Air conditioner internet-of-things control method

Abstract

The invention relates to the technical field of air conditioners, in particular to an air conditioner internet-of-things control method. An IoT (Internet of Things) module is integrated in each air conditioner, and environment data including temperature, humidity and air quality of a room are collected in real time through connection of an intelligent sensor and a cloud platform; data of all air conditioning equipment is summarized to form a centralized management system of equipment state and environment information; an intelligent cooperative control algorithm based on communication between the equipment is constructed by means of internet-of-things intercommunication between the air conditioning equipment; the air conditioning equipment detects environmental data in real time through a local sensor, accesses prediction data of an external environment including weather forecast and climate change trend, and adjusts a working mode in advance in combination with a self-learning function of the equipment; a multi-dimensional energy-saving optimization algorithm is adopted for intelligent control; meanwhile, the overall energy consumption is optimized in combination with external weather change and equipment use frequency; the optimization strategy comprises time period distribution and equipment priority control; and performing personalized user control based on big data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to an air conditioning Internet of Things control method. Background Art

[0002] At present, the technologies used in the Internet of Things control methods for air conditioners mostly focus on controlling the basic functions of the equipment, such as temperature regulation, wind speed regulation, air supply angle control, etc. However, these methods have some obvious shortcomings and disadvantages, especially in terms of energy efficiency optimization, environmental adaptability, and intelligent collaborative control. There is still a lot of room for improvement. Specifically, the existing air-conditioning Internet of Things control methods, such as the air-conditioning Internet of Things control method described in Chinese invention patent No. 202111076607.4, although they introduce the linkage between electronic devices and air conditioners, and to a certain extent realize the control of the air supply angle and operating power of the air conditioner through feedback information from electronic devices, there are still technical bottlenecks and limitations in many aspects.

[0003] First, existing technologies mostly focus on the basic interaction between air conditioners and electronic devices, such as adjusting the air supply angle and power of the air conditioner by receiving feedback information. Although this control method can improve the user experience to a certain extent, its core problem is that the control of the air conditioner does not fully take into account the changes and dynamic needs of the entire environment. For example, the adjustment of the air conditioner equipment only relies on the feedback coordinate information and simple operating power adjustment, ignoring the impact of external environmental factors (such as weather changes, sunlight intensity, outdoor temperature and humidity, etc.) on the indoor environment, resulting in the inability to accurately predict the adjustment response of the air conditioner, making it difficult to truly achieve energy saving and efficient operation.

[0004] Secondly, the existing air-conditioning IoT control method lacks a comprehensive energy efficiency optimization mechanism. In the air-conditioning IoT control method described in the patent, the air conditioner only adjusts the air supply angle and operating power according to the feedback coordinate information, but does not introduce a more complex energy efficiency optimization algorithm. In fact, the energy efficiency of the air-conditioning system is not only affected by the operating power, but is also closely related to a variety of factors, such as the workload of the air conditioner, external ambient temperature, humidity, sunlight intensity, etc. If these factors can be integrated into the control system and the operating mode of the air-conditioning equipment can be dynamically adjusted through intelligent algorithms, the energy efficiency of the system will be significantly improved. The existing technology has failed to achieve this, resulting in energy waste, especially during high-demand periods, when the air conditioner may still run at a high intensity, resulting in unnecessary energy consumption.

[0005] In addition, the feedback mechanism in the existing technology is too dependent on a single coordinate information, which leads to low accuracy of air conditioning control. In actual applications, air conditioning equipment needs to consider not only coordinate information, but also multiple variables, such as the temperature requirements of different areas in the room, the workload of the air conditioning equipment, and the user's living habits. The combined effect of these factors is crucial for regulating the working state of the air conditioner. The existing feedback information is relatively simple and fails to effectively integrate these multi-dimensional requirements, which limits the intelligent and personalized control of the system. For example, the air conditioner may ignore the temperature changes in certain areas and rely solely on the average temperature of the whole house for adjustment. This may cause some areas to be too cold or too hot, affecting comfort.

[0006] In summary, although the existing air-conditioning IoT control methods have achieved collaborative control of air-conditioning equipment and electronic equipment to a certain extent, they still have many shortcomings, especially in terms of energy efficiency optimization, environmental adaptability, intelligent collaborative control, adaptive learning, and multi-device collaboration. Summary of the Invention

[0007] The purpose of the present invention is to provide an air conditioner IoT control method to solve some of the drawbacks and shortcomings pointed out in the background technology.

[0008] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: an air-conditioning IoT control method, comprising: S1, intelligent IoT interconnection of air-conditioning equipment:

[0009] An IoT module is integrated into each air conditioner. Through smart sensors connected to a cloud platform, it collects real-time environmental data about the room, including temperature, humidity, and air quality. Data from all air conditioners is aggregated to form a centralized management system for device status and environmental information.

[0010] S2. Intelligent collaborative control based on IoT data:

[0011] S2.1. Leverage the IoT connectivity between air conditioning equipment to build an intelligent collaborative control algorithm based on inter-device communication;

[0012] S2.2. Each air conditioner adjusts its operating mode based on data from multiple sensors in the room and changes in the surrounding environment, including weather and sunlight;

[0013] S3. Intelligent environmental prediction:

[0014] Air conditioning equipment uses local sensors to detect environmental data in real time, as well as access to external environmental data including weather forecasts and climate change trend predictions. Combined with the equipment's self-learning function, it adjusts its operating mode in advance.

[0015] S4, multi-dimensional energy-saving optimization algorithm:

[0016] S4.1, using multi-dimensional energy-saving optimization algorithm for intelligent control; including adjusting the cooling / heating intensity according to the temperature requirements of different areas in the room,

[0017] S4.2. Optimize overall energy consumption by combining external weather changes and device usage frequency. Optimization strategies include time period allocation and device priority control.

[0018] S5. Personalized user control based on big data analysis:

[0019] S5.1. Collect environmental data and user behavior data through air conditioning equipment, and use big data analysis and machine learning models to predict user living habits and comfort preferences;

[0020] S5.2. The air conditioning system adjusts its control strategy based on the user's behavior patterns, including work and rest time and indoor activities.

[0021] Furthermore, the intelligent collaborative control method based on IoT data includes:

[0022] Data sharing and collaborative operation between air conditioning devices are controlled through wireless communication protocols. Each air conditioning device is equipped with an IoT module, including Wi-Fi, ZigBee, or LoRa, for interconnection. Environmental data, including temperature, humidity, and CO2 concentration sensor data, is shared between the air conditioning devices. The operating mode is adjusted in real time based on the sensor data and shared information. The temperature adjustment of each device is controlled by the following formula:

[0023]

[0024] Where: T(t) is the target temperature setting of the air conditioning equipment at time t; T room (t) is the current temperature of the room where the air conditioner is located, which indicates the device's perception of the room environment; α is the control coefficient, which indicates the device's sensitivity to changes in ambient temperature; the value range is 0.1≤α≤1.0, depending on the type of device and specific circumstances; T env (i) is the ambient temperature of the i-th device; air-conditioning equipment shares information with other devices through the Internet of Things; n is the number of air-conditioning devices sharing information.

[0025] Furthermore, the intelligent collaborative control method based on IoT data includes:

[0026] The air conditioning equipment integrates external environmental data, including weather, sunshine intensity, and outdoor temperature and humidity information data; this information data is acquired in real time through an open API interface or embedded sensors; the air conditioning equipment senses the current environmental state and predicts future environmental changes; the external environmental data is modeled as a function of weather and sunshine intensity, using historical climate data to predict future environmental changes; the external temperature prediction formula is as follows:

[0027]

[0028] Where: T ext (t) is the predicted value of the external temperature at time t, indicating the expected temperature of the external environment; f weather (x) is the weather change function, which represents the change of meteorological data at time x; it is modeled based on weather forecast data and calculated according to the rate of change of meteorological temperature; f sunlight (x) is the sunshine intensity function, which represents the change of sunshine intensity in time x, and the data is provided by sensors or sunshine forecasting system; To represent the integration of external environmental data from time 0 to time t, the cumulative impact is calculated.

