Air conditioner intelligent energy-saving control method and equipment based on Internet of Things and medium

Abstract

The invention relates to the technical field of intelligent adjustment, in particular to an air conditioner intelligent energy-saving control method and equipment based on the Internet of Things and a medium. An internet of things device comprising an MEMS wind speed and direction sensing chip and a temperature sensor is placed at an air outlet of an air conditioner, air sweeping of the air conditioner is controlled to collect data, and a target air outlet direction which is high in wind speed and has the minimum included angle with the initial direction is screened out; a multi-stage temperature excitation strategy of low speed in the early stage and high speed in the later stage is adopted, indoor and outdoor temperature and wind speed data are synchronously collected, multi-dimensional feature vectors are constructed to recognize the air conditioner type, the energy efficiency level and the power number, and an energy-saving control strategy is matched. According to the method, the effective wind speed direction is accurately extracted, the influence of placement deviation is eliminated, steady-state and transient differential features of the air conditioner are excited, the recognition accuracy is better than that of a traditional method, and intelligent energy-saving control is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent regulation technology, and in particular to an intelligent energy-saving control method, device and medium for air conditioning based on the Internet of Things. Background Technology

[0002] With the rapid development of IoT technology, smart home appliance systems have been widely applied in residential and commercial settings. IoT-based air conditioning control systems typically connect to air conditioning units via Wi-Fi, Bluetooth, or dedicated communication protocols to obtain real-time operating status, model information, and energy efficiency parameters, thereby achieving precise energy-saving scheduling and comfort optimization. However, many older air conditioners (such as fixed-frequency models, devices without communication interfaces, or those only supporting infrared remote control) lack standard data interaction capabilities and cannot be effectively integrated into the unified management of existing IoT platforms, resulting in long-term inefficient operation and hindering their ability to enjoy the energy savings and improved user experience brought by smart technology.

[0003] To address these issues, the industry has attempted to use non-invasive monitoring methods to infer the performance of older air conditioners. For example, current sensors can be used to collect the overall power consumption waveform, which, combined with the start-stop cycle, can determine whether the unit is a fixed-frequency model. Alternatively, room temperature variation trends can be used to roughly estimate cooling capacity. However, these methods generally suffer from limitations in their single identification dimension and robustness: on the one hand, air conditioners of different brands, capacities, or energy efficiency ratings may exhibit significant overlap in their power consumption or temperature rise curves; on the other hand, environmental factors (such as outdoor temperature fluctuations and differences in room sealing) can easily interfere with the observed signals, leading to a high false positive rate and hindering the generation of refined control strategies. Furthermore, existing solutions generally neglect the crucial physical dimension of wind field characteristics, failing to effectively distinguish the fundamental differences in wind speed regulation behavior between variable-frequency and fixed-frequency air conditioners.

[0004] Therefore, there is an urgent need for a non-intrusive intelligent identification and control method for air conditioners suitable for the Internet of Things (IoT) environment. This method should be able to accurately extract multi-dimensional performance parameters (including type, energy efficiency rating, and estimated horsepower) of older air conditioners through a combination of external sensing and active excitation, without relying on device communication protocols, and then match personalized energy-saving control strategies accordingly. This method should possess high environmental adaptability, strong feature discrimination capabilities, and seamless integration with existing IoT control terminals, truly achieving intelligent upgrades for existing older air conditioners. Summary of the Invention

[0005] To address one of the aforementioned technical problems, the present invention adopts the following technical solution: According to one aspect of the present invention, an intelligent energy-saving control method for air conditioning based on the Internet of Things is provided, the method comprising the following steps: S1: Place the IoT control device equipped with MEMS wind speed and direction sensing chip and temperature sensor in front of the air outlet of the air conditioner to be controlled, and make its initial orientation face the front of the air conditioner.

[0006] S2: Send an infrared control command to the air conditioner to put it into swing mode and run it continuously for a preset time.

