Novel intelligent efficient heating and ventilation system for realizing zero-carbon building
By introducing intelligent regulatory means in HVAC systems, including prediction models, real-time data analysis and weight priority allocation, the problem of lack of accuracy and intelligence in traditional HVAC systems during regulation is solved, and efficient energy utilization and The dual demand for green buildings.
Patent Information
- Application Number
- CN202411742355.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-02-11
AI Technical Summary
Traditional HVAC systems lack accuracy and intelligence during regulation, resulting in low energy utilization efficiency and difficult to meet the dual needs of green buildings for comfort and energy efficiency.
A new intelligent and efficient HVAC system was designed, and the prediction model was established through the model building module, the parameter acquisition and processing modules were used to obtain environmental parameters in real time, the data analysis module analyzed the correlation of temperature changes, the weight division module assigned weight priority, and the regulation definition module was formulated Differentiated control strategies, the instruction generation module generates specific control instructions.
It has achieved precise regulation of environmental parameters of each floor in the building, reduced energy consumption, improved energy utilization efficiency, and promoted the development of green buildings.
Smart Images
Figure CN119412791A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of HVAC systems, and in particular to a novel intelligent and efficient HVAC system for realizing zero-carbon buildings. Background Art
[0002] As global climate change and resource consumption become increasingly serious, green buildings, as a building model that aims to reduce environmental impact and improve energy efficiency and resource utilization efficiency, are gradually becoming a development trend in the global construction industry. However, the application of traditional HVAC systems in green buildings faces a series of technical problems and challenges.
[0003] For example, traditional HVAC systems are often regulated based on fixed operating modes and parameters, lacking real-time perception and adaptability to environmental changes. This fixed regulation method makes it impossible for the system to be accurately adjusted according to actual needs during operation, resulting in a large amount of energy waste. Especially in green buildings, because the building itself uses high-performance thermal insulation materials and advanced energy-saving technologies, the traditional HVAC system is particularly inefficient in terms of energy efficiency.
[0004] Secondly, the traditional HVAC system lacks intelligent control means and cannot realize real-time monitoring and prediction of environmental parameters on each floor of the building. This results in the system being unable to fully consider the correlation between temperature changes and the difference in the number of personnel activities between floors during control, and thus unable to formulate differentiated control strategies. This single control method is not only difficult to meet the dual needs of green buildings for comfort and energy efficiency, but may also cause excessive energy consumption and environmental deterioration. Due to the lack of precision and intelligence in the control of traditional HVAC systems, energy utilization efficiency is low. On the one hand, the system cannot be accurately adjusted according to actual needs during operation, resulting in overheating or overcooling on some floors or areas; on the other hand, the system lacks rationality in energy distribution, and it is difficult to make priority adjustments according to the weight priority of each floor, resulting in energy waste and uneven distribution.
[0005] Therefore, it is necessary to provide a new intelligent and efficient HVAC system for realizing zero-carbon buildings to solve the above technical problems. Summary of the invention
[0006] In order to solve the above technical problems, the present invention provides a new intelligent and efficient HVAC system for realizing zero-carbon buildings, which is used to solve the problem that traditional HVAC systems lack precision and intelligence in regulation, resulting in low energy utilization efficiency and difficulty in meeting the dual needs of green buildings for comfort and energy efficiency.
[0007] The present invention provides a novel intelligent and efficient HVAC system for realizing zero-carbon buildings, the HVAC system comprising: A model building module is used to build a prediction model based on obtaining historical environmental parameters of each floor of the building; The parameter acquisition and processing module is used to obtain the current environmental parameters of each floor in the building in real time through sensors, and input them into the prediction model to predict the changes in environmental parameters in the future; The data analysis module is used to analyze the correlation of temperature changes between floors based on the predicted changes in environmental parameters in the future and obtain correlation analysis results; The weight division module is used to assign weights to each floor in the building according to the current number of personnel activities on each floor in the building, and obtain the weight priority of each floor; The control definition module is used to formulate differentiated control strategies based on the correlation analysis results and the weight priority of each floor; The instruction generation module is used to generate specific control instructions based on the control strategy and send them to the actuator of the HVAC system to execute the control.
