An intelligent temperature control method and system based on smart buildings

Abstract

The present invention relates to the technical field of temperature regulation, and particularly to an intelligent temperature regulation method and system based on a smart building. The method includes the following steps: simulating the air fluidity in the functional areas based on the structural design data of the smart building to obtain the air conditioning flow simulation data for the functional areas; performing air circulation adjustment for different functional areas on the air conditioning flow simulation data for the functional areas to obtain the temperature-coupled air circulation adjustment data; performing indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data according to the real-time monitoring data of the number of people to obtain the indoor temperature circulation adjustment compensation data; performing training and optimization on the control logic for the indoor temperature circulation adjustment compensation data to obtain the optimized temperature control logic data; and performing automated firmware design based on the optimized temperature control logic data to obtain the temperature control logic firmware. The present invention makes the temperature regulation technology more perfect through the optimization of the temperature regulation technology.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and particularly 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 means to achieve multiple goals of energy conservation, comfort, and environmental protection. In a smart building, a widely deployed sensor network can continuously monitor environmental and population activity data such as indoor and outdoor temperature, humidity, carbon dioxide concentration, and human flow, and deeply analyze this information through Internet of Things (IOT) and big data technologies to identify the best temperature control strategies. The intelligent temperature control system not only realizes personalized settings by learning user preferences and activity patterns but also makes predictive adjustments by combining external factors such as weather forecasts, energy consumption data, and regional electricity peaks, significantly improving energy utilization efficiency. The intelligent design of temperature control combines artificial intelligence (AI) algorithms such as machine learning, neural networks, and optimization algorithms, continuously optimizing the control logic through adaptive and self-learning functions, reducing unnecessary energy consumption and carbon emissions. In addition, the intelligent control method is integrated with the building's central air conditioning system, ventilation system, and window control system to achieve multi-level linkage responses, enhancing environmental adaptability and improving indoor comfort. By working in coordination with the energy management system, this method can effectively control the operation of refrigeration, heating, and ventilation equipment without affecting the user experience, reducing energy consumption costs and extending the equipment lifespan. Intelligent temperature control not only helps to improve the automation and intelligence level of building management but also provides important technical support for building a green and low-carbon smart city, driving the future building environment towards a more energy-efficient, healthy, and efficient direction. However, a traditional intelligent temperature control method based on a smart building has the problems of being unable to accurately control according to the personnel density and external climate changes, and resulting in 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, an intelligent temperature control method based on a smart building, the method includes the following steps:

[0005] Step S1: Obtain the structural design data of the smart building and the real-time monitoring data of the pedestrian flow; perform a functional area air mobility simulation on the structural design data of the smart building to obtain functional area air conditioning flow simulation data;

[0006] Step S2: Perform air circulation adjustment for different functional areas on the air conditioning flow simulation data of the functional area to obtain temperature-coupled air circulation adjustment data; perform indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data according to the real-time monitoring data of the number of people to obtain indoor temperature circulation adjustment compensation data;

[0007] Step S3: Perform training and optimization on the control logic of the indoor temperature circulation adjustment compensation data to obtain optimized temperature control logic data;

[0008] Step S4: Based on the optimized temperature control logic data, perform automated firmware design to obtain temperature control logic firmware, and embed the temperature control logic firmware into the intelligent building control platform to execute intelligent temperature control.

[0009] By simulating the air fluidity in the functional areas, the present invention can identify the bottlenecks and dead ends of air flow, thereby optimizing the air circulation path. This helps to ensure that all areas can obtain uniform air distribution, avoiding 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 the delivery of cold (warm) air. It is not just a single air regulation without temperature control. By reducing the excessive operation of the air conditioning and ventilation systems, the energy use efficiency is optimized. Uniform air flow helps to enhance the comfort level in the building and improve the working and living environment. According to the simulation data, temperature-coupled air circulation adjustment can be carried out to more precisely control the temperature of each functional area. This helps to achieve personalized temperature control in different areas, meeting the needs of different functional areas, such as offices, meeting rooms, rest areas, etc. Precise adjustment of air circulation can avoid unnecessary heating or cooling, further saving energy expenses. By adjusting the air flow during peak demand, a comfortable indoor environment can be maintained while reducing energy consumption. Real-time monitoring data of the number of people enables the system to dynamically adjust the temperature and air flow, responding in real time to the increase or decrease of people. This adaptive adjustment can ensure that the indoor temperature remains within a comfortable range even during peak or off-peak periods. Appropriate temperature circulation compensation helps to maintain indoor air quality and prevent the decline of air quality caused by crowded people. Good air quality has a positive impact on human health and work efficiency. The system can automatically adjust, reducing the need for manual intervention, lowering the maintenance and operation costs. At the same time, the overuse of air conditioning and heating equipment is reduced, extending the service life of the equipment. Through the optimization of control logic training, the temperature control algorithm can be improved to make it respond more precisely to environmental changes. This helps to improve the response speed and accuracy of the adjustment system, ensuring 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 number of people, thereby enhancing the intelligent level and adaptive ability of the system. Through training and optimization, the system can reduce the adjustment errors caused by prediction errors or incomplete data, thereby improving the overall temperature control effect. This means that a more comfortable indoor environment can be better maintained, reducing the discomfort caused by temperature fluctuations. The optimized control logic can more effectively adjust the temperature control strategy, thereby reducing energy consumption. For example, the system can predict and reduce unnecessary heating or cooling, saving energy costs. After embedding the optimized control logic firmware into the intelligent building control platform, the system can automatically execute temperature control without manual intervention. This enables the building to continuously operate in the best state, improving the management efficiency and comfort level. The firmware can process data and execute adjustments in real time, with a rapid response, making timely adjustments to environmental changes to ensure that the indoor temperature always remains within the predetermined range.Automation and optimization of firmware design help reduce human operation errors and system failures, thereby improving system stability and reliability. Integrating and optimizing the firmware makes system maintenance simpler and more efficient. Maintenance personnel can identify and solve problems more quickly, enhancing the long-term stability and operational efficiency of the system. Through automated and intelligent firmware design, users can enjoy a temperature control experience that better meets their individual needs, such as personalized temperature settings for different areas and real-time responses. The system can continuously optimize the temperature control effect, improve the overall indoor comfort level, and meet the needs of different people and usage scenarios. Therefore, the present invention optimizes a traditional intelligent temperature control method based on a smart building, solves the problem that the traditional intelligent temperature control method based on a smart building cannot accurately adjust according to the personnel density and external climate changes, resulting in high energy consumption, and improves the ability to accurately adjust according to the personnel density and external climate changes, effectively controlling the problem of high energy consumption.

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

[0011] Step S11: Obtain the smart building structure design data and real-time pedestrian flow monitoring data;

[0012] Step S12: Classify the smart building structure design data by functional areas to obtain the smart building functional area structure data;

[0013] Step S13: Identify the air conditioning mode for the smart building functional area structure data to obtain the functional area air conditioning mode data;

[0014] Step S14: Simulate the air fluidity in the functional area for the functional area air conditioning mode data to obtain the functional area air conditioning flow simulation data.

[0015] The acquisition of structural design data and real-time monitoring data in the present invention provides a solid data foundation for subsequent analysis and optimization. These data include the building layout, functional areas of the building, and the current pedestrian flow situation. The real-time monitoring data allows the system to instantly understand the personnel distribution and activity patterns within the building, enabling the air conditioning to make rapid adjustments according to the 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 regulation. It enables a more accurate understanding of the actual usage, improving 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, meeting rooms, rest areas, etc.) can be clarified. This helps to understand the specific needs and challenges of each functional area. Clear classification of functional areas helps to design air conditioning solutions that better meet the actual needs, avoiding the situation where a general design fails to meet the requirements 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, improving the pertinence and effectiveness of the air conditioning system. Resources and energy can be more effectively allocated to each functional area, avoiding over-regulation or under-regulation. By analyzing the structural data of the functional areas, the air conditioning demand patterns of different areas can be identified. For example, meeting 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, thereby formulating more refined and effective regulation strategies. By identifying and understanding the air conditioning patterns of each functional area, the air flow, temperature, and humidity can be better regulated, improving the accuracy and comfort of the 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 mobility of the functional areas, the air flow patterns in different areas can be visually observed, and potential problems and optimization points can be identified. For example, it is found that there are problems such as air stagnation or poor air flow in certain areas. The simulation data can be used to formulate improvement plans, such as adjusting the position of the ventilation openings or optimizing the air flow path, to improve the efficiency and uniformity of air flow. Through air mobility simulation, problems can be anticipated and improved before actual installation, thereby enhancing the performance and efficiency of the overall system. The optimized air flow performance effectively improves the indoor comfort, avoiding excessive or too low temperature in the area and improving the user experience.