[0007] S3: Collect time series data of wind speed and direction during the sweeping process through MEMS wind speed and direction sensing chip, select high wind speed sampling points in the top 15% of the data, and select the sampling point with the smallest angle between the wind direction and the initial orientation of the equipment among the high wind speed sampling points. Use its wind direction as the target air outlet direction and the corresponding wind speed as the maximum effective wind speed.

[0008] S4: Control the air conditioner to stop swinging and fix the air outlet direction to the target air outlet direction.

[0009] S5: Control the air conditioner to perform multi-stage temperature excitation according to a preset temperature adjustment sequence, which includes an initial slow temperature adjustment stage and a later fast temperature adjustment stage. In the initial slow temperature adjustment stage, the set temperature is gradually changed at a first adjustment rate, and each set temperature is maintained for a sufficient duration to stabilize the indoor temperature. In the later fast temperature adjustment stage, the set temperature is switched at a second adjustment rate greater than the first adjustment rate, and the holding time at each set temperature is shortened to stimulate its transient response characteristics.

[0010] S6: Simultaneously collect indoor temperature change data and wind speed change data at each temperature setting stage, and acquire outdoor temperature data. Based on the data, construct a multi-dimensional feature vector that includes steady-state temperature fluctuation characteristics, transient temperature response characteristics, wind speed and set temperature linkage characteristics, and environmental correction characteristics.

[0011] S7: Identify the type, energy efficiency rating, and estimated horsepower of the air conditioner based on the multidimensional feature vector.

[0012] S8: Match the corresponding intelligent energy-saving control strategy based on the identification result, and perform subsequent control of the air conditioner based on the strategy.

[0013] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the above-described intelligent energy-saving control method for air conditioning based on the Internet of Things.

[0014] According to a third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described Internet of Things-based intelligent energy-saving control method for air conditioning.

[0015] This invention has at least one of the following beneficial effects: This invention accurately extracts the maximum effective wind speed and main air outlet direction of the air conditioner through dual constraints: "high wind speed point screening in swing mode and minimizing the angle between the wind direction and the initial orientation of the equipment." This design fully considers the characteristics of older air conditioners (especially fixed-frequency units) with fixed air outlet direction and limited wind speed settings: their maximum wind speed is usually only stably output in the central area, while the edges or corners are easily affected by eddy currents, leading to measurement distortion. Geometric correction eliminates the influence of user placement deviations, ensuring that the collected wind speed accurately reflects the equipment's capabilities. Simultaneously, the S5 employs an active excitation strategy of "slow temperature adjustment in the early stage + rapid temperature adjustment in the later stage," respectively stimulating the differentiated behavior of the air conditioner under steady-state (e.g., small temperature fluctuations in inverter units, large fluctuations in fixed-frequency units) and transient (e.g., fast response in high-efficiency units, slow response in older units) conditions. Combined with wind speed linkage characteristics (e.g., inverter units automatically increase airflow according to cooling demand, while fixed-frequency units maintain a constant wind speed), a highly discriminative multi-dimensional feature vector is constructed. Therefore, it is possible to accurately distinguish the type, energy efficiency level and horsepower, with a significantly higher accuracy rate than traditional methods that rely solely on power consumption or a single temperature curve.

[0016] Meanwhile, the performance of traditional air conditioners is easily affected by environmental factors such as indoor-outdoor temperature differences, room volume, and the opening and closing of doors and windows, causing identification schemes that rely solely on temperature changes to fail. This invention introduces "environmental correction features" (such as the ratio of indoor-outdoor temperature difference to the temperature drop per unit time) and integrates wind speed response data to achieve dynamic compensation for environmental disturbances. For example, when high outdoor temperatures cause a general decrease in cooling efficiency, the system no longer judges the air conditioner as inefficient solely based on "slow cooling," but rather makes a comprehensive judgment based on multi-dimensional evidence, such as whether its maximum effective wind speed meets the standard and whether temperature fluctuations conform to fixed-frequency characteristics. This "actively stimulated environmental normalization" design significantly reduces the impact of environmental noise on the identification results and improves the reliability of the solution in real-world home scenarios.