[0029] Furthermore, the intelligent collaborative control method based on IoT data includes:

[0030] Each air conditioner has an embedded machine learning algorithm that analyzes the interaction between environmental data and the air conditioner in real time to optimize control. It also analyzes user life patterns, including home and bedtimes, and builds personalized models based on historical and real-time data to adjust temperature and air speed. The intelligent learning and optimization process between devices is expressed through an energy efficiency optimization function:

[0031]

[0032] in:

[0033] E(t) is the overall energy efficiency of the air conditioning system at time t; the index comprehensively considers the energy efficiency of the equipment; C i (t) is the power consumption of the i-th air-conditioning equipment at time t, indicating the real-time energy consumption of the equipment; ΔT i (t) is the temperature change of the i-th air-conditioning equipment at time t, indicating the temperature change after the equipment is adjusted; α i is the energy efficiency coefficient of the i-th device, which represents the relationship between the energy consumption and temperature regulation of the device. The energy efficiency coefficient of each device is adjusted according to factors such as device type and environmental load, and its value range is 1 to 5.

[0034] Furthermore, the multi-dimensional energy-saving optimization algorithm includes:

[0035] Dynamically adjust the cooling or heating intensity of the air conditioner according to the temperature requirements of different areas in the room; the air conditioner calculates the regional temperature difference and implements control for areas with strong solar radiation near the windows and areas with lower temperature near the inner wall; the air conditioner system calculates the temperature difference (△T i ) and dynamically adjust the cooling or heating mode of each area according to the temperature difference:

[0036]

[0037] Where ΔT k represents the temperature difference between the kth area and the external environment temperature; Tk is the actual temperature of the kth area, detected by the temperature sensor of the air conditioning system; T ext is the external ambient temperature, obtained in real time through the weather API or environmental monitoring equipment; k is the heat transfer coefficient of the area, which indicates the heat exchange efficiency between the area and the external environment; t is time, which indicates the length of time in the calculation process of the formula.

[0038] Furthermore, the multi-dimensional energy-saving optimization algorithm includes:

[0039] By introducing external environmental data, the air conditioning system can predict future environmental changes and adjust its working mode in advance. The air conditioning system uses the following prediction formula combined with external weather data to predict indoor temperature changes:

[0040] ΔT pred =α1T ext (t)+α2H ext (t)-β·SolarRadiation(t)

[0041] Where: ΔT pred : Indicates the indoor temperature change predicted by the system, which is used to adjust the air conditioning working mode; T ext (t): external temperature, reflecting the temperature change of the outdoor environment; H ext (t): external humidity, which affects the cooling or heating effect of the air conditioner; SolarRadiation(t): sunlight intensity, which determines the impact of sunlight on the room temperature; α1, α2, β: response coefficients to changes in the regional external environment, which can be adjusted according to specific circumstances and determine the degree of influence of external environmental parameters on indoor temperature changes.

[0042] Furthermore, the multi-dimensional energy-saving optimization algorithm includes:

[0043] By monitoring the usage frequency (U) and load condition (L i ), so that the equipment can dynamically adjust its working state. The calculation formula for air-conditioning equipment load balancing is as follows:

[0044]

[0045] Among them, LoadFactor i represents the load factor of the i-th air conditioner, indicating the load proportion of the equipment; L i is the current load of the i-th air conditioner, which is determined by the power consumption and cooling or heating intensity parameters of the air conditioner; U is the usage frequency of the i-th air conditioner, which reflects the working condition of the air conditioner and affects the load distribution; Threshold i is the usage frequency threshold of the i-th air conditioner, which is used to determine whether the device enters energy-saving mode; N is the total number of air conditioners, which is used to calculate the overall load balance.

[0046] Furthermore, the multi-dimensional energy-saving optimization algorithm includes:

[0047] Energy usage is optimized through intelligent time allocation and device priority control. Regional comfort is ensured based on energy demand during different time periods, including weekdays, weekends, daytime, and nighttime. Devices are prioritized based on their energy efficiency and frequency of use. The calculation method for time period and device priority optimization is as follows:

[0048]

[0049] Among them, E opt It represents the optimized energy consumption of the entire system in a specific time period, minimizing the total energy consumption by adjusting the device priority and operation mode; i (t) represents the power demand of the i-th air conditioner at a certain time t, which can be dynamically adjusted according to factors such as ambient temperature and set temperature; η i is the energy efficiency coefficient of the i-th air conditioner, which indicates the energy efficiency performance of the equipment in actual operation. A higher η value means that the equipment is more energy-efficient; f i (t) is the priority function of the i-th air conditioner, which determines the operation priority of the equipment according to factors such as time period, regional demand and equipment status.

[0050] The air-conditioning IoT control method of the present invention has significant beneficial effects through a multi-dimensional energy-saving optimization algorithm combined with intelligent control and real-time data feedback, which are mainly reflected in the following aspects:

[0051] 1. Improve energy efficiency: By introducing information from multiple dimensions, such as external environmental data, indoor temperature differences, and equipment usage frequency, the present invention can monitor and dynamically adjust the working status of air-conditioning equipment in real time, ensuring that the air-conditioning operates in the optimal energy efficiency range and avoiding energy waste.

[0052] 2. Optimize air conditioner load distribution: This method intelligently adjusts the operating status of each air conditioner by calculating its load factor, taking into account its frequency of use, current load, and energy efficiency coefficient. The system dynamically adjusts the load based on the load of each air conditioner, ensuring optimal load distribution. This prevents some air conditioners from overworking while others remain idle, thereby reducing equipment overload, extending equipment life, and reducing maintenance costs.

[0053] 3. Improved Comfort and a Healthy Environment: This IoT-based air conditioning control approach not only focuses on energy optimization but also dynamically adjusts cooling or heating intensity based on the temperature requirements of different areas within the room. In areas near windows, exposed to direct sunlight, and near interior walls, where temperatures are lower, the system automatically adjusts the air conditioning mode to ensure that each area remains within a comfortable temperature range, enhancing user comfort. This approach, by precisely controlling and predicting future temperature changes, avoids indoor temperature fluctuations caused by delayed response times in the air conditioning system, further improving comfort.

[0054] 4. System Adaptation and Intelligent Optimization: This system dynamically adjusts the operating status, priority, and energy efficiency of air conditioners based on real-time data and changes in the external environment, making it adaptive. By analyzing energy demand across time periods, weekdays and weekends, and daytime and nighttime, the system rationally assigns device priorities and operating modes, ensuring efficient operation of each air conditioner in diverse environments and improving overall system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the air-conditioning IoT control method of the present invention.

[0056] Figure 2 This is a flow chart of the intelligent collaborative control method based on IoT data of the present invention.

[0057] Figure 3 This is a flow chart of the multi-dimensional energy-saving optimization algorithm of the present invention. DETAILED DESCRIPTION

[0058] The following is a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.

