An energy-saving intelligent central air-conditioning constant temperature control system and device

Abstract

The present invention discloses an energy-saving intelligent central air-conditioning constant temperature control system and device, which relates to the technical field of control systems, including a control platform, wherein the control platform is communicatively connected with a sensor module, a data acquisition module, an environmental load analysis module, a dynamic adjustment module, and a user interaction module, wherein the modules are connected with electrical signals; the sensor module is used to deploy various types of sensors to monitor indoor and outdoor environmental parameters and personnel flow parameters, wherein the environmental parameters include indoor and outdoor temperature, humidity, and carbon dioxide concentration. The present invention accurately matches current and future environmental requirements with system output by integrating real-time monitoring, environmental load prediction, and dynamic adjustment functions. The system automatically adjusts the set temperature and wind speed according to the real-time changes in the environmental load, avoiding the common phenomenon of over-cooling or heating in traditional air-conditioning systems, thereby significantly reducing unnecessary energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to an energy-saving intelligent central air-conditioning constant temperature control system and device. Background Art

[0002] Against the backdrop of global warming and energy shortages, energy conservation and emission reduction have become a global consensus. As the main part of building energy consumption, the energy conservation and consumption reduction of central air-conditioning systems are of great significance for reducing building operating costs and carbon emissions. As people's quality of life improves, the requirements for the comfort of the indoor environment are also getting higher and higher. Traditional commercial central air-conditioning systems often have problems such as inaccurate control and slow response speed, and cannot meet the comfort requirements of modern buildings.

[0003] For example, an air conditioning energy-saving control system disclosed in Chinese Patent Publication No. CN105783187A includes a central controller and a detection switch arranged on an indoor ventilation window, and also includes a temperature sensor arranged indoors. A timer is arranged on the central controller.

[0004] In the prior art, a temperature sensor is set indoors to detect the indoor temperature in real time, and whether to turn on the air conditioner is determined based on the detected indoor temperature value. However, commercial central air-conditioning systems are often configured based on the maximum load of the building environment and leave a large margin. In actual use, daily temperature and ventilation volume requirements are usually far lower than the full load state, and there is a problem of energy waste. Therefore, how to monitor and predict various indoor and outdoor parameters, dynamically adjust the output of the central air-conditioning system, and calculate the optimal set temperature and wind speed in different time periods, and flexibly adjust the central air-conditioning system are the problems we need to solve. To this end, an energy-saving intelligent central air-conditioning constant temperature control system and device are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an energy-saving intelligent central air-conditioning constant temperature control system and device to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] In a first aspect, an energy-saving intelligent central air-conditioning constant temperature control system includes a control platform, wherein the control platform is communicatively connected to a sensor module, a data acquisition module, an environmental load analysis module, a dynamic adjustment module, and a user interaction module, wherein electrical signals are connected between the modules;

[0008] The sensor module is used to deploy various types of sensors to monitor indoor and outdoor environmental parameters and personnel flow parameters, and provide real-time data support for the system. The environmental parameters include indoor and outdoor temperature, humidity and carbon dioxide concentration, and the personnel flow parameters include the number of inflows, the number of outflows and the density of personnel flow.

[0009] The data acquisition module is used to receive data from the sensor module and perform preprocessing, such as data cleaning and format conversion, and upload the preprocessed data to the control platform to provide a basis for algorithm analysis and control decision-making;

[0010] The environmental load analysis module uses machine learning algorithms to analyze indoor and outdoor environmental parameters, and combines personnel flow parameters to evaluate future environmental loads and predict future temperature and ventilation requirements;

[0011] The dynamic adjustment module calculates the optimal set temperature and wind speed in different time periods based on real-time monitoring data and prediction results, dynamically adjusts the output of the central air-conditioning system, adjusts the operating parameters of the air-conditioning system, achieves energy conservation and emission reduction, reduces energy consumption, ensures that the system operates in the optimal way in different time periods, and reduces unnecessary energy consumption;

[0012] The user interaction module provides and sets a user-friendly operation interface, allowing the user to manually set preferences and view system operation status and energy consumption data.

