Intelligent temperature regulation and control method and system based on intelligent building

Abstract

The invention relates to the technical field of temperature regulation and control, in particular to an intelligent temperature regulation and control method and system based on an intelligent building. The method comprises the following steps: performing functional area air flow simulation on intelligent building structure design data to obtain functional area air conditioning flow simulation data; performing air circulation regulation of different functional areas on the functional area air regulation flow simulation data to obtain temperature coupling air circulation regulation data; indoor temperature circulation adjustment compensation is conducted on the temperature coupling air circulation adjustment data according to the human flow real-time monitoring data, and indoor temperature circulation adjustment compensation data are obtained; performing regulation and control logic training optimization on the indoor temperature circulation regulation and compensation data to obtain temperature regulation and control logic optimization data; and performing automatic firmware design based on the temperature regulation and control logic optimization data to obtain temperature regulation and control logic firmware. According to the invention, the temperature regulation and control technology is optimized, so that the temperature regulation and control technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to an intelligent temperature control method and system based on a smart building. Background Art

[0002] In modern building management, the indoor temperature environment is optimized through efficient data collection, analysis and control methods to achieve multiple goals of energy saving, comfort and environmental protection. In smart buildings, widely deployed sensor networks can monitor indoor and outdoor temperature, humidity, carbon dioxide concentration, personnel flow and other environmental and crowd activity data in real time, and deeply analyze this information through the Internet of Things (IOT) and big data technology to identify the best temperature control strategy. The intelligent temperature control system not only realizes personalized settings by learning user preferences and activity patterns, but also makes predictive adjustments based on external factors such as weather forecasts, energy consumption data and regional peak electricity consumption, significantly improving energy efficiency. The intelligent design of temperature control combines artificial intelligence (AI) algorithms such as machine learning, neural networks and optimization algorithms, and continuously optimizes control logic through adaptive and self-learning functions, reducing unnecessary energy consumption and reducing carbon emissions. In addition, the intelligent control method is also integrated with the building's central air-conditioning system, ventilation system and window control system to achieve multi-level linkage response, enhance environmental adaptability and improve indoor comfort. By working in collaboration with the energy management system, this method can effectively control the operation of cooling, heating and ventilation equipment without affecting user experience, reduce energy consumption costs and extend equipment life. Intelligent temperature control not only helps to improve the automation and intelligence level of building management, but also provides important technical support for building green and low-carbon smart cities, and promotes the future building environment to be more energy-saving, healthy and efficient. However, the traditional intelligent temperature control method based on smart buildings cannot be accurately controlled according to the population density and external climate changes, and causes high energy consumption. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent temperature control method and system based on a smart building to solve at least one of the above technical problems.

[0004] To achieve the above object, a smart temperature control method based on a smart building is provided, the method comprising the following steps:

[0005] Step S1: Acquire smart building structural design data and real-time monitoring data of human traffic; simulate the air flow of functional areas based on the smart building structural design data to obtain air conditioning flow simulation data of functional areas;

[0006] Step S2: performing air circulation regulation of different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation regulation data; performing indoor temperature circulation regulation compensation on the temperature-coupled air circulation regulation data according to the real-time monitoring data of human flow to obtain indoor temperature circulation regulation compensation data;

[0007] Step S3: Performing control logic training optimization on the indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data;

[0008] Step S4: Perform automated firmware design based on the temperature control logic optimization data to obtain temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

[0009] By simulating the air flow in functional areas, the present invention can identify bottlenecks and dead corners of air flow, thereby optimizing the air flow path. This helps to ensure that all areas can get uniform air distribution and avoid overcooling or overheating in some areas. By accurately simulating air flow, energy consumption can be reduced. It should be noted that the central air conditioning system and ventilation system of the building are integrated, that is, when adjusting the air, there is also cold air (heating) transmission, not just air conditioning without temperature control, which reduces the excessive operation of the air conditioning and ventilation systems and optimizes energy efficiency. Uniform air flow helps to improve the comfort in the building and improve the working and living environment. Temperature-coupled air circulation adjustment based on simulation data can more accurately control the temperature of each functional area. This helps to achieve personalized temperature control in different areas to meet the needs of different functional areas, such as offices, conference rooms, rest areas, etc. Accurately adjusting air circulation can avoid unnecessary heating or cooling, further saving energy expenses. By adjusting air flow during peak demand, a comfortable indoor environment can be maintained while reducing energy consumption. Real-time traffic monitoring data enables the system to dynamically adjust temperature and air flow and respond to the increase or decrease of personnel in real time. This adaptive regulation ensures that the indoor temperature is kept within a comfortable range even during peak or off-peak periods. Proper temperature cycle compensation helps maintain indoor air quality and prevents air quality degradation caused by dense crowds. Good air quality has a positive impact on human health and work efficiency. The system can adjust automatically, reducing the need for manual intervention and reducing maintenance and operating costs. At the same time, it reduces the overuse of air conditioning and heating equipment and extends the service life of the equipment. Through the training and optimization of the control logic, the temperature control algorithm can be improved to respond to environmental changes more accurately. This helps to improve the response speed and accuracy of the control system and ensure that the temperature of each area remains within the preset range. The optimized logic can better adapt to various actual usage scenarios, such as different weather conditions or sudden changes in the flow of people, thereby improving the intelligence level and adaptive ability of the system. Through training and optimization, the system can reduce the adjustment error caused by prediction errors or incomplete data, thereby improving the overall temperature control effect. This means that a comfortable indoor environment can be better maintained and the discomfort caused by temperature fluctuations can be reduced. The optimized control logic can adjust the temperature control strategy more effectively, thereby reducing energy consumption. For example, the system can predict and reduce unnecessary heating or cooling, saving energy costs. After the optimized control logic firmware is embedded in the smart building control platform, the system can automatically perform temperature control without human intervention. This allows the building to continue to operate at its best, improving management efficiency and comfort. The firmware can process data and perform adjustments in real time, respond quickly, and make timely adjustments to environmental changes to ensure that the indoor temperature always remains within the predetermined range.The automation and optimization of firmware design helps to reduce human operating errors and system failures, thereby improving the stability and reliability of the system. Integrated optimized firmware makes system maintenance simpler and more efficient. Maintenance personnel can identify and solve problems more quickly, improving the long-term stability and operational efficiency of the system. Through automated and intelligent firmware design, users can enjoy a temperature control experience that is more in line with their personal needs, such as personalized temperature settings and real-time responses in different areas. The system can continuously optimize the temperature control effect, improve the overall indoor comfort, and meet the needs of different personnel and usage scenarios. Therefore, the present invention is an optimization of a traditional intelligent temperature control method based on smart buildings, which solves the problem that a traditional intelligent temperature control method based on smart buildings cannot be accurately regulated with changes in population density and external climate, and causes high energy consumption. It improves the ability to accurately regulate population density and changes in external climate, and effectively controls the problem of high energy consumption.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining smart building structure design data and real-time monitoring data of human traffic;

[0012] Step S12: classifying the smart building structural design data into functional areas to obtain the smart building functional area structural data;

[0013] Step S13: performing air conditioning mode recognition on the functional area structure data of the smart building to obtain the functional area air conditioning mode data;

[0014] Step S14: Perform functional area air flow simulation on the functional area air conditioning mode data to obtain functional area air conditioning flow simulation data.

[0015] The present invention obtains structural design data and real-time monitoring data to provide a solid data foundation for subsequent analysis and optimization. These data include the building layout, functional areas and current traffic conditions of the building. Real-time monitoring data allows the system to instantly understand the distribution and activity patterns of people in the building, so that air conditioning can be quickly adjusted according to actual usage. Collecting and integrating these data helps to establish a comprehensive model of the internal environment of the building, providing a reference for further analysis of air flow and conditioning. It can more accurately understand the actual usage and improve the simulation and optimization accuracy in subsequent steps. By classifying the structural design data, the boundaries and characteristics of different functional areas (such as offices, conference rooms, rest areas, etc.) can be clarified. This helps to understand the specific needs and challenges of each functional area. Clear functional area classification helps to design air conditioning solutions that are more in line with actual needs and avoid situations where general designs cannot meet the needs of specific areas. After identifying the functional areas, personalized air conditioning strategies can be formulated according to the usage nature and requirements of different areas to improve the pertinence and effectiveness of the air conditioning system. Resources and energy are allocated to each functional area more efficiently, avoiding excessive or insufficient regulation. By analyzing the structural data of the functional areas, the air conditioning demand patterns of different areas are identified. For example, conference rooms require more frequent air and temperature updates, while offices require more stable temperature control. It is possible to identify and summarize the air conditioning patterns of different functional areas, so as to develop more refined and effective adjustment strategies. By identifying and understanding the air conditioning patterns of each functional area, air flow, temperature and humidity can be better adjusted to improve the accuracy and comfort of air conditioning. Effective pattern recognition can avoid unnecessary air conditioning, reduce energy waste, and improve the energy efficiency of the system. By simulating the air flow of functional areas, the air flow patterns of different areas can be intuitively seen, and potential problems and optimization points can be identified. For example, it is found that there are problems with air stagnation or poor flow in certain areas. The simulation data can be used to develop improvement plans, such as adjusting the location of vents or optimizing air flow paths to improve the efficiency and uniformity of air flow. Through air flow simulation, problems can be foreseen and improved before actual installation, thereby improving the performance and efficiency of the overall system. The optimized air flow performance can effectively improve indoor comfort, avoid excessively high or low temperatures in the area, and improve the user experience.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Analyze the internal and external temperature coupling effect according to the smart building structure design data to obtain the internal and external temperature coupling effect data of the structure;

[0018] Step S22: Based on the temperature coupling effect data inside and outside the structure, the functional area air conditioning flow simulation data is used to perform air circulation regulation for different functional areas to obtain temperature coupling air circulation regulation data;

[0019] Step S23: Calculate the crowd density distribution of the real-time crowd flow monitoring data to obtain crowd density distribution data;

[0020] Step S24: performing indoor temperature circulation adjustment compensation for different functional areas on the temperature-coupled air circulation adjustment data according to the crowd density distribution data to obtain indoor temperature circulation adjustment compensation data.