[0059] Combined with attachment Figure 1The core of the air conditioner IoT control method lies in the intelligent interconnection of air conditioners through IoT technology, achieving more efficient energy management and comfort control. First, the key to step S1 lies in the intelligent IoT interconnection of air conditioners. Each air conditioner is integrated with an IoT module. This module connects to a cloud platform via smart sensors, collecting and transmitting real-time room environmental data, including temperature, humidity, and air quality. This data is transmitted to the cloud platform via a wireless network in real time. The cloud platform aggregates and processes this information to form a centralized device status and environmental information management system. Each air conditioner maintains continuous two-way communication with the system through the IoT module, allowing the operating status and environmental information of the air conditioner to be dynamically updated and adjusted. The air conditioner provides real-time feedback to the cloud platform on its operating status, including operating mode, set temperature, and cooling / heating mode, ensuring that the cloud platform receives accurate device data. Furthermore, the cloud platform can adjust the air conditioner based on environmental data. For example, if the temperature in a room is too high, the cloud platform can control the air conditioner to adjust the temperature within the set range, achieving precise control and avoiding energy waste from excessive cooling or heating.

[0060] Step S2 focuses on intelligent collaborative control among air conditioning units through IoT technology. The goal is to optimize the overall efficiency and comfort of the air conditioning system through data sharing and collaboration between devices. In S2.1, the IoT interconnection between air conditioning units is first used to build an intelligent collaborative control algorithm based on inter-device communication. Specifically, air conditioning units not only adjust based on their own room's environmental data but also share data with other devices through IoT modules. This data includes, but is not limited to, room temperature, humidity, air quality, and occupant activity. Through two-way communication between devices, air conditioning units gain real-time visibility into the operating status of other units and changes in the surrounding environment, enabling them to collaborate based on this information. At the algorithmic level, air conditioning units dynamically adjust their operating modes based on real-time temperature and humidity data and external environmental changes (such as weather and sunlight intensity), in conjunction with their respective operating needs. This intelligent collaborative control not only improves air conditioning accuracy and comfort, but also avoids duplicate cooling or heating by multiple units in different rooms or areas, thereby optimizing energy consumption and improving overall system efficiency. In S2.2, each air conditioner adjusts its operating mode based on data from multiple sensors within the room (such as temperature, humidity, and CO2 concentration), combined with information about changes in the surrounding environment, such as weather forecasts and changes in sunlight intensity. For example, if the room where the air conditioner is located receives weather change data predicting a future temperature increase, the air conditioner can switch to cooling mode in advance to prevent excessive energy consumption caused by high indoor temperatures. In winter or when the weather turns cold, the air conditioner can switch to heating mode to avoid unnecessary energy consumption caused by changes in ambient temperature. Through intelligent control algorithms and data sharing, each air conditioner not only adjusts according to its own environmental data, but also makes corresponding adjustments based on the status of surrounding air conditioners and changes in the external environment, enabling coordinated operation between air conditioners, thereby optimizing the overall performance of the air conditioning system and achieving more energy-efficient and comfortable results.

[0061] Step S3 focuses on intelligent environmental prediction. Its core is to use local sensors to monitor real-time environmental data. Combined with external environmental information, intelligent algorithms are used to predict future environmental changes, enabling proactive adjustments to the air conditioner's operating mode. First, the air conditioner uses built-in sensors, such as temperature, humidity, and CO2 concentration sensors, to monitor the room's environmental data in real time. This data is transmitted to a cloud platform or the device's local control system in real time, serving as the basis for adjusting the air conditioner's operating mode. Simultaneously, the air conditioner also accesses external environmental data, including weather forecasts and climate change trends, to further enhance its ability to perceive environmental changes. Weather forecasts provide short-term (e.g., a few hours to a day) data on temperature, humidity, and wind speed, while climate change trends help the system analyze long-term environmental changes, such as seasonal temperature fluctuations and climate anomalies. This external data is transmitted to the air conditioner system via the IoT module, providing a reference for air conditioning decision-making. Combining this real-time environmental data with external predictions, the air conditioner uses intelligent self-learning capabilities to dynamically learn and adapt based on historical data and user habits. Self-learning algorithms can analyze user demand patterns across different time periods and environmental conditions. For example, users typically adjust the air conditioner to a certain temperature upon waking in the morning or require increased cooling intensity in the evening. By analyzing these behavioral patterns, the air conditioner can anticipate user needs and proactively adjust its operating mode before they arise. For example, if the weather forecast indicates a sharp rise in temperature within the next few hours, the air conditioner system can start up in advance and switch to cooling mode to prevent overheating and avoid energy waste caused by premature activation. Similarly, if the external environment indicates an impending drop in temperature, the air conditioner can switch to energy-efficient heating mode to avoid excessive energy consumption. This intelligent environmental prediction allows the air conditioner to react and prepare in advance, improving user comfort and energy efficiency.

[0062] Step S4 primarily utilizes a multi-dimensional energy-saving optimization algorithm to maximize the energy efficiency of the air conditioning system through intelligent control and data analysis. In S4.1, the multi-dimensional energy-saving optimization algorithm firstly implements intelligent control of the air conditioning operating mode. The core purpose is to precisely adjust the cooling or heating intensity based on the temperature requirements of different zones within the room. Specifically, within a single room, temperature requirements vary significantly across different zones. For example, areas near windows experience higher temperatures due to direct sunlight, while areas further away from the windows experience lower temperatures. Traditional air conditioning systems typically only regulate the temperature of the entire room, resulting in over-cooling or over-heating in certain areas and wasting energy. By incorporating a multi-dimensional energy-saving optimization algorithm, the air conditioning system can precisely adjust the cooling or heating intensity based on the temperature requirements and environmental changes of each zone, achieving more efficient energy utilization and a comfortable indoor environment. Based on real-time sensor data and the distribution of heat loads across different zones, the algorithm dynamically adjusts the air conditioning operating mode to avoid energy waste and ensure temperature balance across all zones. Next, in S4.2, the algorithm incorporates information such as external weather changes and device usage frequency to further optimize overall energy consumption. External weather changes (such as temperature, humidity, and sunlight intensity) directly impact air conditioning demand. For example, rising temperatures increase cooling demand, while falling temperatures increase heating demand. By combining this weather data with the frequency of air conditioning unit usage, the system can adjust air conditioning operation based on weather forecasts, avoiding premature startup or excessive operation, and optimizing overall energy consumption. Furthermore, optimization strategies include time allocation and device priority control. Specifically, the air conditioning system allocates energy appropriately based on demand during different time periods. For example, during daytime working hours, the system prioritizes temperature control in offices and high-traffic areas, while adjusting to actual user needs at night or during off-peak hours to avoid unnecessary energy consumption. Furthermore, device priority control dynamically adjusts the operating status of air conditioning units based on factors such as their energy consumption capacity, efficiency, and load. The system prioritizes the operation of high-efficiency units, while reducing the load on less efficient units or even shutting them down when not needed, ensuring maximum overall energy efficiency for the air conditioning system.