[0013] A further improvement of the technical solution of the present invention is that: in the sensor module, the monitoring process of the environmental parameters and the personnel flow parameters is:

[0014] Deploy various types of sensors indoors and outdoors to monitor indoor and outdoor environmental parameters and personnel flow parameters, including temperature sensors, humidity sensors, carbon dioxide concentration sensors, personnel counters, and personnel density sensors;

[0015] The temperature sensor uses a thermocouple to measure temperature, measures and records indoor and outdoor temperature data in real time, and monitors the ambient temperature. The humidity sensor uses a resistive sensor to measure humidity. It is installed indoors to monitor the humidity level of the indoor air. The carbon dioxide concentration sensor uses an electrochemical sensor to detect the carbon dioxide concentration in the indoor air to monitor the air quality. The personnel counter is deployed at the entrance and exit, and counts the number of people flowing in and out in real time through infrared sensing. The personnel density sensor uses the infrared principle to monitor the distribution and density of people in the control area.

[0016] Each type of sensor detects signal changes through built-in detection elements and converts the response signal into an electrical signal. After amplification, filtering, and linearization, the electrical signal is converted into a standard digital signal.

[0017] A further improvement of the technical solution of the present invention is that: in the data acquisition module, the process of receiving data from the sensor module for preprocessing is: the data acquisition module establishes a connection with the sensor module through a wireless communication interface, and uses a corresponding communication protocol (such as Modbus, MQTT, TCP / IP, HTTP / HTTPS, CoAP, etc.) to read data from the sensor module, including environmental parameters such as temperature, humidity, carbon dioxide concentration, personnel count, and personnel density, and personnel flow parameters;

[0018] Preprocessing the collected environmental parameters and personnel flow parameter data, wherein the preprocessing operations include data cleaning, format conversion, signal conditioning and data calibration, and synchronizing the preprocessed environmental parameters and personnel flow parameter data, adding a timestamp to each data point;

[0019] A data warehouse is established to store the pre-processed environmental parameters and personnel flow parameter data in the data warehouse, and the pre-processed data is synchronously sent to the control platform using a wireless network for subsequent analysis. During the transmission process, encryption technology is used to ensure the security of the data. A further improvement of the technical solution of the present invention is that in the environmental load analysis module, the process of evaluating the future environmental load is:

[0020] Extract pre-processed environmental parameters and personnel flow parameter data from the data warehouse to provide the data required for training and prediction for the machine learning model, and extract features related to environmental load prediction based on historical data and business needs, namely environmental features and flow features. Environmental features include historical temperature features, humidity features, and carbon dioxide concentration features, and flow features include personnel counting features and personnel density features, forming environmental feature data sets and flow feature data sets;

[0021] Filter out characteristic data related to temperature, humidity and carbon dioxide concentration from the environmental characteristic data set, train the neural network model, establish the environmental assessment model, conduct comprehensive analysis to obtain the environmental assessment index, and analyze the changing trend of the environment;

[0022] Filter out feature data related to personnel count and personnel density from the traffic feature data set, train the random forest model, establish a personnel flow model, conduct comprehensive analysis to obtain the traffic evaluation index, and analyze the personnel flow trend;

[0023] Traverse the characteristic data related to environmental load prediction in the environmental characteristic data set and the traffic characteristic data set, combine the analysis results of environmental change trend and personnel flow trend, establish an environmental load prediction model to evaluate the future environmental load, and obtain the environmental load assessment coefficient through comprehensive analysis;

[0024] The pre-processed real-time data is input into the trained environmental load prediction model, and the future environmental load is analyzed in combination with the environmental load assessment coefficient, and the future temperature and ventilation demand are analyzed based on the prediction results. A further improvement of the technical solution of the present invention is that the expression of the environmental assessment index is: ;

[0025] in, is the environmental assessment index, is the number of environmental samples in the time period, For the The temperature value of each sample, is the reference temperature value, usually the comfort temperature value or the historical average temperature value. For the The humidity value of each sample, For the The carbon dioxide concentration value of each sample, is the humidity threshold, used for exponential decay calculation;

[0026] The expression of the flow evaluation index is:

[0027] ;

[0028] in, is the flow evaluation index, is the number of traffic samples in the time period, For the The number of people in the sample, is the baseline people count, usually the historical average flow of people, For the The population density of each sample (person / square meter), is the population density threshold (people / square meter), used for exponential decay calculation.

