Air conditioner temperature control method and device, air conditioner and storage medium

Abstract

The application discloses an air conditioner temperature control method and device, an air conditioner and a storage medium. The application relates to the technical field of air conditioners, and comprises the following steps: collecting a heat source distribution image of an indoor environment, and determining the number of human bodies on a bed body and the position coordinates of the human bodies in the indoor environment according to the heat source distribution image; obtaining user setting information, and generating a partition control parameter according to the user setting information, the number of human bodies and the position coordinates of the human bodies; and controlling the temperature of an air conditioner according to the partition control parameter. According to the application, the number of human bodies on the bed body and the position coordinates of the human bodies are determined according to the heat source distribution image of the indoor environment, the partition control parameter is generated according to the user setting information, the number of human bodies and the position coordinates of the human bodies, and the temperature of the air conditioner is controlled, so that the different body temperature regulation requirements of each person are met, and the comfort of the air conditioner when multiple people sleep together is improved.

Description

Technical Field

[0001] This invention relates to the field of air conditioning technology, and in particular to an air conditioner temperature control method, device, air conditioner, and storage medium. Background Technology

[0002] As people pay increasing attention to sleep quality, air conditioners, as important devices for regulating the sleep environment, directly impact sleep comfort with their level of intelligence and personalization. Traditional air conditioners typically only offer a fixed "sleep curve" function, which gradually adjusts the temperature within a set sleep period to simulate the body's nighttime metabolic changes. For example, after activating the sleep function for one hour, the set temperature is fixedly raised or lowered by 1°C, and then raised or lowered by 1°C again after two hours. However, this method cannot meet the different temperature regulation needs of each individual in a shared sleeping environment, thus failing to ensure comfort for everyone throughout the sleep process. Summary of the Invention

[0003] This invention provides an air conditioner temperature control method, device, air conditioner, and storage medium, aiming to solve the problem of low comfort in existing air conditioners when multiple people are sleeping together.

[0004] In a first aspect, embodiments of the present invention provide an air conditioner temperature control method, comprising: Acquire images of heat source distribution in the indoor environment, and determine the number of people on the bed and their location coordinates based on the images. The system acquires user-defined information, generates zone control parameters based on the user-defined information, the number of human bodies, and the coordinates of the human body positions, and controls the temperature of the air conditioner based on the zone control parameters.

[0005] Secondly, embodiments of the present invention also provide an air conditioner temperature control device, comprising: The acquisition and determination unit is used to acquire images of the heat source distribution in the indoor environment and determine the number of people on the bed and their location coordinates based on the images of the heat source distribution. A control unit is generated to acquire user-defined information, generate zone control parameters based on the user-defined information, the number of human bodies, and the coordinates of the human body positions, and control the temperature of the air conditioner based on the zone control parameters.

[0006] Thirdly, embodiments of the present invention also provide an air conditioner including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0007] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0008] This invention provides an air conditioner temperature control method, device, air conditioner, and storage medium. The method includes: acquiring a heat source distribution image of an indoor environment, and determining the number and coordinates of people on a bed in the indoor environment based on the heat source distribution image; acquiring user-defined information, and generating zone control parameters based on the user-defined information, the number of people, and the coordinates of the people; and controlling the temperature of the air conditioner according to the zone control parameters. The technical solution of this invention, by determining the number and coordinates of people on a bed based on a heat source distribution image of the indoor environment, and generating zone control parameters based on user-defined information, the number of people, and the coordinates of the people, to control the temperature of the air conditioner, thereby meeting the different temperature regulation needs of each individual and improving the comfort of the air conditioner when multiple people are sleeping together. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of an air conditioner temperature control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a sub-process of an air conditioner temperature control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of another sub-process of an air conditioner temperature control method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of another sub-process of an air conditioner temperature control method provided in an embodiment of the present invention; Figure 5 A schematic flowchart of an air conditioner temperature control method provided in another embodiment of the present invention; Figure 6 A schematic flowchart of an air conditioner temperature control method provided in another embodiment of the present invention; Figure 7 A simplified flowchart of an air conditioner temperature control method according to an embodiment of the present invention; Figure 8 A schematic block diagram of an air conditioner temperature control device provided in an embodiment of the present invention; Figure 9 This is a schematic block diagram of an air conditioner provided in an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0013] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0014] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0015] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0016] Please see Figure 1 , Figure 1 This is a schematic flowchart of an air conditioner temperature control method according to an embodiment of the present invention. The air conditioner temperature control method will be described in detail below. Figure 1 As shown, the method includes the following steps S110-S120.

[0017] S110. Collect images of the heat source distribution in the indoor environment, and determine the number of people on the bed and their location coordinates based on the images of the heat source distribution.