[0063] Step S5 focuses on personalized user control based on big data analytics. It aims to provide a more intelligent and personalized air conditioning control experience by collecting and analyzing user behavior and environmental data. In S5.1, air conditioning equipment uses integrated sensors and IoT technology to collect real-time environmental data, including indoor temperature, humidity, and CO2 concentration, as well as user behavior data such as temperature adjustment preferences, usage frequency, and air conditioning on / off times. This data is uploaded to the cloud via the IoT platform, forming a data pool. Leveraging big data analytics and machine learning models, the air conditioning system can conduct in-depth analysis of this data to extract user habits and comfort preferences. For example, a user may prefer to set the air conditioning temperature to a specific value between 10 PM and 12 AM, or may set the air conditioning temperature lower during the day and higher at night. Using this information, the system can predict future user needs and optimize the air conditioning's operating mode. By analyzing user feedback and preferences at different times and in different environments, the machine learning model continuously adjusts the prediction algorithm, improving prediction accuracy and the effectiveness of personalized recommendations. In S5.2, the air conditioning system adjusts based on predicted user behavior patterns and dynamically optimizes its control strategy. Specifically, the system intelligently adjusts the air conditioner's operating strategy based on the user's sleep schedule, indoor activities, and other information. For example, if the system detects through data analysis that a user wakes up at 6:00 AM every day and turns on the air conditioner, it will adjust the air conditioner's preset temperature in advance to provide a comfortable environment when the user arrives. If the user is resting, the air conditioner can reduce unnecessary cooling or heating based on the user's sleep pattern, ensuring minimal energy consumption and maintaining a comfortable sleeping temperature. If the system detects that the user will be away from home for an extended period, the air conditioner can be adjusted to energy-saving mode or turned off to avoid unnecessary energy waste. Through this personalized control, the air conditioner not only adapts to the user's needs but also self-learns based on the user's behavioral patterns, continuously improving service quality and efficiency.

[0064] Example 1:

[0065] Combined with attachment Figure 2 In this example, multiple air conditioners are installed in an office building. Each unit is equipped with an IoT module, enabling data sharing and collaboration with other devices via Wi-Fi. During operation, each unit adjusts its operating mode in real time based on the temperature, humidity, and CO2 concentration in each room, as well as environmental data shared by other units. This ensures the most efficient, comfortable, and energy-efficient temperature regulation for the entire building.

[0066] 1. IoT module and data sharing

[0067] The office building has five air conditioners, four of which are located in employee work areas and one in the public lounge area. Each air conditioner connects to other devices and a cloud platform via a Wi-Fi module, collecting real-time environmental data such as temperature, humidity, and CO2 concentration, and sharing this data with other air conditioners. For example, if the temperature in a room with one air conditioner is 22°C and the temperature in a room with another air conditioner is 24°C, the air conditioner will upload this data to the IoT platform and share this information with other devices. Using IoT communication protocols such as Wi-Fi or ZigBee, the air conditioners can work together and dynamically adjust their operating modes.

[0068] 2. Temperature regulation formula

[0069] The temperature regulation of each air conditioning unit is controlled by the following formula:

[0070]

[0071] T(t): Target temperature setting of the air conditioning equipment at time t.

[0072] T room (t): The current temperature of the room where the air conditioner is located, indicating the device's perception of the room environment.

[0073] α: Control coefficient, which indicates the device's sensitivity to changes in ambient temperature. The value range is 0.1≤α≤1.0. The specific value is set according to the type and function of the device.

[0074] T env (i): The ambient temperature of the i-th device, which refers to the ambient temperature of the room where other air conditioners are located.

[0075] n: The number of air-conditioning devices that share information, indicating the amount of data shared among all air-conditioning devices.

[0076] 3. Application: Control strategy and calculation results

[0077] Assume that there are 5 air conditioners in the office, and the current ambient temperatures of the rooms are:

[0078] Air conditioner 1 (Staff Room A): T room (1)=22℃

[0079] Air Conditioner 2 (Staff Room B): T room (2)=23℃

[0080] Air Conditioner 3 (Staff Room C): T room (3)=24℃

[0081] Air conditioner 4 (Staff Room D): T room (4)=25℃

[0082] Air Conditioner 5 (rest area): T room (5)=22℃

[0083] At this time, the ambient temperature data in the shared information are set as follows:

[0084] T env (1)=22℃

[0085] T env (2)=23℃

[0086] T env (3)=24℃

[0087] T env (4)=25℃

[0088] T env (5)=22℃

[0089] And set the control coefficient α = 0.5, that is, the device's response sensitivity to ambient temperature changes is moderate. Using the above temperature adjustment formula, the target temperature setting for each air conditioner can be calculated:

[0090] For Air Conditioner 1:

[0091] T(1)=22+0.5·((22+23+24+25+22)-22)

[0092] T(1)=22+0.5·(116-22)=22+0.5·94=22+47=69℃

[0093] Obviously, the result of 69°C is an extreme value in the calculation process, indicating that a more reasonable limit or feedback mechanism is needed to control the temperature change. In actual operation, this value can be adjusted to a temperature that better meets actual usage needs, setting the upper temperature limit to 28°C.

[0094] For Air Conditioner 2:

[0095] T(2)=23+0.5·((22+23+24+25+22)-23)

[0096] T(2)=23+0.5·(116-23)=23+0.5·93=23+46.5=69.5℃

[0097] Similarly, the calculation results of air conditioner 2 also show that further control strategies are needed to limit the temperature range.

[0098] In this way, air conditioners dynamically adjust their operating modes based on environmental data and the status of other devices, creating an intelligent collaborative operation. This not only optimizes the temperature control needs of each room, but also reduces energy waste and ensures comfort.

[0099] The innovation of this solution lies in the interconnection of the Internet of Things between air-conditioning devices, which enables each air-conditioner to obtain the temperature information of the environment where other devices are located and adjust its own working mode in real time based on this information.

[0100] Consider an office building with five air conditioners. Each unit is equipped with an IoT module that collects real-time data on ambient temperature, humidity, and sunlight intensity. This data comes not only from the unit's internal sensors but also from weather information obtained through open APIs or external embedded sensors. This external data is transmitted to a cloud platform for real-time processing and analysis. The air conditioners can then adjust their operating modes based on this environmental data and predictions, thereby improving building comfort and energy efficiency.

[0101] 1. Air conditioning equipment integrates external environment data

[0102] Air conditioners integrate weather, sunlight intensity, and outdoor temperature and humidity information to sense the current environmental conditions and make appropriate predictions. Specifically, data on weather changes, sunlight intensity, and outdoor temperature and humidity are acquired through sensors or transmitted to the air conditioners in real time via open APIs (e.g., data from weather service providers). The air conditioners use this information to adjust their temperature control strategies to ensure a comfortable indoor temperature and optimal energy efficiency.

[0103] 2. External ambient temperature prediction formula

[0104] Air conditioning equipment uses historical climate data to predict future environmental changes and respond to future temperature fluctuations. The external temperature prediction formula is:

[0105]

[0106] in:

[0107] T ext (t) represents the predicted value of the external temperature at time t, that is, the expected temperature of the external environment.

[0108] f weather (x) is the weather change function, which represents the change of meteorological data within time x. It is usually provided by the weather forecast system and is modeled based on the rate of change of temperature.

[0109] f sunlight(x) is the sunshine intensity function, which represents the change of sunshine intensity within time x, usually provided by sensors or sunshine forecast systems.

[0110] It integrates the external environment data from time 0 to time t and calculates the cumulative impact.

[0111] 3. Sample Application and Data Display

[0112] Assuming today is a sunny weekday, the weather forecast shows that the outside temperature will gradually rise and the sunshine intensity will gradually increase in the next 6 hours. The specific meteorological data is as follows:

[0113] At 6 a.m., the outside temperature is 18°C, and the weather change rate is 0.5°C / hour.

[0114] The sunlight intensity gradually increases from 6 am to 200W / m at 8 am. 2 , gradually increased to 800W / m 2 .

[0115] Using the above external temperature prediction formula, the air conditioning equipment can predict future temperature changes based on the temperature changes and sunlight intensity of the external environment.

[0116] First, the weather change function f weather (x) and the solar radiation intensity function f sunlight The data of (x) is modeled by hourly interpolation. It is assumed that between 6:00 and 12:00, the temperature gradually rises from 18°C ​​to 24°C, and the sunshine intensity also gradually increases.

[0117] Set the weather change function to: f weather (x) = 0.5x, that is, the temperature changes by 0.5°C per hour.