[0029] A further improvement of the technical solution of the present invention is that the expression of the environmental load assessment coefficient is:

[0030] ;

[0031] in, is the environmental load assessment coefficient, is the length of the time series, indicating the number of time periods for environmental load assessment, For in time Environmental assessment index, For in time The flow evaluation index, and is the weight coefficient, which is used to adjust the relative importance of the environmental assessment index and the flow assessment index in the calculation. and increase, will tend to increase, indicating an increase in environmental load.

[0032] A further improvement of the technical solution of the present invention is that in the dynamic adjustment module, the process of adjusting the operating parameters of the central air-conditioning system is:

[0033] Collect the current operating status data of the central air-conditioning system, including the current indoor temperature, humidity, carbon dioxide concentration, personnel flow, wind speed, and system energy consumption. Use the future environmental load assessment coefficient output by the environmental load prediction model, combined with historical data and current trends, to analyze the environmental load situation in the future.

[0034] According to different environmental load conditions, different control levels are preset, namely, level one, level two and level three. As the environmental load increases, the control level gradually increases from level one to level three to increase the control intervention efficiency, and corresponding control thresholds are set for each control level. Level one: mild intervention, suitable for low load conditions; level two: moderate intervention, suitable for medium load conditions; level three: high intervention, suitable for high load conditions;

[0035] According to the preset control level and the environmental load assessment results, the current control level is determined, and based on the control level, the optimal operating parameter records of the central air-conditioning system under different loads, the energy efficiency ratio of the central air-conditioning system, and the cooling / heating capacity, the optimal set temperature and wind speed in different time periods are calculated to minimize energy consumption while meeting the environmental comfort requirements;

[0036] The calculated optimal set temperature and optimal wind speed parameters are converted into control instructions and sent to the actuator of the central air-conditioning system to adjust the temperature setting, wind speed and wind direction to achieve energy conservation and emission reduction.

[0037] A further improvement of the technical solution of the present invention is that: the multiple control levels correspond to the multiple control thresholds, wherein the control thresholds include an upper threshold and a lower threshold;

[0038] The multiple control levels and the multiple control thresholds satisfy the following relationship:

[0039] First-level regulation level ;

[0040] Secondary control level ;

[0041] Three levels of regulation ;

[0042] in, is the environmental load assessment coefficient, is the lower threshold corresponding to the first-level regulation level and the upper threshold corresponding to the second-level regulation level, It is the lower threshold corresponding to the second-level regulation level and the upper threshold corresponding to the third-level regulation level.

[0043] A further improvement of the technical solution of the present invention is that in the user interaction module, the setting process of the operation interface is:

[0044] Preset the user interface and display different operation interfaces and function options according to the user's role and permissions;

[0045] Equipped with a preference page for personalized settings, users can customize the temperature range, humidity preference, and wind speed level in the preference page, and allow users to set automatic tasks such as timed power on and off and temperature adjustment to improve convenience;

[0046] The user interface displays the current indoor temperature, humidity, wind speed, carbon dioxide concentration and environmental parameters as well as system energy consumption data in real time, and displays the current device status;

[0047] Provide historical data and statistical analysis of energy consumption to help users understand energy usage, and provide personalized energy-saving suggestions and optimization plans based on users' actual usage and system operation status to guide users in energy-saving operations;

[0048] Records all user operation histories, including set preferences and adjusted parameters, allowing users to query and review past system operating status and energy consumption data. It also provides a user feedback channel to collect user opinions and suggestions on the system for subsequent improvement and optimization.

[0049] In the second aspect, an energy-saving intelligent central air-conditioning constant temperature control device includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the energy-saving intelligent central air-conditioning constant temperature control system.