[0018] In this embodiment of the invention, an infrared thermal imaging array is used to acquire images of the heat source distribution in the indoor environment. Understandably, the infrared thermal imaging array is a high-resolution infrared thermal imaging array, mounted on the panel of the air conditioner to facilitate the acquisition of heat source distribution images. After acquiring the heat source distribution images, the number of people on the bed and their location coordinates in the indoor environment are determined based on these images.

[0019] In this embodiment, such as Figure 2 As shown, step S110 specifically includes steps S111-S115: S111. Preprocess the heat source distribution image to obtain a heat source region image; S112. Extract candidate heat source regions from the heat source region image and calculate the morphological feature parameters of the candidate heat source regions; S113. Filter out non-human areas from the candidate heat source areas according to the preset morphological feature threshold and the morphological feature parameters to obtain effective human heat source areas; S114. Extract the centroid coordinates of the effective human heat source region, and use a clustering algorithm based on the centroid coordinates to determine the number of human bodies on the bed in the indoor environment; S115. Normalize the centroid coordinates to obtain the human body position coordinates of each human body on the bed, wherein the human body position coordinates include lateral position coordinates and longitudinal position coordinates.

[0020] In this embodiment of the invention, step S111 specifically includes: performing noise filtering on the heat source distribution image to obtain a filtered heat source distribution image; performing background thermal field modeling on the filtered heat source distribution image to obtain a modeled heat source distribution image; and performing threshold segmentation on the modeled heat source distribution image to obtain the heat source region image. Preprocessing the heat source distribution image is a crucial foundation for subsequent accurate identification. This step aims to eliminate noise interference and enhance the effective signal. Specifically, firstly, a Gaussian filtering algorithm or a median filtering algorithm is used to effectively filter out random noise points caused by the sensor's own thermal noise or minor airflow disturbances in the environment, smoothing the image data to obtain the filtered heat source distribution image. Subsequently, background thermal field modeling is performed, that is, by taking the average value of multiple samples, a temperature distribution benchmark model including static heat sources such as the bed, quilt, and pillow is established. This temperature distribution benchmark model is subtracted from the real-time image, thereby significantly enhancing the contrast between dynamic heat sources such as the human body and the background environment. Finally, through threshold segmentation, a temperature threshold is set, such as 28°C, to convert the modeled heat source distribution image into a binary heat source region image. Pixels with temperatures above the threshold are marked as potential human heat sources, while those below the threshold are considered background. Connected Component Labeling (CCL) is used to analyze the binary heat source region image to identify and label all connected pixel blocks, each initially defined as a candidate heat source region. For each labeled candidate heat source region, a series of morphological feature parameters are calculated for subsequent discrimination. These morphological feature parameters include: area (the total number of pixels contained within the connected candidate heat source region); bounding box (describing the approximate range and orientation of the candidate heat source region in the heat source region image); aspect ratio (the ratio of the width to the height of the bounding box, used to initially determine the shape of the candidate heat source region); and contour complexity, typically evaluated by calculating the ratio of the contour perimeter to the area or the difference from circularity.

[0021] Furthermore, based on preset morphological feature thresholds, the candidate heat source regions are filtered to eliminate non-human areas, thereby selecting valid human heat source regions. These discrimination thresholds are typically set as follows: the region area must be between 100 and 5000 pixels to exclude excessively small noise points (such as thermal noise) or excessively large heat sources (such as heated furniture under sunlight); the region aspect ratio must be between 0.2 and 5.0 to ensure that its shape roughly conforms to the morphological characteristics of a human lying posture, thereby excluding strip-shaped (such as wires) or square (such as heating pads) interference; the contour complexity must also be within a set range, as overly regular or extremely irregular shapes are usually non-human. After obtaining the valid human heat source regions, the centroid coordinates (cx, cy) of each valid human heat source region are calculated. The centroid coordinates (cx, cy) are obtained by calculating the average of the coordinates of all pixels within the valid human heat source region, representing the geometric center position of the heat source in the heat source region image. Subsequently, based on the centroid coordinates of all identified areas, a clustering algorithm, such as K-means clustering or density-based DBSCAN clustering, is used to analyze the centroid coordinates. The clustering algorithm automatically identifies different clusters based on the spatial aggregation of coordinate points; each cluster is considered an independent individual, and the number of clusters represents the number of people identified. This method effectively handles situations where multiple people are close together and heat source areas may become adhered.