[0008] Preferably, the step of establishing a prediction model based on acquiring historical environmental parameters of each floor of the building comprises the following specific steps: Collect historical environmental parameters of each floor of the building over a period of time in the past, where the period of time is specifically half a month or a month, and the historical environmental parameters specifically include temperature, humidity, carbon dioxide concentration, and light intensity; The historical environmental parameters were preprocessed, and the correlation between humidity, carbon dioxide concentration, light intensity and temperature was analyzed by statistical analysis methods; The correlation between humidity, carbon dioxide concentration, light intensity and temperature was used as the training set, and the long short-term memory network architecture was selected for training to obtain the prediction model.
[0009] Preferably, the current environmental parameters of each floor in the building are obtained in real time by sensors, and input into the prediction model to predict the change of environmental parameters in the future, and the specific steps are: The current environmental parameters of each floor in the building are obtained in real time through sensors, where the current environmental parameters specifically include temperature, humidity, carbon dioxide concentration and light intensity; Preprocess the current environmental parameters of each floor to obtain the current environmental parameters after preprocessing; The features of the current environmental parameters after preprocessing are extracted and input into the prediction model to predict the temperature changes of each floor in the future period of time, where the future period of time is specifically 1 day or 2 days.
[0010] Preferably, the method of analyzing the correlation of temperature changes between floors based on the prediction of environmental parameter changes within a period of time in the future to obtain the correlation analysis result comprises the following specific steps: Compare the temperature changes of each floor in the future period of time to obtain the comparison results; Based on the comparison results, the statistical analysis method is used to analyze the correlation of temperature changes between floors, and the correlation analysis results of temperature changes on each floor are obtained.
[0011] Preferably, the specific steps of allocating weights to the floors in the building according to the current number of personnel activities on each floor in the building to obtain the weight priority of each floor are as follows: Through the access control system or personnel positioning system, the number of people currently active on each floor can be counted in real time; Based on the real-time statistics of the current number of personnel activities on each floor, each floor is assigned a weight priority, wherein the more the current number of personnel activities on each floor, the higher the weight priority assigned.
[0012] Preferably, the differentiated control strategy is formulated based on the correlation analysis results and the weight priority of each floor, and the specific steps are: Based on the correlation analysis results of the temperature changes on each floor, each floor is divided into different temperature control areas through the preset correlation threshold segments. For each control area, set a unified temperature control target for preliminary control; Based on the preliminary control strategy, the control strategy is adjusted according to the weight priority of each floor. That is, when the temperature parameters of each floor change, adjustments are made according to the weight priority of each floor. Specifically, when a system failure or abnormal situation occurs, problems of floors with high weights are given priority, and the energy resources of the HVAC system are allocated according to the weight priority of the floors.
[0013] Preferably, the specific control instructions are generated based on the control strategy and sent to the actuator of the HVAC system to perform the control, and the specific steps are as follows: Generate specific parameter control instructions according to the control strategy; The control instructions are sent to the actuator of the HVAC system through the communication protocol. The actuator makes adjustments based on the received instructions and generates a report based on the execution results.
[0014] Preferably, the parameter collection and processing module includes: Temperature sensor, used to collect real-time temperature of each floor; Humidity sensor, used to collect real-time humidity on each floor; Infrared carbon dioxide detector, used to collect carbon dioxide concentration on each floor; Light intensity sensor, used to collect light intensity on each floor; The data processing module is used to predict the temperature changes of each floor in the future through a prediction model.
[0015] Compared with the related art, the novel intelligent and efficient HVAC system for realizing zero-carbon buildings provided by the present invention has the following beneficial effects: The present invention collects and analyzes historical environmental parameters, establishes a prediction model, and obtains current environmental parameters in real time to predict environmental changes in the future, providing a good basis for formulating control strategies. In addition, differentiated control strategies can be formulated based on the correlation analysis results and weight priorities of temperature changes on each floor to ensure maximum energy utilization. The precise control of the system helps to reduce unnecessary energy consumption, achieve energy conservation and emission reduction, and promote the development of green buildings. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 This is a system block diagram of a new intelligent and efficient HVAC system for realizing zero-carbon buildings according to the present invention; Figure 2 It is a structural schematic diagram of the parameter acquisition and processing module of the present invention. DETAILED DESCRIPTION
[0017] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.