[0118] Sunlight intensity variation function: f sunlight (x) = 0.6x, where x is the number of hours, indicating that the sunshine intensity gradually increases from 0 W / m2 starting from 6 o'clock.

[0119] According to this model, the predicted value of external temperature T is calculated. ext (t):

[0120]

[0121] For the time period 0≤t≤6 (from 6am to 12pm):

[0122]

[0123] Therefore, after 6 hours, the outside temperature is predicted to be 19.8°C.

[0124] This predicted value provides the air conditioning equipment with reference data of the future environment, and the air conditioning equipment will pre-adjust the indoor temperature based on this data.

[0125] 4. Intelligent collaborative control of air conditioning equipment

[0126] Assume that there are 5 air conditioners in an office building, and their control modes are as follows:

[0127] Air Conditioner 1: Located in Employee A's room. The current temperature is 22°C.

[0128] Air Conditioner 2: Located in employee B's room. The current temperature is 23°C.

[0129] Air Conditioner 3: Located in employee room C, the current temperature is 24°C.

[0130] Air conditioner 4: Located in employee room D, the current temperature is 25℃.

[0131] Air conditioner 5: Located in the public lounge area, the current temperature is 22℃.

[0132] Each air conditioner will adjust its operating mode based on the shared data and the predicted outside temperature. For example, air conditioner 1 will calculate its target temperature based on the ambient data of other air conditioners and the predicted outside temperature. The target temperature formula for air conditioner 1 is:

[0133]

[0134] in:

[0135] T room (1) is the current temperature of the room where air conditioner 1 is located (22°C).

[0136] T env (i) is the ambient temperature of other air-conditioning equipment.

[0137] α=0.5 is the control coefficient, which indicates the response sensitivity of the device to changes in ambient temperature.

[0138] When calculating the actual value of air conditioner 1, the following results will be obtained:

[0139] T1(t)=22+0.5·((22+23+24+25+22)-22)

[0140] T1(t)=22+0.5·(116-22)=22+0.5·94=22+47=69℃

[0141] Clearly, this calculation result is too extreme, indicating that the air conditioning system needs further limiting mechanisms. For example, the target temperature could be limited to a reasonable range (such as a maximum temperature of 28°C) to ensure that excessive reliance on external data in the forecast does not lead to unrealistic temperature control requirements.

[0142] The scenario involves implementing an intelligent collaborative air conditioning control system based on IoT data in a commercial building. The building houses multiple air conditioners, each equipped with embedded machine learning algorithms that analyze environmental data in real time and optimize control based on interactions between units. Each unit also analyzes user patterns, including homecoming and bedtimes, to create personalized models based on historical and real-time data to adjust temperature and air speed.

[0143] Suppose an office is equipped with five air conditioners, located in different offices and public areas. Each air conditioner uses sensors to collect real-time environmental data, including room temperature, humidity, and air quality. In addition, they can also share this data with other air conditioners to form a collaborative network.

[0144] Each air conditioner's embedded machine learning algorithm learns from the user's historical behavior (such as home time, work time, and bedtime) and real-time data (such as current temperature, humidity, and external environmental data) to build a personalized temperature control model. This model helps the air conditioner predict future needs based on the user's behavioral patterns and adjust the indoor temperature and air speed accordingly. For example, if the system finds that a user typically returns home around 5 pm and enters "comfort" mode (temperature set to 22°C, air speed set to low), the air conditioner will begin adjusting the temperature and air speed at 4 pm to ensure that the indoor environment is ideal when the user returns home.

[0145] To further optimize energy efficiency, intelligent collaborative control among air-conditioning devices is expressed through an energy efficiency optimization function. The specific formula is as follows:

[0146]

[0147] in:

[0148] E(t) represents the overall energy efficiency of the air-conditioning system at time t, which is the comprehensive evaluation value of the energy efficiency of all air-conditioning equipment.

[0149] C i (t) is the power consumption of the i-th air conditioner at time t, representing the real-time energy consumption of the device. Power consumption is typically related to the device's operating mode (such as cooling / heating mode, wind speed, etc.) and environmental loads (such as outdoor temperature, room size, and device efficiency).

[0150] ΔT i (t) is the temperature change of the i-th air conditioner at time t, which represents the temperature change after the device is adjusted. The temperature change is the adjustment target set by the air conditioner based on sensor data and external environment predictions.

[0151] α i is the energy efficiency coefficient of the i-th device, representing the relationship between its energy consumption and temperature regulation. The device's energy efficiency coefficient is adjusted based on factors such as device type (e.g., combined air conditioner and heater, standalone air conditioner), environmental load, and frequency of use, and ranges from 1 to 5.

[0152] On a certain day, the real-time data of the air conditioning equipment in the office building is as follows:

[0153] Air conditioner 1 (Office A): The current temperature is 24°C, the target temperature is 22°C (temperature change is 2°C), the power consumption is 300W, and the energy efficiency coefficient α1=3.

[0154] Air conditioner 2 (Office B): The current temperature is 23°C, the target temperature is 22°C (temperature change is 1°C), the power consumption is 250W, and the energy efficiency coefficient α2 = 2.5.

[0155] Air conditioner 3 (Office C): Current temperature is 25°C, target temperature is 22°C (temperature change is 3°C), power consumption is 350W, and energy efficiency coefficient α3=4.

[0156] Air conditioner 4 (rest area): the current temperature is 26°C, the target temperature is 22°C (temperature change is 4°C), the power consumption is 400W, and the energy efficiency coefficient α4=5.

[0157] Air conditioner 5 (conference room): current temperature is 22°C, target temperature is 22°C (temperature change is 0°C), power consumption is 200W, and energy efficiency coefficient α5=2.

[0158] The energy efficiency optimization function of the air conditioning system is calculated as follows:

[0159]

[0160] E(t)=200+100+262.5+320=882.5

[0161] Therefore, the overall energy efficiency of the air conditioning system at time t is 882.5.

[0162] Air conditioners not only adjust the temperature based on real-time environmental data but also analyze the user's lifestyle patterns through embedded machine learning algorithms. For example, Air Conditioner 1 is located in Employee A's office. Employee A enters the office around 7:00 AM and leaves at 6:00 AM every day. If the machine learning algorithm identifies this pattern in historical data, the air conditioner will pre-cool the room to 22°C before 6:00 AM and switch to energy-saving mode after the employee leaves. Therefore, the temperature change ΔT1(t) of Air Conditioner 1 will be influenced by the employee's lifestyle, achieving the goal of energy conservation.

[0163] If user behavior changes, for example, employee A starts coming into the office earlier or later, the machine learning algorithm of Air Conditioner 1 will relearn based on the new data and adjust the temperature control strategy to ensure optimal comfort and energy efficiency at all times.

[0164] This solution optimizes the coordinated operation of air conditioning units through machine learning and intelligent algorithms. It also uses energy efficiency optimization functions to adjust the air conditioner's temperature, air speed, and energy efficiency across multiple dimensions. The machine learning algorithm not only adjusts the air conditioner's operating status in real time but also makes personalized adjustments based on user behavior and changes in the external environment. By leveraging a rational energy efficiency optimization function, the system minimizes energy waste and achieves efficient energy utilization.

[0165] Example 2:

[0166] See Figure 3 Flowchart, in this case, the "multi-dimensional energy-saving optimization algorithm" of the air conditioning IoT control method is explained in detail. This method achieves energy saving by dynamically adjusting the cooling or heating intensity of the air conditioner according to the temperature requirements of different areas in the room. In particular, the air conditioning system adjusts the temperature difference (△T i ) to adjust the working mode to optimize energy efficiency.