[0022] Furthermore, to transform the position in the image coordinate system into physical location information that guides air conditioning airflow, the centroid coordinates are normalized. Using the physical dimensions of the bed as a reference, the pixel width (W) and height (H) of the image are mapped to a normalized coordinate system of 0 to 1. For each centroid coordinate (cx, cy), its corresponding normalized lateral position coordinate is X = (cx − X min) / (X max -X min ), where X min X represents the pixel coordinates of the leftmost edge of the bed in the heat source distribution image. max Let X be the pixel coordinates of the rightmost edge of the bed in the heat source distribution image. Understandably, if X is 0.5, it indicates the person is lying in the center of the bed; if X is 0, it indicates the person is close to the left side of the bed; if X is 1, it indicates the person is close to the right side of the bed. The normalized vertical position coordinate is Y = (cx−Y). min) / (Y max -Y min ), where Y min Y represents the pixel coordinates of the topmost part of the bed (usually near the head of the bed) in the heat source distribution image; maxThis represents the pixel coordinates of the bottom of the bed (usually near the foot of the bed) in the heat source distribution image. Understandably, if Y = 0.5, it indicates the person is lying in the center of the bed; if Y = 0, it indicates the person is lying at the top (head); and if Y = 1, it indicates the person is lying at the bottom (foot). The final output is the coordinates of each person's position on the bed, providing core input parameters for subsequent zoned airflow control.

[0023] It should be noted that in this embodiment, a user ID (e.g., UserID is U1) is assigned to each valid human heat source region. The nearest neighbor tracking algorithm is used to maintain the consistency of the user ID, avoiding frequent changes in the user ID when turning over. The horizontal position coordinates (X) and vertical position coordinates (Y) of each user are mapped to the [0,1] interval to form standardized coordinates. The output is in JSON format as follows: [{"UserID": "U1", "X": 0.3, "Y": 0.6}, {"UserID": "U2", "X": 0.8, "Y": 0.6}] S120. Obtain user setting information, and generate zoning control parameters based on the user setting information, the number of human bodies, and the coordinates of the human body positions, and control the temperature of the air conditioner based on the zoning control parameters.

[0024] In this embodiment of the invention, user-defined information is obtained, such as... Figure 3As shown, step S120 specifically includes steps S121-S123: S121, if the number of human bodies is N, then N independent air zones are generated, where N≥1; S122, for each independent air zone, the independent air zone is bound to the corresponding human body position coordinates, and a unique identifier is assigned to the independent air zone; S123, an initial temperature control curve is generated according to the user setting information, and the zoning control parameters are generated according to the initial temperature control curve and the user setting information. Specifically, if the number of human bodies identified is N (N≥1), then N corresponding independent air zones are automatically generated. Specifically, each independent air zone corresponds one-to-one with each identified user, ensuring that each user enjoys a dedicated temperature control area. Subsequently, each independent air zone is dynamically bound to the corresponding user's human body coordinates: through a coordinate mapping algorithm, the normalized horizontal position coordinates (X-coordinate) of the human body are associated with the horizontal air delivery range of the air zone, and the vertical position coordinates (Y-coordinate) are associated with the air delivery angle and distance. A unique identifier (such as Zone-L, Zone-C, Zone-R) is assigned to each air zone to achieve precise matching between the air zone and the user. After the air zone binding is completed, an initial personalized initial temperature control curve is generated based on the user information. This initial temperature control curve comprehensively considers sleep stage characteristics (e.g., cooling during sleep onset, constant temperature during deep sleep, and warming up before waking), and is adjusted according to the initial ambient temperature. Finally, based on the initial temperature control curve and user settings, zone control parameters are generated for each independent air zone. These parameters include target temperature, fan speed level, and air direction angle, and are output to the control command via a JSON format instruction set to achieve precise zone air delivery control.

[0025] It should be noted that if there are 2 users, two independent air zones are defined. If the user's X-coordinate is < 0.5, it is bound to Zone-L; if the user's X-coordinate is ≥ 0.5, it is bound to Zone-R. Similarly, if there are 3 users, three independent air zones are defined. If the user's X-coordinate is ≤ 0.3, it is bound to Zone-L; if the user's X-coordinate is 0.3 < X < 0.6, it is bound to Zone-C; if the user's X-coordinate is ≥ 0.6, it is bound to Zone-R. It should also be noted that the air conditioner in this embodiment supports independent control of temperature, fan speed, and air direction for the left and right air ducts (or multiple air outlets). Specifically, this includes: each air duct is equipped with an independent electronic expansion valve or damper system; the air direction motor controls the left / right / up / down air delivery angle; the fan speed is adjusted by a variable frequency fan, with a low-noise mode suitable for sleep scenarios; and the temperature is controlled by the variable frequency compressor's cooling / heating capacity matching the temperature control curve. The control module in the air conditioner reads the target temperature at the current time every minute and sends control commands to the bound air zone, for example: json { "Zone-L": { "set_temp": 24.5, "fan_speed": "low", "swing_angle": 30° } } In practical applications, the air conditioner's control module uses I... 2 C or PWM signals drive the motor and compressor to work together.