[0021] The present invention analyzes the coupling effect of internal and external temperature, which helps to understand the influence of external environment (such as outside temperature, wind speed, solar radiation, etc.) on the internal temperature of the building. This can accurately predict the temperature changes inside the building under different external conditions. By identifying the coupling effect of internal and external temperature, the insulation of the building can be optimized and the interference of the external environment on the internal temperature regulation can be reduced. After understanding the temperature coupling effect, the air conditioning system of the building can be adjusted to cope with the external temperature changes, thereby avoiding the increase of energy consumption due to over-regulation. Targeted energy-saving measures can be implemented. The air circulation adjustment based on the data of the coupling effect of internal and external temperature makes the air conditioning of each functional area more in line with actual needs. The air flow pattern that needs to be adjusted due to the difference in internal and external temperature in different areas will be optimized. Accurate air circulation adjustment can ensure uniform air flow in each functional area and maintain a comfortable indoor environment temperature. By optimizing the air circulation adjustment data, the frequent adjustment of the system is reduced, and the stability and efficiency of the system are improved. Reasonable air circulation helps to maintain good air quality and avoid air stagnation or accumulation of pollutants. The calculation of the crowd density distribution data can reflect the distribution and changes of people in the building in real time, ensuring that the system can adjust the air conditioning strategy according to the actual crowd density. By identifying the distribution of crowd density, we can understand the usage intensity and crowd gathering in different areas, so as to carry out targeted air conditioning in these areas. According to the crowd density distribution data, the allocation of air conditioning resources is optimized, so that high-density areas can obtain more air flow support, improving the overall air quality and comfort. By reasonably adjusting the air conditioning in different areas, unnecessary energy consumption can be reduced and the energy efficiency of the system can be improved. According to the crowd density distribution data, the temperature-coupled air circulation adjustment data can be compensated to achieve more precise adjustment of the temperature of different functional areas. This means that in high-density areas, the system will automatically increase air conditioning to cope with the additional heat load. Compensation adjustment can ensure that even in high-density crowd areas, a comfortable indoor temperature can be maintained, improving user satisfaction. By compensating for the heat changes caused by changes in crowd density in real time, indoor temperature fluctuations caused by changes in crowd density can be avoided, and the stability of temperature control can be improved. The adjustment strategy integrating crowd density data makes the system more intelligent and adaptable, and can be dynamically adjusted to meet the actual environmental needs.

[0022] Preferably, step S22 includes the following steps:

[0023] Step S221: performing structural heat transfer energy conversion evaluation on the temperature coupling effect data inside and outside the structure to obtain structural heat transfer energy conversion data;

[0024] Step S222: performing airflow thermal guidance simulation of different functional areas on the functional area air conditioning flow simulation data based on the structural heat transfer energy conversion data to obtain airflow thermal guidance simulation data;

[0025] Step S223: calculating the convective heat transfer coefficients of different functional zones on the functional zone air conditioning flow simulation data according to the airflow heat guidance simulation data, to obtain the convective heat transfer coefficients;

[0026] Step S224: performing thermal distribution trend mutation analysis on the airflow thermal guidance simulation data to obtain thermal distribution trend mutation data;

[0027] Step S225: performing air circulation regulation on different functional areas based on the functional area air conditioning flow simulation data according to the convective heat transfer coefficient and the heat distribution trend mutation data to obtain temperature-coupled air circulation regulation data.

[0028] The present invention can understand in detail how the building structure transfers and converts heat energy under different environmental conditions by evaluating the temperature coupling effect inside and outside the structure. Through this evaluation, the efficiency of heat transfer from the external environment to the internal structure or vice versa can be quantified, thereby identifying key areas of heat loss or gain. These data help designers understand the thermal insulation performance of the building and its impact on the indoor temperature, optimize indoor temperature regulation, and reduce energy consumption. In addition, the evaluation also provides important information for optimizing the design of building systems, such as the thermal insulation effect of windows, walls and roofs, to ensure that the structure maintains a stable indoor environment under various climatic conditions. Using the structural heat transfer data obtained in step S221, airflow thermal guidance simulation is performed for air conditioning flows in different functional areas. This process helps understand how airflow carries and distributes heat in different areas, and identifies which areas will have overheating or overcooling problems due to uneven airflow. By simulating the thermal guidance effect of airflow, the air conditioning system can be adjusted so that it can guide and distribute heat more effectively and ensure that the temperature of each functional area is evenly distributed. This sophisticated simulation result makes the air conditioning system more in line with actual needs, thereby improving the overall comfort and energy efficiency of the system. By calculating the convective heat transfer coefficient of each functional area in the airflow thermal guidance simulation data, the heat exchange efficiency between air flow and functional areas is evaluated. The convective heat transfer coefficient is a key indicator to measure the contribution of airflow to heat transfer, which helps to understand the heat transfer effect of air flow in each area. This calculation can reveal which areas have poor airflow for heat transfer, and then guide the adjustment of air flow design to improve heat exchange efficiency. This helps to ensure that the air conditioning system can effectively achieve the expected temperature control goals, reduce energy waste, and enhance the overall performance of the system. By performing thermal distribution trend mutation analysis on airflow thermal guidance simulation data, mutations or abnormal points in the heat distribution are identified. Thermal distribution trend mutation analysis helps to find areas with drastic changes in heat distribution, which are local overheating or overcooling due to uneven airflow or structural design defects. Through this analysis, problem areas in heat distribution can be accurately located and targeted adjustments can be made to achieve a more uniform temperature distribution. This detailed analysis provides substantial data support for adjusting air conditioning strategies, which helps to optimize air flow design and ensure comfort in each area. Considering the convective heat transfer coefficient and thermal distribution trend mutation data, the air circulation adjustment of each functional area is optimized. With this data, the air conditioning system can be precisely adjusted to ensure that the air flow in each functional area can effectively cope with the demand for heat transfer and the sudden change of heat distribution. This adjustment not only improves the efficiency of the air conditioning system, but also ensures the stability and comfort of the temperature in each functional area. Ultimately, these optimization measures help improve the energy efficiency of the entire building, reduce energy waste, and improve the comfort experience of users.

[0029] Preferably, performing thermal distribution trend mutation analysis on airflow thermal guidance simulation data comprises the following steps:

[0030] Performing airflow conduction path identification on the airflow heat guide simulation data to obtain airflow conduction path data;

[0031] Performing an air flow thermal field intensity analysis on the air flow heat guide simulation data according to the air flow conduction path data to obtain the air flow thermal field intensity data;

[0032] The convective heat energy accumulation rate is calculated for the air flow thermal field intensity data to obtain the convective heat energy accumulation rate data;

[0033] According to the convective heat energy accumulation rate data, the heat energy nonlinear accumulation point is located on the air flow thermal field intensity data to obtain the heat energy nonlinear accumulation point location data;

[0034] According to the convective heat energy accumulation rate data, the heat distribution trend mutation data is analyzed on the heat energy nonlinear accumulation point positioning data to obtain the heat distribution trend mutation data.

[0035] The present invention can reveal the flow route and distribution characteristics of airflow in space by identifying the conduction path of airflow. This identification process helps to understand how airflow transfers heat in different areas and clarify the main flow channels and paths of airflow in functional areas. The obtained airflow conduction path data provides basic information for subsequent thermal field intensity analysis and heat distribution optimization, so that designers can accurately locate the key channels and areas of heat transfer, thereby effectively optimizing the air conditioning system, ensuring uniform temperature distribution in each area, and improving the overall thermal management performance of the system. Using the airflow conduction path data, the airflow thermal field intensity analysis of the airflow heat guide simulation data can be performed to calculate and evaluate the thermal field intensity of the airflow at different times. This analysis helps to understand the heat distribution and intensity changes of the airflow at different time points, revealing the dynamic characteristics of the thermal field in time. Through the airflow thermal field intensity data, designers can find the changing trend of the thermal field intensity, thereby dynamically adjusting the air conditioning strategy of the system, ensuring the effective distribution and management of heat under various operating conditions, and improving the responsiveness and stability of the thermal management system. By calculating the convective heat energy accumulation rate in the airflow thermal field intensity data, the speed and degree of heat energy accumulation of the airflow in different areas can be evaluated. This calculation reveals the accumulation process of heat energy in space and helps identify areas where heat energy accumulates faster. The convective heat energy accumulation rate data provides an important basis for the subsequent location of nonlinear heat energy accumulation points, allowing designers to more accurately locate heat concentration areas, optimize air conditioning strategies, prevent local overheating problems caused by excessive heat energy accumulation, and ensure the safety and efficiency of system operation. By analyzing the convective heat energy accumulation rate data, the thermal field intensity data is used to locate the nonlinear heat energy accumulation points. This process can identify nonlinear heat energy accumulation points in space, that is, those areas where heat concentration changes dramatically. The nonlinear heat energy accumulation point location data helps to find abnormal points or mutation areas in heat distribution, which usually require special attention and adjustment. By locating these accumulation points, designers can carry out targeted air conditioning and thermal management measures to optimize heat distribution and improve the overall performance and comfort of the system. By combining the convective heat energy accumulation rate data with the nonlinear heat energy accumulation point location data to perform heat distribution trend mutation analysis, we can deeply explore the mutation trend in heat distribution. This analysis can reveal dramatic changes and abnormal trends in thermal field distribution, helping to identify potential problems in system design. The obtained thermal distribution trend mutation data provides an important reference for further optimizing air conditioning strategies, helping designers to adjust system settings in time to cope with sudden changes in heat distribution and improve the stability and efficiency of thermal management systems.

