain the current air flow design and only conduct routine monitoring without making additional adjustments;

[0104] For low-risk areas, the software system will continuously collect dynamic data on air flow and temperature through a sensor network and input this data into the monitoring module in the BIM system. Since the air flow in this area is balanced and the risk of thermal effects is low, the software system will regularly analyze the dynamic environmental information of this area based on the preset monitoring frequency. If the monitored data shows that the air flow and temperature remain within the normal range, the system will not make further adjustments and only keep the records. The reason for this is to reduce unnecessary interventions, maintain the existing design, and ensure that the air flow continues to be smooth through regular monitoring to avoid energy waste and unnecessary maintenance.

[0105] For medium-risk areas with a medium risk of causing thermal effects, the optimization measures taken are to adjust the air flow path, increase local ventilation and flow stabilization devices, and monitor air flow changes in real time;

[0106] In medium-risk areas, the software system will analyze and identify unbalanced areas of the air flow path based on the air velocity and temperature data obtained by the sensor in real time, combined with the fluid mechanics simulation module in the BIM software. In specific implementation, the software will automatically calculate the optimal path for air flow, recommend adjusting the position or airflow direction of the existing ventilation system, and evaluate the effect of the optimization through virtual simulation. To enhance the stability of air flow, the software can also recommend adding local ventilation equipment. The reason for real-time monitoring is that there is a certain risk of thermal effects in this area, and it is necessary to optimize the airflow path and monitor to ensure that the thermal effect does not deteriorate further.

[0107] For high-risk areas with a high risk level of thermal effects, the control measures taken are to immediately carry out comprehensive optimization of air flow paths, introduce auxiliary ventilation systems and local cooling devices to significantly reduce the risk of thermal effects.

[0108] For high-risk areas, the software system will trigger emergency optimization measures when the sensor network detects significant air flow imbalance and high temperature effects. The specific implementation methods include re-planning the air flow path through the simulation model of the BIM software and immediately adjusting the layout of the ventilation system, which may involve the introduction of auxiliary ventilation systems to accelerate air flow. The system will also determine the specific cooling needs based on the calculation of the thermal effect index, and automatically enable local cooling devices such as adding fans or refrigeration equipment through the intelligent control system. The reason for this is that there is already a large temperature imbalance in the high-risk area. If drastic adjustments are not made immediately, it may lead to more serious thermal effects and energy waste.

[0109] Continuously monitor the dynamic environmental information of each area, dynamically adjust the air flow path, regularly update the analysis model, and optimize the air flow design and thermal effect control strategy of the double-skin curtain wall.

[0110] The way to achieve continuous monitoring mainly depends on the integration of sensor networks and BIM systems. Sensors deployed in various areas will collect dynamic environmental information such as air flow rate, temperature, wind direction, etc. in real time, and send these data to the central monitoring system through wireless communication (such as Wi-Fi, LoRa) or wired transmission technology. The data processing module in the BIM system will automatically archive and analyze these data, and display the air flow and temperature conditions of each area in real time. Through the BIM monitoring module, operators can view real-time data changes in each area on a centralized interface. The reason for doing this is to ensure that any imbalance in air flow or temperature anomalies can be detected at the first time so that corresponding adjustments can be made to prevent the deterioration of thermal effects.

[0111] When the monitoring system detects abnormal air flow in a certain area, the software will start the dynamic adjustment function. Through the built-in fluid mechanics analysis module, the BIM system will automatically calculate and simulate the optimal air flow path, and re-plan the ventilation path based on parameters such as the geometry of the area, wind speed and temperature. The simulation module will recommend how to adjust the air flow path, such as adding or moving the location of the vents, guiding the airflow, etc. These adjustment suggestions can be automatically generated by the system and prompted to the user, or directly adjusted by remotely controlling the ventilation equipment in the building through the intelligent control system. This is done to quickly respond to the problem of uneven air flow and ensure that the air can circulate smoothly and evenly to reduce the generation of local thermal effects.

[0112] The analysis model in the BIM system is regularly updated based on new sensor data. By learning from historical data and inputting current data, the system automatically adjusts the parameters of the prediction model to more accurately assess future air flow trends and thermal effect risks. This is achieved by applying machine learning algorithms to optimize the model so that it can adjust itself based on the changing dynamic information of the environment. This approach ensures that the analysis model always maintains high accuracy and provides reliable data support for the building's air flow design and thermal effect control. The reason for this is that the environmental conditions inside and outside the building change at any time, and the distribution of air flow and temperature will fluctuate accordingly. Regularly updating the model can ensure the accuracy and timeliness of decision-making.

[0113] Based on the real-time data collected by sensors and the updated analysis model, the BIM system will periodically re-evaluate the air flow design within the double-skin curtain wall and make optimization suggestions. This includes optimizing ventilation paths, adjusting the operation of ventilation equipment, and improving thermal effect control strategies. For example, the system may recommend adding auxiliary ventilation equipment in specific areas or adjusting the curtain wall structure design to better guide airflow. This strategy optimization can not only be achieved through software simulation to obtain the best solution, but can also be verified through historical data to ensure that the optimization measures can effectively reduce energy consumption and extend the service life of the curtain wall. Continuous optimization is to ensure that the air flow design of the curtain wall always remains in the best state when facing different external environmental changes, thereby improving the overall energy efficiency of the building.