[0050] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0051] The present invention provides an energy-saving intelligent central air-conditioning constant temperature control system and device, which accurately matches current and future environmental requirements with system outputs by integrating real-time monitoring, environmental load prediction and dynamic adjustment functions. The system automatically adjusts the set temperature and wind speed according to real-time changes in environmental load, avoiding the over-cooling or over-heating phenomenon commonly seen in traditional air-conditioning systems, thereby significantly reducing unnecessary energy consumption.

[0052] The present invention provides an energy-saving intelligent central air-conditioning constant temperature control system and device, which optimizes energy use by real-time monitoring of indoor and outdoor environmental parameters and personnel flow, combined with prediction algorithms and dynamic adjustment strategies, and can automatically adjust the temperature and wind speed of the air conditioner according to the environmental load assessment coefficient and the preferences set by the user to meet the needs of different time periods and different areas, which not only improves energy efficiency but also reduces unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a module structure diagram of the present invention;

[0055] Figure 2 Flowchart for assessing future environmental loads for the present invention;

[0056] Figure 3 The present invention is a flow chart for adjusting the operating parameters of the central air-conditioning system. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides an energy-saving intelligent central air-conditioning constant temperature control system, including a control platform, the control platform is communicatively connected with a sensor module, a data acquisition module, an environmental load analysis module, a dynamic adjustment module and a user interaction module, wherein the modules are connected by electrical signals;

[0059] The sensor module is used to deploy various types of sensors to monitor indoor and outdoor environmental parameters and personnel flow parameters, and provide real-time data support for the system. The environmental parameters include indoor and outdoor temperature, humidity and carbon dioxide concentration. The personnel flow parameters include the number of people flowing in, the number of people flowing out and the density of personnel flow. Various types of sensors are deployed indoors and outdoors to monitor indoor and outdoor environmental parameters and personnel flow parameters. The sensors include temperature sensors, humidity sensors, carbon dioxide concentration sensors, personnel counters and personnel density sensors. The temperature sensor uses a thermocouple to measure temperature, measures and records indoor and outdoor temperature data in real time, and monitors the ambient temperature. The humidity sensor uses a resistive sensor to measure humidity, is installed indoors, and monitors the humidity level of indoor air. The carbon dioxide concentration sensor uses an electrochemical sensor to detect the carbon dioxide concentration in the indoor air to monitor the air quality. The personnel counter is deployed at the entrance and exit, and counts the number of people flowing in and out in real time through infrared sensing. The personnel density sensor uses the infrared principle to monitor the distribution and density of people in the control area. Various types of sensors detect signal changes through built-in detection elements and convert the response signals into electrical signals. After amplification, filtering and linearization, the electrical signals are converted into standard digital signals.

[0060] The data acquisition module is used to receive data from the sensor module and perform preprocessing, such as data cleaning and format conversion, and upload the preprocessed data to the control platform to provide a basis for algorithm analysis and control decision-making. The data acquisition module establishes a connection with the sensor module through the wireless communication interface and uses the corresponding communication protocol (such as Modbus, MQTT, TCP / IP, HTTP / HTTPS, CoAP, etc.) to read data from the sensor module, including environmental parameters and personnel flow parameters such as temperature, humidity, carbon dioxide concentration, personnel count, and personnel density, and preprocess the collected environmental parameters and personnel flow parameter data. The preprocessing operation includes data cleaning, format conversion, signal conditioning, and data calibration, and synchronizes the preprocessed environmental parameters and personnel flow parameter data, adding time for each data point. Time stamping is used to ensure the time sequence and synchronization of data, and the clock synchronization of all sensors and data acquisition modules is ensured to facilitate data comparison and analysis. Among them, data cleaning removes invalid data, fills missing values, removes outliers, and ensures the integrity and accuracy of data. Format conversion converts sensor data from the original format to a unified and standardized format for subsequent processing and analysis. Signal conditioning includes amplification and filtering. For sensors with weak signal strength, signal amplification is performed to improve the signal-to-noise ratio. Low-pass, high-pass, band-pass or band-stop filters are applied to remove noise and interference, extract valid signals, and establish a data warehouse. The pre-processed environmental parameters and personnel flow parameter data are stored in the data warehouse, and the pre-processed data is synchronously sent to the control platform using a wireless network for subsequent analysis. During the transmission process, encryption technology is used to ensure data security;