[0026] Furthermore, the user settings information includes sleep duration and temperature preferences, such as... Figure 4As shown, step S123 specifically includes steps S1231-S1233: S1231, based on the user's identity identifier, retrieves historical sleep data from the database within the same time period within the most recent preset number of days, using the sleep time period as the query condition; S1232, constructs a sleep temperature curve model based on the historical sleep data and the temperature preference; S12333, acquires environmental data, and generates the initial temperature curve based on the environmental data and the sleep temperature curve model. Specifically, based on the user's identity identifier, and using the set sleep time period as the key query condition, sleep data within the same time period within the most recent 7 to 30 days is retrieved from the database. This data typically includes the distribution characteristics of each sleep stage (such as sleep onset, deep sleep, light sleep, and wakefulness), corresponding environmental temperature and humidity records, and user thermal comfort feedback indirectly obtained through an infrared thermal imaging array. This process aims to identify the user's sleep patterns and temperature preference patterns within this specific time period. Then, the retrieved historical sleep data is fused and analyzed with the user's actively set temperature preference. A personalized sleep temperature curve model, encompassing the optimal temperature range for each sleep stage, is trained using machine learning algorithms (such as cluster analysis or Bayesian neural networks). This model defines a gradient cooling strategy during sleep onset, a constant temperature maintenance strategy during deep sleep, a slow temperature increase strategy during light sleep, and a rapid temperature increase strategy before wakefulness, and determines the slope of temperature changes between each stage. Finally, real-time environmental data, including ambient temperature and humidity, is acquired. The environmental data is then adapted to the constructed sleep temperature curve model. For example, if the current ambient temperature differs from the model's baseline temperature, the entire curve is shifted to compensate; if the ambient humidity is too high, the target temperature value is appropriately lowered to counteract the stuffiness. After this environmental parameter adaptation and adjustment, an executable initial temperature curve is generated that perfectly adapts to the current environmental conditions. It should be noted that during the generation of the initial temperature curve, the entire night's sleep period is first divided into multiple finely segmented time periods (e.g., one hour per period), and temperature control points are set at key nodes in each period. Based on users' historical sleep data and temperature preferences, linear interpolation or cubic spline curve fitting algorithms are used to generate a smooth, continuous temperature change sequence, ensuring a natural and comfortable temperature transition. Simultaneously, parameters such as real-time ambient temperature and user-set wake-up time are incorporated to apply boundary constraints to the curve, preventing discomfort caused by sudden temperature changes. In practice, a personalized sleep temperature control strategy table is created for each user, storing the target temperature values ​​for each time point in JSON format, for example: json { {"time": "00:00", "target_temp": 25}, {"time": "02:00", "target_temp": 24.5}, {"time": "04:00", "target_temp": 24}, {"time": "06:30", "target_temp": 25.5} } The temperature control strategy table is intelligently linked to the user's individual airflow zone, ensuring that each zone executes its corresponding personalized initial temperature control curve. During curve execution, it continuously monitors changes in ambient temperature and user physiological feedback, dynamically adjusting temperature settings for subsequent periods to achieve closed-loop optimized control. This time-segmented, smoothly transitioning temperature control method not only meets the physiological needs of each sleep stage but also avoids discomfort caused by sudden temperature changes, significantly improving sleep comfort.

[0027] Figure 5 A schematic flowchart of an air conditioner temperature control method according to another embodiment of the present invention is shown below. Figure 5 As shown, in this embodiment, the method includes steps S110-S150. That is, in this embodiment, after step S120 in the above embodiment, the method further includes steps S130-S150.