[0036] Preferably, step S24 comprises the following steps:

[0037] Step S241: analyzing crowd flow behavior characteristics according to crowd flow density distribution data to obtain crowd flow density behavior characteristic data;

[0038] Step S242: quantifying the heat source distribution of the crowd flow on the crowd flow density distribution data according to the crowd flow density behavior characteristic data, and obtaining the behavior characteristic heat source distribution quantified data;

[0039] Step S243: performing a gridded regional disturbance simulation on the behavior characteristic heat source distribution quantified data to obtain heat source distribution regional disturbance data;

[0040] Step S244: performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data to obtain indoor temperature cycle adjustment compensation data.

[0041] The present invention can reveal the behavior patterns and characteristics of human flow at different times and spaces by conducting a detailed analysis of the crowd density distribution data. By identifying high-density human flow areas and their behavioral characteristics (such as moving speed and residence time), we can gain a deep understanding of the actual impact of human flow on the environment. These human flow behavioral characteristic data provide a basis for the subsequent quantification and adjustment of heat source distribution, enabling designers to optimize the spatial layout and facility settings according to the actual human flow conditions to improve the responsiveness and comfort of the system, while effectively predicting and managing the impact of high-density human flow on the indoor environment. The heat source distribution of the crowd density distribution is quantified using the data obtained from the analysis of the human flow behavioral characteristics. By converting the human flow density behavior characteristics into specific quantitative data of the heat source distribution, the contribution and distribution of human flow to heat can be accurately evaluated. This quantitative result can reveal the actual impact of human flow activities on the heat in different areas, helping designers to consider the additional heat sources generated by human flow in the air conditioning system, thereby optimizing the heat distribution and adjustment strategy. Ensure that appropriate temperature control is provided in high-flow areas, thereby improving the overall comfort and energy efficiency of the system. By performing gridded regional perturbation simulation on the quantified heat source distribution data, it is possible to simulate and analyze subtle changes in the heat source distribution in different areas. By dividing the heat source distribution data into grid areas and performing disturbance simulation, designers can observe and evaluate the fluctuation of heat sources in the space and its impact on the thermal field. This simulation result helps to identify and predict potential problem areas in the heat source distribution, and then adjust the air conditioning strategy to cope with heat fluctuations and optimize the temperature stability and comfort of the indoor environment. According to the disturbance data of the heat source distribution area, the indoor temperature cycle adjustment of different functional areas is compensated. By considering the impact of heat source disturbance on temperature distribution, the settings of the air conditioning system can be accurately adjusted to ensure that the temperature of each functional area remains within the ideal range under heat source interference. This can effectively deal with the uneven temperature problem caused by changes in heat source distribution and achieve more accurate and efficient temperature control. Finally, the indoor temperature cycle adjustment compensation data provides a specific compensation solution for optimizing the air conditioning system, improving the spatial comfort and the overall performance of the system.

[0042] Preferably, performing gridded regional disturbance simulation on the behavior characteristic heat source distribution quantitative data comprises the following steps:

[0043] Perform heat source intensity regional grid division on the behavior characteristic heat source distribution quantitative data to obtain behavior characteristic heat source intensity grid data;

[0044] Based on the behavior characteristic heat source intensity grid data, the behavior characteristic airflow influence turbulence evaluation between grids in different regions is performed to obtain the behavior characteristic airflow influence turbulence data;

[0045] According to the behavior characteristic airflow influence turbulence data, the heat source instability increment between grids in different regions is calculated for the behavior characteristic heat source intensity grid data, and the behavior characteristic heat source instability increment data is obtained;

[0046] According to the behavior characteristic airflow impact turbulence data and behavior characteristic heat source instability increment data, gridded regional disturbance simulation is carried out to obtain the heat source distribution regional disturbance data.

[0047] The present invention can clarify the distribution of heat source intensity in different regions by dividing the quantitative data of the behavior characteristic heat source distribution into multiple regional grids. This grid division makes the spatial distribution of the heat source intensity clearer, and can identify the areas of high heat source intensity and low heat source intensity. The obtained behavior characteristic heat source intensity grid data provides a detailed heat source intensity map, which lays the foundation for the subsequent airflow impact assessment and heat source instability calculation, so that the system can more accurately understand the distribution of heat sources in various regions, thereby optimizing air conditioning and heat management strategies. By evaluating the turbulence of the airflow impact between grids in different regions, the degree of interference of the change in heat source intensity on the airflow can be evaluated. This evaluation reveals the stability and turbulence of the airflow when facing heat sources of different intensities, and helps identify potential problem areas in the airflow flow. These behavior characteristic airflow impact turbulence data can help designers understand and predict the changes in the airflow after being affected by the heat source, thereby optimizing the air conditioning system, ensuring the stability and uniformity of the airflow, and improving the overall performance and comfort of the system. By combining the airflow impact turbulence data and performing instability increment calculation on the heat source intensity grid data, the degree of instability of the heat source between grids in different regions can be evaluated. This calculation can identify the instability of heat sources caused by airflow turbulence, that is, which areas of heat sources have overheating or insufficient heat sources due to airflow instability. The obtained incremental data of heat source instability provides a basis for further adjustment and optimization of air conditioning strategies, which helps to improve the stability and thermal management effect of the system and avoid local temperature anomalies caused by heat source instability. By comprehensively considering the airflow impact turbulence data and heat source instability incremental data, a gridded regional disturbance simulation is performed. This simulation can comprehensively evaluate the disturbance of heat source distribution in different areas and reveal the changes in heat distribution caused by heat source instability and airflow turbulence. These heat source distribution regional disturbance data help designers understand the performance of heat sources in actual operation, optimize system settings, and adjust air circulation and regulation strategies to cope with heat fluctuations and uneven distribution problems in actual use, thereby improving the overall system performance and environmental comfort.

[0048] Preferably, performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data comprises the following steps:

[0049] The disturbance temperature interaction effect is simulated on the disturbance data of the heat source distribution area to obtain the disturbance temperature interaction effect data;

[0050] According to the disturbance temperature interaction data and the heat source distribution area disturbance data, the airflow control rate of different areas is calculated for the temperature-coupled air circulation adjustment data to obtain the temperature airflow control rate data;

[0051] According to the disturbance temperature mutual effect data and the heat source distribution area disturbance data, the variable air outlet angle design of different areas is performed on the temperature coupled air circulation adjustment data to obtain the variable air outlet angle design data;

[0052] According to the temperature and airflow control rate data and the variable air outlet angle design data, the temperature-coupled air circulation adjustment data is used to perform indoor temperature circulation adjustment compensation in different functional areas to obtain indoor temperature circulation adjustment compensation data.

[0053] The present invention can understand the mutual influence of temperature changes in different areas in detail by simulating the disturbance temperature interaction effect of the heat source distribution area disturbance data. This simulation process reveals the overall impact of the heat source distribution disturbance on the indoor temperature field, especially how the temperatures between different areas interact and influence each other. This analysis is crucial for identifying and preventing the problem of uneven temperature caused by heat source disturbance. The obtained disturbance temperature interaction data provides a basis for subsequent airflow control and tuyere angle adjustment, making temperature control more accurate and ensuring that each functional area can maintain ideal comfort and temperature stability in actual operation. By combining the disturbance temperature interaction data and the heat source distribution area disturbance data, the airflow control rate is calculated, and the necessary rate of airflow adjustment in different areas can be determined. These rate data show how to adjust the airflow of the air circulation system in order to cope with the temperature changes caused by the heat source disturbance. Accurate calculation of the airflow control rate helps to dynamically adjust the air conditioning system, ensure that a stable temperature is maintained in each area, thereby improving the efficiency and comfort of the system, and avoiding the problem of temperature fluctuation caused by insufficient or excessive airflow. By analyzing the disturbance temperature interaction data and the heat source distribution area disturbance data, the variable tuyere angle is designed to meet the needs of different areas. The design of variable vent angle can dynamically adjust the vent angle according to the specific heat source and airflow influence to optimize air distribution and temperature regulation. The vent designed in this way can more accurately send airflow to the area that needs to be adjusted, thereby improving local temperature and comfort and reducing temperature unevenness caused by inappropriate vent angle. Combining the temperature airflow control rate data and the variable vent angle design data, indoor temperature cycle adjustment compensation is performed for different functional areas. This compensation process adjusts the parameters of the air conditioning system based on the data of the first two steps to cope with temperature changes and heat source disturbances in different areas. By comprehensively adjusting the airflow rate and vent angle, the temperature distribution of each functional area is optimized to achieve temperature balance and comfort improvement of the overall environment. The indoor temperature cycle adjustment compensation data obtained provides an accurate adjustment plan, so that the air conditioning system can effectively cope with various temperature changes in actual operation, improving the overall efficiency and user experience of the system.

[0054] Preferably, step S3 comprises the following steps:

[0055] Step S31: normalizing the indoor temperature cycle adjustment compensation data to obtain indoor temperature adjustment compensation normalized data;

[0056] Step S32: constructing a building intelligent temperature control model for the indoor temperature adjustment compensation normalized data using a gradient boosting tree algorithm to obtain a building intelligent temperature control model;

[0057] Step S33: Based on the building intelligent temperature control model, the control logic training and optimization are performed on the indoor temperature control compensation normalized data to obtain temperature control logic optimization data.