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

[0017] Step S21: Analyze the internal and external temperature coupling effect based on the intelligent building structural design data to obtain the internal and external temperature coupling effect data of the structure;

[0018] Step S22: Perform air circulation regulation for different functional areas on the air conditioning flow simulation data of the functional areas based on the internal and external temperature coupling effect data to obtain the temperature-coupled air circulation regulation data;

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

[0020] Step S24: Perform 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 analysis of the internal and external temperature coupling effect in the present invention helps to understand the influence of the external environment (such as external air temperature, wind speed, solar radiation, etc.) on the internal temperature of the building. This can accurately predict the internal temperature changes of the building under different external conditions. By identifying the coupling effect of the internal and external temperatures, the building's heat insulation 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 external temperature changes, thereby avoiding increased energy consumption due to over-regulation. Targeted energy-saving measures can be implemented. The air circulation adjustment based on the internal and external temperature coupling effect data makes the air conditioning in each functional area more in line with the actual needs. The air flow patterns that need to be adjusted due to the internal and external temperature differences in different areas will be optimized. Precise air circulation adjustment can ensure uniform air flow in each functional area and maintain a comfortable indoor environmental 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 crowd density distribution, the usage intensity and personnel aggregation in different areas can be understood, and targeted air conditioning can be carried out in these areas. Optimize the allocation of air conditioning resources according to the crowd density distribution data, 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 is reduced, and the energy usage efficiency of the system is improved. According to the crowd density distribution data, compensating the temperature-coupled air circulation adjustment data can achieve more precise adjustment of the temperature in different functional areas. This means that in high-density areas, the system will automatically increase the air conditioning to cope with the additional heat load. The compensation adjustment can ensure that even in high-density crowd areas, a comfortable indoor temperature can be maintained, improving user satisfaction. By compensating in real time for the heat changes brought about by the change in crowd density, the indoor temperature fluctuations caused by the change in personnel density are avoided, and the stability of temperature control is improved. The adjustment strategy integrating crowd density data makes the system more intelligent and adaptable, and can dynamically adjust to meet the actual environmental needs.

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

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

[0024] Step S222: Based on the structural heat transfer energy conversion data, conduct an air flow heat guidance simulation for different functional areas on the air conditioning flow simulation data of the functional areas to obtain the air flow heat guidance simulation data;

[0025] Step S223: Calculate the convective heat transfer coefficient for different functional areas on the air conditioning flow simulation data of the functional areas according to the air flow heat guidance simulation data to obtain the convective heat transfer coefficient;

[0026] Step S224: Conduct a mutation analysis of the thermal distribution trend on the air flow heat guidance simulation data to obtain the mutation data of the thermal distribution trend;

[0027] Step S225: Conduct air circulation adjustment for different functional areas on the air conditioning flow simulation data of the functional areas according to the convective heat transfer coefficient and the mutation data of the thermal distribution trend to obtain the temperature coupling air circulation adjustment data.

[0028] By evaluating the temperature coupling effect inside and outside the structure, the present invention can understand in detail how the building structure transfers and converts thermal energy under different environmental conditions. 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 insulation performance of the building and its impact on indoor temperature, optimize indoor temperature regulation to reduce energy consumption. In addition, this evaluation also provides important information for optimizing the design of building systems, such as the thermal insulation effects of windows, walls, and roofs, to ensure a stable indoor environment in various climate conditions. Using the structural heat transfer data obtained in step S221, conduct an air flow heat guidance simulation for the air conditioning flow in different functional areas. This process helps understand how air flow carries and distributes heat in different areas, and identify which areas will have problems of overheating or overcooling due to uneven air flow. By simulating the heat guidance effect of the air flow, the air conditioning system can be adjusted to more effectively guide and distribute heat, ensuring a uniform temperature distribution in each functional area. Such refined simulation results make the air conditioning system more in line with actual needs, thereby enhancing the overall comfort and energy efficiency of the system. By calculating the convective heat transfer coefficient of each functional area in the air flow heat guidance simulation data, evaluate the heat exchange efficiency between air flow and functional areas. The convective heat transfer coefficient is a key indicator to measure the contribution of air flow to heat transfer, helping to understand the heat transfer effect of air flow in each area. This calculation can reveal which areas have poor air flow heat transfer effects, and then guide the adjustment of air flow design to improve heat exchange efficiency. This helps ensure that the air conditioning system can effectively achieve the expected temperature control goal, reduce energy waste, and enhance the overall performance of the system. By conducting a sudden change analysis of the thermal distribution trend of the air flow heat guidance simulation data, identify sudden changes or abnormal points in the thermal distribution. The sudden change analysis of the thermal distribution trend helps to discover areas of drastic changes in heat distribution, which are caused by uneven air flow or structural design defects resulting in local overheating or overcooling. Through this analysis, the problem areas in the heat distribution can be accurately located and targeted adjustments can be made to achieve a more uniform temperature distribution. Such detailed analysis results provide substantial data support for adjusting the air conditioning strategy, helping to optimize the air flow design and ensure the comfort of each area. Considering the convective heat transfer coefficient and the sudden change data of the thermal distribution trend, optimize the air circulation regulation of each functional area. Through these data, the air conditioning system can be accurately adjusted to ensure that the air flow in each functional area can effectively respond to the needs of heat transfer and the sudden changes in thermal 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 to improve the energy utilization efficiency of the overall building, reduce energy waste, and at the same time improve the user's comfort experience.

[0029] Preferably, the thermal distribution trend mutation analysis of the air flow thermal guidance simulation data includes the following steps:

[0030] Identify the air flow conduction path of the air flow thermal guidance simulation data to obtain air flow conduction path data;

[0031] Perform the time-air flow thermal field intensity analysis on the air flow thermal guidance simulation data according to the air flow conduction path data to obtain the time-air flow thermal field intensity data;

[0032] Calculate the convective heat energy accumulation rate of the time-air flow thermal field intensity data to obtain the convective heat energy accumulation rate data;

[0033] Locate the non-linear heat energy accumulation points of the time-air flow thermal field intensity data according to the convective heat energy accumulation rate data to obtain the non-linear heat energy accumulation point location data;

[0034] Perform the thermal distribution trend mutation analysis on the non-linear heat energy accumulation point location data according to the convective heat energy accumulation rate data to obtain the thermal distribution trend mutation data.

[0035] By identifying the conduction path of the air flow, the present invention can reveal the flow route and distribution characteristics of the air flow in space. This identification process helps to understand how the air flow transfers heat in different regions, and clarifies the main flow channels and paths of the air flow in the functional areas. The obtained air flow conduction path data provides basic information for subsequent thermal field intensity analysis and heat distribution optimization, enabling designers to accurately locate the key channels and regions of heat transfer, thereby effectively optimizing the air conditioning system, ensuring uniform temperature distribution in each region, and improving the overall thermal management performance of the system. Using the air flow conduction path data to perform transient air flow thermal field intensity analysis on the air flow thermal guiding simulation data can calculate and evaluate the thermal field intensity of the air flow at different times. This analysis helps to understand the heat distribution and intensity changes of the air flow at different time points, revealing the dynamic characteristics of the thermal field over time. Through the transient air flow thermal field intensity data, designers can discover the change trend of the thermal field intensity, thereby dynamically adjusting the air conditioning strategy of the system to ensure effective distribution and management of heat under various operating conditions, and improving the response ability and stability of the thermal management system. By calculating the convective heat energy accumulation rate in the transient air flow thermal field intensity data, the rate and degree of heat energy accumulation of the air flow in different regions can be evaluated. This calculation reveals the process of heat energy accumulation in space and helps to identify the regions where heat energy accumulates relatively quickly. The convective heat energy accumulation rate data provides an important basis for subsequent positioning of non-linear heat energy accumulation points, enabling designers to more accurately locate the heat concentration regions, optimize the air conditioning strategy, 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, non-linear heat energy accumulation point positioning of the thermal field intensity data is performed. This process can identify the non-linear heat energy accumulation points in space, that is, the regions where the heat concentration changes violently. The non-linear heat energy accumulation point positioning data helps to discover the abnormal points or mutation regions in the heat distribution. These regions usually require special attention and adjustment. By locating these accumulation points, designers can take targeted air conditioning and thermal management measures to optimize the heat distribution and improve the overall performance and comfort of the system. By combining the convective heat energy accumulation rate data with the non-linear heat energy accumulation point positioning data for thermal distribution trend mutation analysis, the mutation trend in the heat distribution can be explored in depth. This analysis can reveal the drastic changes and abnormal trends existing in the 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 the air conditioning strategy, enabling designers to timely adjust the system settings to cope with the mutations in the heat distribution and enhance the stability and efficiency of the thermal management system.