[0028] S130. If the turning frequency is detected to be greater than the preset turning frequency, the initial temperature control curve is adjusted to obtain the target temperature control curve, and the zone control parameters are adjusted according to the target temperature control curve. S140. If the user's body temperature is detected to be rising continuously, the temperature rise mode is entered to adjust the initial temperature control curve to obtain the target temperature control curve, and the step of adjusting the zone control parameters according to the target temperature control curve is executed. S150. Control the temperature of the air conditioner according to the adjusted zone control parameters, and save the target temperature control curve to the database; In this embodiment of the invention, the user's body movement is continuously monitored by an infrared thermal imaging array. When a turning frequency greater than 15 times / hour is detected, environmental sensor data is read: if the room temperature is less than 22°C, it is determined that the environment is too cold and causing discomfort, and the set temperature is increased by 0.5°C; if the room temperature is greater than 24°C, it is determined that the environment is too hot and causing discomfort, and the set temperature is decreased by 0.5°C; if the room temperature is within the comfortable range of 22-24°C, the current temperature setting is maintained. During the temperature adjustment process, the restriction that the single adjustment range does not exceed ±0.5°C and the cumulative adjustment range does not exceed ±1°C is strictly enforced to ensure that the temperature control changes smoothly and naturally. When the infrared thermal imaging array detects a continuous rise in the user's body temperature, multi-dimensional data analysis is initiated: if the conditions of rising body temperature, frequent turning over, and room temperature < 22℃ are met simultaneously, it is determined as "increased metabolism to resist cold," and a 0.5℃ temperature increase is performed; if the conditions of rising body temperature, less turning over, and room temperature > 24℃ are met simultaneously, it is determined as "the environment is too hot or the covering is too thick," and a 0.5℃ temperature decrease is performed; if the body temperature rises but the ambient temperature is within the comfortable range of 22-24℃, the current setting is maintained, and only relevant data is recorded for subsequent analysis. All judgment logic is based on at least 30 minutes of data trend analysis to avoid erroneous adjustments due to instantaneous fluctuations. The body temperature curve, turning over frequency, environmental parameters, and adjustment effects are recorded every night, and a user-specific sleep temperature control model is established through machine learning algorithms. The target temperature control curve after each adjustment is timestamped and encrypted and stored in the database as a training sample for optimizing the next sleep curve, realizing the continuous evolution of personalized temperature control strategies.

[0029] Figure 6 This is a flowchart illustrating an air conditioner temperature control method according to another embodiment of the present invention, as shown below. Figure 6 As shown, in this embodiment, the method includes steps S110-S160. That is, in this embodiment, the method further includes step S160 after step S150 in the above embodiment.

[0030] S160. If the user setting information includes a wake-up comfort strategy, the wake-up mode is activated within a first preset time before the preset wake-up time to adjust the ambient temperature using a stepped heating strategy, and the target comfort temperature is reached within a second preset time before the preset wake-up time.

[0031] In this embodiment of the invention, if the user settings include a wake-up comfort strategy, the wake-up comfort strategy is activated before the user's set wake-up time (e.g., 07:00). Specifically, the wake-up mode is automatically entered at a first preset time before the preset wake-up time (e.g., 30 minutes earlier, i.e., 06:30). In this mode, a stepped heating strategy is used to adjust the ambient temperature. If the latter half of sleep is in cooling mode, the ambient temperature will be gradually adjusted to the target comfortable temperature set in the morning (e.g., 26°C) at a gentle rate of 0.3–0.5°C every 10 minutes. At the same time, the air conditioner fan speed is automatically adjusted to medium or gentle mode to avoid strong airflow causing user discomfort. In addition to temperature adjustment, smart lighting devices can also be linked to gradually turn on soft night lights or simulated natural light at a second preset time (e.g., 10 minutes before wake-up) to further enhance wake-up comfort through photothermal synergy.

[0032] Please see Figure 7 , Figure 7 This is a simplified flowchart illustrating an air conditioner temperature control method according to an embodiment of the present invention. Figure 7 As shown, the air conditioner temperature control method of the present invention includes user preference setting, location sensing, zone matching, temperature control curve generation, independent airflow zone control, feedback adjustment, and air conditioner control. Specifically, user preference setting involves the user setting personal sleep temperature, fan speed, and other preferred parameters via an app or remote control. Location sensing uses an infrared thermal imaging array to identify the number and spatial distribution of people on the bed. Zone matching maps the position of people on the bed to the air outlet area of ​​the air conditioner, establishing a one-to-one binding relationship. Temperature control curve generation generates a personalized temperature control curve based on user preferences and environmental characteristics. Independent airflow zone control independently controls parameters such as air outlet temperature, fan speed, and airflow direction for each zone. Feedback adjustment collects user status and environmental change data in real time and adjusts the initial temperature control curve. Air conditioner control involves receiving control commands and driving components such as dampers, louvers, fans, and compressors to complete control actions. For ease of understanding, examples are given below: 1. Scene Initialization and User Recognition: On a summer night, parents (using a double bed) and their child (using a crib) sleep together in a bedroom. After the air conditioner is turned on, an infrared thermal imaging array scans the sleeping area, acquiring images of heat source distribution. After image preprocessing, human contour extraction, and cluster analysis, three valid human heat source regions are successfully identified, and their normalized position coordinates are calculated: U1 (Father): Located on the left side of the double bed, coordinates (X=0.3, Y=0.6) U2 (Mother): Located on the right side of the double bed, coordinates (X=0.8, Y=0.6) U3 (Child): Located at the crib, coordinates (X=0.5, Y=0.2) 2. Air Zone Binding and Personalized Parameter Mapping: Based on preset spatial matching logic (binding to Zone-L if X < 0.5, binding to Zone-R if X ≥ 0.5, with children's bed areas defined separately as Zone-C), a binding relationship is established between users and air zones, and user preference settings stored in the database are invoked. •{"Zone-L": ["U1"], "Zone-R": ["U2"], "Zone-C": ["U3"]} After binding is complete, an initial partition control instruction set (JSON format) is generated and sent to the air conditioner actuator: json { "Zone-L": {"set_temp": 25.0, "fan_speed": "low", "swing_angle": 20}, "Zone-R": {"set_temp": 26.5, "fan_speed": "medium", "swing_angle":40}, "Zone-C": {"set_temp": 26.0, "fan_speed": "silent", "swing_angle":30}} Based on this, the air conditioner's control module drives the three independent air ducts to start operating according to the preset curve.