[0017] Furthermore, this invention not only completes the identification process but also directly uses the results to generate suitable control strategies. Because older air conditioners lack adaptive adjustment capabilities, their optimal operating mode highly depends on their inherent performance. For example, for equipment identified as "old, inefficient fixed-frequency units," a strategy of "extending the start-stop cycle and avoiding frequent temperature switching" can be adopted to reduce compressor wear; while for "high-efficiency variable-frequency units," more aggressive temperature tracking and fan speed linkage logic can be enabled. This strategy matching is feasible because it is based on the refined performance profile constructed by S5–S7. To achieve optimal control for energy saving and comfort, it is necessary to accurately grasp the dynamic response characteristics of the air conditioning equipment, that is, to clarify its specific attributes in the two modes of "rapid response but significant fluctuations" or "smooth response but stable operation." Based on this, this technical solution effectively solves the limitations of traditional general control algorithms in terms of equipment adaptability, achieving the technical goal of customizing personalized energy-saving strategies for different models. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of an intelligent energy-saving control method for air conditioning based on the Internet of Things provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] As one possible embodiment of the present invention, such as Figure 1 As shown, an intelligent energy-saving control method for air conditioners based on the Internet of Things is provided, the method including the following steps: S1: Place the IoT control device, equipped with a MEMS wind speed and direction sensing chip and a temperature sensor, in front of the air outlet of the air conditioner to be controlled, ensuring its initial orientation is aligned with the front of the air conditioner. Specifically, the MEMS wind speed and direction sensing chip is a thermal sensing array without moving parts, integrated inside the IoT control device. In this embodiment, the air conditioner to be controlled is a fixed-frequency or variable-frequency air conditioner without a communication protocol interface, i.e., the old-style air conditioner described in the background art. The initial orientation is defined as the direction in which the front of the device points towards the air conditioner, and its opposite direction (180°) is the ideal airflow direction from the air conditioner. The IoT control device has a built-in infrared remote control code library for multiple brands of air conditioners and supports automatic adaptation to infrared control commands of unknown models through the user remote control learning mode, ensuring reliable transmission of commands such as air sweeping, temperature setting, and air guide plate locking.

[0022] MEMS wind speed and direction sensing chips typically measure wind speed and direction based on thermal principles (thermal film or thermistor). Their core structure comprises multiple miniature heating elements and temperature sensors arranged in a symmetrical array. When air flows across the chip surface, the airflow carries away heat, causing an asymmetrical temperature distribution upstream and downstream of the heating elements. By detecting the temperature difference in each direction or the difference in heating power required to maintain a constant temperature, the wind speed and direction of the incoming flow can be calculated.

[0023] This approach integrates the sensing unit into a single IoT device, avoiding the installation complexity and signal interference associated with external probes, making it particularly suitable for self-deployment scenarios by home users. Since older air conditioners generally lack self-reporting capabilities, their physical airflow characteristics (such as prevailing wind direction and maximum wind speed) become crucial external indicators of their internal structure and performance. Guiding users to initially align the air conditioner with the front provides a reference direction for subsequent airflow geometry correction, a prerequisite for achieving non-invasive and accurate sensing. This design fully considers the practical constraints of limited user operation skills and cluttered environments associated with older air conditioners, balancing ease of use with measurement reliability.

[0024] S2: Sends an infrared control command to the air conditioner, causing it to enter the swing mode and run continuously for a preset duration. The preset duration is 1 to 5 minutes, used to ensure that at least one complete swing cycle is completed.

[0025] Enabling the air-sweeping mode is a key means of actively stimulating the omnidirectional airflow distribution of an air conditioner. For older air conditioners that only support infrared remote control, their air deflectors typically have basic oscillation functions but cannot provide feedback on the current angle. By forcibly entering the air-sweeping state, the air outlet can cover the main areas of both the horizontal and vertical planes (if the air conditioner supports vertical air-sweeping), thus providing MEMS sensors with a sufficiently diverse range of wind direction and speed sampling points. The 1- to 5-minute duration setting takes into account the differences in air-sweeping speeds among different brands of air conditioners, ensuring that at least one complete cycle is completed and avoiding misjudgments of the main wind direction due to incomplete sampling. This step embodies the "active excitation + passive sensing" approach of this invention and is a crucial technical support for overcoming the "black box" characteristics of older air conditioners.

