Air conditioning equipment and control method of air conditioning equipment

Abstract

The invention relates to air conditioning equipment and a control method of the air conditioning equipment, and relates to the field of air conditioners. The controller is configured to acquire user activity data, and the user activity data is acquired by the non-imaging sensor; displacement amplitude information, activity intensity information and activity time information of user activities are extracted according to the user activity data; and operation of the air conditioning equipment is controlled according to the displacement amplitude information, the activity intensity information and the activity time information. By adopting the technical scheme, the use convenience of the air conditioning equipment can be improved.

Description

Technical Field

[0001] This application relates to the field of air conditioning equipment technology, and in particular to an air conditioning device and a control method for the air conditioning device. Background Technology

[0002] With the continuous improvement of modern living standards and the development of refrigeration and heating technologies, air conditioning equipment has become widely used and has become an essential household appliance for improving indoor environmental comfort, providing people with basic temperature regulation functions.

[0003] In traditional technology, air conditioning equipment still relies primarily on manual operation by the user. Users need to actively set parameters such as temperature, fan speed, and operating mode via remote control, device panel, or mobile application based on their own physical sensations and actual needs, and the air conditioning equipment then operates according to these parameters.

[0004] Although the introduction of mobile applications in recent years has alleviated the operational difficulties of lost or hard-to-find remote controls to some extent, they are still essentially still manual adjustment modes. Every time user needs change, relevant parameters must be reset through interface interaction, making the process still relatively cumbersome. Summary of the Invention

[0005] Therefore, it is necessary to provide an air conditioning device and a control method for the air conditioning device that can improve the ease of use of the above-mentioned technical problems.

[0006] In a first aspect, this application provides an air conditioning device, comprising:

[0007] Non-imaging sensors are configured to collect user activity data;

[0008] The controller is configured as follows:

[0009] User activity data is acquired through non-imaging sensors.

[0010] Extract the displacement amplitude, activity intensity, and activity time information of user activities from user activity data;

[0011] The operation of the air conditioning equipment is controlled based on displacement amplitude information, activity intensity information, and activity time information.

[0012] Technical Effects: Firstly, by collecting user activity data using non-imaging sensors, effective perception of user activity can be achieved while fully protecting user privacy. Since non-imaging sensors do not acquire image or video information, they can balance perception capability and privacy protection at the technical level. Subsequently, by extracting displacement amplitude, activity intensity, and activity time information from the user activity data, a quantitative representation of the user's activity state is achieved. Furthermore, by controlling the operation of the air conditioning equipment based on the displacement amplitude, activity intensity, and activity time information, the air conditioning equipment can proactively and promptly respond to changes in the user's activity state. Thus, when the user's activity state changes, the air conditioning equipment can effectively sense the change and quickly adjust its operating parameters accordingly. This allows the air conditioning equipment's operating state to rapidly match the changed user activity state, ensuring that the actual temperature environment continuously matches the user's actual needs arising from changes in activity state. This achieves a shift from passive response to proactive adaptation, effectively reducing manual operation by the user and improving the ease of use of the air conditioning equipment.

[0013] Secondly, this application provides a method for controlling an air conditioning device, including:

[0014] User activity data is acquired through non-imaging sensors.

[0015] Extract the displacement amplitude, activity intensity, and activity time information of user activities from user activity data;

[0016] The operation of the air conditioning equipment is controlled based on displacement amplitude information, activity intensity information, and activity time information.

[0017] Technical Effects: Firstly, by collecting user activity data using non-imaging sensors, effective perception of user activity can be achieved while fully protecting user privacy. Since non-imaging sensors do not acquire image or video information, they can balance perception capability and privacy protection at the technical level. Subsequently, by extracting displacement amplitude, activity intensity, and activity time information from the user activity data, a quantitative representation of the user's activity state is achieved. Furthermore, by controlling the operation of the air conditioning equipment based on the displacement amplitude, activity intensity, and activity time information, the air conditioning equipment can proactively and promptly respond to changes in the user's activity state. Thus, when the user's activity state changes, the air conditioning equipment can effectively sense the change and quickly adjust its operating parameters accordingly. This allows the air conditioning equipment's operating state to rapidly match the changed user activity state, ensuring that the actual temperature environment continuously matches the user's actual needs arising from changes in activity state. This achieves a shift from passive response to proactive adaptation, effectively reducing manual operation by the user and improving the ease of use of the air conditioning equipment. Attached Figure Description

[0018] Figure 1 A schematic diagram of the hardware configuration of the refrigerant circulation loop provided in some embodiments of this application;

[0019] Figure 2 This is a schematic diagram of the hardware configuration of an air conditioning device provided in some embodiments of this application;

[0020] Figure 3 This is a schematic diagram of the hardware configuration of an air conditioning device provided in other embodiments of this application;

[0021] Figure 4 A flowchart illustrating the energy-saving control method provided in some embodiments of this application;

[0022] Figure 5 Timing diagrams for implementing energy-saving control methods according to some embodiments of this application;

[0023] Figure 6 A flowchart illustrating the process of controlling the operation of an air conditioning device according to some embodiments of this application;

[0024] Figure 7 A flowchart illustrating the process of determining a target use scenario provided in some embodiments of this application;

[0025] Figure 8 A timing diagram illustrating the process of determining a target use case as provided in some embodiments of this application;

[0026] Figure 9 The following are schematic diagrams illustrating home Internet of Things (IoT) scenarios provided in some embodiments of this application;

[0027] Figure 10 A flowchart illustrating the linkage control process provided in some embodiments of this application;

[0028] Figure 11 A flowchart illustrating the process of determining a target use scenario provided for other embodiments of this application;

[0029] Figure 12 A flowchart illustrating the preset time period customization process provided in some embodiments of this application;

[0030] Figure 13 A flowchart illustrating the step-by-step adjustment process of air outlet parameters provided in some embodiments of this application;

[0031] Figure 14 A flowchart illustrating the information extraction process in a multi-user scenario provided in some embodiments of this application;

[0032] Figure 15 This is a flowchart illustrating a method for controlling an air conditioning device according to some embodiments of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0034] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0036] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] In the description of the embodiments in this application, the term "and / or" is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, the character " / " in this document generally indicates that the related objects before and after are in an "or" relationship, and the term "multiple" refers to two or more (including two).

[0039] In this application embodiment, air conditioning equipment refers to intelligent devices with temperature and / or humidity regulation functions, such as intelligent air conditioning equipment.

[0040] In this application, the air conditioning equipment can achieve cooling or heating functions through its internal refrigerant circulation loop. The refrigerant circulation includes a series of processes, mainly involving four major processes: compression, condensation, expansion, and evaporation, and ultimately achieves air temperature regulation through heat exchange between the refrigerant and the air.

[0041] like Figure 1 As shown, the refrigerant circulation loop 10 includes a compressor 102, a condenser 104, an expansion valve 106, and an evaporator 108.

[0042] Compressor 102 compresses the refrigerant gas at a low temperature and low pressure and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into condenser 104. Condenser 104 condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through the condensation process.

[0043] Expansion valve 106 expands the high-temperature, high-pressure liquid refrigerant condensed in condenser 104 into a low-temperature, low-pressure liquid refrigerant. Evaporator 108 evaporates the refrigerant that expands in expansion valve 106 and returns the low-temperature, low-pressure refrigerant gas to compressor 102.

[0044] Throughout the cycle, the air conditioning unit can regulate the indoor temperature through heat exchange between the refrigerant and the indoor air.

[0045] Air conditioning equipment typically consists of two parts: an outdoor unit and an indoor unit.

[0046] Air conditioning equipment typically includes an outdoor unit and an indoor unit. The outdoor unit may include a compressor and an outdoor heat exchanger, while the indoor unit may include an indoor heat exchanger. An expansion valve may be located in either the indoor or outdoor unit.