[0058] The present invention can convert data of different ranges and scales into a unified standard format by normalizing the indoor temperature cycle adjustment compensation data. This processing can eliminate the difference in data magnitude and the influence of units, making the data easier to analyze and model. The normalized data enables various temperature adjustment compensation information to be compared and processed under the same standard, thereby improving the accuracy of subsequent modeling and analysis. This process is the basis for ensuring data consistency and reliability, and helps to build a more accurate model and optimize the control effect of the system. The gradient boosting tree algorithm is used to construct a building intelligent temperature control model, which can effectively process and analyze the normalized indoor temperature adjustment compensation data. The gradient boosting tree is a powerful ensemble learning method that can improve the prediction accuracy by optimizing the model through multiple iterations. The algorithm improves its prediction ability by gradually reducing the model error, so it can provide an accurate intelligent model for building temperature control. This model can accurately predict and adjust the indoor temperature, optimize the energy efficiency and comfort of the building, and provide strong support for the realization of intelligent temperature control management. Based on the building intelligent temperature control model, the control logic training optimization can further refine the temperature control strategy. This process uses the model's predictive power to train normalized data and optimize the control logic, making temperature control more accurate and efficient. The trained and optimized data can reveal the optimal temperature control strategy and logic, ensuring that the temperature adjustment of each area is more in line with actual needs and comfort standards. This process helps to improve the response speed and adaptability of the building temperature control system, thereby enhancing the overall system performance and user experience.

[0059] Preferably, the present invention further provides an intelligent temperature control system based on a smart building, which is used to execute the intelligent temperature control method based on a smart building as described above. The intelligent temperature control system based on a smart building includes:

[0060] Functional area air flow simulation module is used to obtain smart building structural design data and real-time monitoring data of human flow; simulate the functional area air flow based on the smart building structural design data to obtain functional area air conditioning flow simulation data;

[0061] The circulation adjustment module is used to perform air circulation adjustment in different functional areas based on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation adjustment data; perform indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data based on the real-time monitoring data of human flow to obtain indoor temperature circulation adjustment compensation data;

[0062] A control logic training module is used to perform control logic training optimization on indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data;

[0063] The control execution module is used to perform automated firmware design based on the temperature control logic optimization data, obtain the temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

[0064] The beneficial effect of the present invention is that by simulating the air flow of functional areas, the bottlenecks and dead corners of air flow can be identified, thereby optimizing the air flow path. This helps to ensure that all areas can get uniform air distribution and avoid overcooling or overheating in some areas. By accurately simulating air flow, energy consumption can be reduced. It should be noted that the central air conditioning system and ventilation system of the building are integrated, that is, when adjusting the air, there is also cold air (heating) transmission, not just air conditioning without temperature control, which reduces the excessive operation of the air conditioning and ventilation systems and optimizes energy efficiency. Uniform air flow helps to improve the comfort in the building and improve the working and living environment. Temperature-coupled air circulation adjustment based on simulation data can more accurately control the temperature of each functional area. This helps to achieve personalized temperature control in different areas to meet the needs of different functional areas, such as offices, conference rooms, rest areas, etc. Accurately adjusting air circulation can avoid unnecessary heating or cooling, further saving energy expenses. By adjusting air flow during peak demand, a comfortable indoor environment can be maintained while reducing energy consumption. Real-time traffic monitoring data enables the system to dynamically adjust temperature and air flow and respond to the increase or decrease of personnel in real time. This adaptive regulation ensures that the indoor temperature is kept within a comfortable range even during peak or off-peak periods. Proper temperature cycle compensation helps maintain indoor air quality and prevents air quality degradation caused by dense crowds. Good air quality has a positive impact on human health and work efficiency. The system can adjust automatically, reducing the need for manual intervention and reducing maintenance and operating costs. At the same time, it reduces the overuse of air conditioning and heating equipment and extends the service life of the equipment. Through the training and optimization of the control logic, the temperature control algorithm can be improved to respond to environmental changes more accurately. This helps to improve the response speed and accuracy of the control system and ensure that the temperature of each area remains within the preset range. The optimized logic can better adapt to various actual usage scenarios, such as different weather conditions or sudden changes in the flow of people, thereby improving the intelligence level and adaptive ability of the system. Through training and optimization, the system can reduce the adjustment error caused by prediction errors or incomplete data, thereby improving the overall temperature control effect. This means that a comfortable indoor environment can be better maintained and the discomfort caused by temperature fluctuations can be reduced. The optimized control logic can adjust the temperature control strategy more effectively, thereby reducing energy consumption. For example, the system can predict and reduce unnecessary heating or cooling, saving energy costs. After the optimized control logic firmware is embedded in the smart building control platform, the system can automatically perform temperature control without human intervention. This allows the building to continue to operate at its best, improving management efficiency and comfort. The firmware can process data and perform adjustments in real time, respond quickly, and make timely adjustments to environmental changes to ensure that the indoor temperature always remains within the predetermined range.The automation and optimization of firmware design helps to reduce human operating errors and system failures, thereby improving the stability and reliability of the system. Integrated optimized firmware makes system maintenance simpler and more efficient. Maintenance personnel can identify and solve problems more quickly, improving the long-term stability and operational efficiency of the system. Through automated and intelligent firmware design, users can enjoy a temperature control experience that is more in line with their personal needs, such as personalized temperature settings and real-time responses in different areas. The system can continuously optimize the temperature control effect, improve the overall indoor comfort, and meet the needs of different personnel and usage scenarios. Therefore, the present invention is an optimization of a traditional intelligent temperature control method based on smart buildings, which solves the problem that a traditional intelligent temperature control method based on smart buildings cannot be accurately regulated with changes in population density and external climate, and causes high energy consumption. It improves the ability to accurately regulate population density and changes in external climate, and effectively controls the problem of high energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the steps of an intelligent temperature control method based on a smart building;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0071] To achieve this, please refer to Figure 1 to Figure 2 , an intelligent temperature control method based on a smart building, the method comprising the following steps:

[0072] Step S1: Acquire smart building structural design data and real-time monitoring data of human traffic; simulate the air flow of functional areas based on the smart building structural design data to obtain air conditioning flow simulation data of functional areas;

[0073] Step S2: performing air circulation regulation of different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation regulation data; performing indoor temperature circulation regulation compensation on the temperature-coupled air circulation regulation data according to the real-time monitoring data of human flow to obtain indoor temperature circulation regulation compensation data;

[0074] Step S3: Performing control logic training optimization on the indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data;

[0075] Step S4: Perform automated firmware design based on the temperature control logic optimization data to obtain temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

[0076] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of an intelligent temperature control method based on a smart building of the present invention. In this example, the intelligent temperature control method based on a smart building includes the following steps:

[0077] Step S1: Acquire smart building structural design data and real-time monitoring data of human traffic; simulate the air flow of functional areas based on the smart building structural design data to obtain air conditioning flow simulation data of functional areas;

[0078] In the embodiment of the present invention, in the process of obtaining the structural design data of the smart building and the real-time monitoring data of the flow of people, high-resolution cameras are installed at key locations such as the main entrance, corridor, and functional area of ​​the building. These cameras need to have real-time video streaming function and be able to provide clear images under different lighting conditions to obtain real-time monitoring data of the flow of people. Through intelligent sensing terminals, such as laser rangefinders and infrared imaging devices, the structural parameters of different floors and areas in the building are collected, including detailed design data such as wall thickness, room height, and window positions (the structural design data of the building can also be obtained from the building design builder). After data collection, computational fluid dynamics (CFD) is used to simulate air flow. The specific operation steps include importing the structural design data into the fluid dynamics simulation software and building a three-dimensional building model based on the actual building design drawings. Then, air flow simulation is performed on each functional area, and the parameters such as the flow direction, velocity field, and vortex formation of the air are simulated by dividing the grid units, defining the boundary conditions and initial conditions. The air flow characteristics of different functional areas are obtained by directly solving the momentum equation and energy equation in the flow field, including data such as flow path, ventilation efficiency, and air residence time. These data provide a basis for the subsequent steps to regulate the air circulation in the functional areas, and finally generate the functional area air conditioning flow simulation data.

[0079] Step S2: performing air circulation regulation of different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation regulation data; performing indoor temperature circulation regulation compensation on the temperature-coupled air circulation regulation data according to the real-time monitoring data of human flow to obtain indoor temperature circulation regulation compensation data;

[0080] In the embodiment of the present invention, the functional area air conditioning flow simulation data is used to adjust the air circulation of different functional areas. First, a multi-zone coupled ventilation analysis method is used to determine the air exchange path and flow rate between each functional area. Air conditioning equipment, such as a variable air volume control system (VAV) and an intelligent air valve, is used to optimize the air circulation of the functional area by adjusting the air valve opening, controlling the air supply speed, etc. The air circulation adjustment step includes setting the air supply temperature and humidity parameters of different functional areas according to the air flow simulation data, and manually calibrating the position of the air valve to ensure the target flow and temperature. Then, according to the purpose of each functional area (such as a conference room, office, rest area, etc.), the circulating air volume is adjusted according to the frequency of crowd activities to ensure reasonable and efficient air circulation. Then, combined with the real-time monitoring data of the flow of people, the activity hotspots of the crowd in the building are analyzed, and the changes in the density of the crowd in each area are captured by infrared cameras and thermal imagers to calculate the regional temperature load. Based on these monitoring data, the original temperature-coupled air circulation adjustment scheme is compensated and adjusted. By dynamically adjusting the air supply temperature and humidity, the temperature distribution of different areas is balanced, and the indoor temperature tends to be uniform, and finally the indoor temperature circulation adjustment compensation data is obtained. These operations ensure that the indoor environment can maintain a comfortable temperature distribution under different passenger flow loads.

