Partitioned independent temperature control method and apparatus

Abstract

The application discloses a partitioned independent temperature control method and device, and the method comprises the following steps: periodically collecting personnel position distribution information and personnel state information of personnel in a to-be-divided region by each sensing unit; dividing the to-be-divided region according to the personnel position distribution information and / or space information of the to-be-divided region, to obtain a plurality of independent temperature control regions; determining target temperatures corresponding to the independent temperature control regions according to personnel state information and environmental information in each independent temperature control region; and generating temperature control instructions according to the target temperatures and delivering the temperature control instructions to temperature control equipment corresponding to each independent temperature control region. Thus, the regional temperature control is combined with the periodically collected personnel position and personnel state, and the individualized somatosensory comfort demand is effectively met.

Description

Technical Field

[0001] This invention relates to the field of zoned temperature control technology, and in particular to a method and apparatus for independent zoned temperature control. Background Technology

[0002] As people's demands for comfort in their living and working environments continue to rise, traditional, single-mode temperature control can no longer meet diverse and personalized usage needs. Intelligent air conditioning control edge devices that combine energy efficiency and comfort are gradually becoming the mainstream trend in the industry. In various application scenarios such as commercial buildings, residences, and offices, the operating efficiency and temperature control effect of air conditioning edge devices directly affect energy consumption levels and user experience. Therefore, developing efficient, intelligent, and personalized air conditioning temperature control technologies has significant practical importance and application value for reducing social energy consumption, reducing carbon emissions, and improving user comfort.

[0003] Currently, the widely used air conditioning control edge terminals on the market are mainly divided into two categories: central air conditioning edge terminals and split air conditioning edge terminals. Both operate with unified temperature control or simple constant temperature control as their core operating mode. In practical applications, whether it is a central air conditioning system covering an entire floor or an entire office, or a split air conditioning system serving a single room, the common approach is to set a single target temperature for the target space. This involves collecting overall temperature data within the space through temperature sensors and then adjusting the cooling or heating power to maintain the set temperature.

[0004] These types of air conditioners have simple control logic at the periphery and low installation and maintenance costs, meeting basic temperature regulation needs for a certain period. However, with the increasing complexity of human activity patterns and users' pursuit of personalized comfort experiences, existing uniform temperature control solutions cannot make differentiated adjustments based on the distribution of people in different areas of the space, easily leading to ineffective energy consumption and failing to meet personalized comfort needs. Summary of the Invention

[0005] This invention provides a method and device for independent temperature control in different zones, which solves the technical problem that existing uniform temperature control schemes cannot make differentiated adjustments according to the distribution of people in different areas of a space, easily leading to ineffective energy consumption and difficulty in meeting personalized comfort needs.

[0006] This invention provides a zoned independent temperature control method applied to an edge region. The edge region is communicatively connected to temperature control devices and multiple sensing units within the area to be divided. The sensing units are deployed in a grid pattern. The method includes: Each of the aforementioned sensing units periodically collects information on the location distribution and status of personnel within the area to be divided. According to the personnel location distribution information and / or the spatial information of the area to be divided, the area to be divided is divided to obtain multiple independent temperature control areas; Based on the personnel status information and environmental information within each of the independent temperature control zones, the target temperature corresponding to each of the independent temperature control zones is determined; Temperature control commands are generated according to the target temperatures and sent to the temperature control devices corresponding to the independent temperature control zones.

[0007] Optionally, the step of dividing the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided to obtain multiple independent temperature control zones includes: If there is a physical barrier in the spatial information of the area to be divided, then the area to be divided is divided according to the physical barrier to obtain multiple initial temperature control areas; If any of the initial temperature control areas also have functional partitions, then the initial temperature control areas are divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided only contains functional partitions, then the area to be divided is divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided does not have physical barriers or functional partitions, then the area is divided and clustered according to the personnel location distribution information to obtain multiple independent temperature control areas.

[0008] Optionally, it also includes: If any of the independent temperature control areas is an unmanned area, a stop command is sent to the temperature control device corresponding to the unmanned area to minimize the output of the temperature control device; When a person is detected moving within the perimeter of the unmanned area and the distance between the person and the unmanned area continues to decrease, or when a pre-temperature control command is received, a temperature control command is sent to the temperature control device corresponding to the unmanned area according to the preset temperature or the specified temperature corresponding to the pre-temperature control command.