[0026] S3: Collect time series data of wind speed and direction during the sweeping process through MEMS wind speed and direction sensing chip, select high wind speed sampling points in the top 15% of the data, and select the sampling point with the smallest angle between the wind direction and the initial orientation of the equipment (i.e., 180°) among the high wind speed sampling points. Use its wind direction as the target air outlet direction and the corresponding wind speed as the maximum effective wind speed.

[0027] This step addresses the physical characteristic of older air conditioners where airflow is concentrated in the central front area. It employs a dual criterion of "high wind speed screening + minimum geometric angle" to effectively eliminate interference from edge eddies, lateral air leakage, or user-placed misalignment. If only the maximum wind speed point is considered, a strong side wind point might be mistakenly selected when the device is not strictly aligned with the air conditioner, leading to distortion of subsequent wind speed characteristics. Introducing the constraint of "minimum angle with initial orientation" essentially uses the user's rough alignment as a priori, correcting for the actual main wind axis and significantly improving the representativeness of the maximum effective wind speed. This wind speed value is directly related to the air conditioner's heat exchange capacity and horsepower rating, and is one of the key inputs for constructing the performance feature vector.

[0028] S4: Control the air conditioner to stop swinging and fix the air outlet direction to the target air outlet direction.

[0029] After identifying the main wind direction, the air guide plate is locked to the target air outlet direction via infrared commands to ensure stable and consistent wind field conditions during the subsequent temperature excitation stage (S5). If the air guide plate continues to oscillate, it will cause drastic fluctuations in the wind speed signal, making it difficult to accurately capture the steady-state wind speed response of the air conditioner at a specific set temperature (such as whether it automatically adjusts the airflow according to cooling requirements). For older fixed-frequency air conditioners, their fan speed settings are usually fixed, while some variable-frequency models have fan speed linkage logic. Therefore, fixing the air outlet direction is a necessary prerequisite for distinguishing the differences in behavior between the two. This step demonstrates the high importance this invention places on the controllability of test conditions, laying the foundation for high-precision feature extraction.

[0030] S5: Control the air conditioner to perform multi-stage temperature excitation according to a preset temperature adjustment sequence, which includes an initial slow temperature adjustment stage and a later fast temperature adjustment stage. In the initial slow temperature adjustment stage, the set temperature is gradually changed at a first adjustment rate, and each set temperature is maintained for a sufficient duration to stabilize the indoor temperature. In the later fast temperature adjustment stage, the set temperature is switched at a second adjustment rate greater than the first adjustment rate, and the holding time at each set temperature is shortened to stimulate its transient response characteristics.

[0031] Specifically, during the initial slow temperature adjustment phase, the switching interval between adjacent set temperature points is 25–35 minutes, which is used to collect the fluctuation range after the temperature stabilizes.

[0032] During the later rapid temperature adjustment phase, the switching interval between adjacent set temperature points is 8–12 minutes, which is used to collect the response time from the current room temperature to the new set temperature.

[0033] Initial slow temperature adjustment phase (25–35 minute intervals): Setting the temperature adjustment rate: In this stage, the air conditioner's set temperature changes gradually at the first adjustment rate. This rate should be slow enough to ensure that the indoor temperature gradually approaches the set value over time and eventually reaches a stable state. The key to this stage is to observe and record the differences between fixed-frequency and variable-frequency air conditioners, especially the temperature fluctuations caused by the compressor starting and stopping.

[0034] Data acquisition strategy: For each set temperature point, the indoor temperature will be continuously monitored and recorded for at least 25 to 35 minutes. By analyzing this data, the standard deviation or peak-to-peak value of the temperature at each set temperature can be calculated to evaluate the steady-state performance of the air conditioner.