[0018] Example A new intelligent and efficient HVAC system for achieving zero-carbon buildings, including: A model building module is used to build a prediction model based on obtaining historical environmental parameters of each floor of the building; The parameter acquisition and processing module is used to obtain the current environmental parameters of each floor in the building in real time through sensors, and input them into the prediction model to predict the changes in environmental parameters in the future; Wherein, the parameter acquisition and processing module includes: Temperature sensor, used to collect real-time temperature of each floor; Humidity sensor, used to collect real-time humidity on each floor; Infrared carbon dioxide detector, used to collect carbon dioxide concentration on each floor; Light intensity sensor, used to collect light intensity on each floor; The data processing module is used to predict the temperature change of each floor in the future through the prediction model; The data analysis module is used to analyze the correlation of temperature changes between floors based on the predicted changes in environmental parameters in the future and obtain correlation analysis results; The weight division module is used to assign weights to each floor in the building according to the current number of personnel activities on each floor in the building, and obtain the weight priority of each floor; The control definition module is used to formulate differentiated control strategies based on the correlation analysis results and the weight priority of each floor; The instruction generation module is used to generate specific control instructions based on the control strategy and send them to the actuator of the HVAC system to execute the control.
[0019] In the specific implementation process, the specific steps performed by the model building module are: Collect historical environmental parameters of each floor of the building over a period of time in the past, where the period of time is specifically half a month or a month, and the historical environmental parameters specifically include temperature, humidity, carbon dioxide concentration, and light intensity; Specifically, based on the log records, the historical environmental parameters of each floor of the building over a period of time in the past are extracted and collected from the database, including temperature, humidity, carbon dioxide concentration and light intensity.
[0020] The historical environmental parameters were preprocessed, and the correlation between humidity, carbon dioxide concentration, light intensity and temperature was analyzed by statistical analysis methods; Specifically, the collected data needs to be cleaned and organized to remove outliers and duplicate data. Then, the data needs to be normalized to ensure the comparability between different parameters. Next, correlation analysis or regression analysis in data statistical analysis methods is used to analyze the relationship between humidity, carbon dioxide concentration, light intensity and temperature. The correlation obtained from the analysis can provide a basis for the subsequent establishment of a prediction model.
[0021] The correlation between humidity, carbon dioxide concentration, light intensity and temperature is used as the training set, and the long short-term memory network architecture is selected for training to obtain the prediction model; Specifically, the association data obtained from the previous analysis is organized into a training set, where the training set includes input features (humidity, carbon dioxide concentration, and light intensity) and target variables (temperature). In this embodiment, a long short-term memory network (LSTM) is selected as the architecture of the prediction model, and the training set is input into the LSTM model for training. During the training process, the model will learn the mapping relationship between the input features and the target variables, and continuously optimize its internal parameters to improve the prediction accuracy. After the training is completed, the prediction model is obtained.
[0022] In the specific implementation process, the specific steps performed by the parameter acquisition and processing module are: The current environmental parameters of each floor in the building are obtained in real time through sensors, where the current environmental parameters specifically include temperature, humidity, carbon dioxide concentration and light intensity; Specifically, it is clear which floors' data need to be collected, and which environmental parameters (such as temperature, humidity, carbon dioxide concentration, and light intensity) need to be collected on each floor. Then, the real-time temperature of each floor is collected through the temperature sensor; the real-time humidity of each floor is collected through the humidity sensor; the carbon dioxide concentration of each floor is collected through the infrared carbon dioxide detector; and the light intensity sensor collects the light intensity of each floor. It should be noted that in order to ensure the continuity and accuracy of the data, a reasonable data collection frequency needs to be set. In this embodiment, data is collected every hour or every half hour.