[0081] Step S3: Performing control logic training optimization on the indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data;

[0082] In an embodiment of the present invention, the control logic training and optimization are performed on the indoor temperature cycle adjustment compensation data. Using data mining technology, a training set for temperature control is established by analyzing the historical data of actual indoor temperature adjustment. These data include indoor temperature changes at different times and under different traffic conditions. An optimization algorithm, such as a genetic algorithm or a gradient descent method, is applied to train the control logic, and the control rules are continuously adjusted through optimization iterations to achieve the best temperature control effect. In this process, an algorithm script is written in a programming language (such as Python or MATLAB), the adjustment data is processed, and control logic optimization data is generated. These optimized data reflect the best temperature control logic, which can effectively meet the needs of various actual usage scenarios and ensure accurate control of indoor temperature.

[0083] Step S4: Perform automated firmware design based on the temperature control logic optimization data to obtain temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

[0084] In an embodiment of the present invention, automated firmware design is performed based on the temperature control logic optimization data generated in step S3. First, the firmware functional specifications are designed according to the optimization data, including the definition of input and output interfaces, the implementation of control algorithms, and the arrangement of data processing flows. Use embedded development tools (such as Keil or IAR Embedded Workbench) to program the firmware and convert the optimized control logic into embedded code. Write a control program and set the parameters of the controller to ensure that it can be adjusted in real time according to the temperature control logic optimization data. After the firmware design is completed, it is embedded in the smart building control platform. The platform is connected to the HVAC system and sensors in the building through a hardware interface to perform intelligent temperature control tasks. The embedded firmware can automatically adjust system parameters according to real-time sensor data to ensure the stability and comfort of the indoor environment.

[0085] Preferably, step S1 comprises the following steps:

[0086] Step S11: Obtaining smart building structure design data and real-time monitoring data of human traffic;

[0087] Step S12: classifying the smart building structural design data into functional areas to obtain the smart building functional area structural data;

[0088] Step S13: performing air conditioning mode recognition on the functional area structure data of the smart building to obtain the functional area air conditioning mode data;

[0089] Step S14: Perform functional area air flow simulation on the functional area air conditioning mode data to obtain functional area air conditioning flow simulation data.

[0090] In the embodiment of the present invention, the structural design data and real-time monitoring data of the intelligent building are first collected, and high-resolution cameras are installed at key locations such as the main entrance, corridor, and functional area of ​​the building. These cameras need to have real-time video streaming function and be able to provide clear images under different lighting conditions to obtain real-time monitoring data of human flow. Through intelligent sensing terminals, such as laser rangefinders and infrared imaging devices, the structural parameters of different floors and areas in the building are collected, including detailed design data such as wall thickness, room height, and window positions (the structural design data of the building can also be obtained from the building design and construction company). The structural design data includes the floor plan of the building, the layout of each functional area, the wall material and thickness, the window position, etc. These data are input into the system through the building information modeling (BIM) tool to generate a detailed three-dimensional model of the building. At the same time, the personnel flow data of each functional area is collected in real time through the sensors of the human flow monitoring system (such as infrared sensors or video monitoring equipment). These data include the number of people, flow speed, and time distribution. The two types of data are integrated and stored in the database to provide a basis for subsequent analysis and processing. The smart building structural design data is classified into functional areas to obtain the functional area structural data of the smart building. The building three-dimensional model is classified using architectural design software (such as Revit), and the areas in the model are divided into office areas, conference rooms, rest areas, etc. by function. The characteristics and requirements of each functional area are marked by setting regional attribute labels. Subsequently, the classified functional area data is exported as a structural data table, and the size, layout, ventilation facilities and other information of each functional area are recorded in detail, and integrated into the system for further analysis. The air conditioning mode is identified for the functional area structural data of the smart building to obtain the functional area air conditioning mode data. First, the design requirements and air conditioning needs of each functional area are analyzed. For example, the office area requires constant temperature control, while the conference room needs to cope with frequent temperature changes. Based on the design characteristics and air flow data of the functional area, the appropriate air conditioning mode is determined, such as constant temperature mode, rapid cooling mode or comfort mode. By establishing an air conditioning mode database, the parameter settings and applicable conditions of each mode are recorded. These mode data are summarized to form the functional area air conditioning mode data for the next step of simulation and optimization. Perform air flow simulation on the functional area air conditioning mode data to obtain the functional area air conditioning flow simulation data. First, input the functional area air conditioning mode data into the fluid mechanics simulation software (such as ANSYS Fluent) and set the simulation parameters, including air flow rate, temperature change, fan speed, etc. Perform simulation calculations to observe the air flow, temperature distribution and circulation effect in each functional area. During the simulation process, the air conditioning flow simulation data of the functional area is generated by adjusting different parameters. These data include the air flow path, velocity distribution and heat transfer, providing detailed air conditioning effect analysis for optimizing the actual air conditioning solution.

[0091] Preferably, step S2 comprises the following steps:

[0092] Step S21: Analyze the internal and external temperature coupling effect according to the smart building structure design data to obtain the internal and external temperature coupling effect data of the structure;

[0093] Step S22: Based on the temperature coupling effect data inside and outside the structure, the functional area air conditioning flow simulation data is used to perform air circulation regulation for different functional areas to obtain temperature coupling air circulation regulation data;

[0094] Step S23: Calculate the crowd density distribution of the real-time crowd flow monitoring data to obtain crowd density distribution data;

[0095] Step S24: performing indoor temperature circulation adjustment compensation for different functional areas on the temperature-coupled air circulation adjustment data according to the crowd density distribution data to obtain indoor temperature circulation adjustment compensation data.

[0096] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0097] Preferably, step S2 comprises the following steps:

[0098] Step S21: Analyze the internal and external temperature coupling effect according to the smart building structure design data to obtain the internal and external temperature coupling effect data of the structure;

[0099] In an embodiment of the present invention, an analysis of the internal and external temperature coupling effect is performed based on the smart building structural design data. Use a heat conduction analysis tool (such as Thermophysical Properties Data System, TPDS) to evaluate the thermal conductivity, thickness and thermal insulation performance of the materials of the walls, windows, roofs and other components of the building. Input the structural design data into the tool and perform simulation calculations to analyze the impact of internal and external temperature changes on the internal temperature of the building. During the calculation process, the external temperature change range is set, and environmental factors such as wind speed and radiation intensity are considered. The data on the internal and external temperature coupling effect of the structure, including the heat transfer coefficient and degree of influence of each component, are obtained by calculation, providing basic data for subsequent air conditioning.

[0100] Step S22: Based on the temperature coupling effect data inside and outside the structure, the functional area air conditioning flow simulation data is used to perform air circulation regulation for different functional areas to obtain temperature coupling air circulation regulation data;

[0101] In the embodiment of the present invention, based on the temperature coupling effect data of the structure inside and outside, the functional area air conditioning flow simulation data is used to perform air circulation regulation of different functional areas. First, the internal and external temperature coupling effect data is input into the system using air flow simulation software (such as CFD tools). The air flow pattern of each functional area is adjusted to set the temperature regulation targets of different areas. Through simulation calculation, the temperature coupling air circulation regulation data is obtained, which includes the air flow path, wind speed adjustment and temperature distribution of each functional area, to ensure that the temperature in the functional area meets the set requirements, while taking into account the coupling effect of internal and external temperatures.

[0102] Step S23: Calculate the crowd density distribution of the real-time crowd flow monitoring data to obtain crowd density distribution data;

[0103] In an embodiment of the present invention, the crowd density distribution is calculated for the real-time crowd flow monitoring data. The data collected by the crowd flow monitoring system is analyzed using a data analysis tool (such as a data processing library in MATLAB or Python). First, the crowd flow data in each time period is extracted, and the crowd density distribution of each functional area is calculated. Based on the monitoring data, a crowd density distribution map is drawn to display the crowd density in different time periods and areas. The calculation results include areas with high and low crowd density and their distribution trends, which provide a basis for indoor temperature regulation to cope with the impact of changes in crowd density on air flow and temperature regulation.

[0104] Step S24: performing indoor temperature circulation adjustment compensation for different functional areas on the temperature-coupled air circulation adjustment data according to the crowd density distribution data to obtain indoor temperature circulation adjustment compensation data.

[0105] In an embodiment of the present invention, indoor temperature circulation adjustment compensation is performed on temperature-coupled air circulation adjustment data in different functional areas according to crowd density distribution data. First, the crowd density distribution data is combined with the temperature-coupled air circulation adjustment data to determine the specific impact of crowds in each functional area on air flow and temperature adjustment. The air circulation adjustment data is adjusted using a compensation algorithm (such as a PID control algorithm), and compensation parameters are set to respond to changes in crowd density. The required adjustment compensation amount is calculated to generate indoor temperature circulation adjustment compensation data. Through the adjustment system, the compensation data is applied to the temperature control system to achieve precise temperature adjustment of different functional areas to maintain the comfort of the indoor environment.

[0106] Preferably, step S22 includes the following steps:

[0107] Step S221: performing structural heat transfer energy conversion evaluation on the temperature coupling effect data inside and outside the structure to obtain structural heat transfer energy conversion data;

[0108] Step S222: performing airflow thermal guidance simulation of different functional areas on the functional area air conditioning flow simulation data based on the structural heat transfer energy conversion data to obtain airflow thermal guidance simulation data;

[0109] Step S223: calculating the convective heat transfer coefficients of different functional zones on the functional zone air conditioning flow simulation data according to the airflow heat guidance simulation data, to obtain the convective heat transfer coefficients;

[0110] Step S224: performing thermal distribution trend mutation analysis on the airflow thermal guidance simulation data to obtain thermal distribution trend mutation data;

[0111] Step S225: performing air circulation regulation on different functional areas based on the functional area air conditioning flow simulation data according to the convective heat transfer coefficient and the heat distribution trend mutation data to obtain temperature-coupled air circulation regulation data.