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

[0037] Step S241: Analyze the pedestrian flow behavior characteristics based on the pedestrian flow density distribution data to obtain the pedestrian flow density behavior characteristic data;

[0038] Step S242: Quantify the heat source distribution of the pedestrian flow density distribution data according to the pedestrian flow density behavior characteristic data to obtain the behavior characteristic heat source distribution quantification data;

[0039] Step S243: Conduct a grid region perturbation simulation on the behavior characteristic heat source distribution quantification data to obtain the heat source distribution region perturbation data;

[0040] Step S244: Perform indoor temperature cycle regulation compensation for different functional areas on the temperature coupling air circulation regulation data according to the heat source distribution region perturbation data to obtain the indoor temperature cycle regulation compensation data.

[0041] By analyzing the pedestrian flow density distribution data in detail, the present invention can reveal the behavior patterns and characteristics of pedestrian flow at different times and in different spaces. By identifying high-density pedestrian flow areas and their behavior characteristics (such as moving speed, staying time), the actual impact of pedestrian flow on the environment can be deeply understood. These pedestrian flow behavior 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 pedestrian flow situation, so as to improve the response ability and comfort of the system, and at the same time effectively predict and manage the impact of high-density pedestrian flow on the indoor environment. Using the data obtained from the analysis of pedestrian flow behavior characteristics, the heat source distribution of the pedestrian flow density distribution is quantified. By converting the pedestrian flow density behavior characteristics into specific quantified data of heat source distribution, the contribution and distribution of pedestrian flow to heat can be accurately evaluated. This quantification result can reveal the actual impact of pedestrian flow activities on the heat in different regions, helping designers to consider the additional heat sources generated by pedestrian flow in the air conditioning system, so as to optimize the heat distribution and adjustment strategy. Ensure appropriate temperature control in high-pedestrian flow areas, thereby improving the overall comfort and energy efficiency of the system. By performing grid area perturbation simulation on the quantified heat source distribution data, the subtle changes in heat source distribution in different regions can be simulated and analyzed. By dividing the heat source distribution data into grid areas and performing perturbation simulation, designers can observe and evaluate the fluctuations of heat sources in space and their impact on the heat field. This simulation result helps to identify and predict potential problem areas in 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 heat source distribution area perturbation data, the indoor temperature cycle regulation of different functional areas is compensated. By considering the impact of heat source perturbation 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 cope with the problem of uneven temperature caused by changes in heat source distribution and achieve more accurate and efficient temperature control. Finally, the indoor temperature cycle regulation compensation data provides a specific compensation scheme for optimizing the air conditioning system, improving the spatial comfort and the overall performance of the system.

[0042] Preferably, the grid area perturbation simulation of the behavior characteristic heat source distribution quantified data includes the following steps:

[0043] Dividing the behavior characteristic heat source distribution quantified data into heat source intensity area grids to obtain behavior characteristic heat source intensity grid data;

[0044] Based on the behavior characteristic heat source intensity grid data, evaluating the disorderliness of the influence of behavior characteristics air flow between different area grids to obtain behavior characteristic air flow influence disorderliness data;

[0045] Calculate the heat source instability increment between grids in different regions of the heat source intensity grid data of behavioral characteristics according to the airflow influence disorder data of behavioral characteristics, and obtain the heat source instability increment data of behavioral characteristics;

[0046] Conduct a grid-based regional perturbation simulation according to the airflow influence disorder data of behavioral characteristics and the heat source instability increment data of behavioral characteristics, and obtain the perturbation data of the heat source distribution area.

[0047] By dividing the heat source distribution quantization data of behavioral characteristics into multiple regional grids, the present invention can clarify the heat source intensity distribution in different regions. This grid-based division makes the spatial distribution of heat source intensity clearer, and can identify high-heat-source-intensity and low-heat-source-intensity regions. The obtained heat source intensity grid data of behavioral characteristics provides a detailed heat source intensity map, laying a foundation for subsequent airflow influence assessment and heat source instability calculation, enabling the system to more accurately understand the distribution of heat sources in each region, and thus optimizing air conditioning and heat management strategies. By evaluating the disorder of airflow influence between different regional grids, the degree of interference of heat source intensity changes on the airflow can be evaluated. This evaluation reveals the stability and disorder of the airflow in the face of heat sources of different intensities, and helps to identify potential problem areas in the airflow. These airflow influence disorder data of behavioral characteristics can help designers understand and predict the changes in the airflow after being affected by heat sources, so as to optimize the air conditioning system, ensure the stability and uniformity of the airflow, and improve the overall performance and comfort of the system. By combining the airflow influence disorder data, calculating the instability increment of the heat source intensity grid data can evaluate the instability degree of the heat source between different regional grids. This calculation can identify the heat source instability caused by airflow disorder, that is, in which regions the heat source has problems such as overheating or insufficiency due to the instability of the airflow. The obtained heat source instability increment data provides a basis for further adjusting and optimizing the air conditioning strategy, helps to improve the stability and heat management effect of the system, and avoids local temperature anomalies caused by heat source instability. By comprehensively considering the airflow influence disorder data and the heat source instability increment data, a grid-based regional perturbation simulation is carried out. This simulation can comprehensively evaluate the perturbation of the heat source distribution in different regions, and reveal the change of heat distribution caused by heat source instability and airflow disorder. These perturbation data of the heat source distribution area help designers understand the performance of the heat source in actual operation, optimize the system settings, adjust the air circulation and regulation strategies to cope with the heat fluctuations and uneven distributions that occur in actual use, and thus improve the overall performance and environmental comfort of the system.

[0048] Preferably, the indoor temperature cycle regulation compensation for different functional areas of the temperature-coupled air circulation regulation data according to the perturbation data of the heat source distribution area includes the following steps:

[0049] Simulate the mutual effect of disturbance temperature on the disturbance data of the heat source distribution area to obtain the mutual effect data of disturbance temperature;

[0050] Calculate the air flow control rate in different regions of the temperature-coupled air circulation regulation data based on the mutual effect data of disturbance temperature and the disturbance data of the heat source distribution area to obtain the temperature air flow control rate data;

[0051] Design the variable air outlet angle in different regions of the temperature-coupled air circulation regulation data based on the mutual effect data of disturbance temperature and the disturbance data of the heat source distribution area to obtain the variable air outlet angle design data;

[0052] Perform indoor temperature cycle regulation compensation in different functional areas on the temperature-coupled air circulation regulation data according to the temperature air flow control rate data and the variable air outlet angle design data to obtain the indoor temperature cycle regulation compensation data.

[0053] Through the simulation of the mutual effect of disturbance temperature on the disturbance data of the heat source distribution area, the present invention can understand in detail the mutual influence of temperature changes in different areas. This simulation process reveals the overall influence of the heat source distribution disturbance on the indoor temperature field, especially how the temperatures in different areas interact and influence each other. This analysis is crucial for identifying and preventing temperature unevenness problems caused by heat source disturbances. The obtained mutual effect data of disturbance temperature provides a basis for subsequent air flow regulation and air outlet angle adjustment, making the temperature regulation more accurate and ensuring that each functional area can maintain ideal comfort and temperature stability during actual operation. By combining the mutual effect data of disturbance temperature and the disturbance data of the heat source distribution area, the calculation of the air flow regulation rate can be carried out to determine the necessary rates of air flow adjustment in different areas. These rate data indicate how to adjust the air flow volume of the air circulation system in response to the temperature changes brought about by heat source disturbances. Accurately calculating the air flow regulation rate helps to dynamically adjust the air conditioning system, ensuring a stable temperature is maintained in each area, thereby improving the efficiency and comfort of the system and avoiding temperature fluctuation problems caused by insufficient or excessive air flow. By analyzing the mutual effect data of disturbance temperature and the disturbance data of the heat source distribution area, variable air outlet angles are designed to meet the requirements of different areas. The design of variable air outlet angles can dynamically adjust the angles of the air outlets according to the specific heat source and air flow effects to optimize air distribution and temperature regulation. The air outlets designed in this way can deliver the air flow to the areas that need to be adjusted more accurately, thereby improving the local temperature and comfort and reducing the temperature unevenness phenomenon caused by inappropriate air outlet angles. Combining the temperature air flow regulation rate data and the variable air outlet angle design data, indoor temperature cycle regulation compensation is carried out for different functional areas. This compensation process adjusts the parameters of the air conditioning system according to the data of the previous two steps to cope with the temperature changes and heat source disturbances in different areas. By comprehensively adjusting the air flow rate and the air outlet angle, the temperature distribution in each functional area is optimized to achieve the temperature balance and comfort improvement of the overall environment. The obtained indoor temperature cycle regulation compensation data provides an accurate adjustment plan, enabling the air conditioning system to effectively cope with various temperature changes during actual operation and improving the overall efficiency and user experience of the system.