[0035] The influence of inherent hardware parameters: Older air conditioners, especially fixed-frequency air conditioners, will shut off the compressor after reaching the set temperature, causing the temperature to rise again, and then restart the compressor for cooling / heating, creating obvious periodic fluctuations. In contrast, high-efficiency inverter air conditioners can maintain a relatively stable indoor temperature, which makes the two exhibit drastically different behavior patterns during this phase.

[0036] Later rapid temperature adjustment phase (8–12 minute intervals): Accelerated set temperature switching: During this phase, the air conditioner rapidly changes the set temperature at a second adjustment rate greater than the first adjustment rate. The shorter set temperature hold time (8 to 12 minutes) is designed to stimulate the transient response characteristics of the air conditioner, that is, its speed and ability to react to environmental changes.

[0037] Response time and efficiency assessment: By comparing the time required for different air conditioner models to transition from the current room temperature to the newly set temperature, and the changes in parameters such as fan speed and temperature during this process, it is possible to effectively distinguish between older, inefficient models and high-performance models. Older models typically require a longer time to complete the temperature transition and may exhibit lower cooling / heating efficiency.

[0038] This step, through differentiated temperature adjustment rhythms, elicits the typical behavioral fingerprints of older air conditioners under steady-state and transient conditions. The initial slow temperature adjustment allows the system to fully converge; at this point, fixed-frequency air conditioners exhibit significant temperature oscillations (large peak-to-peak values) due to compressor start-stop, while high-efficiency inverter air conditioners maintain smaller fluctuations, thus allowing for the determination of temperature control stability and energy efficiency rating. The subsequent rapid temperature adjustment forces the air conditioner to frequently respond to new setpoints; older, less efficient models often respond slowly and have low cooling rates, while high-performance models can quickly establish a new equilibrium. This "dual-mode excitation" strategy fully utilizes the lack of adaptive capability in older air conditioners and the fact that their dynamic characteristics are determined by inherent hardware parameters, thereby generating highly discriminative temporal characteristics to achieve multi-dimensional parameter identification.

[0039] S6: Simultaneously collect indoor temperature change data and wind speed change data at each temperature setting stage, and obtain outdoor temperature data (obtain current outdoor temperature data from the Internet weather service interface). Based on the data, construct a multi-dimensional feature vector that includes steady-state temperature fluctuation characteristics, transient temperature response characteristics, wind speed and set temperature linkage characteristics, and environmental correction characteristics.

[0040] A multidimensional feature vector includes at least three of the following: (1) Standard deviation or peak-to-peak value of indoor temperature at each set temperature point. In the air conditioning temperature control scenario of this embodiment, the peak-to-peak value refers to the difference between the highest and lowest temperatures recorded within a certain observation period after the indoor temperature reaches a steady state at a certain set temperature.

[0041] (2) The average rate of change required for the temperature to change from the first set value to the second set value.

[0042] (3) Whether the maximum effective wind speed changes significantly under different set temperatures. 'Significant change' is defined as wind speed change exceeding a preset threshold (e.g., 0.8 m / s). For fixed-frequency air conditioners, the change is usually less than the threshold because the wind speed setting is fixed, while for variable-frequency air conditioners, the wind speed increases with the increase of cooling load, resulting in a larger change.

[0043] (4) The ratio of the indoor-outdoor temperature difference to the temperature drop per unit time.

[0044] The constructed multidimensional feature vector integrates thermodynamics, fluid mechanics and environmental factors to form a three-dimensional characterization of the comprehensive performance of old-fashioned air conditioners. Among them, feature (1) reflects the temperature control accuracy, feature (2) reflects the cooling / heating response capability, feature (3) is the key criterion for distinguishing between fixed frequency (constant wind speed) and variable frequency (wind speed adjusted with load), and feature (4) is used to eliminate the interference of outdoor high temperature or low temperature on the evaluation of cooling efficiency.