[0047] Indoor and outdoor heat exchangers function as either condensers or evaporators. When the indoor heat exchanger is used as a condenser, the air conditioning unit functions as a heater in heating mode; when the indoor heat exchanger is used as an evaporator, the air conditioning unit functions as a cooler in cooling mode.

[0048] Figure 2 Block diagrams of air conditioning equipment provided in some embodiments of this application. For example... Figure 2 As shown in the figure, the air conditioning equipment 20 of this application embodiment includes a refrigerant circulation loop 10, an outdoor fan 202, an indoor fan 206, and a controller 204.

[0049] The refrigerant circulation loop 10 includes a compressor 102, a condenser 104, an expansion valve 106, and an evaporator 108, which are used to realize the cooling or heating cycle of the refrigerant.

[0050] The outdoor fan 202 is used to drive outdoor air through the outdoor heat exchanger by rotation, so that the refrigerant can exchange heat with the outdoor air. The outdoor heat exchanger is used as a condenser 102 in the refrigeration cycle and as an evaporator 104 in the heating cycle.

[0051] The indoor fan 206 is used to drive indoor air through the indoor heat exchanger by rotation, so that the refrigerant and the indoor air can exchange heat. The indoor heat exchanger is used as an evaporator 104 in the refrigeration cycle and as a condenser 102 in the heating cycle.

[0052] As an example, in the refrigeration cycle, the low-temperature, low-pressure refrigerant gas is compressed in the compressor 102 of the outdoor unit, becoming a high-temperature, high-pressure gas. The high-temperature, high-pressure gas flows into the outdoor heat exchanger of the outdoor unit, where it acts as a condenser 104, releasing heat to the outdoor air with the help of an outdoor fan, and condensing into a medium-temperature, high-pressure liquid. The medium-temperature, high-pressure liquid flows through the expansion valve 106, where its pressure and temperature drop sharply, becoming a low-temperature, low-pressure vapor-liquid mixture. The low-temperature, low-pressure refrigerant enters the indoor heat exchanger of the indoor unit, where it acts as an evaporator 108, absorbing heat from the indoor air with the help of an indoor fan, and evaporating into a low-temperature, low-pressure gas, thereby lowering the indoor temperature. Subsequently, the gas returns to the compressor 102, starting the next cycle.

[0053] As an example, in the heating cycle, the low-temperature, low-pressure refrigerant gas is compressed in the compressor 102 of the outdoor unit, becoming a high-temperature, high-pressure gas. The high-temperature, high-pressure gas flows into the indoor heat exchanger of the indoor unit, where it acts as a condenser 104, releasing heat to the indoor air with the help of an indoor fan, condensing into a medium-temperature, high-pressure liquid, thereby raising the indoor temperature. The medium-temperature, high-pressure liquid flows through the expansion valve 106, where the pressure and temperature drop sharply, becoming a low-temperature, low-pressure vapor-liquid mixture. The low-temperature, low-pressure refrigerant enters the outdoor heat exchanger of the outdoor unit, where it acts as an evaporator 108, absorbing heat from the outdoor air with the help of an outdoor fan, and evaporating into a low-temperature, low-pressure gas. Subsequently, the gas returns to the compressor 102, starting the next cycle.

[0054] like Figure 3 As shown, the air conditioning device 20 includes a controller 204 and a non-imaging sensor 208.

[0055] Non-imaging sensors 208 can refer to sensors that do not acquire information by capturing and processing images or videos, including at least one of millimeter-wave radar sensors, passive infrared sensors, ultrasonic sensors, pressure sensors, etc.

[0056] Non-imaging sensors 208 can indirectly sense the presence, state, and motion of a target by detecting changes in specific physical quantities or signals in the physical environment and converting them into electrical signals or digital data that can be used for analysis. The physical quantities or signals that the non-imaging sensors 208 can detect may include at least one of displacement, thermal radiation, pressure, and electromagnetic waves.

[0057] Compared to imaging sensors such as cameras, non-imaging sensors 208 do not record visual appearance information, thus providing the advantage of protecting user privacy while achieving sensing functions.

[0058] Millimeter-wave radar sensors, by emitting millimeter-wave signals in a specific frequency band and receiving the echo signals reflected by the human body, analyze the changes in frequency, phase, and time difference between the echo signals and the emitted signals. This allows them to effectively measure the user's distance, speed, and movement characteristics such as breathing. Compared to passive infrared sensors and lidar sensors, millimeter-wave radar has significant advantages in sensing sensitivity and information richness.

[0059] Passive infrared sensors detect user activity by detecting changes in infrared thermal radiation of specific wavelengths emitted by the human body. Passive infrared sensors struggle to detect stationary individuals and typically cannot provide quantitative information such as distance, speed, or subtle movements, thus limiting their ability to distinguish specific activity states. However, they offer significant advantages such as extremely low cost and very low power consumption.

[0060] LiDAR sensors construct high-precision environmental distance point cloud maps by emitting laser beams and measuring their reflection time, thereby enabling accurate detection of user position, attitude, and movement paths. However, LiDAR sensors are inferior to millimeter-wave radar in terms of information richness and sensitivity, and their higher cost typically limits their widespread application in consumer products.

[0061] The controller 204 is configured to: acquire user activity data, which is obtained through non-imaging sensors; extract displacement amplitude information, activity intensity information, and activity time information of the user activity based on the user activity data; and control the operation of the air conditioning equipment based on the displacement amplitude information, activity intensity information, and activity time information.

[0062] In some embodiments, such as Figure 4 As shown, controller 204 is configured to perform the following steps:

[0063] Step 402: Acquire user activity data, which is obtained through non-imaging sensors;

[0064] Step 404: Extract the displacement amplitude information, activity intensity information, and activity time information of the user activity based on the user activity data;

[0065] Step 406: Control the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information.

[0066] User activity data can refer to signals or data collected by non-imaging sensors that reflect changes in the physical field caused by user activity. This includes human movement speed and position coordinate changes detected by millimeter-wave radar sensors; or heat source movement and distribution changes sensed by passive infrared sensor arrays; etc. After processing, user activity data can be further extracted to extract displacement amplitude information, activity intensity information, and activity time information, which can be used to objectively determine the user's actual thermal comfort needs.

[0067] Displacement amplitude information refers to a quantitative indicator describing the distance a user moves in space, treating the user as a whole. For example, it could be the average distance a user moves per unit time or the maximum displacement span calculated from radar point cloud trajectories. The specific details can be determined based on actual conditions and test results, and this embodiment does not impose any limitations on this.

[0068] Activity intensity information refers to quantitative indicators that describe the intensity of a user's activity, which can be characterized by the amplitude, frequency, or rate of change of user activity data. For example, based on the micro-Doppler characteristics of radar or the frequency of change of passive infrared sensor signals, the speed of a user's movements and the amplitude of limb swings can be analyzed to distinguish user activities of different intensities, such as sitting still, walking, and jumping.

[0069] Activity time information describes when or for how long a user activity occurred. For example, the moment or time range during which a user activity was detected.

[0070] Displacement amplitude information, activity intensity information, and activity time information are quantitative representations of a user's activity status.

[0071] In some feasible embodiments, user activity data can be time-series user activity data. For example, non-imaging sensors can collect data once at preset acquisition time intervals, and the data collected continuously multiple times can form user activity time-series data, which can be used for user activity analysis and air conditioning equipment operation control.

[0072] As an example, after receiving the latest data from a non-imaging sensor, the air conditioning equipment can use this latest data as the last data point of the user activity time series data, and then acquire a preset number of historical data that are continuous with the latest data received before it, to form the user activity time series data, which is used for user activity analysis and air conditioning equipment operation control.

[0073] As an example, displacement amplitude information may include, but is not limited to: the user's average movement rate and cumulative movement distance within the time interval corresponding to the user's activity time series data, or the maximum displacement span of the user's center of mass in space.

[0074] As an example, activity intensity information may include, but is not limited to: the peak amplitude, root mean square value, and time rate of change of signal energy of the user activity time-series data waveform, or the dominant frequency component and bandwidth in its spectrum.