[0112] In the embodiment of the present invention, in step S221, a structural heat transfer energy conversion evaluation is performed to obtain structural heat transfer energy conversion data. The data of the temperature coupling effect inside and outside the structure are analyzed in detail using a building thermodynamics calculation tool (such as a heat conduction calculation formula and a heat balance equation). First, the thermophysical parameters of components such as walls, windows, and roofs in the structural design data are extracted, including thermal conductivity, specific heat capacity, density, etc. According to these parameters, the heat transfer capacity of each material is calculated using a heat transfer equation (such as a Fourier heat conduction equation), taking into account the temperature difference between inside and outside and the heat exchange efficiency. Through simulation calculations, the heat transfer energy conversion process of different components is quantified to obtain heat transfer energy conversion data for each functional area. These data include the heat flow, heat transfer efficiency, and degree of influence on indoor temperature of each component, which serve as basic data for subsequent analysis. In step S222, based on the structural heat transfer energy conversion data, an airflow thermal guidance simulation is performed on the functional area air conditioning flow simulation data to obtain airflow thermal guidance simulation data. First, the structural heat transfer energy conversion data is input into an airflow simulation tool (such as a fluid mechanics simulation software). Simulation parameters are set, including airflow velocity, air temperature, structural heat transfer data, etc. Perform airflow thermal guidance simulation, and identify the effect of airflow on heat guidance in different areas by analyzing the flow path and heat distribution of air in different functional areas. Generate airflow thermal guidance simulation data, including the airflow's ability to guide heat, the heat distribution of each area, and the thermal interaction between airflow and structure. These data provide necessary information for the subsequent calculation of convective heat transfer coefficient and analysis of heat distribution trend. In step S223, the convective heat transfer coefficient of the functional area air conditioning flow simulation data is calculated according to the airflow thermal guidance simulation data. First, the heat guidance obtained in the airflow thermal guidance simulation data is combined with the air conditioning flow simulation data to analyze the convective heat transfer effect of the airflow in each functional area. The convective heat transfer coefficient of each functional area is calculated using the convective heat transfer formula (such as the Nusselt number and Glashow number formula), taking into account factors such as air velocity, temperature difference and airflow direction. The specific operation includes setting the airflow conditions of each functional area in the flow simulation software, performing convective heat transfer simulation calculation, and obtaining the convective heat transfer coefficient of each functional area. These coefficients describe the heat transfer efficiency and heat transfer capacity of the airflow in different areas, and provide a quantitative basis for the optimization of air circulation regulation. In step S224, the thermal distribution trend mutation analysis is performed on the airflow thermal guidance simulation data to obtain the thermal distribution trend mutation data. First, the heat distribution in the airflow thermal guidance simulation data is extracted, and the heat distribution is statistically analyzed. The thermal distribution analysis tool (such as a thermal distribution map or a thermal gradient analysis method) is used to identify the mutation points and uneven areas in the heat distribution. When performing the thermal distribution trend mutation analysis, the thermal gradient calculation and trend analysis algorithm are used to identify the sudden change points or hot spots in the heat distribution.Finally, the heat distribution trend mutation data is generated, including the changing trend of heat distribution, the specific location of the mutation point and its impact on the overall heat distribution. In step S225, the functional area air conditioning flow simulation data is air circulated according to the convective heat transfer coefficient and the heat distribution trend mutation data. The convective heat transfer coefficient is combined with the heat distribution trend mutation data to formulate an air circulation regulation strategy. The specific operation includes adjusting the air conditioning parameters of each functional area according to the convective heat transfer coefficient, such as fan speed, air outlet temperature, etc., to optimize the heat distribution and heat exchange effect of the airflow. At the same time, the heat distribution trend mutation data is used to adjust the air circulation mode to solve the problem of heat distribution mutation and ensure that the air flow can evenly adjust the temperature. Generate temperature-coupled air circulation regulation data, including adjusted airflow parameters and improved heat distribution, to provide an effective solution for achieving stable indoor temperature control.

[0113] Preferably, performing thermal distribution trend mutation analysis on airflow thermal guidance simulation data comprises the following steps:

[0114] Performing airflow conduction path identification on the airflow heat guide simulation data to obtain airflow conduction path data;

[0115] Performing an air flow thermal field intensity analysis on the air flow heat guide simulation data according to the air flow conduction path data to obtain the air flow thermal field intensity data;

[0116] The convective heat energy accumulation rate is calculated for the air flow thermal field intensity data to obtain the convective heat energy accumulation rate data;

[0117] According to the convective heat energy accumulation rate data, the heat energy nonlinear accumulation point is located on the air flow thermal field intensity data to obtain the heat energy nonlinear accumulation point location data;

[0118] According to the convective heat energy accumulation rate data, the heat distribution trend mutation data is analyzed on the heat energy nonlinear accumulation point positioning data to obtain the heat distribution trend mutation data.

[0119] In an embodiment of the present invention, when the airflow conduction path of the airflow heat guide simulation data is identified, it is first necessary to extract the velocity field and temperature field information of the airflow from the simulation data. The flow of the airflow inside the building is discretized using numerical calculation methods, such as finite element analysis (FEA) or finite difference method (FDM). Through these methods, the airflow velocity vector and the corresponding temperature data of each grid point are calculated. Next, a path tracking algorithm, such as the shortest path algorithm or the streamline algorithm, is applied to identify the conduction path of the airflow. These algorithms determine the main flow path of the airflow by tracking the direction of the airflow velocity vector. The path identification results will be saved as a path data set, including the starting point, end point and each node information passed by each airflow path. These path data will provide a basis for subsequent thermal field intensity analysis. In the analysis of the thermal field intensity of the airflow in time and space, it is first necessary to combine the airflow conduction path data with the temperature field information in the simulation data. With time as a variable, the thermal field intensity of the airflow at each time step is analyzed. The temperature field data is smoothed between the grid points using data interpolation technology (such as cubic spline interpolation) to obtain an accurate thermal field intensity distribution. By integrating the heat transfer on the airflow path, the thermal field intensity of each path can be calculated. The thermal field intensity results of each time step are summarized to form a temporal airflow thermal field intensity data set. This data set will show the changes in thermal field intensity of airflow passing through different paths at different time points, reflecting the distribution trend of thermal energy. The calculation of convective heat accumulation rate is based on the temporal airflow thermal field intensity data. In each time step, the accumulation rate of heat energy on the airflow path is evaluated by calculating the spatial gradient of the thermal field intensity (using numerical differentiation methods such as central difference). The specific implementation method is to divide each airflow path into multiple small segments and calculate the change in thermal energy on each small segment. Adding these changes can get the accumulation rate of convective heat energy. The data can be processed using the cumulative integration method to calculate the total accumulation rate in each time step. The final convective heat accumulation rate data set will contain the accumulation rate information of each airflow path at different time steps. In order to locate the nonlinear accumulation points of thermal energy, it is necessary to analyze the convective heat accumulation rate data first. Nonlinear regression analysis methods (such as polynomial regression) are applied to fit the data to identify the nonlinear accumulation trend of thermal energy in space. The thermal energy accumulation rate and its rate of change are calculated for each location, and mutation points or outliers in the data are found. These points are potential nonlinear thermal energy accumulation points. The location of nonlinear accumulation points can be determined by comparing data at different time steps to determine which locations have the most dramatic changes in thermal energy accumulation rate, thereby marking these locations as nonlinear thermal energy accumulation points. This process can be combined with graphical tools (such as heat maps or contour maps) to visualize the spatial distribution of nonlinear accumulation points. In the thermal distribution trend mutation analysis, the convective thermal energy accumulation rate data is first combined with the thermal energy nonlinear accumulation point location data.Perform detailed thermal field intensity data analysis on the areas around each nonlinear aggregation point to evaluate the changing trend of heat distribution in these areas. By calculating the statistical characteristics of the thermal energy distribution in these areas (such as mean, variance, peak value, etc.), detect whether there are significant mutations or trend changes. Change point detection algorithms (such as CUSUM algorithm or sliding window method) can be used to identify mutation points in the data. These mutation points will show the trend changes of thermal distribution and mark the mutation locations of thermal distribution. The final thermal distribution trend mutation data set will provide a detailed description of the trend changes of thermal energy distribution, helping to understand and optimize the thermal distribution characteristics of intelligent temperature control systems.

[0120] Preferably, step S24 comprises the following steps:

[0121] Step S241: analyzing crowd flow behavior characteristics according to crowd flow density distribution data to obtain crowd flow density behavior characteristic data;

[0122] Step S242: quantifying the heat source distribution of the crowd flow on the crowd flow density distribution data according to the crowd flow density behavior characteristic data, and obtaining the behavior characteristic heat source distribution quantified data;

[0123] Step S243: performing a gridded regional disturbance simulation on the behavior characteristic heat source distribution quantified data to obtain heat source distribution regional disturbance data;

[0124] Step S244: performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data to obtain indoor temperature cycle adjustment compensation data.