[0114] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0116] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A BIM forward design method for building curtain walls, characterized in that: The specific steps include: The air layer inside the double-layer curtain wall is evenly divided into several areas according to the curtain wall height, wind speed difference and air flow characteristics, and a sensor network is deployed in each area; Acquire the environmental dynamic information of each area in real time, analyze it after acquisition, and generate the air flow balance coefficient and thermal effect index of each area; A thermal effect risk model is constructed based on the air flow balance coefficient and thermal effect index of each area, and the thermal effect risk coefficient of each area is generated. After the generation, the thermal effect risk coefficient is analyzed to predict the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-layer curtain wall, and the air layer inside the double-layer curtain wall is divided into high-risk area, medium-risk area and low-risk area according to the prediction results; According to the division results of each area of ​​the air layer inside the double-skin curtain wall, corresponding air flow optimization and thermal effect control measures are taken; Continuously monitor the dynamic environmental information of each area, dynamically adjust the air flow path, regularly update the analysis model, and optimize the air flow design and thermal effect control strategy of the double-skin curtain wall.

2. A building curtain wall BIM forward design method according to claim 1, characterized in that: The environmental dynamic information of each area is obtained in real time, and analyzed after acquisition to generate the air flow balance coefficient and thermal effect index of each area respectively, which specifically includes the following steps: Acquire environmental dynamic information of each area in real time and perform preprocessing after acquisition; Extracting air flow information and temperature conduction information from the pre-processed environmental dynamic information of each area; The air flow information and temperature conduction information in the extracted environmental dynamic information of each area are analyzed to generate the air flow balance coefficient and thermal effect index of each area respectively.

3. A building curtain wall BIM forward design method according to claim 2, characterized in that: The logic for obtaining the air flow balance coefficient of each area is as follows: The air flow information is extracted from the preprocessed environmental dynamic information of each area, including the actual wind speed at the entrance of each area at different times within a period of time, the actual wind speed at the exit of each area, the longest path length and the shortest path length in the flow path of each area, and they are calibrated as and It represents the actual wind speed of the air at the entrance of the ith area at time m within a period of time. It represents the actual wind speed of the air at the outlet of the ith area at time m within a period of time. It represents the longest path length of the air flow path in the ith region at time m within a period of time. represents the shortest path length of the air flow path in the i-th region at time m within a period of time, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers; Calculate the air flow balance coefficient of each area. The specific calculation formula is as follows: In the formula, ABC i is the air flow balance coefficient of the ith area.

4. A building curtain wall BIM forward design method according to claim 3, characterized in that: The logic for obtaining the thermal effect index of each area is as follows: The temperature conduction information is extracted from the preprocessed environmental dynamic information of each area, including the average temperature of each area at different times within a period of time, the surface area of ​​air conduction, the average thickness of the air in each area at different times within a period of time, and the thermal conductivity of the air, and calibrated as and δ, represents the average temperature of the ith region at time m within a period of time, represents the surface area of ​​air conduction in the ith region at time m within a period of time, represents the average thickness of the air in the ith region at time m within a period of time, δ represents the thermal conductivity of the air, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., g, k and g are both positive integers; The average temperature of each area at different times over a period of time is constructed into a set, and the maximum and minimum values ​​in the set are marked as TAX respectively. i and TIN i ,TAX i Indicates the maximum value of the average temperature of the ith region over a period of time, TIN i It represents the minimum value of the average temperature of the ith region within a period of time; Calculate the thermal effect index of each area. The specific calculation formula is as follows: Where, TEI i is the thermal effect index of the ith region.

5. A building curtain wall BIM forward design method according to claim 4, characterized in that: A thermal effect risk model is constructed based on the generated air flow balance coefficient and thermal effect index of each area to generate the thermal effect risk coefficient of each area, which specifically includes the following steps: Collect the air flow balance coefficients, thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, and calibrate them as and x represents the numbers of several air flow balance coefficients, several thermal effect indexes and corresponding thermal effect risk coefficients of various regions generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set; Select the multivariate regression model as the thermal effect risk model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined; Using the final regression coefficient, the constructed thermal effect risk model is input with the air flow balance coefficient ABC for each area generated in real time. i and thermal effect index TEI i , generate the thermal effect risk coefficient ERC of each area in real time i .

6. A building curtain wall BIM forward design method according to claim 5, characterized in that: The thermal effect risk coefficient ERC of each area will be generated i The pre-set thermal effect risk factor threshold interval [ERC min , ERC max ] and predicted the risk level of thermal effect caused by unbalanced air flow in each area of ​​the air layer inside the double-skin curtain wall according to the comparison results. The air layer inside the double-skin curtain wall was divided into high-risk area, medium-risk area and low-risk area according to the prediction results. The specific comparison analysis and division are as follows: If ERC i <ERC min , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is low risk, so the area is classified as a low-risk area; If ERC min ≤ERC i ≤ERC max , the risk level of thermal effect caused by uneven air flow in the air layer inside the double-skin curtain wall is medium risk, so the area is classified as a medium risk area; If ERC i >ERC max , the risk level of thermal effect caused by uneven air flow in this area of ​​the air layer inside the double-skin curtain wall is high, so this area is classified as a high-risk area.

7. A building curtain wall BIM forward design method according to claim 6, characterized in that: According to the division results of each area of ​​the air layer inside the double-skin curtain wall, corresponding air flow optimization and thermal effect control measures are taken, specifically: For low-risk areas with a low risk of inducing thermal effects, the control measures taken are to maintain the current air flow design and only conduct routine monitoring without making additional adjustments; For medium-risk areas with a medium risk of causing thermal effects, the optimization measures taken are to adjust the air flow path, increase local ventilation and flow stabilization devices, and monitor air flow changes in real time; For high-risk areas with a high risk level of causing thermal effects, the control measures taken are to immediately carry out comprehensive optimization of air flow paths, introduce auxiliary ventilation systems and local cooling devices.

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