[0061] The environmental load analysis module uses machine learning algorithms to analyze indoor and outdoor environmental parameters, and combines personnel flow parameters to evaluate future environmental loads, predict future temperature and ventilation requirements, and extract pre-processed environmental parameter and personnel flow parameter data from the data warehouse to provide the data required for training and prediction for the machine learning model. Based on historical data and business needs, it extracts features related to environmental load prediction, namely environmental features and flow features. Environmental features include historical temperature features, humidity features, and carbon dioxide concentration features, and flow features include personnel counting features and personnel density features, forming environmental feature data sets and flow feature data sets. Feature data related to temperature, humidity, and carbon dioxide concentration are screened out from the environmental feature data sets to train the neural network. Model, establish an environmental assessment model, conduct comprehensive analysis to obtain an environmental assessment index, analyze the changing trend of the environment, filter out feature data related to personnel count and personnel density from the flow feature data set, train a random forest model, establish a personnel flow model, conduct comprehensive analysis to obtain a flow assessment index, analyze the flow trend of personnel, traverse the feature data related to environmental load prediction in the environmental feature data set and the flow feature data set, combine the analysis results of environmental change trend and personnel flow trend, establish an environmental load prediction model to evaluate the future environmental load, conduct comprehensive analysis to obtain the environmental load assessment coefficient, input the preprocessed real-time data into the trained environmental load prediction model, analyze the future environmental load in combination with the environmental load assessment coefficient, and analyze the future temperature and ventilation volume requirements based on the prediction results;

[0062] Furthermore, the expression of the environmental assessment index is:

[0063] ;

[0064] in, is the environmental assessment index, is the number of environmental samples in the time period, For the The temperature value of each sample, is the reference temperature value, usually the comfort temperature value or the historical average temperature value. For the The humidity value of each sample, For the The carbon dioxide concentration value of each sample, is the humidity threshold, which is used for exponential decay calculation. When the temperature deviates from the baseline value and the humidity and carbon dioxide concentration are low, Higher, and vice versa, as humidity or carbon dioxide concentration increases, exponential decay will lead to The value is reduced, and the comfort of the environment is evaluated by comparing the difference between the actual temperature and the reference temperature, while considering the influence of humidity and carbon dioxide concentration. Increase, while high humidity and carbon dioxide concentration will reduce their influence through the attenuation factor, thus comprehensively reflecting the comfortable state of the environment;

[0065] The expression of the flow evaluation index is:

[0066] ;

[0067] in, is the flow evaluation index, is the number of traffic samples in the time period, For the The number of people in the sample, is the baseline people count, usually the historical average flow of people, For the The population density of each sample (person / square meter), is the population density threshold (person / square meter), which is used for exponential decay calculation. When the population count is higher than the benchmark value and the population density is low, Higher, and vice versa, as the population density increases, exponential decay will lead to The value is reduced, and the personnel flow trend is evaluated by comparing the difference between the actual personnel count and the benchmark personnel count. At the same time, the influence of personnel density is considered. A high flow of people will Increase, while high density will reduce its influence through the attenuation factor, thus comprehensively reflecting the state of personnel flow. The environmental assessment index and flow assessment index enable the system to dynamically adjust the operation strategy of the central air-conditioning according to changes in the environment and personnel flow, so as to achieve energy saving and improve comfort. The system can monitor and predict the environment and personnel flow in real time, so as to achieve intelligent adjustment and optimization control;

[0068] Furthermore, the expression of the environmental load assessment coefficient is:

[0069] ;

[0070] in, is the environmental load assessment coefficient, is the length of the time series, indicating the number of time periods for environmental load assessment, For in time Environmental assessment index, For in time The flow evaluation index, and is the weight coefficient, which is used to adjust the relative importance of the environmental assessment index and the flow assessment index in the calculation. and increase, will tend to increase, indicating that the environmental load is increasing, and the specific growth rate will be affected and The adjustment, It is an exponential function used to simulate the exponential impact of environmental parameters (such as temperature, humidity, and carbon dioxide concentration) on environmental load. To adjust the intensity of this effect,