[0075] As an example, activity time information may include, but is not limited to, the timestamp of each data point in the user activity time series data. By combining this with the determination of the activity intensity represented by the data points, more advanced time features can be further extracted from the activity time information, such as the start and end times of user activities of a specific intensity, and the activity duration calculated therefrom.

[0076] In some embodiments, such as Figure 5As shown, non-imaging sensors can detect changes in the physical field caused by user activity in the indoor space corresponding to the air conditioning equipment in real time, periodically, or triggeredly, thus obtaining user activity data. The controller can acquire the user activity data collected by the non-imaging sensors in real time, periodically, or triggeredly, and extract three key quantitative features characterizing user activity from the user activity data through a preset signal processing algorithm: displacement amplitude information representing the user's spatial movement range, activity intensity information reflecting the intensity of the activity, and activity time information recording the duration and time distribution of the activity. Subsequently, the controller uses the extracted displacement amplitude information, activity intensity information, and activity time information as input parameters, generates operation control commands according to preset control logic, and sends the operation control commands to the corresponding actuators. The actuators drive their associated physical components to perform actions based on the received operation control commands, providing a temperature environment that matches the user activity.

[0077] In some feasible embodiments, user activity data collected by non-imaging sensors can be directly sent to the air conditioning equipment for subsequent user activity analysis and air conditioning equipment operation control.

[0078] In other feasible embodiments, user activity data collected by non-imaging sensors can be stored in a designated path of the memory, and the controller can retrieve it from that designated path as needed.

[0079] In some feasible embodiments, the preset control logic includes, but is not limited to, rule table lookup, fuzzy control algorithm or threshold judgment mechanism.

[0080] In some feasible embodiments, the actuator may include at least one of a compressor, a fan, and a deflector.

[0081] In some feasible embodiments, the operation control commands may include at least one of the following: adjusting the compressor frequency, fan speed, air guide vane angle, and switching operating modes.

[0082] In some feasible embodiments, user activity data may include raw point cloud data collected multiple times consecutively by a millimeter-wave radar sensor; the method of extracting displacement amplitude information from user activity data may include: identifying and tracking the same target user in multiple consecutive frames of raw point cloud data through target detection and clustering algorithms, locating the spatial position information of the target user (which can be represented by distance and orientation) based on each frame of raw point cloud data, forming a position trajectory sequence corresponding to the target user; subsequently, trajectory analysis can be performed on this position trajectory sequence to extract displacement amplitude information.

[0083] As an example, displacement magnitude information may include at least one of the following:

[0084] The Euclidean distance between the start and end positions of the user's target;

[0085] The azimuth difference between the start and end positions of the user target;

[0086] Calculate the straight-line distance between any two consecutive locations in the location trajectory sequence, and sum these straight-line distances to obtain the cumulative path length.

[0087] In this embodiment, user activity data is first collected using a non-imaging sensor, enabling effective perception of user activity while fully protecting user privacy. Since non-imaging sensors do not acquire image or video information, they can technically balance perception capability and privacy protection. Subsequently, displacement amplitude, activity intensity, and activity time information are extracted from the user activity data to achieve a quantitative representation of the user's activity state. Furthermore, the operation of the air conditioning equipment is controlled based on these displacement amplitude, activity intensity, and activity time information, allowing the air conditioning equipment to proactively and promptly respond to changes in the user's activity state. Thus, when the user's activity state changes, the air conditioning equipment can effectively sense this change and quickly adjust its operating parameters accordingly, ensuring that the air conditioning equipment's operating state rapidly matches the changed user activity state. This allows the actual temperature environment in which the user is located to continuously match their actual needs arising from changes in activity state. This achieves a shift from passive response to proactive adaptation, effectively reducing manual operation by the user and improving the ease of use of the air conditioning equipment.

[0088] In some embodiments, such as Figure 6 As shown, in the process of controlling the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information, the controller is further configured to perform the following steps:

[0089] Step 602: Based on the preset scene matching rules, determine the target usage scene that matches the displacement amplitude information, activity intensity information, and activity time information;

[0090] Step 604: Control the air conditioning equipment to operate according to the operating mode corresponding to the target usage scenario.

[0091] It should be noted that with the rapid development of artificial intelligence technology, using deep learning models for data analysis and directly outputting optimized air conditioning control parameters has gradually become a cutting-edge research direction. However, such technical solutions still face significant challenges in practical deployment and application:

[0092] First, the training and optimization of the model highly depend on the accumulation of long-term, large-scale, and high-quality user behavior and comfort feedback data, and continuous iterative training is required to adapt to the personalized needs of different regions and families. This results in high costs for both data collection in the early stage and computing power investment for model maintenance in the later stage.

[0093] Secondly, the inference process of such models is typically computationally complex, posing a significant challenge to the local computing power of air conditioning equipment. Currently, the only solution is to upload the collected user activity data to the cloud, where a powerful computing server deployed in the cloud completes the entire model's inference calculations, and then sends the generated control commands to the terminal. This method is highly dependent on network bandwidth and stability. In scenarios with network fluctuations, latency, or limited bandwidth, it is difficult to guarantee the real-time performance of the control, severely impacting the air conditioning's response speed and the consistency of the user experience.

[0094] Finally, deep learning models themselves are "black box" characteristics, lacking transparency and interpretability in their decision-making process. When the control results output by the model do not match the user's actual experience, the user finds it difficult to understand the decision-making logic, nor can they manually intervene or correct the rules. This may lead to a decrease in the user's trust in the function and weaken their willingness to use it.

[0095] Among them, the preset scene matching rules can refer to a set of judgment logic or algorithms that are pre-stored in the controller and used to map the combination of displacement amplitude information, activity intensity information and activity time information to a specific use scenario.

[0096] As an example, the preset scenario matching rules can be a predefined set of conditional judgment logic statements. By comparing the displacement amplitude information, activity intensity information, and activity time information with the thresholds or conditions set in these conditional judgment logic statements, when all three pieces of information simultaneously meet all the conditions defined in a certain statement, it is determined that the current user activity matches the usage scenario corresponding to that statement, thereby identifying the target usage scenario.

[0097] As another example, the preset scenario matching rule can also be a preset mapping table. The index or key value of this mapping table consists of a combination of quantized ranges or discrete levels of displacement amplitude information, activity intensity information, and activity time information. Each entry in this mapping table is associated with a preset use scenario. By querying this mapping table based on the displacement amplitude information, activity intensity information, and activity time information, the corresponding target use scenario can be directly determined.

[0098] Use cases can refer to the classification of user activity states. As an example, use cases can include at least one of the following: daily life scenarios, away scenarios, sleep scenarios, exercise scenarios, office scenarios, and dining scenarios.

[0099] The target use case can refer to a use case that is determined based on displacement amplitude information, activity intensity information, and activity time information and matches the current user activity state.

[0100] Operating mode can refer to a set of coordinated operating parameters and control strategies that are predefined for air conditioning equipment to adapt to specific target usage scenarios.

[0101] In some feasible embodiments, the quantitative correlation between each preset usage scenario and displacement amplitude, activity intensity, and activity time indicators can be predefined based on expert experience, historical operating data, laboratory testing, or actual comfort requirements. Simultaneously, a set of operating mode parameters can be configured for each usage scenario.

[0102] In some feasible embodiments, during the actual use of the air conditioning equipment, users can adjust the quantitative correlation between various usage scenarios and displacement amplitude indicators, activity intensity indicators, and activity time indicators according to their actual needs. The controller can respond to user operation commands and personalize the quantitative correlation between usage scenarios and displacement amplitude indicators, activity intensity indicators, and activity time indicators. For example, assuming the activity time indicator for the sleep scenario is predefined as 22:00 to 8:00 the next day; for a user who works the night shift, their sleep time is usually from 8:00 to 18:00, and this user can adjust the activity time indicator for the sleep scenario to 8:00 to 18:00 according to their actual work and rest schedule; for users who have a habit of taking a nap, the activity time indicator for the sleep scenario can also be adjusted to 22:00 to 8:00 the next day, and 13:00 to 15:00.