[0167] Consider a modern office building with a smart air conditioning system installed. The building consists of multiple rooms and zones, each with varying temperature requirements. For example, areas near windows receive strong solar radiation and therefore experience higher temperatures in the summer, while areas near interior walls are less exposed to the outside environment and experience lower temperatures. To ensure comfort and conserve energy, the air conditioning system must adapt to these temperature differences, requiring precise control of areas with strong solar radiation and cooler temperatures.

[0168] In order to achieve this precise regulation, the air conditioning equipment uses the following formula to calculate the temperature difference (△T i ):

[0169]

[0170] in:

[0171] ΔT k is the temperature difference of the kth region, which represents the difference between the region and the external environment temperature;

[0172] T k is the actual temperature of the kth area, which is detected in real time by the temperature sensor of the air-conditioning equipment;

[0173] T ext is the external ambient temperature, usually obtained through weather APIs or environmental monitoring equipment;

[0174] λ k is the heat transfer coefficient of the kth region, which indicates the heat exchange efficiency between this region and the external environment;

[0175] t is time, which represents the duration of the calculation in the formula, usually in minutes or hours.

[0176] The core of this formula is to calculate the temperature change within a region based on the region's thermal conductivity and the changes in the external ambient temperature. This calculation process dynamically reflects the relationship between the temperature within the region and the external environment, thereby determining whether the air conditioning equipment needs to increase cooling or heating intensity.

[0177] In a conference room in an office building, the air conditioning system uses sensors and an API to obtain real-time environmental data. The conference room is divided into two areas: the window area and the inner wall area. To better understand the practical application of this algorithm, the following data is set:

[0178] The external ambient temperature T=30° C., which is the real-time external temperature obtained through the weather API.

[0179] Window area temperature T 窗 =28℃, which is the actual temperature in the window area of ​​the conference room.

[0180] Inner wall area temperature T 墙 =25℃, which is the actual temperature of the wall area inside the conference room.

[0181] Thermal conductivity λ 窗 =0.2 (thermal conductivity coefficient of window area), λ 墙 =0.1 (heat conduction coefficient of inner wall area). These coefficients represent the heat exchange efficiency between each area and the external environment. The value range is generally 0.1 to 0.5. The specific value depends on the material properties and building design.

[0182] Time t = 1 hour (set the air conditioning equipment to recalculate the temperature difference and adjust the cooling / heating mode every hour).

[0183] First, calculate the temperature difference (△T) between the two areas using the formula i ):

[0184] Window area:

[0185]

[0186] ΔT 窗 =28-30·(1-e -0.21 )

[0187] ΔT 窗 =28-30·(1-e -0.2)

[0188] ΔT 窗 ≈28-30·(1-0.8187)=28-30·0.1813≈28-5.44=22.56℃

[0189] Therefore, the temperature difference in the window area is 22.56℃.

[0190] Interior wall area:

[0191]

[0192] ΔT 墙 =25-30·(1-e -0.1·1 )

[0193] ΔT 墙 =25-30·(1-e -0.1 )

[0194] ΔT 墙 ≈25-30·(1-0.9048)=25-30·0.0952≈25-2.86=22.14℃

[0195] Therefore, the temperature difference in the inner wall area is 22.14℃.

[0196] Dynamically adjust air conditioning mode

[0197] Based on the above calculations, the air conditioning system can dynamically adjust its operating mode based on the temperature difference between different areas. Since the temperature difference in the window area is slightly larger (22.56°C vs. 22.14°C), this area requires stronger cooling. However, the temperature difference in the interior wall area is smaller, so energy consumption can be reduced by adjusting the air conditioning speed and temperature slightly. Therefore, the air conditioning system can adopt the following strategies:

[0198] Window area: Stronger cooling is required, and the air conditioning equipment will increase the cooling power, reduce the temperature difference, and ensure a comfortable indoor environment.

[0199] Inner wall area: You can reduce the cooling intensity, or even switch some air-conditioning equipment to wind speed adjustment mode instead of forced cooling to reduce energy consumption.

[0200] By dynamically calculating the temperature differences between different areas of a room and adjusting the cooling or heating mode of the air conditioning equipment accordingly, the air conditioning system can intelligently adjust to the needs of different areas, significantly improving energy efficiency. For example, in a conference room, the window area is more affected by solar radiation and requires more cooling, while the interior wall area can meet comfort needs through air speed adjustment, thereby reducing the overall energy consumption of the air conditioning system.

[0201] Consider a modern office building with a smart air conditioning system. The building has multiple zones, each with different temperature requirements. For example, areas near windows experience higher temperatures in the summer due to stronger sunlight, while areas further away from windows experience lower temperatures. To ensure a comfortable environment in each zone and maximize energy savings, the air conditioning system needs to be precisely adjusted to meet the needs of each zone. External environmental data (such as temperature, humidity, and sunlight intensity) is used to predict indoor temperature changes, allowing the air conditioning operating mode to be adjusted in advance.

[0202] The air conditioning system predicts the change in indoor temperature using the following formula:

[0203] ΔT pred =α1T ext (t)+α2H ext (t)-β·SolarRadiation(t)

[0204] in:

[0205] ΔT pred is the predicted indoor temperature change;

[0206] Text(t) is the external temperature, reflecting the temperature change of the external environment;

[0207] H ext (t) is the external humidity, which affects the cooling or heating effect of the air conditioner;

[0208] SolarRadiation(t) is the sunlight intensity, which determines the effect of sunlight on the room temperature;

[0209] α1, α2, and p are adjustment coefficients related to the external environment, and these coefficients can be adjusted according to specific regions or environments.

[0210] Assume that an air conditioning system is installed in a conference room. The conference room is divided into two areas: the window area and the inner wall area. Each area is affected differently by the external environment. For ease of calculation, the following data is set:

[0211] External temperature T ext =30℃;

[0212] External humidity H ext =60%;

[0213] Solar Radiation = 800W / m 2 ;

[0214] The adjustment coefficients of the air-conditioning system are: α1=0.6, α2=0.3, β=0.1.

[0215] To calculate indoor temperature changes using the above formula, first input the external environment data and make a prediction based on the adjustment coefficient.

[0216] △T pred =0.6×30+0.3×60-0.1×800

[0217] The calculation process is as follows:

[0218] ΔT pred =18+18-80=-44℃

[0219] This result indicates that, based on external environmental conditions, the indoor temperature is expected to drop by 44°C (this is an idealized model, and more complex corrections will be made in actual applications). Based on this prediction, the air conditioning system can predict environmental changes in advance and adjust its operating mode (such as increasing heating or cooling intensity).

[0220] Based on the prediction results, the air conditioning system can take the following measures:

[0221] 1. Areas with large temperature differences (window areas): Due to strong solar radiation, the temperature changes in window areas are more significant. Therefore, the air conditioning system will increase the cooling capacity of this area to cope with the rapid rise in indoor temperature.

[0222] 2. Areas with smaller temperature differences (inner wall areas): The inner wall areas are less affected by the external environment, and the air conditioner can reduce its cooling intensity or even adjust the air flow mode to reduce energy consumption.

[0223] For example, let's assume that the air conditioning system calculates a temperature difference of 4°C in the window area, while in the inner wall area, the temperature difference is 2°C. Based on these results, the air conditioning system will adopt the following strategies:

[0224] Window areas: Air conditioning equipment will increase cooling power to ensure that the room temperature is maintained within the preset range to avoid overheating.

[0225] Interior wall areas: Air conditioning equipment will reduce cooling effort to maintain a comfortable temperature and adjust fan speed appropriately to save energy.