[0071] The square root function is used to simulate the square root impact of personnel flow (such as personnel count and personnel density) on environmental load. To adjust the intensity of this effect, The sum of and divided by the length of the time series , calculate the average value, which reflects the average impact of environmental load during the period of environmental load assessment. , predicting the future environmental load under different environmental and personnel flow conditions, based on Based on the prediction results, the intelligent central air conditioning system can dynamically adjust the temperature setting and ventilation volume to optimize energy efficiency and indoor environmental quality;

[0072] The dynamic adjustment module calculates the optimal set temperature and wind speed in different time periods based on real-time monitoring data and prediction results, dynamically adjusts the output of the central air-conditioning system, adjusts the operating parameters of the air-conditioning system, achieves energy conservation and emission reduction, reduces energy consumption, ensures that the system operates in the optimal way in different time periods, and reduces unnecessary energy consumption;

[0073] The user interaction module provides and sets a user-friendly operation interface, allowing users to manually set preferences and view system operation status and energy consumption data.

[0074] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the dynamic adjustment module, the process of adjusting the operating parameters of the central air-conditioning system is:

[0075] Collect the current operating status data of the central air-conditioning system, including the current indoor temperature, humidity, carbon dioxide concentration, personnel flow, wind speed, and system energy consumption. Use the future environmental load assessment coefficient output by the environmental load prediction model, combined with historical data and current trends, to analyze the environmental load situation in the future. According to different environmental load conditions, different control levels are preset, namely, the first-level control level, the second-level control level, and the third-level control level. As the environmental load increases, the control level gradually increases from the first level to the third level to increase the control intervention efficiency, and set corresponding control thresholds for each control level. The first-level control level: mild intervention, suitable for low load conditions, Secondary control level: moderate intervention, suitable for medium load conditions; third control level: high intervention, suitable for high load conditions. According to the preset control level and the environmental load assessment results, the current control level is determined, and based on the control level, the optimal operating parameter records of the central air-conditioning system under different loads, the energy efficiency ratio of the central air-conditioning system, and the cooling / heating capacity, the optimal set temperature and wind speed in different time periods are calculated. While meeting the environmental comfort requirements, energy consumption is minimized, and the calculated optimal set temperature and optimal wind speed parameters are converted into control instructions and sent to the actuator of the central air-conditioning system to adjust the temperature setting, wind speed and wind direction to achieve energy saving and emission reduction;

[0076] Furthermore, the multiple control levels correspond to multiple control thresholds, wherein the control thresholds include an upper threshold and a lower threshold;

[0077] Multiple control levels and multiple control thresholds satisfy the following relationship:

[0078] First-level regulation level ;

[0079] Secondary control level ;

[0080] Three levels of regulation ;

[0081] in, is the environmental load assessment coefficient, is the lower threshold corresponding to the first-level regulation level and the upper threshold corresponding to the second-level regulation level, The lower threshold corresponding to the second-level regulation level and the upper threshold corresponding to the third-level regulation level;

[0082] In the user interaction module, the setting process of the operation interface is as follows:

[0083] The user interface is preset, and different operation interfaces and function options are displayed according to the user's role and authority. It is equipped with a preference setting page for personalized settings. Users can customize the temperature range, humidity preference, and wind speed level in the preference setting page, and allow users to set timed power on and off, and adjust the temperature for automated tasks to improve ease of use. The user interface displays the current indoor temperature, humidity, wind speed, carbon dioxide concentration and environmental parameters as well as system energy consumption data in real time, and displays the current device status. It provides historical energy consumption data and statistical analysis to assist users in understanding energy usage, and provides personalized energy-saving suggestions and optimization plans based on the user's actual usage and system operation status, guides users to perform energy-saving operations, and records all user operation histories, including set preferences and adjusted parameters, allowing users to query and review past system operation status and energy consumption data. At the same time, it provides user feedback channels to collect user opinions and suggestions on the system for subsequent improvement and optimization.