[0023] Preprocess the current environmental parameters of each floor to obtain the current environmental parameters after preprocessing; Specifically, the collected current environmental parameters of each floor are cleaned to remove outliers, duplicate values, and missing values; outliers may be caused by sensor failure or sudden environmental changes, and need to be deleted or replaced with reasonable values; duplicate values need to be merged or deleted to avoid data redundancy; missing values need to be filled or deleted according to the specific situation; then, the data needs to be transformed to make it more in line with the needs of subsequent analysis; for example, parameters such as temperature and humidity can be converted into relative values or percentages to more intuitively compare the differences between different floors. Finally, the data needs to be normalized to eliminate the dimensional differences and numerical range differences between different parameters to obtain the current environmental parameters after preprocessing.
[0024] The features of the current environmental parameters after preprocessing are extracted and input into the prediction model to predict the temperature changes of each floor in the future period of time, where the future period of time is specifically 1 day or 2 days.
[0025] In the specific implementation process, the specific steps performed by the data analysis module are: Compare the temperature changes of each floor in the future period of time to obtain the comparison results; Specifically, the predicted temperature changes of each floor in the future will be compared separately. It should be noted that since the data collection time of different floors may be different, the data needs to be time-aligned before comparison. At the same time, in order to eliminate the differences in temperature values between different floors, the data needs to be standardized so that the temperature data of each floor are comparable. The temperature change data of each floor are plotted into a comparison chart, such as a line chart, a bar chart or a scatter plot. Through the comparison chart, the temperature change trend and difference of each floor in the future can be intuitively understood. According to the comparison chart, the temperature difference, change trend and abnormal points between the floors are analyzed.
[0026] Based on the comparison results, the correlation of temperature changes between floors is analyzed using statistical analysis methods to obtain the correlation analysis results of temperature changes on each floor; Specifically, according to the comparison results and data characteristics, a statistical analysis method is selected, specifically correlation analysis, regression analysis or cluster analysis is used, so as to reveal the correlation between the temperature changes between floors through analysis, and calculate the correlation index of the temperature changes between floors, where the correlation index may include a correlation coefficient, a regression coefficient or a cluster center, which can quantify the degree of correlation between the temperature changes between floors. According to the calculated correlation index, the correlation between the temperature changes between floors is analyzed. For example, if the correlation coefficient is high, it means that the temperature changes between floors have a strong correlation; if the cluster centers are close, it means that the temperature change trends between floors are similar.
[0027] For example, after comparing the temperature changes on each floor, a correlation analysis method is chosen to analyze the correlation between the temperature changes on each floor. By calculating the correlation coefficient matrix, it is found that the correlation coefficient between two floors is relatively high (such as above 0.8), indicating that the temperature changes between them have a strong correlation, indicating that the two floors are similar in terms of building structure, insulation measures or air-conditioning system settings.
[0028] In the specific implementation process, the specific steps performed by the weight division module are: Through the access control system or personnel positioning system, the number of people currently active on each floor can be counted in real time; Specifically, the current number of people active on each floor is obtained from an access control system equipped with sensor technology and data recording functions.
[0029] Based on the real-time statistics of the current number of personnel activities on each floor, each floor is assigned a weight priority, wherein the more the number of current personnel activities on each floor, the higher the weight priority assigned; Specifically, the weight distribution principle is set according to the number of personnel activities on each floor, specifically, the more personnel activities a floor has, the higher its weight priority; and different weight levels are set, specifically high priority, medium priority and low priority; or specific numerical values are used to represent the weight size, specifically, the weighted average method or the maximum and minimum method is used to calculate the weight value.
[0030] For example, the first floor currently has the largest number of personnel activities and the highest weight value, so it is classified as high priority; the second floor has the second largest number of personnel activities and a medium weight value, so it is classified as medium priority; the third floor has the least number of personnel activities and the lowest weight value, so it is classified as low priority.
[0031] In the specific implementation process, the specific steps performed by the control definition module are: Based on the correlation analysis results of the temperature changes on each floor, each floor is divided into different temperature control areas through the preset correlation threshold segments; Specifically, based on the correlation analysis results, correlation threshold segments are set to ensure that they can accurately reflect the correlation of temperature changes between floors. In this embodiment, the high correlation segment is set to 0.8-1.0, the medium correlation segment is set to 0.5-0.8, and the low correlation segment is set to 0-0.5; according to the set correlation threshold segments, each floor is divided into different temperature control areas, floors with correlations higher than 0.8 are divided into the same control area, and floors with correlations lower than 0.5 are divided into another control area; For each control area, set a unified temperature control target for preliminary control; Specifically, for each divided temperature control area, a unified temperature control target is set, which is specifically a specific temperature value or a temperature range.