[0103] In some feasible embodiments, during actual use of the air conditioning equipment, users can adjust the set of operating mode parameters corresponding to each usage scenario according to their actual needs. For example, assuming that the temperature parameter in the pre-configured operating mode parameter set for the office scenario is 25°C and medium fan speed; for users who are prone to feeling cold or are sensitive to airflow, this default setting may still feel too cold or uncomfortable due to the wind. Such users can adjust the temperature parameter in the pre-configured operating mode parameter set for the office scenario to 26°C, low fan speed, and anti-direct airflow; for users who use computers and other high-heat electronic devices for extended periods of time, this default setting may not be able to effectively offset the ambient temperature rise caused by the heat dissipation of the equipment. Such users can adjust the temperature parameter in the pre-configured operating mode parameter set for the office scenario to 24°C and high fan speed.

[0104] In some feasible embodiments, everyday scenarios correspond to daily living activities or light physical activity, such as a person walking around indoors, doing housework, or occasionally moving around while watching TV. Everyday scenarios indicate that the user is in a relaxed and leisurely state, with minimal activity but noticeable movement. The displacement amplitude index for everyday scenarios can be non-stationary activity, the activity intensity index can be low-intensity or moderate-intensity exercise, and the activity time index can be any period of time. The set of operating mode parameters for everyday scenarios can aim to provide a comfortable and pleasant environment, ensuring a cool environment while avoiding excessive cooling or direct airflow.

[0105] In some feasible implementations, the displacement amplitude, activity intensity, and activity time indicators corresponding to leaving the scene can be null values, meaning no user activity was detected. In this case, the air conditioning unit automatically switches to energy-saving mode or standby mode to reduce the prolonged idling of the air conditioning unit when no one is present.

[0106] In some feasible embodiments, the displacement amplitude index corresponding to the sleep scenario can be stationary activity, the activity intensity index can be stillness or low activity intensity, and the activity time index can be nighttime or midday, or other user-defined sleep periods. At this time, the air conditioning device automatically switches to sleep mode, with the control objective of providing a quiet environment where the temperature gradually adapts to the body's nighttime needs.

[0107] In some feasible embodiments, if the user is detected to be waking up in a sleep scenario, for example, if the activity intensity increases and continues for a period of time, the sleep mode can be exited and the user can gradually transition to a daily scenario or other matching usage scenario.

[0108] In some feasible embodiments, the displacement amplitude index corresponding to the exercise scenario can be stationary activity or non-stationary activity, the activity intensity index can be high activity intensity, and the activity time index can be any time period or a user-defined exercise time period. At this time, the air conditioning equipment automatically switches to exercise mode, appropriately increases the temperature, reduces the fan speed, and sets up anti-direct blowing to prevent the user from getting cold due to excessive cold air while sweating during exercise.

[0109] In some feasible implementations, the displacement amplitude index corresponding to the office scenario can be stationary activity, the activity intensity index can be low activity intensity and occasional medium activity intensity, and the activity time index can be the afternoon or a user-defined office time period. This scenario typically corresponds to a situation where someone is sitting still, standing still, or focused on something in the room. At this time, the air conditioning equipment automatically switches to office mode, prioritizing a quiet, comfortable environment and stable temperature to facilitate user focus.

[0110] In some feasible embodiments, the displacement amplitude index corresponding to the dining scenario can be stationary activity, the activity intensity index can be moderate activity intensity, and the activity time index can be early morning, noon, evening, or a user-defined dining time. At this time, the air conditioning equipment automatically switches to dining mode. Compared with the office scenario, the human body's metabolism increases slightly in the dining scenario, so the air conditioning can slightly increase the cooling intensity or decrease the heating temperature to offset the heat generated by multiple people gathering and eating. However, the overall strategy is to prioritize comfortable ventilation, creating a dining atmosphere with a suitable temperature, good air circulation, and no direct airflow.

[0111] In some embodiments, the controller uses the extracted displacement amplitude information, activity intensity information, and activity time information as input parameters, calls pre-stored preset scenario matching rules, comprehensively analyzes the input parameters, and selects the scenario that best represents the current overall state of user activity from multiple preset usage scenarios as the target usage scenario. Then, based on the set of operating mode parameters pre-configured for the target usage scenario, it generates an operating control command and sends the operating control command to the corresponding actuator. The actuator drives its associated physical components to perform actions based on the received operating control command, causing the air conditioning equipment to switch to the operating mode corresponding to the target usage scenario.

[0112] In this embodiment, regarding cost and deployment, the scene matching rules can be initially constructed and optimized offline through expert experience or small-sample learning, and can be fully deployed and run on the local controller of the air conditioning terminal device, greatly reducing data accumulation costs, computing resource investment, and deployment and maintenance complexity. Regarding real-time performance and reliability, in this embodiment, information extraction, scene matching, and pattern invocation can all be completed locally on the air conditioning device, effectively reducing dependence on cloud servers and stable network connections. This allows the air conditioning device to quickly respond to changes in user activity, and even in the event of a network interruption, the normal operation of the function is not affected, significantly improving the reliability of the function and the consistency of the user experience. Regarding interpretability and controllability, the preset scene matching rule logic is transparent and the results are traceable, making it easy for users to understand the scene identification logic and parameter adjustment logic. Furthermore, users can intervene and personalize the control strategy and control effect by adjusting rule thresholds or preset mode parameters. This transparent and controllable design effectively enhances users' trust and acceptance of the system.

[0113] In some embodiments, such as Figure 7 As shown, in the process of determining the target use scenario that matches the displacement amplitude information, activity intensity information, and activity time information according to the preset scenario matching rules, the controller is further configured to perform the following steps:

[0114] Step 702: Based on the preset scenario matching rules, determine the alternative use scenarios that match the displacement amplitude information, activity intensity information, and activity time information, and start timing;

[0115] Step 704: Return to the step of obtaining user activity data until the timer reaches the preset timer threshold;

[0116] Step 706: If the alternative use scenario changes, restart the timing and return to the step of obtaining user activity data until the timing reaches the preset timing threshold.

[0117] Step 708: When the timer reaches the preset timer threshold, the alternative use scenario is determined as the target use scenario.

[0118] It's important to note that brief, sporadic user activities (such as getting up to get water, bending over to pick up an object, or short periods of walking) can easily trigger mismatches in the scene and frequent switching of the air conditioning operating mode. For example, when a user is sitting still, standing still, or focused on something while working, their brief stillness might be misinterpreted as a sleep scene, automatically switching to a sleep mode with a higher temperature and lower fan speed. Although the system will switch back to the original mode once the user resumes normal slight activity, the user may already be experiencing discomfort due to the increased ambient temperature. As another example, if a user briefly gets out of bed and walks quickly to the bathroom during actual sleep, this relatively high-intensity short activity might be misinterpreted by the system as a medium-to-high-intensity activity scene, triggering a cooling mode with a lower temperature and increased fan speed. Although the system will switch back to sleep mode when the user returns to bed, the user's metabolism is still at a low level during the short period of getting up to use the restroom, and the sudden increase in cooling can easily lead to feeling too cold and causing discomfort.

[0119] Therefore, it can be seen that such scene misjudgment and mode oscillation caused by instantaneous activity interference not only cause frequent start-stop of air conditioners, fluctuations in compressor and fan load, and increased energy consumption and equipment wear, but also seriously damage the stability of the indoor environment due to the continuous changes in supply air temperature and wind speed, directly affecting the user's continuous comfort experience.

[0120] The preset timing threshold can refer to the shortest time required for the user activity state corresponding to a certain use scenario to be continuously maintained when a certain use scenario is identified as the target use scenario.

[0121] The preset timing thresholds may vary depending on the usage scenario. The specific threshold can be determined based on the actual situation and test results, and this embodiment does not impose any restrictions.