[0009] Optionally, the edge terminal is also connected to the cloud for communication, and the personnel status information includes the surface temperature and activity status of each personnel; the step of determining the target temperature corresponding to each of the independent temperature control areas based on the personnel status information and environmental information within each independent temperature control area includes: The body temperature calculation model is invoked to calculate the initial body temperature of each person in each of the independent temperature control areas based on the environmental information and the activity status of the people in each of the independent temperature control areas. According to the population type corresponding to each person, a correction coefficient is matched from the preset coefficient table respectively; The initial perceived temperature of each person is corrected using the correction coefficient, and the perceived temperature of each person is determined accordingly. The input features for each person are constructed using the average perceived temperature of the person, the surface temperature of the person, the activity status of the person, and the environmental information, respectively. The cloud-based personalized temperature control model is invoked to predict the temperature based on the input features, thereby determining the initial comfort temperature for each individual. The cloud calculates the weighted average of all initial comfort temperatures associated with each of the independent temperature-controlled zones as the target temperature and sends it to the edge device.

[0010] Optionally, before performing the step of determining the target temperature corresponding to each of the independent temperature control zones based on the personnel status information and environmental information within each of the independent temperature control zones, the method further includes: When any of the independent temperature control zones has a single preferred temperature, a temperature control command is generated according to the preferred temperature and sent to the corresponding temperature control device; wherein, the preferred temperature is also associated with a preferred time period; When any of the independent temperature control zones has multiple preferred temperatures, the average preferred temperature of the preferred temperatures is calculated, and a confirmation request is sent to the setting terminal of each preferred temperature. If no confirmation instruction for the confirmation request is received from each of the preset terminals within the preset time period, then the step of determining the target temperature corresponding to each of the independent temperature control areas based on the personnel status information and environmental information in each of the independent temperature control areas is executed. If a confirmation instruction for the confirmation request is received from each of the preset terminals within a preset time period, a temperature control instruction is generated according to the average preferred temperature and sent to the corresponding temperature control device, and the preferred temperature corresponding to the independent temperature control area is updated to the average preferred temperature.

[0011] Optionally, it also includes: When the activity state of the person is detected to be in a sleep state, the corresponding personalized sleep curve is retrieved according to the independent temperature control zone to which the activity state of the person belongs; Based on the temperature values ​​within the personalized sleep curve, a temperature control command is generated and sent to the temperature control device corresponding to the independent temperature control zone.

[0012] Optionally, the sensing unit further includes multiple gas concentration sensors, and the method further includes: The concentrations of various gases within each of the independent temperature control zones are obtained using multiple gas concentration sensors. If any of the gas concentrations is not within the preset standard range, an air quality optimization command corresponding to the gas concentration will be output according to the preset optimization strategy table to activate the fresh air function of the temperature control equipment in the independent temperature control area. When the fresh air function is activated, and the predicted temperature change in the independent temperature control area exceeds the preset change threshold, a new temperature control command is generated according to the predicted temperature change and sent to the temperature control device corresponding to the independent temperature control area.

[0013] Optionally, the edge device also communicates with various IoT devices, and the method further includes: When an abnormal temperature location that does not match the environmental information occurs in any of the independent temperature control areas, it is determined whether the abnormal temperature location is associated with an Internet of Things (IoT) device. If the location of the temperature anomaly is associated with an IoT device and no personalized user instruction has been received, a temperature optimization instruction is sent to the IoT device to eliminate the temperature anomaly location through the IoT device.

[0014] Optionally, it also includes: In response to a constant temperature requirement command or a user-customized command sent by a person within any of the independent temperature control areas, the temperature control equipment within the independent temperature control area is adjusted.

[0015] The present invention also provides a zoned independent temperature control device applied at an edge, wherein the edge is communicatively connected to temperature control devices and multiple sensing units within the area to be divided, the sensing units being deployed in a grid, and the device comprising: The information acquisition module is used to periodically acquire the personnel location distribution information and personnel status information of the personnel in the area to be divided through each of the sensor units; The area division module is used to divide the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided, so as to obtain multiple independent temperature control areas; The temperature determination module is used to determine the target temperature corresponding to each of the independent temperature control areas based on the personnel status information and environmental information in each of the independent temperature control areas; The temperature control command issuing module is used to generate temperature control commands according to each of the target temperatures and issue them to the temperature control devices corresponding to each of the independent temperature control zones.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This invention periodically collects personnel location and status information within a designated area using various sensing units. Based on the personnel location and / or spatial information of the area, it divides the area into multiple independent temperature-controlled zones. The target temperature for each independent temperature-controlled zone is determined according to the personnel status and environmental information within that zone. Temperature control commands are generated based on each target temperature and sent to the corresponding temperature control devices in each independent temperature-controlled zone. This combined periodic collection of personnel location and status information allows for zoned temperature control, effectively meeting personalized comfort needs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of a zoned independent temperature control method provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a zoned independent temperature control device provided in an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a method and apparatus for independent temperature control in different zones, which solves the technical problem that existing uniform temperature control schemes cannot make differentiated adjustments based on the distribution of people in different areas of a space, easily leading to ineffective energy consumption and difficulty in meeting personalized comfort needs.