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

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

[0056] Step S32: Use the gradient boosting tree algorithm to construct a building intelligent temperature regulation model for the normalized indoor temperature regulation compensation data to obtain the building intelligent temperature regulation model;

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

[0058] In the present invention, by normalizing the indoor temperature cycle regulation compensation data, data with different ranges and scales can be converted into a unified standard format. This processing can eliminate the differences in data magnitude and the influence of units, making the data easier to analyze and model. The normalized data enables various temperature regulation compensation information to be compared and processed under the same standard, 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 regulation effect of the system. Using the gradient boosting tree algorithm to construct the building intelligent temperature control model can effectively process and analyze the normalized indoor temperature regulation compensation data. The gradient boosting tree is a powerful ensemble learning method that can improve the prediction accuracy by iteratively optimizing the model multiple times. This algorithm improves its prediction ability by gradually reducing the model error, so it can provide an accurate intelligent model for building temperature control. Such a model can accurately predict and adjust the indoor temperature, optimize the energy efficiency and comfort of the building, and provide strong support for realizing intelligent temperature control management. Based on the building intelligent temperature control model, performing regulation logic training and optimization can further refine the temperature regulation strategy. This process uses the prediction ability of the model to train the normalized data and optimize the regulation logic, making the temperature regulation more accurate and efficient. The data after training and optimization can reveal the optimal temperature control strategy and logic, ensuring that the temperature regulation in each area is more in line with the 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 also provides an intelligent temperature control system based on a smart building for executing 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] A functional area air flow simulation module, configured to obtain the smart building structure design data and the real-time monitored data of the number of people; perform functional area air flow simulation on the smart building structure design data to obtain functional area air regulation flow simulation data;

[0061] A cycle regulation module, configured to perform air cycle regulation in different functional areas on the functional area air regulation flow simulation data to obtain temperature-coupled air cycle regulation data; perform indoor temperature cycle regulation compensation on the temperature-coupled air cycle regulation data according to the real-time monitored data of the number of people to obtain indoor temperature cycle regulation compensation data;

[0062] A regulation logic training module, which is used to perform regulation logic training and optimization on indoor temperature cycle adjustment compensation data to obtain optimized temperature regulation logic data;

[0063] A regulation execution module, which is used to perform automated firmware design based on the optimized temperature regulation logic data to obtain a temperature regulation logic firmware, and embed the temperature regulation logic firmware into the intelligent building control platform to execute intelligent temperature regulation.

[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 help reduce human operation errors and system failures, thereby improving the stability and reliability of the system. Integrating and optimizing the firmware makes system maintenance simpler and more efficient. Maintenance personnel can identify and solve problems more quickly, enhancing the long-term stability and operational efficiency of the system. Through automated and intelligent firmware design, users can enjoy a temperature control experience that better meets their individual needs, such as personalized temperature settings for different areas and real-time responses. The system can continuously optimize the temperature control effect, enhance the overall indoor comfort, and meet the needs of different people and usage scenarios. Therefore, the present invention optimizes a traditional intelligent temperature control method based on a smart building, solves the problem that the traditional intelligent temperature control method based on a smart building cannot accurately adjust according to the personnel density and external climate changes, and results in high energy consumption, improves the ability to accurately adjust according to the personnel density and external climate changes, and effectively controls the problem of high energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of the steps of an intelligent temperature control method based on a smart building;

[0066] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0067] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts belong to the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the 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 in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. 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 related items.

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

[0072] Step S1: Obtain the structural design data of the smart building and the real-time monitoring data of the number of people; perform a functional area air flow simulation on the structural design data of the smart building to obtain functional area air conditioning flow simulation data;

[0073] Step S2: Perform air circulation adjustment for different functional areas 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 according to the real-time monitoring data of the number of people to obtain indoor temperature circulation adjustment compensation data;

[0074] Step S3: Perform regulation logic training and optimization on the indoor temperature circulation adjustment compensation data to obtain temperature regulation logic optimization data;

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

[0076] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of an intelligent temperature control method based on a smart building according to the present invention. In this example, the intelligent temperature control method based on a smart building comprises the following steps:

[0077] Step S1: Obtain the structural design data of the smart building and the real-time monitoring data of the number of people; perform a functional area air flow simulation on the structural design data of the smart building to obtain functional area air conditioning flow simulation data;

[0078] In the embodiments of the present invention, during the process of obtaining the intelligent building structure design data and the real-time monitoring data of the pedestrian flow, high-resolution cameras are installed at key positions such as the main entrances, corridors, and functional areas of the building. These cameras need to have the function of real-time video streaming and be able to provide clear images under different lighting conditions to obtain the real-time monitoring data of the pedestrian 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 opening positions (the structural design data of the building can also be obtained from the building design and construction contractor). After the data is collected, computational fluid dynamics (CFD) is used for air flow simulation. The specific operation steps include importing the structural design data into the fluid dynamics simulation software and constructing a three-dimensional building model based on the actual building design drawings. Then, air flow simulation is carried out for each functional area. By dividing grid cells, defining boundary conditions and initial conditions, parameters such as the air flow direction, velocity field, and vortex formation are simulated. By directly solving the momentum equation and energy equation in the flow field, the air flow characteristics of different functional areas are obtained, including data such as flow paths, ventilation efficiency, and air residence time. These data are used as a basis for subsequent steps to adjust the air circulation in the functional areas, and finally, air conditioning flow simulation data for the functional areas is generated.

[0079] Step S2: Perform air circulation adjustment for different functional areas on the air conditioning flow simulation data for the functional areas to obtain temperature-coupled air circulation adjustment data; perform indoor temperature circulation adjustment compensation on the temperature-coupled air circulation adjustment data according to the real-time monitoring data of the pedestrian flow to obtain indoor temperature circulation adjustment compensation data;

[0080] In the embodiments of the present invention, for the air conditioning flow simulation data of the functional areas, air circulation adjustment of different functional areas is carried out. First, a multi-zone coupled ventilation analysis method is adopted to determine the air exchange paths and flow rates between the functional areas. Air conditioning equipment such as variable air volume control systems (VAV) and intelligent air valves are used to optimize the air circulation in the functional areas by adjusting the air valve opening degrees and controlling the supply air speeds. The air circulation adjustment steps include setting the supply air temperature and humidity parameters of different functional areas according to the air flow simulation data, and manually calibrating the positions of the air valves to ensure the achievement of the target flow rate and temperature. Then, according to the uses of each functional area (such as meeting rooms, offices, rest areas, etc.), the circulating air volume is adjusted according to the frequency of people's activities to ensure reasonable and efficient air circulation. Next, combined with the real-time monitoring data of the number of people, the activity hotspots of people in the building are analyzed, the density changes of people in each area are captured by infrared cameras and thermal imagers, and the regional temperature load is calculated. Based on these monitoring data, a compensatory adjustment is made to the original temperature-coupled air circulation adjustment scheme. By dynamically adjusting the supply air temperature and humidity, the temperature distribution in different areas is balanced, the indoor temperature tends to be uniform, and finally the indoor temperature circulation adjustment compensation data is obtained. These operations enable the indoor environment to maintain a comfortable temperature distribution under different people flow loads.

[0081] Step S3: Carry out training and optimization of the control logic for the indoor temperature circulation adjustment compensation data to obtain optimized temperature control logic data;

[0082] In the embodiments of the present invention, training and optimization of the control logic for the indoor temperature circulation adjustment compensation data are carried out. Using data mining techniques, a training set for temperature control is established by analyzing the historical data of actual indoor temperature adjustments. This data includes the indoor temperature changes under different times and different numbers of people. Optimization algorithms such as genetic algorithms or gradient descent methods are applied to train the control logic. By optimizing and iteratively adjusting the control rules, the best temperature control effect is achieved. During this process, algorithm scripts are written using programming languages (such as Python or MATLAB) to process the adjustment data and generate optimized temperature control logic data. These optimized data reflect the best temperature control logic and can effectively meet the requirements of various actual usage scenarios to ensure precise control of the indoor temperature.

[0083] Step S4: Based on the optimized temperature control logic data, carry out automated firmware design to obtain temperature control logic firmware, and embed the temperature control logic firmware into the intelligent building control platform to perform intelligent temperature control.

[0084] In the embodiments of the present invention, an automated firmware design is performed based on the temperature regulation logic optimization data generated in step S3. First, the firmware function 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. The firmware is programmed using embedded development tools (such as Keil or IAR Embedded Workbench), and the optimized regulation logic is converted into embedded code. A control program is written to set the parameters of the controller to ensure that it can perform real-time adjustment according to the temperature regulation logic optimization data. After the firmware design is completed, it is embedded into the intelligent building control platform. This platform is connected to the HVAC system and sensors in the building through hardware interfaces to perform intelligent temperature regulation tasks. The embedded firmware can automatically adjust the system parameters according to the real-time sensor data to ensure the stability and comfort of the indoor environment.