[0122] In some feasible embodiments, the preset timing thresholds for each use scenario are arranged from longest to shortest, which can be in the following order: sleep scenario, exercise scenario, office scenario, departure scenario, and dining scenario.

[0123] As an example, the preset timing threshold for the sleep scenario can be 60 minutes; the preset timing threshold for the exercise scenario can be 20 minutes; the preset timing threshold for the office scenario can be 15 minutes; the preset timing threshold for the departure scenario can be 10 minutes; and the preset timing threshold for the dining scenario can be 5 minutes.

[0124] In some feasible embodiments, the preset timing threshold for any usage scenario matched after exiting sleep mode can be 5 to 15 minutes to avoid misjudging getting up to go to the toilet as waking up.

[0125] In some embodiments, such as Figure 8 As shown, the controller takes the extracted displacement amplitude information, activity intensity information, and activity time information as input parameters, calls the pre-stored preset scenario matching rules, performs comprehensive analysis on the input parameters, and selects the usage scenario that best represents the current comprehensive state of user activity from multiple preset usage scenarios as a candidate usage scenario. The timer is started and the preset timing threshold corresponding to the candidate usage scenario is determined. Then, it returns to the step of obtaining user activity data, continuously monitors the user activity status, and determines whether the user activity status has changed.

[0126] If the alternative use scenario matched based on the new displacement amplitude information, activity intensity information, and activity time information is different from the previously determined alternative use scenario, it is determined that the alternative use scenario has changed, indicating that the user's activity state has changed. In this case, the new alternative use scenario replaces the previously determined alternative use scenario, instructs the timer to be reset to zero or restart, and re-determines the corresponding preset timing threshold based on the new alternative use scenario.

[0127] If the candidate use scenario matched based on the new displacement amplitude information, activity intensity information, and activity time information is the same as the previously determined candidate use scenario, then there is no need to update the candidate use scenario or reset the timer. Continue monitoring for changes in the user's activity status until the timer reaches the preset timer threshold corresponding to the candidate use scenario, at which point the candidate use scenario is determined as the target use scenario.

[0128] In this embodiment, the timing mechanism can effectively filter out short-term, occasional user activity noise (such as getting up, short walks, picking up items, etc.), thereby overcoming the technical defects of scene misjudgment and frequent pattern oscillation caused by momentary behavior interference. It can not only improve the stability of indoor environmental parameters, but also reduce the frequent load changes and start-stop of key components such as compressors and fans caused by pattern oscillation, which is conducive to reducing system energy consumption, reducing equipment mechanical wear, and improving overall reliability.

[0129] In some embodiments, such as Figure 9 As shown, the air conditioning unit is connected to the home IoT network, which in turn connects to at least one other household device; such as Figure 10 As shown, after determining the target usage scenario that matches the displacement amplitude information, activity intensity information, and activity time information according to the preset scenario matching rules, the controller is further configured to perform the following steps:

[0130] Step 1002: Generate linkage control commands based on the target usage scenario;

[0131] Step 1004: Send linkage control commands to other home devices through the home Internet of Things.

[0132] It's important to note that most household appliances currently operate in isolation, leading to significant shortcomings when facing complex life scenarios. Take a user's office environment as an example: a user might need to coordinate multiple devices such as air conditioning, lighting, and audio equipment. This involves manually lowering the air conditioning to counteract the heat generated by other devices, increasing the lighting to ensure adequate illumination, and turning on the audio system to play music. Even if each device could be adjusted independently based on the user's activity, the entire process still requires manual operation, resulting in a fragmented and cumbersome experience.

[0133] A deeper problem lies in the fact that, in an architecture lacking inter-device collaboration, multiple home appliances such as air conditioners, lights, and audio systems need to collect and store similar environmental and user activity data separately to achieve the same scenario goal, and each must run a complete set of sensing, analysis, and decision-making logic. This leads to data redundancy, duplicate occupation of storage resources, and significant waste of computing energy.

[0134] Among them, linkage control instructions can refer to a set or sequence of instructions generated by the controller based on the target usage scenario, which aims to coordinate multiple home devices to achieve the overall environmental goal under the target usage scenario.

[0135] In some embodiments, after determining the target usage scenario based on user activity information, the controller can invoke a cross-device linkage strategy pre-configured for the target usage scenario. This cross-device linkage strategy predefines other household devices that need to be controlled collaboratively in the current scenario, in addition to the air conditioner's own operating parameters, and their target operating parameters. Based on the cross-device linkage strategy, the controller transforms the scenario intent into a series of specific linkage control commands that can be recognized and executed by each of the other household devices. Subsequently, through its network interface and relying on the communication protocol of the home IoT, the controller addresses and sends the control commands for different other household devices in the linkage control commands to the corresponding other household devices, thereby achieving joint control of all household devices connected to the home IoT.

[0136] In this embodiment, by accessing the home Internet of Things, the target usage scenario identified by the air conditioner can be applied to the linkage control of other home appliances, effectively reducing the scene adaptation operations performed by users on multiple devices, and reducing the repeated data collection, storage and scene calculation of other home appliances, thereby saving storage space and hardware requirements of other home appliances, and reducing the overall energy consumption of the home and the purchase cost of home appliances.

[0137] In some embodiments, the non-imaging sensor includes a millimeter-wave radar sensor; the displacement amplitude information includes the amount of position change; the activity intensity information includes the current fluctuation amplitude; such as Figure 11 As shown, in the process of determining the target use scenario that matches the displacement amplitude information, activity intensity information, and activity time information according to the preset scenario matching rules, the controller is further configured to perform the following steps:

[0138] Step 1102: Determine the target displacement type of the user activity based on the change in position. If the change in position exceeds a preset change threshold, the target displacement type of the user activity is determined to be a non-stationary activity type. If the change in position does not exceed the preset change threshold, the target displacement type of the user activity is determined to be a stationary activity type.

[0139] Step 1104: Determine the current activity intensity level of the user activity based on the current fluctuation range. The current activity intensity level is positively correlated with the current fluctuation range.

[0140] Step 1106: By matching the activity time information with multiple preset time periods, determine the target time period in which the user's activity is located from each preset time period;

[0141] Step 1108: Based on the preset scene matching rules, determine the target usage scene that matches the target displacement type, the current activity intensity level, and the target time period.

[0142] It should be noted that directly using raw displacement amplitude, activity intensity, and activity time information for scene matching increases the computational burden on the controller and the complexity of the algorithm due to the multi-dimensionality, continuous numerical values, and complex meanings of the raw data. This also reduces the interpretability and debuggability of the matching results. Ultimately, overly complex rules or excessive coupling may affect the accuracy and real-time performance of scene recognition.

[0143] Among them, the change in location can refer to the quantitative measurement of the distance or range by which a user moves in space.

[0144] The preset change threshold can refer to the pre-set critical value for location change used to distinguish between in-situ activity types and non-in-situ activity types.

[0145] Non-stationary activities can refer to categories of activities in which users undergo significant changes in location within an indoor space. Examples include walking around the room, moving from the sofa to the dining table, or taking a walk indoors.

[0146] "Stationary activities" can refer to activities in which the user's body remains in a relatively constant spatial position. Examples include working while sitting in a chair, cooking while standing in a fixed position, and turning over in bed.

[0147] The current fluctuation amplitude can refer to the degree of drastic change in dynamic characteristic values ​​(such as radial velocity, signal energy, etc.) extracted from millimeter-wave radar echo signals that are related to the user's body movements.

[0148] As an example, the echo signal detected by the millimeter-wave radar sensor can be processed first. The radial velocity of the target user can be calculated by analyzing the Doppler frequency shift of the echo signal, and a radial velocity curve showing the change of radial velocity over time can be generated. Subsequently, by analyzing the radial velocity curve, the two key features of amplitude and frequency can be extracted, and the current fluctuation amplitude can be determined based on these features.