[0084] Example 3. Based on Examples 1-2, the present invention also provides an energy-saving intelligent central air-conditioning constant temperature control device, including at least one control processor and a memory for communicating with at least one control processor; the memory stores instructions that can be executed by at least one control processor, and the instructions are executed by at least one control processor so that at least one control processor can execute an energy-saving intelligent central air-conditioning constant temperature control system.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An energy-saving intelligent central air-conditioning constant temperature control system, including a control platform, characterized in that: The control platform is communicatively connected with a sensor module, a data acquisition module, an environmental load analysis module, a dynamic adjustment module and a user interaction module, wherein electrical signals are connected between the modules; The sensor module is used to deploy various types of sensors to monitor indoor and outdoor environmental parameters and personnel flow parameters, wherein the environmental parameters include indoor and outdoor temperature, humidity and carbon dioxide concentration, and the personnel flow parameters include the number of inflows, the number of outflows and the density of personnel flow. In the sensor module, the monitoring process of environmental parameters and personnel flow parameters is as follows: Deploy various types of sensors indoors and outdoors to monitor indoor and outdoor environmental parameters and personnel flow parameters, including temperature sensors, humidity sensors, carbon dioxide concentration sensors, personnel counters, and personnel density sensors; The temperature sensor uses a thermocouple to measure temperature, measures and records indoor and outdoor temperature data in real time, and monitors the ambient temperature. The humidity sensor uses a resistive sensor to measure humidity. It is installed indoors to monitor the humidity level of the indoor air. The carbon dioxide concentration sensor uses an electrochemical sensor to detect the carbon dioxide concentration in the indoor air to monitor the air quality. The personnel counter is deployed at the entrance and exit, and counts the number of people flowing in and out in real time through infrared sensing. The personnel density sensor uses the infrared principle to monitor the distribution and density of people in the control area. Each type of sensor detects the change of the signal through the built-in detection element and converts the response signal into an electrical signal. After amplification, filtering and linearization, the electrical signal is converted into a standard digital signal. The data acquisition module is used to receive data from the sensor module, perform preprocessing, and upload the preprocessed data to the control platform. In the data acquisition module, the process of receiving data from the sensor module and performing preprocessing is as follows: The data acquisition module establishes a connection with the sensor module through a wireless communication interface, and the data acquisition module reads data from the sensor module, including environmental parameters such as temperature, humidity, carbon dioxide concentration, personnel count, personnel density, and personnel flow parameters; Preprocessing the collected environmental parameters and personnel flow parameter data, wherein the preprocessing operations include data cleaning, format conversion, signal conditioning and data calibration, and synchronizing the preprocessed environmental parameters and personnel flow parameter data, adding a timestamp to each data point; Establish a data warehouse, store the pre-processed environmental parameters and personnel flow parameter data in the data warehouse, and use the wireless network to synchronously send the pre-processed data to the control platform; The environmental load analysis module uses a machine learning algorithm to analyze indoor and outdoor environmental parameters, and combines personnel flow parameters to evaluate future environmental loads and predict future temperature and ventilation requirements. In the environmental load analysis module, the process of evaluating future environmental loads is as follows: Extract pre-processed environmental parameters and personnel flow parameter data from the data warehouse, and extract features related to environmental load prediction based on historical data and business needs, namely environmental features and flow features. Environmental features include historical temperature features, humidity features, and carbon dioxide concentration features, and flow features include personnel counting features and personnel density features, forming environmental feature data sets and flow feature data sets; Filter out characteristic data related to temperature, humidity and carbon dioxide concentration from the environmental characteristic data set, train the neural network model, establish the environmental assessment model, conduct comprehensive analysis to obtain the environmental assessment index, and analyze the changing trend of the environment; Filter out feature data related to personnel count and personnel density from the traffic feature data set, train the random forest model, establish a personnel flow model, conduct comprehensive analysis to obtain the traffic evaluation index, and analyze the personnel flow trend; Traverse the characteristic data related to environmental load prediction in the environmental characteristic data set and the traffic characteristic data set, combine the analysis results of environmental change trend and personnel flow trend, establish an environmental load prediction model to evaluate the future environmental load, and obtain the environmental load assessment coefficient through comprehensive analysis; The pre-processed real-time data is input into the trained environmental load prediction model, and the future environmental load is analyzed in combination with the environmental load assessment coefficient. The future temperature and ventilation volume requirements are analyzed based on the prediction results. The expression of the environmental assessment index is: ； in, is the environmental assessment index, is the number of environmental samples in the time period, For the The temperature value of each sample, is the reference temperature value, For the The humidity value of each sample, For the The carbon dioxide concentration value of each sample, is the humidity threshold; The expression of the flow evaluation index is: ； in, is the flow evaluation index, is the number of traffic samples in the time period, For the The number of people in the sample, For the baseline headcount, For the The population density of the samples, is the personnel density threshold; The expression of the environmental load assessment coefficient is: ； in, is the environmental load assessment coefficient, is the length of the time series, indicating the number of time periods for environmental load assessment, For in time Environmental assessment index, For in time The flow evaluation index, and is the weight coefficient, which is used to adjust the relative importance of the environmental assessment index and the flow assessment index in the calculation; The dynamic adjustment module calculates the optimal set temperature and wind speed in different time periods based on real-time monitoring data and prediction results, dynamically adjusts the output of the central air-conditioning system, and adjusts the operating parameters of the air-conditioning system; The user interaction module provides and sets an operation interface, allowing the user to manually set preferences and view system operation status and energy consumption data.