[0045] For example, two air conditioners may have similar cooling rates at the same room temperature, but their actual energy efficiency may differ significantly if the indoor and outdoor temperature differences are different. By introducing environmental correction features, fair performance comparisons can be achieved across seasons and regions. This feature engineering design directly serves the need for accurate classification of older air conditioners that lack model numbers and parameters.

[0046] S7: Identify the type, energy efficiency rating, and estimated horsepower of the air conditioner based on the multidimensional feature vector.

[0047] On the one hand, S7 can be implemented by following these steps: S7.1: Match the multidimensional feature vector with a pre-defined standard feature library. The standard feature library is constructed based on the measured response data of known models of older air conditioners under the same excitation conditions.

[0048] S7.2: Based on the matching results, identify the type, energy efficiency rating, and estimated horsepower of the air conditioner.

[0049] On the other hand, S7 can be implemented by following these steps: S7.3: Input the multidimensional feature vector into the pre-trained AI classification model. The AI ​​classification model can be a random forest model, support vector machine, or lightweight neural network.

[0050] S7.4: Based on the classification results, identify the type, energy efficiency rating, and estimated horsepower of the air conditioner.

[0051] This invention offers two recognition paths: vector matching and AI models, balancing deployment flexibility and recognition accuracy. The standard feature library is suitable for resource-constrained embedded devices, achieving rapid matching through Euclidean distance or cosine similarity; the AI ​​model is suitable for cloud-based or high-performance terminals, capable of learning more complex nonlinear feature associations. Regardless of the method used, its training / construction is based on a large amount of measured data from older air conditioners under a unified excitation protocol, ensuring a high degree of consistency between the recognition results and actual physical performance. It is worth noting that the "horsepower estimate" is not directly measured, but rather inferred through a combination of maximum wind speed, response rate, and energy efficiency rating, meeting the engineering practice needs of scenarios where older air conditioner parameters are lacking.

[0052] S8: Match the corresponding intelligent energy-saving control strategy based on the identification result, and perform subsequent control of the air conditioner based on the strategy.

[0053] For example, for air conditioners identified as "old fixed-frequency inefficient units", the system will set the temperature offset to +1℃ and extend the single cooling cycle to more than 45 minutes to reduce the number of daily start-ups and shutdowns; while for "high-efficiency inverter units", the system will activate the ±0.3℃ narrow-range temperature control and fan speed adaptive linkage to maintain high comfort.

[0054] This step establishes a closed loop from "identification" to "control." For different types of older air conditioners identified, pre-defined differentiated control strategies are invoked: for example, for "old, inefficient fixed-frequency units," a strategy of extending single-cycle operation and reducing start-stop frequency is adopted to reduce compressor wear; for "high-efficiency inverter units," a more proactive temperature tracking and fan speed linkage logic is employed to improve comfort. This "identification + adaptation" mechanism overcomes the dilemma of traditional general-purpose control algorithms that either "over-control" or "under-control" older air conditioners, truly achieving synergistic optimization of energy saving and comfort.