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

[0021] Please see Figure 1 , Figure 1 The flowchart illustrates a method for independent temperature control in different zones, as provided in an embodiment of the present invention.

[0022] This invention provides a zoned independent temperature control method applied to an edge region. The edge region is communicatively connected to temperature control devices and multiple sensing units within the area to be divided. The sensing units are deployed in a grid pattern. The method includes: Step 101: Periodically collect personnel location distribution information and personnel status information within the area to be divided using each sensing unit; Personnel location distribution information refers to the relevant data collected by the sensing unit regarding the presence, specific location, and crowding patterns of personnel within the area to be divided.

[0023] Personnel status information includes the person's activity status (such as sitting, exercising, sleeping), physiological characteristics (such as body surface temperature), and population classification information (such as elderly, adults, children, men, and women).

[0024] In this embodiment, communication connections are established between the edge device and multiple sets of sensor units deployed in a grid. Infrared thermal sensors (including passive infrared sensors (PIR) and thermal imaging sensors) periodically collect personnel location distribution information and personnel status information, while environmental auxiliary sensors verify the validity of the data. The PIR sensor identifies the presence and approximate location of personnel by detecting human infrared radiation, and the thermal imaging sensor generates a thermal distribution image. Combined with image processing algorithms, the system accurately locates personnel positions, identifies the number of people, and their activity status. During the data collection process, the edge device automatically filters out interference factors such as airflow from air conditioning vents and direct sunlight. Abnormal data is eliminated through cross-validation of data from multiple sensors to ensure the accuracy of the collected information. Simultaneously, the collection frequency is dynamically adjusted according to personnel distribution density, with increased collection frequency in densely populated areas and reduced collection frequency in unoccupied areas to save energy.

[0025] Specifically, for collecting information on the distribution of people's locations, an infrared thermal sensor array can be used to detect the distribution of people in space in real time. When a PIR sensor detects infrared radiation signals from a human body, it outputs a high level, and the edge end records that the spatial area corresponding to that sensor is occupied. The thermal imaging sensor outputs a thermal distribution image, and the edge end uses image processing algorithms (such as threshold segmentation and connected component analysis) to identify the location of human heat sources and extract the coordinates of the human body's center. Triangulation or probabilistic fusion can also be performed using the detection results of multiple sensors to improve positioning accuracy. If a person is detected by sensors A, B, and C simultaneously, the edge end calculates the intersection area of ​​the three sensors' fields of view as the person's precise location. The fusion algorithm uses Kalman filtering or particle filtering, comprehensively considering factors such as sensor accuracy, detection confidence, and historical trajectories, achieving a positioning accuracy of 0.5-1 meter. The number of human heat sources in the thermal distribution image is counted to identify the number of people in the current area. The people counting uses deep learning object detection algorithms (such as YOLO and SSD), training models to identify human heat sources in different postures and angles with an accuracy of >95%. The number of people is used for temperature control decisions. For example, when there are many people, the metabolic heat production is high and the set temperature needs to be lowered. When there are few people, the set temperature can be appropriately increased to save energy.

[0026] To identify the activity status of a person, the movement trajectory and heat distribution characteristics of the human body's heat source can be analyzed. Seated state (e.g., working, watching TV): The heat source position remains relatively unchanged, and the heat distribution is concentrated; Movement state (e.g., walking, exercising): The heat source position moves rapidly, and the heat distribution changes significantly; at the edges, the metabolic rate is high, requiring cooling; Sleep state (e.g., lying in bed): The heat source position remains unchanged, and the heat distribution is in a lying position (long strip); at the edges, the sleep temperature control mode is switched.