[0226] By implementing this multi-dimensional energy-saving optimization algorithm, the air conditioning system can predict external environmental changes in real time and dynamically adjust its operating mode. Compared to traditional air conditioning control methods, this approach can effectively reduce energy consumption and improve energy efficiency. For example, in a conference room, the window area is more affected by solar radiation, so the air conditioning equipment needs to provide more cooling air. However, in the interior wall area, where the temperature difference is smaller, the air conditioning system slightly adjusts the air speed and temperature to maintain a comfortable environment. In this way, the air conditioning system ensures comfort while achieving significant energy savings.

[0227] The load balancing algorithm of air conditioning equipment monitors the usage frequency of air conditioners (U i ) and load (L i ) to dynamically adjust the equipment's operating status, achieving more efficient energy-saving control. Through load balancing algorithms, air conditioning units can dynamically adjust their operating modes based on their usage and load conditions, ensuring optimal energy consumption across the entire air conditioning system.

[0228] Consider an office building with multiple air conditioning units installed to maintain a comfortable temperature throughout the building. Each unit's usage frequency and load profile vary; some units are frequently used during peak hours, while others remain in standby mode for extended periods. To optimize energy usage and load distribution, the air conditioning system needs to dynamically adjust its operating state based on the unit's usage frequency and load profile, ensuring a more balanced load distribution and ultimately achieving energy savings.

[0229] The load balancing of the air conditioning equipment is calculated using the following formula:

[0230]

[0231] in:

[0232] LoadFactor i represents the load factor of the i-th air conditioner, indicating the load proportion of the equipment;

[0233] L i is the current load of the i-th air conditioner, which is usually determined by parameters such as the air conditioner's power consumption, cooling or heating intensity, etc.

[0234] U i is the usage frequency of the i-th air conditioner, which indicates the operating frequency of the air-conditioning equipment and reflects the workload of the air conditioner;

[0235] Thresholdi is the usage frequency threshold of each air conditioner, which is used to determine whether the device enters energy-saving mode;

[0236] N is the total number of air conditioning equipment, which is used to calculate the total load of all equipment.

[0237] Assume that there are five air conditioners in an office building, numbered Air Conditioner 1 to Air Conditioner 5. The following is the usage frequency and load data of each air conditioner:

[0238]

[0239] Based on this data, the air conditioning system needs to dynamically adjust the operating mode of each device according to load balancing. First, the load factor of each air conditioner is calculated.

[0240] According to the formula:

[0241]

[0242] First, calculate the total load of the air conditioning system

[0243]

[0244] Then, calculate the load factor of each air conditioner:

[0245] Air Conditioner 1:

[0246] Air Conditioner 2:

[0247] Air Conditioner 3:

[0248] Air Conditioner 4:

[0249] Air Conditioner 5:

[0250] Based on the calculation results, the load factor for each air conditioner is determined. A higher load factor indicates a greater share of the load for that air conditioner, requiring adjustments to its operating mode to avoid overload and achieve energy savings. For example, air conditioner 3 has the highest load factor. The air conditioning system adjusts its operating mode to reduce its load and avoid energy waste.

[0251] In addition, the air conditioning system will adjust the frequency of use of each device (U i ) and load conditions, dynamically adjust the working state of the equipment. If the frequency of use of an air conditioner is U i Lower than the set usage frequency threshold Threshold i , the device can enter energy-saving mode, reduce its working intensity, and thus reduce energy consumption.

[0252] For example:

[0253] Air conditioner 1 is used 8 times per day, which is higher than its threshold of 6 times per day, so it will continue to operate in normal mode.

[0254] Air conditioner 2 is used 6 times per day, slightly higher than its threshold of 5 times per day. The air conditioning system will set it to a slightly lower cooling / heating intensity to reduce unnecessary energy consumption.

[0255] The usage frequency of air conditioner 5 is 3 times / day, which is lower than its threshold of 3 times / day. The system will consider adjusting it to energy-saving mode to reduce energy waste.

[0256] In practice, the air conditioning system's load balancing algorithm can help reduce unnecessary load distribution. By monitoring the usage frequency and load of each air conditioner in real time, the system can dynamically adjust according to different usage scenarios. If certain air conditioners are idle for extended periods, the system will switch them to energy-saving mode to avoid energy waste. Furthermore, load balancing ensures that each air conditioner's operating load is not excessive, thereby extending the equipment's lifespan and reducing maintenance costs.

[0257] By incorporating a load balancing algorithm, the air conditioning system dynamically adjusts its operating mode based on the frequency of use and load of each device, ensuring more efficient operation. In this case, the air conditioning system successfully switched the heavily loaded devices to energy-saving mode, reducing energy consumption while ensuring overall temperature control. By adjusting the load factor, the air conditioning system achieves energy savings and cost optimization while ensuring a comfortable indoor environment.

[0258] This control method is set for an office building containing multiple air-conditioning equipment. Each air-conditioning unit is intelligently adjusted according to different time periods, weekdays and weekends, and daytime and nighttime needs to minimize energy consumption and ensure regional comfort.

[0259] Assume that an office building has 10 air conditioners installed. The goal is to optimize the operation of each unit through intelligent control to achieve energy conservation and high efficiency. Air conditioner usage requirements vary at different times of day. For example, during daytime office hours, the air conditioner needs to maintain a lower temperature, while the set temperature can be increased at night. To optimize energy usage, the system needs to dynamically prioritize each air conditioner based on the varying demands of weekdays, weekends, and daytime and nighttime.

[0260] In this system, the goal of overall optimization of energy consumption is achieved through the following formula:

[0261]

[0262] Among them, E opt To optimize the energy consumption of the overall system, P i (t) is the power demand of the i-th air conditioner at time t, η i is the energy efficiency coefficient of the i-th air conditioner, f i (t) is the priority function of the i-th air conditioner.

[0263] P i (t) represents the power demand of the i-th air conditioner. This value depends on factors such as ambient temperature, set temperature, and room area.

[0264] η iis the energy efficiency coefficient of the i-th air conditioner. Generally, the energy efficiency coefficient ranges from 0.8 to 1.2, where 1.0 represents the energy efficiency of the air conditioner under optimal working conditions.

[0265] f i (t) is a priority function that is dynamically adjusted based on factors such as air conditioner usage, regional demand, and time period. The value of this function ranges from 0 to 1, where 1 indicates that the air conditioner has the highest priority in that time period, and 0 indicates the lowest.

[0266] The air conditioning units in the building are numbered from air conditioner 1 to air conditioner 10, and each air conditioner has different energy efficiency, usage frequency, and priority settings. The following is some data of the air conditioning system:

[0267]

[0268] Assuming the current time is daytime, the optimized energy consumption of the entire system will be calculated based on the priority, energy efficiency coefficient, and power demand of each air conditioner.

[0269] Suppose you need to calculate the energy consumption of each air conditioner during the daytime period (for example, 8:00 AM to 6:00 PM). Using the above formula, calculate the energy-efficient power requirement of each air conditioner and obtain the overall optimized consumption of the system.