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

[0086] Step S11: Obtain the intelligent building structure design data and real-time monitoring data of the pedestrian flow;

[0087] Step S12: Classify the intelligent building structure design data by functional areas to obtain the intelligent building functional area structure data;

[0088] Step S13: Identify the air conditioning mode of the intelligent building functional area structure data to obtain the functional area air conditioning mode data;

[0089] Step S14: Simulate the air fluidity of the functional area for the functional area air conditioning mode data to obtain the functional area air conditioning flow simulation data.

[0090] In the embodiments of the present invention, first, collect the structural design data and real-time pedestrian flow monitoring data of the intelligent building. Install high-resolution cameras at key locations such as the main entrances, corridors, and functional areas of the building. These cameras need to have real-time video stream functions and be able to provide clear images under different lighting conditions to obtain real-time pedestrian flow monitoring data. Through intelligent sensing terminals such as laser rangefinders and infrared imaging devices, collect the structural parameters of different floors and areas in the building, including detailed design data such as wall thickness, room height, and window opening positions (structural design data of the building can also be obtained from the building design and construction contractors). The structural design data includes the floor plan of the building, the layout of each functional area, wall materials and thickness, window positions, etc. These data are input into the system through building information modeling (BIM) tools to generate a detailed 3D model of the building. At the same time, through the sensors of the pedestrian flow monitoring system (such as infrared sensors or video monitoring devices), collect the personnel flow data of each functional area in real time. These data include the number of people, flow speed, and time distribution, etc. Integrate and store these two types of data in the database to provide a basis for subsequent analysis and processing. Classify the structural design data of the intelligent building by functional area to obtain the structural data of the intelligent building functional area. Use architectural design software (such as Revit) to classify the 3D model of the building, and divide the areas in the model into functional areas such as office areas, meeting rooms, and rest areas according to functions. By setting area attribute tags, mark the characteristics and requirements of each functional area. Subsequently, export the classified functional area data as a structural data table, record in detail the information such as the size, layout, and ventilation facilities of each functional area, and integrate it into the system for further analysis. Identify the air conditioning mode for the structural data of the intelligent building functional area to obtain the air conditioning mode data of the functional area. First, analyze the design requirements and air conditioning needs of each functional area. For example, the office area requires constant temperature control, while the meeting room needs to cope with frequent temperature changes. Based on the design characteristics of the functional area and air flow data, determine the suitable air conditioning mode, such as constant temperature mode, rapid cooling mode, or comfort mode. By establishing an air conditioning mode database, record the parameter settings and applicable conditions of each mode. Summarize these mode data to form the air conditioning mode data of the functional area for the next step of simulation and optimization. Conduct air flow simulation on the air conditioning mode data of the functional area to obtain the air conditioning flow simulation data of the functional area. First, input the air conditioning mode data of the functional area into fluid dynamics simulation software (such as ANSYS Fluent), and set the simulation parameters, including air flow rate, temperature change, fan speed, etc. Execute the simulation calculation to observe the air flow situation, temperature distribution, and circulation effect in each functional area. During the simulation process, generate the air conditioning flow simulation data of the functional area by adjusting different parameters. These data contain the air flow path, speed distribution, and heat transfer situation, providing a detailed analysis of the air conditioning effect for optimizing the actual air conditioning plan.

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

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

[0093] Step S22: Based on the internal and external temperature coupling effect data, perform air circulation adjustment for different functional areas on the air conditioning flow simulation data of the functional areas to obtain the temperature-coupled air circulation adjustment data;

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

[0095] Step S24: According to the population density distribution data, perform indoor temperature circulation adjustment compensation for different functional areas on the temperature-coupled air circulation adjustment data to obtain the 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 includes the following steps:

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

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

[0100] Step S22: Based on the internal and external temperature coupling effect data, perform air circulation adjustment for different functional areas on the air conditioning flow simulation data of the functional areas to obtain the temperature-coupled air circulation adjustment data;

[0101] In the embodiments of the present invention, based on the data of the temperature coupling effect inside and outside the structure, the air circulation adjustment of different functional areas is performed on the air conditioning flow simulation data of the functional areas. First, the data of the temperature coupling effect inside and outside is input into the system using an air flow simulation software (such as a CFD tool). The air flow pattern of each functional area is adjusted, and the temperature adjustment targets of different areas are set. Through simulation calculations, the temperature-coupled air circulation adjustment data is obtained, which includes the air flow path, wind speed adjustment, and temperature distribution of each functional area, ensuring that the temperature in the functional area meets the set requirements while considering the temperature coupling effect inside and outside.

[0102] Step S23: Calculate the population density distribution based on the real-time monitoring data of the population flow, and obtain the population density distribution data;

[0103] In the embodiments of the present invention, the population density distribution is calculated based on the real-time monitoring data of the population flow. A data analysis tool (such as a data processing library in MATLAB or Python) is used to analyze the data collected by the population flow monitoring system. First, the population flow data in each time period is extracted, and the population density distribution of each functional area is calculated. According to the monitoring data, a population density distribution map is drawn to show the population density in different time periods and areas. The calculation results include the high and low population density areas and their distribution trends, providing a basis for indoor temperature adjustment to cope with the impact of population density changes on air flow and temperature adjustment.

[0104] Step S24: Perform indoor temperature cycle adjustment compensation for different functional areas on the temperature-coupled air circulation adjustment data according to the population density distribution data, and obtain the indoor temperature cycle adjustment compensation data.

[0105] In the embodiments of the present invention, indoor temperature cycle adjustment compensation for different functional areas is performed on the temperature-coupled air circulation adjustment data according to the population density distribution data. First, the population density distribution data is combined with the temperature-coupled air circulation adjustment data to determine the specific impact of the population flow in each functional area on air flow and temperature adjustment. A compensation algorithm (such as a PID control algorithm) is used to adjust the air circulation adjustment data, and compensation parameters are set to respond to changes in population density. The required adjustment compensation amount is calculated to generate the indoor temperature cycle 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: Evaluate the energy conversion of structural heat transfer for the data of the temperature coupling effect inside and outside the structure, and obtain the data of the energy conversion of structural heat transfer;

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

[0109] Step S223: Calculate the convective heat transfer coefficient for different functional areas on the functional area air conditioning flow simulation data according to the air flow thermal guidance simulation data to obtain the convective heat transfer coefficient;

[0110] Step S224: Conduct a sudden change analysis of the thermal distribution trend on the air flow thermal guidance simulation data to obtain thermal distribution trend sudden change data;

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

[0112] In the embodiments of the present invention, in step S221, a structural heat transfer energy conversion assessment is performed to obtain structural heat transfer energy conversion data. The coupled temperature effect data inside and outside the structure is analyzed in detail using building thermodynamics calculation tools (such as heat conduction calculation formulas and heat balance equations). 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 ability of each material is calculated using heat transfer equations (such as Fourier's heat conduction equation), considering the temperature difference inside and outside and the heat exchange efficiency. Through simulation calculations, the heat transfer energy conversion processes of different components are quantified, and the heat transfer energy conversion data for each functional area is obtained. These data include the heat flux, heat transfer efficiency of each component, and the degree of influence on the indoor temperature, serving as the basic data for subsequent analysis. In step S222, based on the structural heat transfer energy conversion data, an air flow heat guidance simulation is performed on the air conditioning flow simulation data of the functional area to obtain air flow heat guidance simulation data. First, the structural heat transfer energy conversion data is input into an air flow simulation tool (such as computational fluid dynamics simulation software). The simulation parameters are set, including air flow velocity, air temperature, structural heat transfer data, etc. The air flow heat guidance simulation is executed. By analyzing the flow path and heat distribution of air in different functional areas, the effect of air flow on heat guidance in different areas is identified. Air flow heat guidance simulation data is generated, including the heat guidance ability of air flow, the heat distribution in each area, and the thermal interaction between air flow and the structure. These data provide necessary information for subsequent calculation of the convective heat transfer coefficient and analysis of the heat distribution trend. In step S223, the convective heat transfer coefficient of the air conditioning flow simulation data of the functional area is calculated according to the air flow heat guidance simulation data. First, the heat guidance situation obtained from the air flow heat guidance simulation data is combined with the air conditioning flow simulation data to analyze the convective heat transfer effect of air flow in each functional area. The convective heat transfer coefficient of each functional area is calculated using convective heat transfer formulas (such as Nusselt number and Grashof number formulas), considering factors such as air flow velocity, temperature difference, and air flow direction. The specific operation includes setting the air flow conditions of each functional area in the flow simulation software and performing convective heat transfer simulation calculations to obtain the convective heat transfer coefficient of each functional area. These coefficients describe the heat transfer efficiency and heat transfer ability of air flow in different areas, providing a quantitative basis for the optimization of air circulation regulation. In step S224, a sudden change analysis of the heat distribution trend of the air flow heat guidance simulation data is performed to obtain sudden change data of the heat distribution trend. First, the heat distribution situation in the air flow heat guidance simulation data is extracted, and statistical analysis of the heat distribution is carried out. Using heat distribution analysis tools (such as heat distribution diagrams or heat gradient analysis methods), the sudden change points and non-uniform areas in the heat distribution are identified. When performing the sudden change analysis of the heat distribution trend, heat gradient calculations and trend analysis algorithms are used to identify the sudden change points or hot spot areas in the heat distribution.Finally, heat distribution trend mutation data is generated, including the change trend of heat distribution, the specific positions of the mutation points, and their impacts on the overall heat distribution. In step S225, based on the convective heat transfer coefficient and the heat distribution trend mutation data, air circulation adjustment is performed on the air conditioning flow simulation data of the functional areas. The convective heat transfer coefficient is combined with the heat distribution trend mutation data to formulate an air circulation adjustment strategy. The specific operations include adjusting the air conditioning parameters of each functional area according to the convective heat transfer coefficient, such as the fan speed, outlet air temperature, etc., to optimize the heat distribution and heat transfer effect of the air flow. At the same time, the heat distribution trend mutation data is used to adjust the air circulation mode to solve the mutation problem of heat distribution and ensure that the air flow can evenly adjust the temperature. Temperature-coupled air circulation adjustment data is generated, including the adjusted air flow parameters and the improved heat distribution situation, providing an effective solution for achieving stable indoor temperature control.