2. According to claim 1, an energy-saving intelligent central air-conditioning constant temperature control system is characterized in that: In the dynamic adjustment module, the process of adjusting the operating parameters of the central air conditioning system is as follows: Collect the current operating status data of the central air-conditioning system, including the current indoor temperature, humidity, carbon dioxide concentration, personnel flow, wind speed, and system energy consumption. Use the future environmental load assessment coefficient output by the environmental load prediction model, combined with historical data and current trends, to analyze the environmental load situation in the future. According to different environmental load conditions, different control levels are preset, namely, level one, level two and level three. As the environmental load increases, the control level is gradually increased from level one to level three to increase the control intervention efficiency, and corresponding control thresholds are set for each control level; According to the preset control level and the environmental load assessment results, the current control level is determined, and based on the control level, the optimal operating parameter records of the central air-conditioning system under different loads, the energy efficiency ratio of the central air-conditioning system, and the cooling / heating capacity, the optimal set temperature and wind speed in different time periods are calculated to minimize energy consumption while meeting the environmental comfort requirements; The calculated optimal set temperature and optimal wind speed parameters are converted into control instructions and sent to the actuator of the central air-conditioning system to adjust the temperature setting, wind speed and wind direction.

3. The energy-saving intelligent central air-conditioning constant temperature control system according to claim 2 is characterized in that: The plurality of control levels correspond to the plurality of control thresholds, wherein the control thresholds include an upper threshold and a lower threshold; The multiple control levels and the multiple control thresholds satisfy the following relationship: First-level regulation level ; Secondary control level ; Three levels of regulation ; in, is the environmental load assessment coefficient, is the lower threshold corresponding to the first-level regulation level and the upper threshold corresponding to the second-level regulation level, It is the lower threshold corresponding to the second-level regulation level and the upper threshold corresponding to the third-level regulation level.

4. The energy-saving intelligent central air-conditioning constant temperature control system according to claim 3 is characterized in that: In the user interaction module, the setting process of the operation interface is as follows: Preset the user interface and display different operation interfaces and function options according to the user's role and permissions; Equipped with a preference page for personalized settings, users can customize the temperature range, humidity preference, wind speed level in the preference page, and allow users to set timed power on and off and adjust the temperature for automated tasks; The user interface displays the current indoor temperature, humidity, wind speed, carbon dioxide concentration and environmental parameters as well as system energy consumption data in real time, and displays the current device status; Provide historical data and statistical analysis of energy consumption to help users understand energy usage, and provide personalized energy-saving suggestions and optimization plans based on users' actual usage and system operation status to guide users in energy-saving operations; Records all user operation histories, including set preferences and adjusted parameters, allowing users to query and review past system operating status and energy consumption data, while providing user feedback channels to collect user opinions and suggestions on the system.

5. An energy-saving intelligent central air-conditioning constant temperature control device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the energy-saving intelligent central air-conditioning constant temperature control system as described in any one of claims 1 to 4.

Images