[0033] 3. The dynamic regulation process throughout the sleep cycle: During the sleep stage (22:30 - 23:30): Zone-L provides a cool, breezy environment of 25°C for the father; Zone-R provides a warm airflow of 26.5°C for the mother, with a 40° upward angle to avoid direct airflow; Zone-C provides a quiet, constant temperature environment of 26°C for the child.

[0034] Deep sleep stage (01:00 - 04:00): Dynamic adjustment is initiated. Specifically, the infrared thermal imaging array detects a decreasing trend in the father's (U1) body surface temperature. Combined with his "cool preference" sleep curve, the target temperature in Zone-L is automatically and slightly lowered to 24.5℃. Simultaneously, the frequency of the mother's (U2) turning over is detected to be 12 times within one hour (> the preset threshold of 10 times), indicating she may feel slightly cold. The environmental sensors are checked; if the room temperature is <22℃, the temperature in Zone-R is increased by 0.5℃ to 27.0℃. The parameters in the child's (U3) area remain stable, and Zone-C maintains a constant temperature of 26.0℃.

[0035] Adjustments made in the latter half of the night (04:00 - 06:00): The father entered deep sleep and his physical movements decreased, so Zone-L was maintained at 24.5℃ with a low fan speed. The mother's turning frequency returned to normal, indicating the adjustment was effective, and Zone-R temperature was maintained at 26.8℃. Thermal imaging showed that the child's (U3) skin temperature rose by 0.8℃ within 20 minutes, which was determined to be possibly due to kicking off the blankets. To prevent the child from catching a cold, Zone-C temperature was gently increased to 26.5℃ in advance, and the fan speed was slightly increased to soften the airflow and promote air circulation.

[0036] Wake-up Phase (06:30 - 07:00): Based on the user's preset wake-up time of 07:00, the wake-up program starts at 06:30, employing a stepped heating strategy. Zone-L heats up slowly from 24.5℃ to 25.5℃, with the airflow adjusted to horizontal. Zone-R heats up from 26.8℃ to 27.2℃, with the airflow switching to a gentler "soft breeze" mode.

[0037] Zone-C warms up from 26.5℃ to 27.0℃. At 06:50, it connects with smart home systems to turn on a soft night light in the children's bed area to help the child wake up naturally.

[0038] In this example, a highly personalized sleep environment is provided for each family member through a closed-loop control system of "perception-decision-execution." It not only solves the problem of uneven heating and cooling caused by the "one-size-fits-all" approach of traditional air conditioners, but also allows for dynamic fine-tuning based on real-time physiological feedback. All nighttime adjustment data (including triggering conditions, adjustment range, and effect feedback) is encrypted, recorded, and transmitted back to the database to optimize the next sleep temperature control curve, thus achieving continuous personalized learning and ultimately improving sleep comfort while achieving precise energy-saving control.

[0039] In summary, by constructing an air conditioner temperature control method suitable for multi-person co-sleeping scenarios, precise zoned temperature adjustment can be achieved according to the different users' physical needs, ensuring the comfort of each user during sleep.

[0040] Figure 8 This is a schematic block diagram of an air conditioner temperature control device 200 provided in an embodiment of the present invention. Figure 8 As shown, corresponding to the above-described air conditioner temperature control method, the present invention also provides an air conditioner temperature control device 200. This air conditioner temperature control device 200 includes a unit for performing the above-described air conditioner temperature control method, and the device can be configured in an air conditioner. Specifically, please refer to... Figure 8 The air conditioner temperature control device 200 includes a data acquisition and determination unit 201 and a generation and control unit 202. Detailed descriptions of each functional module are as follows: The acquisition and determination unit 201 is used to acquire heat source distribution images of the indoor environment and determine the number of people on the bed and their location coordinates based on the heat source distribution images. A generation control unit 202 is used to acquire user setting information, generate zoning control parameters based on the user setting information, the number of human bodies, and the coordinates of the human body positions, and control the temperature of the air conditioner based on the zoning control parameters.