[0149] In some feasible embodiments, the peak amplitude or root mean square amplitude of the velocity curve can be calculated, and the peak amplitude or root mean square amplitude can be directly used as the fluctuation amplitude value. Vigorous physical activities of the user (such as jumping, rapid arm swinging, etc.) will produce large-scale velocity changes, resulting in high amplitude values.

[0150] In other feasible embodiments, a comprehensive fluctuation index can be calculated as the fluctuation amplitude value by extracting the dominant frequency of the velocity curve or analyzing its spectral energy, combined with the amplitude. For example, a high-frequency, high-amplitude velocity change can produce a large fluctuation amplitude value.

[0151] Activity intensity level refers to a graded quantitative indicator that represents the intensity of user activity, usually a discrete integer value. For example, activity intensity can be divided into inactivity, low intensity, medium intensity, and high intensity. The higher the current fluctuation range, the higher the activity intensity.

[0152] In some feasible embodiments, different thresholds can be set for different activity intensity levels. As an example, if the current fluctuation amplitude value is lower than a first threshold, the current activity intensity level is determined to be static; if the current fluctuation amplitude value is not lower than the first threshold but lower than a second threshold, the current activity intensity level is determined to be low; if the current fluctuation amplitude value is not lower than the second threshold but lower than a third threshold, the current activity intensity level is determined to be medium; and if the current fluctuation amplitude value is not lower than the third threshold, the current activity intensity level is determined to be high. Wherein, the first threshold is lower than the second threshold, the second threshold is lower than the third threshold, and the third threshold is lower than a fourth threshold.

[0153] In some embodiments, the controller compares the position change amount with a preset change amount threshold stored in the memory. If the position change amount exceeds the preset change amount threshold, it is determined that the user has made a significant overall positional movement, and the target displacement type is determined to be a non-stationary activity type. If the position change amount does not exceed the preset change amount threshold, it is determined that the user is basically staying in place, and the target displacement type is determined to be a stationary activity type.

[0154] The controller compares the current fluctuation amplitude with the preset fluctuation amplitude range or the upper and lower limits of the preset fluctuation amplitude range stored in the memory to determine the target fluctuation amplitude range to which the current fluctuation amplitude belongs, and then determines the activity intensity level corresponding to the target fluctuation range as the current activity intensity level.

[0155] The controller compares the activity time information with the start and end times of a preset time period stored in the memory, and determines the preset time period into which the activity time information falls as the target time period for the current user activity.

[0156] After determining the target displacement type, the current activity intensity level, and the target time period, the target displacement type, the current activity intensity level, and the target time period can be used as input parameters. Pre-stored preset scenario matching rules can be called to comprehensively analyze the input parameters and match the usage scenario that best represents the current comprehensive state of user activity from multiple preset usage scenarios as the target usage scenario.

[0157] In some feasible embodiments, the controller may simply compare the current time with the start and end times of a preset time period.

[0158] In other feasible embodiments, when the user activity data is time-series user activity data, the controller can compare the time range covered by the time-series user activity data with the start and end times of a preset time period. If the proportion of the overlap between the time range covered by the time-series user activity data and a certain preset time period exceeds a preset percentage threshold, then the time range covered by the time-series user activity data is determined to be within the preset time period.

[0159] In this embodiment, by first transforming the original, continuous displacement amplitude information, activity intensity information, and activity time information into three discretized and semantic intermediate features—target displacement type, current activity intensity level, and target time period—the subsequent scene matching process can be effectively simplified, thereby significantly reducing the computational complexity and workload of scene matching and improving the response speed of the air conditioning equipment. Furthermore, reasonable discretization and classification of the original data inherently possesses filtering and anti-interference properties, reducing the direct impact of data noise on the final scene determination result, thus improving the accuracy and stability of the identification.

[0160] In some embodiments, such as Figure 12 As shown, the controller is further configured to perform the following steps:

[0161] Step 1202: In response to the selection operation for the preset time period, the selected preset time period is determined as the time period to be configured;

[0162] Step 1204: In response to a custom operation for the period to be configured, adjust at least one of the start time and end time of the period to be configured.

[0163] It should be noted that while preset time periods can be pre-set based on general sleep patterns, these patterns are difficult to adapt to the unique and varied lifestyles of different user groups or individuals. For example, a preset time period for a sleep scenario based on a general sleep pattern might be from 10:00 PM to 8:00 AM the next day, but for night shift workers, freelancers, or travelers crossing time zones, their actual primary rest periods may be completely different from this preset time period. When using fixed preset time periods for scenario matching, it is easy to cause scenario recognition errors and malfunctions in air conditioning control.

[0164] In some embodiments, the controller first displays a time period setting interface via a display screen on the air conditioner, a display screen on the air conditioner's associated remote control, or a mobile application associated with the air conditioner. This interface presents pre-stored preset time period options corresponding to different usage scenarios. The controller continuously monitors for selection operations on any preset time period. When a selection operation is detected, the controller identifies the selected preset time period as the time period to be configured. Subsequently, time adjustment controls, such as a time selector, a timeline drag-and-drop interface, or a numeric input box, are provided on the user interface for the time period to be configured. The controller continuously listens for user customization operations on the time adjustment controls. When a customization operation is detected, the controller parses the operation content to determine the user's intent, which may include a custom modification object and custom parameter values. The controller adjusts at least one of the start and end times of the time period to be configured based on the custom modification object and the custom parameters.

[0165] In some feasible embodiments, the preset time period options can be displayed in the form of a list, which includes the name of the preset time period and the currently configured time boundary.

[0166] In some feasible embodiments, after determining the time period to be configured, the controller can also update the time period setting interface, highlight or otherwise visually identify the time period to be configured, and provide feedback on the selection result to the user.

[0167] In some feasible implementations, the custom modification object can be the start time, the end time, or both.

[0168] In other feasible embodiments, the custom modification object may also include the number of preset time periods corresponding to the usage scenario. For example, if the original sleep scenario only had a nighttime period, but the user has a habit of taking a nap, a midday period can be added.

[0169] In this embodiment, personalized customization of the time dimension for scene matching is achieved by selecting and customizing preset time periods. Users can flexibly adjust the range of preset time periods corresponding to different usage scenarios according to their actual life patterns, reducing misjudgments of scenarios caused by inconsistencies in work and rest schedules, providing users with operating parameters that better meet their actual needs, and improving the comfort experience.

[0170] In some embodiments, such as Figure 13 As shown, in the process of controlling the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information, the controller is further configured to perform the following steps:

[0171] Step 1302: Obtain the current air outlet parameter value of the air conditioning equipment, and determine the target air outlet parameter value of the air conditioning equipment based on the displacement amplitude information, activity intensity information and activity time information;

[0172] Step 1304: Determine the air outlet parameter adjustment step size based on the current air outlet parameter value and the target air outlet parameter value;

[0173] Step 1306: Adjust the air outlet parameters of the air conditioning equipment step by step according to the preset time interval and air outlet parameter adjustment step size.

[0174] It's important to note that immediately switching the airflow parameters from the current value to the new target value after detecting a change in the environment can easily cause drastic changes in indoor environmental parameters (especially perceived temperature and airflow intensity) within a short period. For example, after a user finishes exercising and returns to their normal routine, if the air conditioner suddenly lowers the airflow temperature and increases the fan speed significantly, it will create a noticeable shock of cold air, making the user feel uncomfortable and potentially causing health problems such as catching a chill. This rapid change in environmental parameters disrupts the continuity and stability of environmental comfort, contradicting the physiological characteristic that the human body needs to gradually adapt to changes in temperature and humidity.

[0175] Among them, the air supply parameter value can refer to the key operating variables that directly affect the air supply characteristics when the indoor unit of the air conditioning equipment delivers conditioned air to the indoor space. These variables are perceptible to the user and can be actively controlled by the system. They include at least one of the following: temperature, wind speed, air volume, wind direction, airflow pattern, humidity, etc.

[0176] The air outlet parameter adjustment step size refers to the amount of change in the air outlet parameter during a single adjustment action.