[0113] Preferably, the heat distribution trend mutation analysis of the air flow heat guidance simulation data includes the following steps:

[0114] Identify the air flow conduction path for the air flow heat guidance simulation data to obtain air flow conduction path data;

[0115] Perform time-air flow heat field intensity analysis on the air flow heat guidance simulation data according to the air flow conduction path data to obtain time-air flow heat field intensity data;

[0116] Calculate the convective heat energy accumulation rate for the time-air flow heat field intensity data to obtain convective heat energy accumulation rate data;

[0117] Locate the non-linear heat energy accumulation points for the time-air flow heat field intensity data according to the convective heat energy accumulation rate data to obtain non-linear heat energy accumulation point location data;

[0118] Perform heat distribution trend mutation analysis on the non-linear heat energy accumulation point location data according to the convective heat energy accumulation rate data to obtain heat distribution trend mutation data.

[0119] In the embodiments of the present invention, when identifying the air flow conduction path of the air flow thermal guidance simulation data, it is first necessary to extract the air flow velocity field and temperature field information from the simulation data. Using numerical calculation methods, such as finite element analysis (FEA) or finite difference method (FDM), the flow of air flow inside the building is discretized. Through these methods, the air flow velocity vector of each grid point and the corresponding temperature data are calculated. Next, apply path tracking algorithms, such as the shortest path algorithm or streamline algorithm, to identify the conduction path of the air flow. These algorithms determine the main flow path of the air flow by tracing the direction of the air flow velocity vector. The path recognition results will be saved as a path data set, including the starting point, ending point, and information of each node passed by each air flow path. These path data will provide a basis for subsequent thermal field intensity analysis. In the time-air flow thermal field intensity analysis, it is first necessary to combine the air flow conduction path data with the temperature field information in the simulation data. Taking time as a variable, analyze the air flow thermal field intensity at each time step. Use data interpolation techniques (such as cubic spline interpolation) to smooth the temperature field data between grid points to obtain an accurate thermal field intensity distribution. By integrating the heat transfer along the air flow path, the thermal field intensity of each path can be calculated. Summarize the thermal field intensity results of each time step to form a time-air flow thermal field intensity data set. This data set will show the change of the thermal field intensity of the air flow passing through different paths at different time points, reflecting the distribution trend of thermal energy. The calculation of the convective thermal energy accumulation rate is based on the time-air flow thermal field intensity data. In each time step, evaluate the accumulation rate of thermal energy on the air flow path 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 air flow path into multiple small segments and calculate the change amount of thermal energy on each small segment. Add these change amounts to obtain the convective thermal energy accumulation rate. The data can be processed using the cumulative integral method to calculate the total accumulation rate within each time step. The final convective thermal energy accumulation rate data set will contain the accumulation rate information of each air flow path at different time steps. In order to locate the non-linear accumulation points of thermal energy, it is first necessary to analyze the convective thermal energy accumulation rate data. Apply non-linear regression analysis methods (such as polynomial regression) to fit the data to identify the non-linear accumulation trend of thermal energy in space. Calculate the thermal energy accumulation rate and its change rate at each position, and find the mutation points or outliers in the data. These points are the potential non-linear accumulation points of thermal energy. The location of the non-linear accumulation points can be determined by comparing the data of different time steps to identify which positions have the most significant change in the thermal energy accumulation rate, and then mark these positions as the non-linear accumulation points of thermal energy. This process can be combined with graphical tools (such as heat maps or contour maps) to visualize the spatial distribution of the non-linear accumulation points. In the analysis of the mutation of the thermal distribution trend, first combine the convective thermal energy accumulation rate data with the data of the location of the non-linear accumulation points of thermal energy.Conduct a detailed analysis of the thermal field intensity data in the surrounding areas of each non-linear clustering point to evaluate the changing trend of the thermal distribution in these areas. By calculating the statistical characteristics (such as mean, variance, peak value, etc.) of the thermal energy distribution in these areas, detect whether there are significant mutations or trend changes. Change point detection algorithms (such as the CUSUM algorithm or the sliding window method) can be used to identify the mutation points in the data. These mutation points will show the trend changes in the thermal distribution and mark the mutation positions of the thermal distribution. The final dataset of the thermal distribution trend mutations will provide a detailed description of the trend changes in the thermal energy distribution, helping to understand and optimize the thermal distribution characteristics in the intelligent temperature control system.

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

[0121] Step S241: Analyze the pedestrian flow behavior characteristics based on the pedestrian flow density distribution data to obtain the pedestrian flow density behavior characteristic data;

[0122] Step S242: Quantify the pedestrian flow heat source distribution for the pedestrian flow density distribution data based on the pedestrian flow density behavior characteristic data to obtain the behavior characteristic heat source distribution quantification data;

[0123] Step S243: Conduct a grid area perturbation simulation on the behavior characteristic heat source distribution quantification data to obtain the heat source distribution area perturbation data;

[0124] Step S244: Perform indoor temperature cycle adjustment compensation for different functional areas on the temperature coupling air circulation adjustment data based on the heat source distribution area perturbation data to obtain the indoor temperature cycle adjustment compensation data.

[0125] In the embodiments of the present invention, in step S241, the personnel flow data in each area of the building is monitored by using a high-resolution sensor network. These sensors include infrared sensors, video surveillance systems, and pressure sensors, which continuously collect real-time personnel flow data. After preprocessing, these data include information such as the flow statistics of personnel in different time periods, the flow patterns between regions, and the residence time of personnel. Using data analysis tools, such as data mining software, these data are analyzed to extract the behavior characteristic data of the personnel flow density. These characteristic data include peak hours of the personnel flow, main concentrated areas, and personnel flow frequencies, etc. By statistically analyzing and visualizing these data, the obtained behavior characteristic data of the personnel flow density will provide a basis for the subsequent quantification of the heat source distribution. In step S242, based on the behavior characteristic data of the personnel flow density obtained in step S241, a quantitative analysis method is used to quantify the heat source distribution of the personnel flow density. In this process, a heat map generation tool is used to convert the behavior characteristic data into heat source distribution data. Specifically, according to the personnel flow density of each area, corresponding heat source intensity values are set, and these values are mapped onto the floor plan of the building. A spatial interpolation algorithm (such as Kriging interpolation or spline interpolation) is used to fill the gaps between the data to form continuous heat source distribution quantification data. These quantification data can accurately reflect the heat source intensity of each area and provide basic data support for the subsequent perturbation simulation. In step S243, the heat source distribution quantification data obtained in step S242 is applied to the grid area perturbation simulation. First, the internal area of the building is divided into uniform grid cells, and the heat source intensity data of each cell is input into the grid model. Then, a heat source simulation software is used to perform perturbation simulation on each grid cell to simulate the heat diffusion effect under different heat source conditions. Through the perturbation model, the heat change situation under different heat source distribution conditions can be obtained, forming heat source distribution area perturbation data. These data reveal the heat flow in each area and its influence on the temperature change under different heat source conditions, providing detailed simulation results for the final temperature regulation. In step S244, according to the heat source distribution area perturbation data obtained in step S243, the indoor temperature of different functional areas in the building is cyclically adjusted and compensated. Using the temperature adjustment algorithm in the temperature control system, the heat source distribution area perturbation data is input, and the system calculates the actual temperature deviation of each functional area based on these data. Then, the system adjusts the air circulation and the settings of heating / cooling equipment in each functional area to compensate for the temperature change caused by the heat source perturbation. For example, if the heat source intensity in a certain area is higher than expected, the system will enhance the cooling capacity of this area; otherwise, the heating function will be increased. Finally, the obtained indoor temperature cyclic adjustment compensation data will ensure that the temperature of each functional area is maintained within the preset comfortable range, improving the overall temperature control efficiency of the building.