[0032] Based on the preliminary control strategy, the control strategy is adjusted according to the weight priority of each floor. That is, when the temperature parameters of each floor change, adjustments are made according to the weight priority of each floor. Specifically, when the system fails or an abnormal situation occurs, the problem of the floor with a high weight is dealt with first, and the energy resources of the HVAC system are allocated according to the weight priority of the floor; Specifically, first, based on the set unified temperature control target, the corresponding parameter preliminary adjustment is generated, including the supply air volume, return air volume or supply air temperature parameters of the air-conditioning system. On the basis of the preliminary adjustment, the temperature changes of each floor are continuously monitored. Then, according to the obtained weight priority of each floor, it is analyzed which floors have more critical or urgent temperature changes. Based on the monitored temperature changes and weight priority analysis, the control strategy is dynamically adjusted.
[0033] For example, if it is found that the temperature on the second floor of area A (first and second floors) suddenly rises to 26°C, while the temperature in area B (third floor) remains stable at 23°C, since the weight priority of the second floor is higher (such as intensive personnel activities, concentrated important equipment, etc.), the air-conditioning system parameters of the second floor are adjusted first (such as increasing the air supply volume, reducing the air supply temperature, etc.) to quickly reduce the temperature.
[0034] In the specific implementation process, the specific steps performed by the instruction generation module are: Generate specific parameter control instructions according to the control strategy; Specifically, based on the control strategy, specific control parameters are set for each actuator of the HVAC system (such as air-conditioning units, fans, water pumps, etc.), including but not limited to supply air volume, supply air temperature, return air volume, return air temperature, chilled water flow rate and cooling water flow rate, etc. According to the set parameters, the software platform or programming tools of the building automatic control system are used to generate specific parameter control instructions.
[0035] The control instructions are sent to the actuator of the HVAC system through the communication protocol. The actuator makes adjustments according to the received instructions and generates a report based on the execution results. Specifically, according to the type and brand of the HVAC system, select the appropriate communication protocol (such as Modbus, BACnet, LonWorks, etc.) to transmit the control command. After receiving the control command, the actuator will make corresponding adjustments according to the parameters in the command. For example, the air conditioning unit will adjust its operating state according to the set air supply temperature and air supply volume to achieve the expected temperature control effect. After completing the adjustment, the actuator will feedback the execution results (such as actual air supply temperature, air supply volume, indoor temperature, etc.) to the building automation system through the communication protocol.
[0036] For example, suppose we send the control command to the air conditioner on the second floor through the BACnet protocol, and the air conditioner adjusts the air supply temperature and air supply volume according to the command. After a period of operation, the building automation system receives the execution result data fed back by the air conditioner. These data show that the temperature on the second floor has stabilized at around 24°C, and the energy consumption data is also within a reasonable range. Based on these data, the building automation system automatically generates a report that records in detail the operating parameters of the air conditioner, the trend of indoor temperature changes, and energy consumption data. Managers can evaluate and improve the performance of the system by viewing this report.
[0037] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0038] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.
[0039] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
Claims
1. A new intelligent and efficient HVAC system for realizing zero-carbon buildings, characterized in that: The HVAC system includes: A model building module is used to build a prediction model based on obtaining historical environmental parameters of each floor of the building; The parameter acquisition and processing module is used to obtain the current environmental parameters of each floor in the building in real time through sensors, and input them into the prediction model to predict the changes in environmental parameters in the future; The data analysis module is used to analyze the correlation of temperature changes between floors based on the predicted changes in environmental parameters in the future and obtain correlation analysis results; The weight division module is used to assign weights to each floor in the building according to the current number of personnel activities on each floor in the building, and obtain the weight priority of each floor; The control definition module is used to formulate differentiated control strategies based on the correlation analysis results and the weight priority of each floor; The instruction generation module is used to generate specific control instructions based on the control strategy and send them to the actuator of the HVAC system to execute the control.