[0055] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0056] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0057] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0058] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0059] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0060] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0061] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0062] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0063] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0064] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0065] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0067] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0068] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0069] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0070] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0071] Furthermore, the accompanying drawings are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes shown in the above drawings do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0072] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0073] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart energy-saving control method for air conditioning based on the Internet of Things, characterized in that, The method includes the following steps: S1: Place the IoT control device equipped with MEMS wind speed and direction sensing chip and temperature sensor in front of the air outlet of the air conditioner to be controlled, and make its initial orientation face the front of the air conditioner. S2: Send an infrared control command to the air conditioner to make it enter the air swing mode and run continuously for a preset time; S3: Collect time series data of wind speed and wind direction during the sweeping process through the MEMS wind speed and wind direction sensing chip, select high wind speed sampling points with wind speed values ​​in the top 15%, and select the sampling point with the smallest angle between the wind direction and the initial orientation of the device among the high wind speed sampling points, take its wind direction as the target air outlet direction, and take the corresponding wind speed as the maximum effective wind speed. S4: Control the air conditioner to stop swinging and fix the air outlet direction to the target air outlet direction; S5: Control the air conditioner to perform multi-stage temperature excitation according to a preset temperature adjustment sequence, the temperature adjustment sequence including an early slow temperature adjustment stage and a later fast temperature adjustment stage; in the early slow temperature adjustment stage, the set temperature is gradually changed at a first adjustment rate, and the set temperature is maintained for a sufficient time at each set temperature to make the indoor temperature tend to stabilize; in the later fast temperature adjustment stage, the set temperature is switched at a second adjustment rate greater than the first adjustment rate, and the holding time at each set temperature is shortened to stimulate its transient response characteristics. S6: Simultaneously collect indoor temperature change data and wind speed change data at each temperature setting stage, and obtain outdoor temperature data. Based on the data, construct a multi-dimensional feature vector that includes steady-state temperature fluctuation characteristics, transient temperature response characteristics, wind speed and set temperature linkage characteristics, and environmental correction characteristics. S7: Identify the type, energy efficiency rating, and estimated horsepower of the air conditioner based on the multidimensional feature vector; S8: Match the corresponding intelligent energy-saving control strategy according to the identification result, and perform subsequent control of the air conditioner based on the strategy.

2. The IoT-based intelligent energy-saving control method for air conditioning as described in claim 1, characterized in that, S7: Based on the multi-dimensional feature vector, identify the type, energy efficiency rating, and estimated horsepower of the air conditioner, including: The multidimensional feature vector is matched with a preset standard feature library; the standard feature library is constructed based on the measured response data of known models of old air conditioners under the same excitation conditions. Based on the matching results, the type, energy efficiency rating, and estimated horsepower of the air conditioner are identified.

3. The intelligent energy-saving control method for air conditioning based on the Internet of Things as described in claim 1, characterized in that, S7: Based on the multi-dimensional feature vector, identify the type, energy efficiency rating, and estimated horsepower of the air conditioner, including: The multidimensional feature vector is input into a pre-trained AI classification model; the AI ​​classification model is a random forest model, a support vector machine, or a lightweight neural network. Based on the classification results, identify the type, energy efficiency rating, and estimated horsepower of the air conditioner.

4. The IoT-based intelligent energy-saving control method for air conditioning as described in claim 1, characterized in that, MEMS wind speed and direction sensor chips are thermal sensing arrays without moving parts, integrated inside the IoT control device.

5. The intelligent energy-saving control method for air conditioning based on the Internet of Things as described in claim 1, characterized in that, In step S2, the preset duration is 1 to 5 minutes, which is used to ensure that at least one complete sweeping cycle is completed.

6. The intelligent energy-saving control method for air conditioning based on the Internet of Things as described in claim 1, characterized in that, The air conditioner to be controlled is a fixed-frequency or variable-frequency air conditioner that does not have a communication protocol interface.

7. The intelligent energy-saving control method for air conditioning based on the Internet of Things as described in claim 1, characterized in that, The switching interval between adjacent set temperature points in the early slow temperature adjustment stage is 25-35 minutes, which is used to collect the fluctuation range after the temperature stabilizes. The switching interval between adjacent set temperature points in the later rapid temperature adjustment stage is 8–12 minutes, which is used to collect the response time from the current room temperature to the new set temperature.

8. The intelligent energy-saving control method for air conditioning based on the Internet of Things as described in claim 1, characterized in that, The multidimensional feature vector includes at least three of the following: (1) The standard deviation or peak-to-peak value of the indoor temperature at each set temperature point; (2) The average rate of change required for the temperature to change from the first set value to the second set value; (3) Whether the maximum effective wind speed changes significantly under different set temperatures; (4) The ratio of the indoor-outdoor temperature difference to the temperature drop per unit time.

9. A non-transitory computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements an Internet of Things-based intelligent energy-saving control method for air conditioning as described in any one of claims 1 to 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements an IoT-based intelligent energy-saving control method for air conditioning as described in any one of claims 1 to 8.

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