[0126] Preferably, the grid area perturbation simulation of the behavior characteristic heat source distribution quantification data includes the following steps:

[0127] Perform grid division on the heat source intensity regional grid for the quantified data of the heat source distribution of behavioral characteristics to obtain the grid data of the heat source intensity of behavioral characteristics;

[0128] Based on the grid data of the heat source intensity of behavioral characteristics, conduct an assessment of the disorderliness of the airflow influence between different regional grids to obtain the data of the disorderliness of the airflow influence of behavioral characteristics;

[0129] According to the data of the disorderliness of the airflow influence of behavioral characteristics, calculate the heat source instability increment between different regional grids for the grid data of the heat source intensity of behavioral characteristics to obtain the data of the heat source instability increment of behavioral characteristics;

[0130] Based on the data of the disorderliness of the airflow influence of behavioral characteristics and the data of the heat source instability increment of behavioral characteristics, conduct a grid-based regional perturbation simulation to obtain the perturbation data of the heat source distribution area.

[0131] In the embodiments of the present invention, when performing grid processing on the quantized data of the heat source distribution of behavioral characteristics, it is first necessary to divide the spatial region of the data into a number of equidistant or non-equidistant grid cells. This process is achieved by selecting an appropriate grid size and distribution method. For example, the uniform grid method can be used to divide the entire region into a number of square grid cells, or the adaptive grid method can be used to adjust the size of the grid according to the change of heat source density. The specific operation steps include: first, input the two-dimensional floor plan of the entire building or region into the grid tool, and then set the resolution and size of the grid according to the heat source intensity data. Automatic grid division is performed in the tool to obtain the heat source intensity data within each grid cell. After completion, the output result is the heat source intensity data of the behavioral characteristics of each grid cell, which is saved in the form of a spreadsheet or database for subsequent analysis. After obtaining the heat source intensity grid data, evaluating the influence of air flow is a key step. This evaluation is completed by simulating the disturbance of air flow between different grid cells. The specific operation is as follows: first, input the heat source intensity data of each grid cell into the simulation environment, and set the boundary conditions and initial state of the air flow model. Then, use the fluid mechanics simulation tool to simulate the flow characteristics of the air flow, calculate the air flow velocity, direction of each grid cell and its influence on the adjacent area. By quantifying the disturbance situation of the air flow, evaluate the influence of the air flow on the heat source intensity of different grid cells. The result will be output in the form of behavioral characteristic air flow influence disorder data, usually numerical data containing the disturbance degree of each grid cell, which is used for subsequent instability increment calculation. According to the air flow influence disorder data, calculate the heat source instability increment. This process involves analyzing the heat source intensity of each grid cell to determine the degree of instability caused by air flow disturbance. The specific operation includes: using a calculation tool or software to input the air flow influence disorder data and the heat source intensity grid data, and set the calculation parameters, such as the instability threshold and the increment range. Calculate the heat source instability increment of each grid cell under different air flow conditions through an algorithm, that is, the change amount of the heat source intensity. During the calculation process, it is necessary to combine the influence factor with the heat source intensity for increment calculation to generate the behavioral characteristic heat source instability increment data. The final output is the numerical value of the instability increment of each grid cell, which is stored in the database or spreadsheet for subsequent disturbance simulation. After obtaining the heat source instability increment data and the air flow influence disorder data, perform grid area disturbance simulation. This simulation generates disturbance data of the heat source distribution by combining the instability increment data with the air flow influence. The operation steps are as follows: first, input the heat source intensity grid data, the air flow influence disorder data and the heat source instability increment data into the disturbance simulation tool. Then, set the simulation parameters, such as the disturbance range, time step, etc. Run the simulation tool to perform dynamic simulation of the heat source distribution between different grid cells, considering the change of the heat source under the influence of air flow. The simulation result is displayed as the disturbance data of the heat source distribution area, showing the change of the heat source distribution in each grid cell.These data can be used to optimize the intelligent temperature control strategy to ensure uniform and comfortable temperature inside the building.

[0132] Preferably, the indoor temperature cycle regulation compensation for different functional areas of the temperature-coupled air circulation regulation data according to the disturbance data of the heat source distribution area includes the following steps:

[0133] Conduct a simulation of the mutual effect of disturbance temperatures on the disturbance data of the heat source distribution area to obtain the mutual effect data of disturbance temperatures;

[0134] Calculate the air flow regulation rate data for different regions of the temperature-coupled air circulation regulation data based on the mutual effect data of disturbance temperatures and the disturbance data of the heat source distribution area;

[0135] Design the variable air outlet angles for different regions of the temperature-coupled air circulation regulation data based on the mutual effect data of disturbance temperatures and the disturbance data of the heat source distribution area to obtain the variable air outlet angle design data;

[0136] Conduct indoor temperature cycle regulation compensation for different functional areas of the temperature-coupled air circulation regulation data based on the temperature air flow regulation rate data and the variable air outlet angle design data to obtain the indoor temperature cycle regulation compensation data.

[0137] In the embodiments of the present invention, disturbance data of the heat source distribution area is collected, including the heat source positions, intensities, types and their time-varying characteristics in different areas. These data are processed, and the mutual effect of disturbance temperature is simulated by using the heat conduction equation and the air convection model. Specifically, the heat source distribution data is input into fluid dynamics software, such as using computational fluid dynamics (CFD) tools, which can finely model the air temperature change. Through simulation calculation, the influence of each heat source on the surrounding air temperature is obtained, including the mutual interference effect between heat sources. Finally, these simulation results are used to generate disturbance temperature mutual effect data, which describes the temperature transfer between different heat sources and its influence on the overall environmental temperature. According to the obtained disturbance temperature mutual effect data, the air flow regulation requirements in different areas are analyzed. By using the air flow distribution analysis method, the air flow regulation rate in each area is calculated. The specific steps include: First, the target temperature of each functional area is set and compared with the current temperature data. Then, by establishing a mathematical model of air flow (such as a model based on the Navier-Stokes equation), the air flow rate is calculated. Using an air flow rate calculation tool (such as fluid dynamics simulation software), the disturbance temperature mutual effect data is input into the model to calculate the required air flow rate to adjust the temperature of each area. Finally, the temperature air flow regulation rate data of each area is generated, which is used to guide the subsequent air flow regulation. Based on the obtained temperature air flow regulation rate data, an appropriate air outlet angle is designed. First, according to the air flow regulation rate data of each area, the air volume and air speed of the air outlet that needs to be adjusted are determined. A air outlet design tool or an air outlet angle adjustment algorithm is used to calculate the optimal air outlet angle in different areas. These tools will optimize the angle setting of the air outlet according to the air flow rate data and the indoor temperature distribution to ensure that the air flow can effectively adjust the temperature of each area. Specifically, the adjustment of the air outlet angle can be achieved through an automatic adjustment system, which finely adjusts the air outlet angle according to real-time data to achieve the best air flow distribution and temperature control. Finally, the variable air outlet angle design data of each area is generated. Based on the obtained variable air outlet angle design data and the temperature air flow regulation rate data, indoor temperature cycle adjustment compensation is implemented. First, the air outlet angle adjustment setting is applied to the actual air outlet device to ensure that each air outlet is adjusted according to the designed angle. Then, the air speed and air volume in the air circulation system are adjusted to match the temperature air flow regulation rate data. Through the air circulation control system, the temperature change in each area is monitored in real time, and the air outlet angle and air flow rate are adjusted according to the actual temperature data to reach the preset target temperature. Finally, the indoor temperature cycle adjustment compensation data is generated based on the comprehensive adjustment result to ensure that the temperature of each functional area is maintained within the set range.