[0041] In some embodiments, such as this one, the acquisition and determination unit 201 is specifically used for: The heat source distribution image is preprocessed to obtain a heat source region image; Candidate heat source regions are extracted from the heat source region image, and morphological feature parameters of the candidate heat source regions are calculated. Based on the preset morphological feature threshold and the morphological feature parameters, non-human regions are filtered from the candidate heat source regions to obtain effective human heat source regions; Extract the centroid coordinates of the effective human heat source region, and use a clustering algorithm based on the centroid coordinates to determine the number of human bodies on the bed in the indoor environment; The centroid coordinates are normalized to obtain the human body position coordinates of each human body on the bed, wherein the human body position coordinates include lateral position coordinates and longitudinal position coordinates.

[0042] In some embodiments, such as this one, the acquisition and determination unit 201 is further configured to: The heat source distribution image is subjected to noise filtering to obtain a filtered heat source distribution image; The filtered heat source distribution image is used to model the background thermal field to obtain the modeled heat source distribution image; The heat source region image is obtained by thresholding the modeled heat source distribution image.

[0043] In some embodiments, such as this one, the generation control unit 202 is specifically used for: If the number of human bodies is N, then N independent wind zones are generated, where N≥1; For each independent wind zone, the independent wind zone is bound to the corresponding human body position coordinates, and a unique identifier is assigned to the independent wind zone; An initial temperature control curve is generated based on the user-defined information, and the zone control parameters are generated based on the initial temperature control curve and the user-defined information.

[0044] In some embodiments, such as this one, the generation control unit 202 is further configured to: Based on the user's identity identifier, historical sleep data within the same time period within the most recent preset number of days is retrieved from the database using the sleep time period as the query condition. A sleep temperature curve model is constructed based on the historical sleep data and the temperature preference. Acquire environmental data, and generate the initial temperature curve based on the environmental data and the sleep temperature curve model.

[0045] In some embodiments, such as this embodiment, the air conditioner temperature control device 200 further includes: The first adjustment unit is used to adjust the initial temperature control curve to obtain a target temperature control curve if the detected turning frequency is greater than the preset turning frequency, and to adjust the zone control parameters according to the target temperature control curve; The second adjustment unit is used to enter the heating mode if the user's body temperature is detected to be rising continuously, so as to adjust the initial temperature control curve to obtain the target temperature control curve, and execute the step of adjusting the zone control parameters according to the target temperature control curve; The control and storage unit is used to control the temperature of the air conditioner according to the adjusted zone control parameters and save the target temperature control curve to the database; The start adjustment unit is used to start the wake-up mode within a first preset time before the preset wake-up time if the user setting information includes a wake-up comfort strategy, so as to adjust the ambient temperature by adopting a step-by-step heating strategy, and reach the target comfort temperature within a second preset time before the preset wake-up time.

[0046] The aforementioned air conditioner temperature control device can be implemented as a computer program, which can, for example... Figure 9 The air conditioner shown is running.

[0047] Please see Figure 9 , Figure 9 This is a schematic block diagram of an air conditioner provided in an embodiment of the present invention. The air conditioner 300 is a device capable of adjusting the operating current of the compressor corresponding to the current power reduction setting.

[0048] See Figure 9 The air conditioner 300 includes a processor 302, a memory, and a network interface 305 connected via a system bus 301. The memory may include a non-volatile storage medium 303 and internal memory 304.

[0049] The non-volatile storage medium 303 may store an operating system 3031 and a computer program 3032. When the computer program 3032 is executed, it causes the processor 302 to execute an air conditioner temperature control method.

[0050] The processor 302 is used to provide computing and control capabilities to support the operation of the entire air conditioner 300.

[0051] The internal memory 304 provides an environment for the operation of the computer program 3032 in the non-volatile storage medium 303. When the computer program 3032 is executed by the processor 302, the processor 302 can execute an air conditioner temperature control method.

[0052] This network interface 305 is used for network communication with other devices. Those skilled in the art will understand that... Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the air conditioner 300 to which the present invention is applied. A specific air conditioner 300 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0053] The processor 302 is used to run a computer program 3032 stored in a memory to implement any embodiment of the above-described air conditioner temperature control method.

[0054] It should be understood that, in this embodiment of the invention, the processor 302 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0055] It will be understood by those skilled in the art 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 may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0056] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform any embodiment of the above-described air conditioner temperature control method.