[0125] In an embodiment of the present invention, in step S241, the flow data of people in each area of ​​the building is monitored by using a high-resolution sensor network. These sensors include infrared sensors, video monitoring systems and pressure sensors, which continuously collect real-time flow data. After preprocessing, these data include information such as flow statistics of people in different time periods, flow patterns between regions and the length of stay of people. Data analysis tools, such as data mining software, are used to analyze these data and extract the behavior characteristic data of the density of people. These characteristic data include peak hours of people flow, main concentrated areas and the frequency of people flow. By statistically and visually processing these data, the obtained behavior characteristic data of the density of people flow will provide a basis for the subsequent quantification of the heat source distribution. In step S242, based on the behavior characteristic data of the density of people flow obtained in step S241, the heat source distribution of the density of people flow is quantified by using a quantitative analysis method. This process uses a heat map generation tool to convert the behavior characteristic data into heat source distribution data. Specifically, according to the density of people flow in each area, the corresponding heat source intensity value is set, and these values ​​are mapped to the floor plan of the building. Use spatial interpolation algorithms (such as Kriging interpolation or spline interpolation) to fill in the gaps between data to form continuous quantitative data of heat source distribution. These quantitative data can accurately reflect the heat source intensity of each region and provide basic data support for subsequent disturbance simulation. In step S243, the quantitative data of heat source distribution obtained in step S242 are applied to gridded regional disturbance simulation. First, the internal area of ​​the building is divided into uniform grid cells, and the heat source intensity data of each unit is input into the grid model. Then, the heat source simulation software is used to perform disturbance simulation on each grid cell to simulate the heat diffusion effect under different heat source conditions. Through the disturbance model, the heat change under different heat source distribution conditions can be obtained to form the heat source distribution regional disturbance data. These data reveal the heat flow in each region and its influence on temperature change under different heat source conditions, and provide detailed simulation results for the final temperature regulation. In step S244, according to the heat source distribution regional disturbance data obtained in step S243, the indoor temperature of different functional areas in the building is cyclically adjusted and compensated. Using the temperature control algorithm in the temperature control system, the disturbance data of the heat source distribution area is input, and the system calculates the actual temperature deviation of each functional area based on this data. Then, the system adjusts the air circulation and heating / cooling equipment settings of each functional area to compensate for the temperature changes caused by the heat source disturbance. For example, if the heat source intensity in a certain area is higher than expected, the system will increase the cooling capacity of the area; otherwise, the heating function will be increased. Ultimately, the indoor temperature cycle adjustment compensation data obtained will ensure that the temperature of each functional area remains within the preset comfort range, improving the overall temperature control efficiency of the building.

[0126] Preferably, performing gridded regional disturbance simulation on the behavior characteristic heat source distribution quantitative data comprises the following steps:

[0127] Perform heat source intensity regional grid division on the behavior characteristic heat source distribution quantitative data to obtain behavior characteristic heat source intensity grid data;

[0128] Based on the behavior characteristic heat source intensity grid data, the behavior characteristic airflow influence turbulence evaluation between grids in different regions is performed to obtain the behavior characteristic airflow influence turbulence data;

[0129] According to the behavior characteristic airflow influence turbulence data, the heat source instability increment between grids in different regions is calculated for the behavior characteristic heat source intensity grid data, and the behavior characteristic heat source instability increment data is obtained;

[0130] According to the behavior characteristic airflow impact turbulence data and behavior characteristic heat source instability increment data, gridded regional disturbance simulation is carried out to obtain the heat source distribution regional disturbance data.

[0131] In an embodiment of the present invention, when the behavior characteristic heat source distribution quantitative data is gridded, it is first necessary to divide the spatial area of ​​the data into a number of equidistant or unequally spaced grid units. This process is achieved by selecting a suitable grid size and distribution method. For example, a uniform grid method can be used to divide the entire area into a number of square grid units, or an adaptive grid method can be used to adjust the size of the grid according to the change in heat source density. The specific operation steps include: first, the two-dimensional plan of the entire building or area is input into the gridding tool, and then the resolution and size of the grid are set according to the heat source intensity data. Automatic grid division is performed in the tool to obtain the heat source intensity data in each grid unit. After completion, the output result is the behavior characteristic heat source intensity data of each grid unit, which is saved in a spreadsheet or database format for subsequent analysis. After obtaining the heat source intensity grid data, it is a key step to evaluate the impact of airflow. This evaluation is completed by simulating the disturbance of airflow between different grid units. The specific operation is: first, the heat source intensity data of each grid unit is input in the simulation environment, and the boundary conditions and initial state of the airflow model are set. Next, the flow characteristics of the airflow are simulated using a fluid mechanics simulation tool to calculate the airflow velocity, direction, and impact on the adjacent area of ​​each grid unit. The impact of the airflow on the heat source intensity of different grid units is evaluated by quantifying the disturbance of the airflow. The results will be output in the form of behavioral characteristic airflow influence turbulence data, usually as numerical data containing the degree of disturbance of each grid unit, for subsequent instability increment calculation. Based on the airflow influence turbulence data, the heat source instability increment calculation is performed. This process involves analyzing the heat source intensity of each grid unit to determine the degree of instability caused by airflow disturbance. The specific operations include: using a calculation tool or software to input airflow influence turbulence data and heat source intensity grid data, and setting calculation parameters such as instability threshold and increment range. The heat source instability increment of each grid unit under different airflow conditions is calculated by an algorithm, that is, the change in heat source intensity. In the calculation process, the influencing factor needs to be combined with the heat source intensity, and the increment calculation is performed to generate behavioral characteristic heat source instability increment data. The final output is the numerical value of the instability increment of each grid unit, which is stored in a database or spreadsheet for subsequent disturbance simulation. After obtaining the incremental data of heat source instability and the turbulence data of airflow influence, a gridded regional disturbance simulation is performed. This simulation generates disturbance data of heat source distribution by combining the incremental instability data with the airflow influence. The operation steps are as follows: First, input the heat source intensity grid data, airflow influence turbulence data and heat source instability incremental data into the disturbance simulation tool. Then, set the simulation parameters, such as disturbance range, time step, etc. Run the simulation tool to dynamically simulate the heat source distribution between different grid cells, considering the changes in heat source under the influence of airflow. The simulation results are displayed as regional disturbance data of heat source distribution, showing the changes in heat source distribution in each grid cell.This data can be used to optimize smart temperature control strategies to ensure uniform and comfortable temperatures inside the building.

[0132] Preferably, performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data comprises the following steps:

[0133] The disturbance temperature interaction effect is simulated on the disturbance data of the heat source distribution area to obtain the disturbance temperature interaction effect data;

[0134] According to the disturbance temperature interaction data and the heat source distribution area disturbance data, the airflow control rate of different areas is calculated for the temperature-coupled air circulation adjustment data to obtain the temperature airflow control rate data;

[0135] According to the disturbance temperature mutual effect data and the heat source distribution area disturbance data, the variable air outlet angle design of different areas is performed on the temperature coupled air circulation adjustment data to obtain the variable air outlet angle design data;

[0136] According to the temperature and airflow control rate data and the variable air outlet angle design data, the temperature-coupled air circulation adjustment data is used to perform indoor temperature circulation adjustment compensation in different functional areas to obtain indoor temperature circulation adjustment compensation data.

[0137] In an embodiment of the present invention, disturbance data of the heat source distribution area is collected, including the location, intensity, type and time-varying characteristics of the heat sources in different areas. These data are processed, and the disturbance temperature interaction effect is simulated using the heat conduction equation and the air convection model. Specifically, the heat source distribution data is input into the fluid dynamics software, such as using the computational fluid dynamics (CFD) tool, which can model the air temperature change in detail. Through simulation calculation, the influence of each heat source on the surrounding air temperature is obtained, including the mutual interference effect between the heat sources. Finally, the simulation results are used to generate disturbance temperature interaction data, which describes the temperature transfer between different heat sources and its influence on the overall ambient temperature. Based on the obtained disturbance temperature interaction data, the airflow control requirements of different areas are analyzed. The airflow distribution analysis method is used to calculate the airflow control rate in each area. The specific steps include: first, setting the target temperature of each functional area and comparing it with the current temperature data. Then, the airflow rate is calculated by establishing a mathematical model of air flow (such as a model based on the Navier-Stokes equation). Using airflow rate calculation tools (such as fluid dynamics simulation software), the disturbance temperature interaction data is input into the model to calculate the required airflow rate to adjust the temperature of each area. Finally, the temperature airflow control rate data of each area is generated to guide the subsequent airflow control. Based on the obtained temperature airflow control rate data, the appropriate air outlet angle is designed. First, according to the airflow control rate data of each area, the air volume and wind speed of the air outlet that needs to be adjusted are determined. The best air outlet angles for different areas are calculated using air outlet design tools or air outlet angle adjustment algorithms. These tools will optimize the angle setting of the air outlet based on the airflow rate data and the indoor temperature distribution to ensure that the airflow can effectively adjust the temperature of each area. Specifically, the adjustment of the air outlet angle can be achieved through automated adjustment systems, which fine-tune the air outlet angle based on real-time data to achieve the best airflow distribution and temperature control. Finally, the variable air outlet angle design data for each area is generated. Based on the obtained variable air outlet angle design data and temperature airflow control rate data, indoor temperature cycle adjustment compensation is implemented. First, the air outlet angle adjustment setting is applied to the actual air outlet equipment to ensure that each air outlet is adjusted according to the design angle. Next, adjust the wind speed and air volume in the air circulation system to match the temperature and airflow control rate data. Through the air circulation control system, monitor the temperature changes in each area in real time, and adjust the air outlet angle and airflow rate according to the actual temperature data to achieve the preset target temperature. Finally, the comprehensive adjustment results generate indoor temperature circulation adjustment compensation data to ensure that the temperature of each functional area is maintained within the set range.

[0138] Preferably, step S3 comprises the following steps:

[0139] Step S31: normalizing the indoor temperature cycle adjustment compensation data to obtain indoor temperature adjustment compensation normalized data;

[0140] Step S32: constructing a building intelligent temperature control model for the indoor temperature adjustment compensation normalized data using a gradient boosting tree algorithm to obtain a building intelligent temperature control model;

[0141] Step S33: Based on the building intelligent temperature control model, the control logic training and optimization are performed on the indoor temperature control compensation normalized data to obtain temperature control logic optimization data.