[0177] In some embodiments, the controller first reads the current air outlet parameter value from the air conditioner's operating status register or sensor feedback. Simultaneously, combining the acquired displacement amplitude information, activity intensity information, and activity time information, it calculates the target air outlet parameter value that the air conditioner should achieve, matching the current user activity state, using a preset calculation model or scene mapping rule. Then, the controller calculates the difference between the target air outlet parameter value and the current air outlet parameter value; this difference reflects the total adjustment required. Next, the controller determines the air outlet parameter adjustment step size according to a preset step size calculation rule. Then, the controller initiates a timed adjustment process, periodically sending adjustment commands to the air conditioner's actuator at preset time intervals. Each adjustment command instructs the actuator to adjust the corresponding air outlet parameter according to the air outlet parameter adjustment step size until the air outlet parameter value reaches or is infinitely close to the target air outlet parameter value.

[0178] In some feasible embodiments, the step size calculation rules may include: a fixed ratio method, such as 20% of the difference; a lookup table method, such as querying a preset step size table based on the size of the difference and the intensity of user activity; an adaptive algorithm, such as adaptively determining the step size by considering auxiliary parameters such as ambient temperature and humidity; etc.

[0179] In this embodiment, by introducing a step-by-step gradual adjustment mechanism, a smooth transition of airflow parameters is achieved during scene switching. This can effectively reduce the impact of sudden changes in wind feel on users. Especially in scenarios that are sensitive to airflow, such as sleeping and reading, users can hardly perceive the parameter change process. This conforms to the dynamic characteristics of human thermal comfort and can effectively improve comfort.

[0180] In some embodiments, such as Figure 14 As shown, during the process of extracting displacement amplitude information, activity intensity information, and activity time information of user activities based on user activity data, the controller is further configured to perform the following steps:

[0181] Step 1402: If it is determined from the user activity data that there are multiple users in the indoor space corresponding to the air conditioning equipment, extract the user activity sub-data corresponding to each user from the user activity data.

[0182] Step 1404: Detect the activity intensity value of each user based on the user activity data;

[0183] Step 1406: Extract the displacement amplitude information, activity intensity information, and activity time information of the user activity based on the user activity sub-data corresponding to the highest activity intensity value.

[0184] It should be noted that there may often be more than one user in the indoor space corresponding to the air conditioning unit. In this case, the air conditioning unit may not be able to determine how to make a decision.

[0185] User activity sub-data can refer to a subset of sensor data that is separated from user activity data and belongs to a specific individual user.

[0186] In some embodiments, after acquiring user activity data, the controller can first perform multi-target detection and tracking processing on the user activity data. If multiple independent movement trajectories or cluster centers are detected in the indoor space, it is determined that multiple independent users exist in the indoor space. Subsequently, the controller can perform clustering processing on the data points in the user activity data, thereby dividing the user activity data into user activity sub-data corresponding to different users. For each group of user activity sub-data obtained in step 1, the controller independently performs activity intensity analysis and calculates the activity intensity value corresponding to each user. Then, the numerical values ​​of the activity intensity values ​​of each user are compared, and the user activity sub-data corresponding to the highest activity intensity value is used as the sole data input source to perform subsequent quantitative feature extraction operations and air conditioning equipment operation control operations.

[0187] In this embodiment, high-activity-intensity data offers significant signal quality advantages in terms of data reliability. Users with higher activity levels typically generate physical signals (e.g., Doppler shift in millimeter-wave radar echoes, infrared energy changes, etc.) with larger amplitudes and more pronounced characteristics. This enables the system to achieve a higher signal-to-noise ratio and stronger anti-interference capabilities during target detection, trajectory tracking, and data extraction, effectively reducing the probability of data loss, misjudgment, or feature ambiguity. Therefore, using the user activity sub-data corresponding to the highest activity intensity as the decision-making basis can significantly improve the stability and accuracy of feature extraction, thereby enhancing the decision reliability of the entire control system. Regarding human thermal comfort needs, users in a high-intensity activity state generate more heat per unit time, making their need for cooling more urgent and sensitive. If the air conditioning system adopts a compromise strategy to pursue "average" comfort, it often results in high-activity-intensity users not receiving timely and sufficient cooling, leading to stuffy or even overheated discomfort. Air conditioning equipment responding according to high-intensity activity scenarios can effectively ensure timely and sufficient cooling for high-intensity activities.

[0188] In some embodiments, after extracting the activity intensity information corresponding to each user based on user activity data, the controller is further configured to perform the following steps:

[0189] In heating mode, based on the user activity sub-data corresponding to the lowest activity intensity value, the displacement amplitude information, activity intensity information, and activity time information of the user activity are extracted.

[0190] In some embodiments, when the controller detects that the air conditioner is currently operating in heating mode, the controller compares the activity intensity values ​​of each user, takes the user activity sub-data corresponding to the lowest activity intensity value as the only data input source, and performs subsequent quantitative feature extraction operations and air conditioning equipment operation control operations.

[0191] In this embodiment, under heating mode, users with the lowest activity levels are often the most heat-sensitive groups, such as the elderly, children, and patients, whose metabolic heat production rate is low and who are more dependent on ambient heating. In contrast, users with higher activity levels can generate enough heat to meet their thermal needs to some extent. With advancements in sensor and data processing technologies, the activities of users with the lowest activity levels in an indoor space should also be accurately sensed and tracked. Against this backdrop, quantitative feature extraction and air conditioning equipment operation control based on the user activity sub-data corresponding to the lowest activity levels can better ensure the thermal comfort needs of users with the lowest activity levels, the least self-generated heat, and the highest sensitivity to ambient temperature.

[0192] In some embodiments, a control method for an air conditioning device is provided, such as Figure 15 As shown, the method includes:

[0193] Step 1502: Acquire user activity data, which is obtained through non-imaging sensors;

[0194] Step 1504: Extract the displacement amplitude information, activity intensity information, and activity time information of the user activity based on the user activity data;

[0195] Step 1506: Control the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information.

[0196] In some embodiments, controlling the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information includes:

[0197] Based on preset scenario matching rules, determine the target usage scenario that matches the displacement amplitude information, activity intensity information, and activity time information;

[0198] Control the air conditioning equipment to operate according to the operating mode corresponding to the target usage scenario.

[0199] In some embodiments, a target usage scenario matching the displacement amplitude information, activity intensity information, and activity time information is determined according to a preset scenario matching rule, including:

[0200] Based on the preset scenario matching rules, select alternative usage scenarios that match the displacement amplitude information, activity intensity information, and activity time information, and start timing;

[0201] Return to the step of retrieving user activity data until the timer reaches the preset timer threshold;

[0202] If the alternative use case changes, the timing will restart and return to the step of obtaining user activity data until the timing reaches the preset timing threshold.

[0203] If the timer reaches the preset timer threshold, the alternative use scenario will be selected as the target use scenario.

[0204] In some embodiments, the air conditioning device is connected to a home Internet of Things (IoT), which in turn connects to at least one other household device; after determining a target usage scenario that matches the displacement amplitude information, activity intensity information, and activity time information according to preset scenario matching rules, the method further includes:

[0205] Generate linkage control commands based on the target usage scenario;

[0206] Send linkage control commands to other home devices through the home Internet of Things.

[0207] In some embodiments, the non-imaging sensor includes a millimeter-wave radar sensor; the displacement amplitude information includes the amount of position change; the activity intensity information includes the current fluctuation amplitude; and a target usage scenario matching the displacement amplitude information, activity intensity information, and activity time information is determined according to a preset scene matching rule, including:

[0208] The target displacement type of user activity is determined based on the change in location. If the change in location exceeds a preset threshold, the target displacement type of user activity is determined to be a non-stationary activity type. If the change in location does not exceed the preset threshold, the target displacement type of user activity is determined to be a stationary activity type.