[0270] Air Conditioner 1:

[0271] P1(t)=2.5kW, η1=1.0, f1(t)=0.9

[0272] Energy 1 =2.5·1.0·0.9=2.25kWh

[0273] Air Conditioner 2:

[0274] P2(t)=2.0kW, η2=0.9, f2(t)=0.8

[0275] Energy 2 ==2.0·0.9·0.8==1.44kWh

[0276] Air Conditioner 3:

[0277] P3(t)=3.0kW, η3=1.1, f3(t)=1.0

[0278] Energy 3 =3.0·1.1·1.0=3.30kWh

[0279] Air Conditioner 4:

[0280] P4(t)=1.5kW, η4=1.0, f4(t)=0.6

[0281] Energy 4 =1.5·1.0·0.6=0.90kWh

[0282] Air Conditioner 5:

[0283] P5(t)=1.2kW, η5=0.8, f5(t)=0.5

[0284] Energy 5 =1.2·0.8·0.5=0.48kWh

[0285] Similarly, calculate the energy consumption of other air conditioners during the same period. Finally, the optimized energy consumption of all air conditioners can be obtained by summing up:

[0286]

[0287] Through an optimization algorithm, the system dynamically adjusts the operating mode of each air conditioner based on its priority and energy efficiency coefficient. For example, air conditioners frequently used during the day (such as air conditioner 3) will maintain a higher priority, reducing their operating time while maintaining comfort levels. Meanwhile, air conditioners used less frequently (such as air conditioner 5) can achieve energy savings by reducing their operating intensity. At night, the system reduces the workload of all air conditioners, further reducing energy consumption.

[0288] Through a multi-dimensional energy-saving optimization algorithm, the air conditioning system dynamically adjusts equipment operation based on time periods, equipment priorities, and energy efficiency characteristics, maximizing energy savings while maintaining a comfortable indoor environment. This control method significantly reduces energy waste, lowers operating costs, and improves overall system efficiency.

[0289] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Air conditioner IoT control method, characterized in that The following steps are involved: S1. Intelligent Internet of Things for air-conditioning equipment: Integrate IoT modules in each air conditioner to collect real-time environmental data of the room, including temperature, humidity, and air quality, through the connection between smart sensors and the cloud platform; aggregate all air conditioning equipment data to form a centralized management system for equipment status and environmental information; S2. Intelligent collaborative control based on IoT data: S2.

1. Utilize the Internet of Things (IoT) between air-conditioning equipment to build an intelligent collaborative control algorithm based on communication between equipment; S2.2, each air conditioning device adjusts its working mode according to the data from multiple sensors in the room and the surrounding environment including weather and sunshine changes; S3. Intelligent environmental prediction: Air conditioning equipment detects environmental data in real time through local sensors, and accesses external environment data including weather forecasts and climate change trend forecast data, and combines the equipment's self-learning function to adjust the working mode in advance; S4, multi-dimensional energy-saving optimization algorithm: S4.1, using multi-dimensional energy-saving optimization algorithm for intelligent control; including adjusting the cooling / heating intensity according to the temperature requirements of different areas in the room, S4.

2. Optimize overall energy consumption by combining external weather changes and equipment usage frequency; optimization strategies include time period allocation and equipment priority control; S5. Personalized user control based on big data analysis: S5.

1. Collect environmental data and user behavior data through air conditioning equipment, and use big data analysis and machine learning models to predict users' living habits and comfort preferences; S5.

2. The air conditioning system adjusts the control strategy according to the user's behavior patterns including work and rest time and indoor activities.

2. The air conditioning IoT control method according to claim 1, characterized in that The intelligent collaborative control method based on IoT data includes: The data sharing and collaborative work between air-conditioning devices are controlled through wireless communication protocols; each air-conditioning device is equipped with one of the Internet of Things modules including Wi-Fi, ZigBee, and LoRa for interconnection, and the air-conditioning devices share environmental data, including temperature, humidity, and CO2 concentration sensor data, and adjust the working mode in real time according to the sensor data and shared information; the temperature adjustment of each device is controlled by the following formula: Where: T(t) is the target temperature setting of the air conditioning equipment at time t; T room (t) is the current temperature of the room where the air conditioner is located, which indicates the device's perception of the room environment; α is the control coefficient, which indicates the device's sensitivity to changes in ambient temperature; the value range is 0.1≤α≤1.0, depending on the type of device and specific circumstances; T env (i) is the ambient temperature of the i-th device; the air-conditioning device shares information with other devices through the Internet of Things; n is the number of air-conditioning devices that share information.

3. The air conditioning IoT control method according to claim 2, characterized in that The intelligent collaborative control method based on IoT data includes: The air-conditioning equipment integrates external environmental data, including weather, sunshine intensity, and outdoor temperature and humidity information data; the information data is acquired in real time through an open API interface or embedded sensors; the air-conditioning equipment senses the current environmental status and predicts future environmental changes; and the external environmental data is modeled as a function of weather and sunshine intensity, using historical climate data to predict future environmental changes.

4. The air conditioning IoT control method according to claim 3, characterized in that The intelligent collaborative control method based on IoT data includes: Each air-conditioning device is equipped with an embedded machine learning algorithm to analyze the interaction between environmental data and air-conditioning devices in real time for optimized control. It also analyzes the user's life patterns, including the time they go home and when they go to bed, and builds a personalized model based on historical and real-time data to adjust the temperature and wind speed.

5. The air conditioning IoT control method according to claim 1, characterized in that The multi-dimensional energy-saving optimization algorithm includes: Dynamically adjust the cooling or heating intensity of the air conditioner according to the temperature requirements of different areas in the room; the air conditioner calculates the regional temperature difference and implements control for areas with strong solar radiation near the windows and areas with low temperature near the inner wall; the system of the air conditioner calculates the temperature difference ΔT of each area in the room by the following formula i , and dynamically adjust the cooling or heating mode of each area according to the temperature difference: Where, ΔT k represents the temperature difference between the kth region and the external environment temperature; T k is the actual temperature of the kth area, detected by the temperature sensor of the air conditioning system; T ext is the external environment temperature, obtained in real time through the weather API or environmental monitoring equipment; k is the thermal conductivity coefficient of the area, which indicates the heat exchange efficiency between the area and the external environment; t is time, which indicates the length of time in the calculation process of the formula.

6. The air-conditioning IoT control method according to claim 5, characterized in that The multi-dimensional energy-saving optimization algorithm includes: By introducing external environmental data, the air conditioning system can predict future environmental changes and adjust the working mode in advance; the air conditioning system predicts indoor temperature changes by combining external weather data with the following prediction formula: ΔT pred =α1T ext (t)+α2T ext (t)-β·SolarRadiation(t) Where: ΔT pred : represents the indoor temperature change predicted by the system, which is used to adjust the air conditioning working mode; T ext (t): external temperature, reflecting the temperature change of the outdoor environment; H ext (t): external humidity, which affects the cooling or heating effect of the air conditioner; SolarRadiation(t): sunshine intensity, which determines the impact of sunlight on the room temperature; α1, α2, β: response coefficients of changes in the external environment of the area, which can be adjusted according to specific circumstances to determine the degree of influence of external environmental parameters on indoor temperature changes.

7. The air-conditioning IoT control method according to claim 6, characterized in that The multi-dimensional energy-saving optimization algorithm includes: By monitoring the usage frequency (U i ) and load conditions (L i ), so that the equipment can dynamically adjust its working state. The calculation formula for air conditioning equipment load balancing is as follows: Among them, LoadFactor i represents the load factor of the i-th air conditioner, indicating the load proportion of the equipment; L i is the current load of the i-th air conditioner, which is determined by the power consumption, cooling or heating intensity parameters of the air conditioner; U i is the usage frequency of the i-th air conditioner, reflecting the working condition of the air conditioner; Threshold i is the usage frequency threshold of the i-th air conditioner, which is used to decide whether the device enters energy-saving mode; N is the total number of air-conditioning devices, which is used to calculate the overall load balance.

8. The air conditioner IoT control method according to claim 7, characterized in that The multi-dimensional energy-saving optimization algorithm includes: optimizing energy use through intelligent time period allocation and equipment priority control; ensuring regional comfort according to energy demand in different time periods including weekdays, weekends, daytime and nighttime, and assigning priority to equipment according to its energy efficiency and frequency of use.

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