2. According to claim 1, a new intelligent and efficient HVAC system for realizing zero-carbon buildings is characterized in that: The specific steps of establishing a prediction model based on obtaining historical environmental parameters of each floor of the building are as follows: Collect historical environmental parameters of each floor of the building over a period of time in the past, where the period of time is specifically half a month or a month, and the historical environmental parameters specifically include temperature, humidity, carbon dioxide concentration, and light intensity; The historical environmental parameters were preprocessed, and the correlation between humidity, carbon dioxide concentration, light intensity and temperature was analyzed by statistical analysis methods; The correlation between humidity, carbon dioxide concentration, light intensity and temperature was used as the training set, and the long short-term memory network architecture was selected for training to obtain the prediction model.
3. According to claim 1, a new intelligent and efficient HVAC system for realizing zero-carbon buildings is characterized in that: The current environmental parameters of each floor in the building are obtained in real time through sensors, and input into the prediction model to predict the changes of environmental parameters in the future. The specific steps are as follows: The current environmental parameters of each floor in the building are obtained in real time through sensors, where the current environmental parameters specifically include temperature, humidity, carbon dioxide concentration and light intensity; Preprocess the current environmental parameters of each floor to obtain the current environmental parameters after preprocessing; The features of the current environmental parameters after preprocessing are extracted and input into the prediction model to predict the temperature changes of each floor in the future period of time, where the future period of time is specifically 1 day or 2 days.
4. According to claim 1, a new intelligent and efficient HVAC system for realizing zero-carbon buildings is characterized in that: The method of analyzing the correlation of temperature changes between floors based on the prediction of environmental parameter changes in the future to obtain correlation analysis results has the following specific steps: Compare the temperature changes of each floor in the future period of time to obtain the comparison results; Based on the comparison results, the statistical analysis method is used to analyze the correlation of temperature changes between floors, and the correlation analysis results of temperature changes on each floor are obtained.
5. According to claim 1, a new intelligent and efficient HVAC system for realizing zero-carbon buildings is characterized in that: The specific steps of allocating weights to the floors in the building according to the current number of personnel activities on each floor in the building and obtaining the weight priority of each floor are as follows: Through the access control system or personnel positioning system, the number of people currently active on each floor can be counted in real time; Based on the real-time statistics of the current number of personnel activities on each floor, each floor is assigned a weight priority, wherein the more the current number of personnel activities on each floor, the higher the weight priority assigned.
6. A new intelligent and efficient HVAC system for realizing zero-carbon buildings according to claim 1, characterized in that: The specific steps of formulating differentiated control strategies based on the correlation analysis results and the weight priority of each floor are as follows: Based on the correlation analysis results of the temperature changes on each floor, each floor is divided into different temperature control areas through the preset correlation threshold segments. For each control area, set a unified temperature control target for preliminary control; Based on the preliminary control strategy, the control strategy is adjusted according to the weight priority of each floor. That is, when the temperature parameters of each floor change, adjustments are made according to the weight priority of each floor. Specifically, when a system failure or abnormal situation occurs, problems of floors with high weights are given priority, and the energy resources of the HVAC system are allocated according to the weight priority of the floors.
7. A new intelligent and efficient HVAC system for realizing zero-carbon buildings according to claim 1, characterized in that: The specific control instructions are generated based on the control strategy and sent to the actuator of the HVAC system to perform the control. The specific steps are as follows: Generate specific parameter control instructions according to the control strategy; The control instructions are sent to the actuator of the HVAC system through the communication protocol. The actuator makes adjustments based on the received instructions and generates a report based on the execution results.
8. According to claim 1, a new intelligent and efficient HVAC system for realizing zero-carbon buildings is characterized in that: The parameter acquisition and processing module includes: Temperature sensor, used to collect real-time temperature of each floor; Humidity sensor, used to collect real-time humidity on each floor; Infrared carbon dioxide detector, used to collect carbon dioxide concentration on each floor; Light intensity sensor, used to collect light intensity on each floor; The data processing module is used to predict the temperature changes of each floor in the future through a prediction model.