[0209] The current activity intensity level of the user activity is determined based on the current fluctuation range, and the current activity intensity level is positively correlated with the current fluctuation range;

[0210] By matching activity time information with multiple preset time periods, the target time period in which the user's activity occurs can be determined from each preset time period;

[0211] Based on preset scenario matching rules, determine the target usage scenario that matches the target displacement type, current activity intensity level, and target time period.

[0212] In some embodiments, the method further includes:

[0213] In response to the selection operation for a preset time period, the selected preset time period is determined as the time period to be configured;

[0214] In response to a custom operation for the period to be configured, adjust at least one of the start and end times of the period to be configured.

[0215] In some embodiments, controlling the operation of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information includes:

[0216] Obtain the current air outlet parameter values ​​of the air conditioning equipment, and determine the target air outlet parameter values ​​of the air conditioning equipment based on displacement amplitude information, activity intensity information, and activity time information;

[0217] Determine the air outlet parameter adjustment step size based on the current air outlet parameter value and the target air outlet parameter value;

[0218] The air outlet parameters of the air conditioning equipment are adjusted step by step according to the preset time interval and the adjustment step size of the air outlet parameters.

[0219] In some embodiments, extracting displacement amplitude information, activity intensity information, and activity time information of user activities based on user activity data includes:

[0220] When it is determined from user activity data that there are multiple users in the indoor space corresponding to the air conditioning equipment, extract the user activity sub-data corresponding to each user from the user activity data.

[0221] Detect the activity intensity value of each user based on user activity data;

[0222] Based on the user activity sub-data corresponding to the highest activity intensity value, extract the displacement amplitude information, activity intensity information, and activity time information of the user activity.

[0223] In some embodiments, after detecting the activity intensity value corresponding to each user based on user activity data, the controller, the method further includes:

[0224] In heating mode, based on the user activity sub-data corresponding to the lowest activity intensity value, the displacement amplitude information, activity intensity information, and activity time information of the user activity are extracted.

[0225] In this embodiment, user activity data is first collected using a non-imaging sensor, enabling effective perception of user activity while fully protecting user privacy. Since non-imaging sensors do not acquire image or video information, they can technically balance perception capability and privacy protection. Subsequently, displacement amplitude, activity intensity, and activity time information are extracted from the user activity data to achieve a quantitative representation of the user's activity state. Furthermore, the operation of the air conditioning equipment is controlled based on these displacement amplitude, activity intensity, and activity time information, allowing the air conditioning equipment to proactively and promptly respond to changes in the user's activity state. Thus, when the user's activity state changes, the air conditioning equipment can effectively sense this change and quickly adjust its operating parameters accordingly, ensuring that the air conditioning equipment's operating state rapidly matches the changed user activity state. This allows the actual temperature environment in which the user is located to continuously match their actual needs arising from changes in activity state. This achieves a shift from passive response to proactive adaptation, effectively reducing manual operation by the user and improving the ease of use of the air conditioning equipment.

[0226] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the methods of the above embodiments.

[0227] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the methods of the above embodiments.

[0228] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods of the above embodiments.

[0229] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0230] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0231] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0232] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An air conditioning device, characterized in that, include: Non-imaging sensors are configured to collect user activity data; The controller is configured as follows: Acquire user activity data, which is obtained through non-imaging sensors; Based on the user activity data, extract the displacement amplitude information, activity intensity information, and activity time information of the user activity; The operation of the air conditioning equipment is controlled based on the displacement amplitude information, the activity intensity information, and the activity time information.

2. The air conditioning equipment according to claim 1, characterized in that, In the process of controlling the operation of the air conditioning equipment based on the displacement amplitude information, the activity intensity information, and the activity time information, the controller is further configured to: Based on preset scenario matching rules, a target usage scenario that matches the displacement amplitude information, the activity intensity information, and the activity time information is determined; Control the air conditioning equipment to operate according to the operating mode corresponding to the target usage scenario.

3. The air conditioning equipment according to claim 2, characterized in that, In the process of determining a target usage scenario that matches the displacement amplitude information, the activity intensity information, and the activity time information according to preset scenario matching rules, the controller is further configured to: According to the preset scenario matching rules, a candidate usage scenario that matches the displacement amplitude information, the activity intensity information, and the activity time information is determined, and the timing is started; Return to the step of obtaining user activity data until the timer reaches the preset timer threshold; If the alternative use case changes, the timing restarts and returns to the step of obtaining user activity data until the timing reaches the preset timing threshold. If the timer reaches a preset timer threshold, the alternative use scenario will be determined as the target use scenario.

4. The air conditioning equipment according to claim 2, characterized in that, The air conditioning equipment is connected to a home Internet of Things (IoT), which in turn connects to at least one other household device. After determining the target usage scenario that matches the displacement amplitude information, the activity intensity information, and the activity time information according to the preset scenario matching rules, the controller is further configured to: Generate linkage control commands based on the target usage scenario; The home IoT system sends the linkage control commands to each of the other home devices.

5. The air conditioning equipment according to claim 2, characterized in that, The non-imaging sensors include millimeter-wave radar sensors; the displacement amplitude information includes the amount of position change; the activity intensity information includes the current fluctuation amplitude. In the process of determining a target usage scenario that matches the displacement amplitude information, the activity intensity information, and the activity time information according to preset scenario matching rules, the controller is further configured to: The target displacement type of the user activity is determined based on the change in position, wherein if the change in position exceeds a preset change threshold, the target displacement type of the user activity is determined to be a non-stationary activity type; if the change in position does not exceed the preset change threshold, the target displacement type of the user activity is determined to be a stationary activity type. The current activity intensity level of the user activity is determined based on the current fluctuation amplitude, and the current activity intensity level is positively correlated with the current fluctuation amplitude; By matching the activity time information with multiple preset time periods, the target time period in which the user's activity is located is determined from each preset time period; Based on preset scenario matching rules, a target usage scenario is determined that matches the target displacement type, the current activity intensity level, and the target time period.

6. The air conditioning equipment according to claim 5, characterized in that, The controller is further configured to: In response to the selection operation for a preset time period, the selected preset time period is determined as the time period to be configured; In response to a custom operation for the period to be configured, at least one of the start time and end time of the period to be configured is adjusted.

7. The air conditioning equipment according to any one of claims 1 to 6, characterized in that, In the process of controlling the operation of the air conditioning equipment based on the displacement amplitude information, the activity intensity information, and the activity time information, the controller is further configured to: Obtain the current air outlet parameter value of the air conditioning equipment, and determine the target air outlet parameter value of the air conditioning equipment based on the displacement amplitude information, the activity intensity information, and the activity time information; The air outlet parameter adjustment step size is determined based on the current air outlet parameter value and the target air outlet parameter value; The air outlet parameters of the air conditioning equipment are adjusted step by step according to the preset time interval and the adjustment step size of the air outlet parameters.

8. The air conditioning equipment according to any one of claims 1 to 6, characterized in that, In the process of extracting displacement amplitude information, activity intensity information, and activity time information of user activities based on the user activity data, the controller is further configured to: If, based on the user activity data, it is determined that there are multiple users in the indoor space corresponding to the air conditioning equipment, then each user's corresponding user activity sub-data is extracted from the user activity data. Detect the activity intensity value of each user based on the user activity data; Based on the user activity sub-data corresponding to the highest activity intensity value, extract the displacement amplitude information, activity intensity information, and activity time information of the user activity.

9. The air conditioning equipment according to claim 8, characterized in that, After detecting the activity intensity value corresponding to each user based on the user activity data, the controller is further configured to: In heating mode, based on the user activity sub-data corresponding to the lowest activity intensity value, the displacement amplitude information, activity intensity information, and activity time information of the user activity are extracted.

10. A control method for an air conditioning device, characterized in that, The method includes: Acquire user activity data, which is obtained through non-imaging sensors; Based on the user activity data, extract the displacement amplitude information, activity intensity information, and activity time information of the user activity; The operation of the air conditioning equipment is controlled based on the displacement amplitude information, the activity intensity information, and the activity time information.

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