[0057] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0058] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0059] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0060] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an air conditioner to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0062] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0063] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0064] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling the temperature of an air conditioner, characterized in that, include: Acquire images of heat source distribution in the indoor environment, and determine the number of people on the bed and their location coordinates based on the images. The system acquires user-defined information, generates zone control parameters based on the user-defined information, the number of human bodies, and the coordinates of the human body positions, and controls the temperature of the air conditioner based on the zone control parameters. The step of determining the number of people and their location coordinates on the bed in the indoor environment based on the heat source distribution image includes: The heat source distribution image is subjected to noise filtering to obtain a filtered heat source distribution image; The filtered heat source distribution image is used to model the background thermal field to obtain the modeled heat source distribution image; The heat source region image is obtained by threshold segmentation of the modeled heat source distribution image; Candidate heat source regions are extracted from the heat source region image, and morphological feature parameters of the candidate heat source regions are calculated. Based on the preset morphological feature threshold and the morphological feature parameters, non-human regions are filtered from the candidate heat source regions to obtain effective human heat source regions; Extract the centroid coordinates of the effective human heat source region, and use a clustering algorithm based on the centroid coordinates to determine the number of human bodies on the bed in the indoor environment; The centroid coordinates are normalized to obtain the human body position coordinates of each human body on the bed, wherein the human body position coordinates include lateral position coordinates and longitudinal position coordinates.

2. The method according to claim 1, characterized in that, The step of generating partition control parameters based on the user settings, the number of human bodies, and the human body location coordinates includes: If the number of human bodies is N, then N independent wind zones are generated, where N≥1; For each independent wind zone, the independent wind zone is bound to the corresponding human body position coordinates, and a unique identifier is assigned to the independent wind zone; An initial temperature control curve is generated based on the user-defined information, and the zone control parameters are generated based on the initial temperature control curve and the user-defined information.

3. The method according to claim 2, characterized in that, The user-defined information includes sleep duration and temperature preference. The step of generating an initial temperature control curve based on the user-defined information includes: Based on the user's identity identifier, historical sleep data within the same time period within the most recent preset number of days is retrieved from the database using the sleep time period as the query condition. A sleep temperature curve model is constructed based on the historical sleep data and the temperature preference. Acquire environmental data, and generate the initial temperature control curve based on the environmental data and the sleep temperature curve model.

4. The method according to claim 3, characterized in that, The method further includes: If the detected turning frequency is greater than the preset turning frequency, the initial temperature control curve is adjusted to obtain the target temperature control curve, and the zone control parameters are adjusted according to the target temperature control curve; If a user's body temperature is detected to be rising continuously, the system enters a temperature rise mode to adjust the initial temperature control curve to obtain the target temperature control curve, and then performs the step of adjusting the zone control parameters according to the target temperature control curve. The temperature of the air conditioner is controlled according to the adjusted zone control parameters, and the target temperature control curve is saved to the database.

5. The method according to claim 4, characterized in that, The method further includes: If the user settings include a wake-up comfort strategy, the wake-up mode will be activated within a first preset time before the preset wake-up time to adjust the ambient temperature using a stepped heating strategy, and the target comfort temperature will be reached within a second preset time before the preset wake-up time.

6. A temperature control device for an air conditioner, characterized in that, include: The acquisition and determination unit is used to acquire images of the heat source distribution in the indoor environment and determine the number of people on the bed and their location coordinates based on the images of the heat source distribution. A control unit is generated to acquire user-defined information, generate zone control parameters based on the user-defined information, the number of human bodies, and the coordinates of the human body positions, and control the temperature of the air conditioner based on the zone control parameters. Specifically, the acquisition and determination unit is used for: The heat source distribution image is subjected to noise filtering to obtain a filtered heat source distribution image; The filtered heat source distribution image is used to model the background thermal field to obtain the modeled heat source distribution image; The heat source region image is obtained by threshold segmentation of the modeled heat source distribution image; Candidate heat source regions are extracted from the heat source region image, and morphological feature parameters of the candidate heat source regions are calculated. Based on the preset morphological feature threshold and the morphological feature parameters, non-human regions are filtered from the candidate heat source regions to obtain effective human heat source regions; Extract the centroid coordinates of the effective human heat source region, and use a clustering algorithm based on the centroid coordinates to determine the number of human bodies on the bed in the indoor environment; The centroid coordinates are normalized to obtain the human body position coordinates of each human body on the bed, wherein the human body position coordinates include lateral position coordinates and longitudinal position coordinates.

7. An air conditioner, characterized in that, The air conditioner includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-5.

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