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

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

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

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

[0142] In the embodiment of the present invention, first collect the indoor temperature cycle adjustment compensation data from the temperature adjustment system of the building. These data include the indoor temperature at different time points, the external environment temperature, and the control signals of the adjustment system, etc. Next, normalize these original data to eliminate the influence of different data magnitudes on the analysis. The specific operation is as follows: calculate the minimum and maximum values of each data feature, then subtract the minimum value from each data point and divide by the difference between the maximum value and the minimum value to obtain the normalized value within the range of [0, 1]. For example, if the temperature data range is between 15°C and 25°C, then 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, improving the accuracy and stability of the model. Use the Gradient Boosting Tree (GBT) algorithm to construct a building intelligent temperature control model. The specific operations include: first, divide the normalized data obtained in step S31 into a training set and a test set. Then, use the gradient boosting tree algorithm to train the model on the training set. The GBT algorithm constructs a series of decision trees, and each tree is optimized based on the previous tree to gradually reduce the prediction error. During the training process, set hyperparameters such as the depth of the tree, the learning rate, and the minimum sample split number, and perform cross-validation to select the best parameters. After the training is completed, the obtained model can perform temperature control prediction 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, perform regulation logic training and optimization on the normalized indoor temperature adjustment compensation data. The specific operation is as follows: 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. By calculating the prediction error (such as the mean square error, absolute error, etc.), further optimize the model. Adjust the model parameters and optimize the loss function in the algorithm to reduce the prediction error and update the regulation logic. The optimized data contains more accurate regulation 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 for implementing 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] A functional area air flow simulation module, configured to obtain the smart building structure design data and the real-time monitoring data of the number of people; perform a functional area air flow simulation on the smart building structure design data to obtain functional area air conditioning flow simulation data;

[0145] A circulation adjustment module, configured to perform air circulation adjustment for different functional areas 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 according to the real-time monitoring data of the number of people to obtain indoor temperature circulation adjustment compensation data;

[0146] A control logic training module, configured to perform control logic training and optimization on the indoor temperature circulation adjustment compensation data to obtain temperature control logic optimization data;

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

[0148] Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0149] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent temperature control method based on a smart building, characterized in that, It includes the following steps: Step S1: Obtain the intelligent building structure design data and real-time pedestrian flow monitoring data; perform air flow simulation in functional areas on the intelligent building structure design data to obtain functional area air conditioning flow simulation data; Step S2: Perform air circulation adjustment in different functional areas 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 according to the real-time pedestrian flow monitoring data to obtain indoor temperature circulation adjustment compensation data; Step S3: Perform regulation logic training and optimization on the indoor temperature circulation adjustment compensation data to obtain temperature regulation logic optimization data; Step S4: Based on the temperature regulation logic optimization data, perform automated firmware design to obtain temperature regulation logic firmware, and embed the temperature regulation logic firmware into the intelligent building control platform to perform intelligent temperature regulation; Step S1 includes the following steps: Step S11: Obtain the intelligent building structure design data and real-time pedestrian flow monitoring data; Step S12: Classify the intelligent building structure design data by functional area to obtain intelligent building functional area structure data; Step S13: Identify the air conditioning mode in the intelligent building functional area structure data to obtain functional area air conditioning mode data; Step S14: Perform air flow simulation in functional areas on the functional area air conditioning mode data to obtain functional area air conditioning flow simulation data Step S2 includes the following steps: Step S21: Analyze the internal and external temperature coupling effect based on the intelligent building structure design data to obtain structure internal and external temperature coupling effect data; Step S22: Based on the structure internal and external temperature coupling effect data, perform air circulation adjustment in different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation adjustment data; Step S23: Calculate the pedestrian flow density distribution on the real-time pedestrian flow monitoring data to obtain pedestrian flow density distribution data; Step S24: Perform indoor temperature circulation adjustment compensation in different functional areas on the temperature-coupled air circulation adjustment data according to the pedestrian flow density distribution data to obtain indoor temperature circulation adjustment compensation data; Step S22 includes the following steps: Step S221: Evaluate the energy conversion of structure heat transfer on the structure internal and external temperature coupling effect data to obtain structure heat transfer energy conversion data; Step S222: Based on the structure heat transfer energy conversion data, perform air flow heat guidance simulation in different functional areas on the functional area air conditioning flow simulation data to obtain air flow heat guidance simulation data; Step S223: Calculate the convective heat transfer coefficient in different functional areas on the functional area air conditioning flow simulation data according to the air flow heat guidance simulation data to obtain the convective heat transfer coefficient; Step S224: Analyze the sudden change of heat distribution trend on the air flow heat guidance simulation data to obtain heat distribution trend sudden change data; Step S225: Based on the convective heat transfer coefficient and heat distribution trend sudden change data, perform air circulation adjustment in different functional areas on the functional area air conditioning flow simulation data to obtain temperature-coupled air circulation adjustment data; Performing mutation analysis on the thermal distribution trend of the simulated data of air flow thermal guidance includes the following steps: Identifying the air flow conduction path of the simulated data of air flow thermal guidance to obtain air flow conduction path data; Performing time-air flow thermal field intensity analysis on the simulated data of air flow thermal guidance according to the air flow conduction path data to obtain time-air flow thermal field intensity data; Calculating the convective heat energy accumulation rate of the time-air flow thermal field intensity data to obtain convective heat energy accumulation rate data; Locating the non-linear heat energy accumulation point of the time-air flow thermal field intensity data according to the convective heat energy accumulation rate data to obtain non-linear heat energy accumulation point location data; Performing mutation analysis on the thermal distribution trend of the non-linear heat energy accumulation point location data according to the convective heat energy accumulation rate data to obtain thermal distribution trend mutation data; Step S24 includes the following steps: Step S241: Analyzing the pedestrian behavior characteristics according to the pedestrian density distribution data to obtain pedestrian density behavior characteristic data; Step S242: Quantifying the pedestrian heat source distribution of the pedestrian density distribution data according to the pedestrian density behavior characteristic data to obtain behavior characteristic heat source distribution quantification data; Step S243: Performing grid area perturbation simulation on the behavior characteristic heat source distribution quantification data to obtain heat source distribution area perturbation data; Step S244: Compensating the indoor temperature cycle regulation of different functional areas for the temperature coupling air circulation regulation data according to the heat source distribution area perturbation data to obtain indoor temperature cycle regulation compensation data; Performing grid area perturbation simulation on the behavior characteristic heat source distribution quantification data includes the following steps: Dividing the heat source intensity area grid of the behavior characteristic heat source distribution quantification data to obtain behavior characteristic heat source intensity grid data; Evaluating the disorder of the behavior characteristic air flow influence between different area grids based on the behavior characteristic heat source intensity grid data to obtain behavior characteristic air flow influence disorder data; Calculating the heat source instability increment between different area grids of the behavior characteristic heat source intensity grid data according to the behavior characteristic air flow influence disorder data to obtain behavior characteristic heat source instability increment data; Performing grid area perturbation simulation according to the behavior characteristic air flow influence disorder data and the behavior characteristic heat source instability increment data to obtain heat source distribution area perturbation data; Compensating the indoor temperature cycle regulation of different functional areas for the temperature coupling air circulation regulation data according to the heat source distribution area perturbation data includes the following steps: Performing simulation on the mutual effect of perturbation temperature on the heat source distribution area perturbation data to obtain perturbation temperature mutual effect data; Calculating the air flow regulation rate of different areas for the temperature coupling air circulation regulation data according to the perturbation temperature mutual effect data and the heat source distribution area perturbation data to obtain temperature air flow regulation rate data; Designing the variable air outlet angle of different areas for the temperature coupling air circulation regulation data according to the perturbation temperature mutual effect data and the heat source distribution area perturbation data to obtain variable air outlet angle design data; Compensating the indoor temperature cycle regulation of different functional areas for the temperature coupling air circulation regulation data according to the temperature air flow regulation rate data and the variable air outlet angle design data to obtain indoor temperature cycle regulation compensation data.

2. The intelligent temperature control method based on an intelligent building according to claim 1, wherein Step S3 includes the following steps: Step S31: Normalize the indoor temperature cyclic regulation compensation data to obtain the normalized indoor temperature regulation compensation data; Step S32: Use the gradient boosting tree algorithm to construct a building intelligent temperature control model for the normalized indoor temperature regulation compensation data to obtain the building intelligent temperature control model; Step S33: Optimize the regulation logic training for the normalized indoor temperature regulation compensation data based on the building intelligent temperature control model to obtain the optimized temperature regulation logic data.

3. An intelligent temperature control system based on a smart building, characterized in that, For implementing the intelligent temperature control method based on a smart building as described in claim 2, the intelligent temperature control system based on a smart building includes: A functional area air flow simulation module, configured to obtain the smart building structure design data and the real-time monitored data of the pedestrian flow; perform a functional area air flow simulation on the smart building structure design data to obtain the functional area air conditioning flow simulation data; A cyclic regulation module, configured to perform air circulation regulation for different functional areas on the functional area air conditioning flow simulation data to obtain the temperature-coupled air circulation regulation data; perform indoor temperature cyclic regulation compensation on the temperature-coupled air circulation regulation data according to the real-time monitored data of the pedestrian flow to obtain the indoor temperature cyclic regulation compensation data; A regulation logic training module, configured to optimize the regulation logic training for the indoor temperature cyclic regulation compensation data to obtain the optimized temperature regulation logic data; A regulation execution module, configured to perform automated firmware design based on the optimized temperature regulation logic data to obtain the temperature regulation logic firmware, and embed the temperature regulation logic firmware into the smart building control platform to perform intelligent temperature control.

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