[0142] In an embodiment of the present invention, indoor temperature cycle adjustment compensation data is first collected from the temperature control system of the building. These data include indoor temperature at different time points, external environment temperature, and control signals of the adjustment system. Next, these raw data are normalized to eliminate the influence of different data magnitudes on the analysis. The specific operation is: calculate the minimum and maximum values ​​of each data feature, then subtract the minimum value from each data point, and then divide it by the difference between the maximum value and the minimum value to obtain a normalized value in the range of [0, 1]. For example, if the temperature data range is between 15°C and 25°C, the data point 25°C will be normalized to 1, and 15°C will be normalized to 0. The normalized data will facilitate subsequent model training and analysis, and improve the accuracy and stability of the model. The building intelligent temperature control model is constructed using the gradient boosting tree (GBT) algorithm. The specific operation includes: first, the normalized data obtained in step S31 is divided into a training set and a test set. Then, the training set is trained using the gradient boosting tree algorithm. The GBT algorithm constructs a series of decision trees, each of which is optimized on the basis of the previous tree to gradually reduce the prediction error. During the training process, hyperparameters such as the depth of the tree, the learning rate, and the minimum number of sample splits are set, and cross-validation is performed to select the best parameters. After the training is completed, the obtained model can predict temperature control based on the input normalized data and provide intelligent control suggestions based on historical data and real-time environment. Based on the building intelligent temperature control model obtained in step S32, the control logic training and optimization of the indoor temperature control compensation normalized data are performed. The specific operation is: use the model to predict the data in the test set, and compare the difference between the temperature control suggestions output by the model and the actual control effect. The model is further tuned by calculating the prediction error (such as mean square error, absolute error, etc.). Adjust the model parameters, optimize the loss function in the algorithm to reduce the prediction error, and update the control logic. The optimized data contains more accurate control rules and strategies to adapt to different indoor environments and external conditions, thereby improving the performance and comfort of the overall temperature control system.

[0143] Preferably, the present invention further provides an intelligent temperature control system based on a smart building, which is used to execute the intelligent temperature control method based on a smart building as described above. The intelligent temperature control system based on a smart building includes:

[0144] Functional area air flow simulation module is used to obtain smart building structural design data and real-time monitoring data of human flow; simulate the functional area air flow based on the smart building structural design data to obtain functional area air conditioning flow simulation data;

[0145] The circulation adjustment module is used to perform air circulation adjustment in different functional areas based on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation adjustment data; perform indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data based on the real-time monitoring data of human flow to obtain indoor temperature circulation adjustment compensation data;

[0146] A control logic training module is used to perform control logic training optimization on indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data;

[0147] The control execution module is used to perform automated firmware design based on the temperature control logic optimization data, obtain the temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

[0148] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0149] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent temperature control method based on smart buildings, characterized in that: The following steps are involved: Step S1: Acquire smart building structural design data and real-time monitoring data of human traffic; simulate the air flow of functional areas based on the smart building structural design data to obtain air conditioning flow simulation data of functional areas; Step S2: performing air circulation regulation of different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation regulation data; Perform indoor temperature cycle adjustment compensation on the temperature-coupled air cycle adjustment data according to the real-time monitoring data of human flow, and obtain indoor temperature cycle adjustment compensation data; Step S3: Performing control logic training optimization on the indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data; Step S4: Perform automated firmware design based on the temperature control logic optimization data to obtain temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

2. The intelligent temperature control method based on smart buildings according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining smart building structure design data and real-time monitoring data of human traffic; Step S12: classifying the smart building structural design data into functional areas to obtain the smart building functional area structural data; Step S13: performing air conditioning mode recognition on the functional area structure data of the smart building to obtain the functional area air conditioning mode data; Step S14: Perform functional area air flow simulation on the functional area air conditioning mode data to obtain functional area air conditioning flow simulation data.

3. The intelligent temperature control method based on smart buildings according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Analyze the internal and external temperature coupling effect according to the smart building structure design data to obtain the internal and external temperature coupling effect data of the structure; Step S22: Based on the temperature coupling effect data inside and outside the structure, the functional area air conditioning flow simulation data is used to perform air circulation regulation for different functional areas to obtain temperature coupling air circulation regulation data; Step S23: Calculate the crowd density distribution of the real-time crowd flow monitoring data to obtain crowd density distribution data; Step S24: performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the crowd density distribution data to obtain indoor temperature cycle adjustment compensation data.

4. The intelligent temperature control method based on smart buildings according to claim 3 is characterized in that: Step S22 includes the following steps: Step S221: performing structural heat transfer energy conversion evaluation on the temperature coupling effect data inside and outside the structure to obtain structural heat transfer energy conversion data; Step S222: performing airflow thermal guidance simulation of different functional areas on the functional area air conditioning flow simulation data based on the structural heat transfer energy conversion data to obtain airflow thermal guidance simulation data; Step S223: calculating the convective heat transfer coefficients of different functional zones on the functional zone air conditioning flow simulation data according to the airflow heat guidance simulation data, to obtain the convective heat transfer coefficients; Step S224: performing thermal distribution trend mutation analysis on the airflow thermal guidance simulation data to obtain thermal distribution trend mutation data; Step S225: performing air circulation regulation on different functional areas based on the functional area air conditioning flow simulation data according to the convective heat transfer coefficient and the heat distribution trend mutation data to obtain temperature-coupled air circulation regulation data.

5. The intelligent temperature control method based on smart buildings according to claim 4 is characterized in that: The thermal distribution trend mutation analysis of airflow thermal guidance simulation data includes the following steps: Performing airflow conduction path identification on the airflow heat guide simulation data to obtain airflow conduction path data; Performing an air flow thermal field intensity analysis on the air flow heat guide simulation data according to the air flow conduction path data to obtain the air flow thermal field intensity data; The convective heat energy accumulation rate is calculated for the air flow thermal field intensity data to obtain the convective heat energy accumulation rate data; According to the convective heat energy accumulation rate data, the heat energy nonlinear accumulation point is located on the air flow thermal field intensity data to obtain the heat energy nonlinear accumulation point location data; According to the convective heat energy accumulation rate data, the heat distribution trend mutation analysis is performed on the heat energy nonlinear accumulation point positioning data to obtain the heat distribution trend mutation data.

6. The intelligent temperature control method based on smart buildings according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: analyzing crowd flow behavior characteristics according to crowd flow density distribution data to obtain crowd flow density behavior characteristic data; Step S242: quantifying the heat source distribution of the crowd flow on the crowd flow density distribution data according to the crowd flow density behavior characteristic data, and obtaining the behavior characteristic heat source distribution quantified data; Step S243: performing a gridded regional disturbance simulation on the behavior characteristic heat source distribution quantified data to obtain heat source distribution regional disturbance data; Step S244: performing indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data to obtain indoor temperature cycle adjustment compensation data.

7. The intelligent temperature control method based on smart buildings according to claim 6 is characterized in that: The gridded regional disturbance simulation of the quantitative data of the behavior characteristic heat source distribution includes the following steps: The behavior characteristic heat source distribution quantitative data is divided into heat source intensity area grids to obtain behavior characteristic heat source intensity grid data; Based on the behavior characteristic heat source intensity grid data, the behavior characteristic airflow influence turbulence evaluation between grids in different regions is performed to obtain the behavior characteristic airflow influence turbulence data; According to the behavior characteristic airflow influence turbulence data, the heat source instability increment between grids in different regions is calculated for the behavior characteristic heat source intensity grid data, and the behavior characteristic heat source instability increment data is obtained; According to the behavior characteristic airflow impact turbulence data and behavior characteristic heat source instability increment data, gridded regional disturbance simulation is carried out to obtain the heat source distribution regional disturbance data.

8. The intelligent temperature control method based on smart buildings according to claim 6 is characterized in that: The indoor temperature cycle adjustment compensation of different functional areas is performed on the temperature-coupled air cycle adjustment data according to the heat source distribution area disturbance data, including the following steps: The disturbance temperature interaction effect is simulated on the disturbance data of the heat source distribution area to obtain the disturbance temperature interaction effect data; According to the disturbance temperature interaction data and the heat source distribution area disturbance data, the airflow control rate of different areas is calculated for the temperature-coupled air circulation adjustment data to obtain the temperature airflow control rate data; According to the disturbance temperature mutual effect data and the heat source distribution area disturbance data, the variable air outlet angle design of different areas is performed on the temperature coupled air circulation adjustment data to obtain the variable air outlet angle design data; According to the temperature and airflow control rate data and the variable air outlet angle design data, the temperature-coupled air circulation adjustment data is used to perform indoor temperature circulation adjustment compensation in different functional areas to obtain indoor temperature circulation adjustment compensation data.

9. The intelligent temperature control method based on smart buildings according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: normalizing the indoor temperature cycle adjustment compensation data to obtain indoor temperature adjustment compensation normalized data; Step S32: constructing a building intelligent temperature control model for the indoor temperature adjustment compensation normalized data using a gradient boosting tree algorithm to obtain a building intelligent temperature control model; Step S33: Based on the building intelligent temperature control model, the control logic training and optimization are performed on the indoor temperature control compensation normalized data to obtain temperature control logic optimization data.

10. An intelligent temperature control system based on smart buildings, characterized in that: Used to execute the intelligent temperature control method based on the smart building as claimed in claim 1, the intelligent temperature control system based on the smart building comprises: Functional area air flow simulation module is used to obtain smart building structural design data and real-time monitoring data of human flow; simulate the functional area air flow based on the smart building structural design data to obtain functional area air conditioning flow simulation data; The circulation adjustment module is used to perform air circulation adjustment in different functional areas based on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation adjustment data; perform indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data based on the real-time monitoring data of human flow to obtain indoor temperature circulation adjustment compensation data; The control logic training module is used to perform control logic training optimization on the indoor temperature cycle adjustment compensation data to obtain temperature control logic optimization data; The control execution module is used to perform automated firmware design based on the temperature control logic optimization data, obtain the temperature control logic firmware, and embed the temperature control logic firmware into the smart building control platform to perform intelligent temperature control.

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