[0027] The deployment density of the sensor units in a grid pattern is determined based on the space size and accuracy requirements, typically one sensor per 15-20 square meters to ensure no blind spots. For example, sensors can be installed from above in the center of the ceiling (maximum field of view, fewest blind spots) or diagonally from a high position on a wall. Avoid installation near air conditioning vents (airflow interference), next to windows (direct sunlight interference), or next to heat-generating equipment (heat source interference). The sensor's field of view must cover major activity areas (such as beds, sofas, desks, conference tables, etc.). Sensor unit types can include, but are not limited to, PIR sensors, thermal imaging sensors (infrared array sensors), temperature and humidity sensors, air quality sensors, light sensors, door and window sensors, and wearable devices. PIR sensors detect human infrared radiation (wavelength 8-14μm, corresponding to a human body temperature of approximately 37℃), detecting the presence, movement, and approximate location of people. The detection distance is 5-10 meters, the detection angle is 110-120 degrees, the response time is <1 second, and the power consumption is <1W. Thermal imaging sensors, employing thermopile arrays or MEMS infrared sensor arrays, can generate thermal distribution images within a region (resolution 8×8, 16×16, or 32×32 pixels). They not only detect the presence of people but also measure human surface temperature (accuracy ±0.5℃), identify the number of people, and analyze their activity status (sitting / exercising / sleeping). They can be deployed in key areas or high-end application scenarios. Environmental temperature and humidity sensors can be installed in various zones to measure ambient temperature (accuracy ±0.3℃) and relative humidity (accuracy ±3% RH) in real time. The sensors use digital output (I2C, SPI interface) for easy data acquisition, with a response time of <10 seconds and a sampling frequency of 1 time / minute. Deployment density typically requires at least one sensor per independent temperature-controlled area (e.g., each room, each office area). For larger spaces, deployment density can be increased (one sensor per 30-50 square meters) to improve accuracy. Air quality sensors can include various gas concentration sensors such as PM2.5 sensors (laser scattering method, measurement range 0-500 μg / m³, accuracy ±10%), CO2 sensors (NDIR nondispersive infrared method, measurement range 0-5000 ppm, accuracy ±50 ppm), formaldehyde sensors (electrochemical method, measurement range 0-2 mg / m³, accuracy ±10%), and VOC sensors (metal oxide semiconductor method, measurement range 0-5000 ppb), monitoring indoor air quality in real time. Air quality sensors are typically deployed at return air vents or in the center of the room, avoiding deployment at air outlets (as measurements will be inaccurate). Illuminance sensors detect indoor light intensity (measurement range 0-100,000 lux, accuracy ±5%), determining whether lights are on, curtains are open, or sunlight is direct, providing auxiliary information for temperature control decisions (e.g., direct sunlight causes rapid temperature increases, requiring pre-cooling). Switch sensors (magnetic induction switches or contact switches) are installed on doors and windows to monitor their open / closed status in real time.When doors and windows are opened, outside air enters and disrupts the temperature balance, requiring the edge device to adjust its control strategy accordingly (e.g., pausing cooling to save energy when doors and windows are open, or prompting the user to close them). Furthermore, the edge device supports connection to wearable devices such as smart bracelets and smartwatches to obtain real-time physiological data such as the user's body temperature, heart rate, activity level, and sleep stage, serving as an important reference for temperature control decisions. For example, if a user is exercising and their heart rate is elevated, the edge device determines that their metabolism is high and the temperature needs to be lowered; if the user enters deep sleep, the edge device adjusts to a sleep temperature control mode accordingly.

[0028] Step 102: Divide the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided, to obtain multiple independent temperature control areas; Spatial information refers to information related to the physical structure (such as walls and partitions), functional layout (such as office area and rest area) and geographical boundaries of the area to be divided.

[0029] An independent temperature-controlled area refers to the smallest unit of area that can independently perform temperature regulation, divided according to personnel distribution and / or spatial characteristics, and has dedicated temperature control equipment.

[0030] In this embodiment, the edge device combines the collected personnel location distribution information with the spatial information of the area to be divided, and uses a multi-strategy fusion method to obtain independent temperature control areas. When dividing based on physical structure, the area is split according to natural boundaries such as walls and partitions. When dividing based on personnel gathering patterns, density clustering algorithm is used to divide areas with concentrated personnel into independent temperature control units, and sparsely populated or unoccupied areas are merged into energy-saving areas. When dividing based on functional layout, the area is differentiated according to its use (such as sleeping area, office area) to ensure the accuracy of temperature control in key areas. After division, each independent temperature control area is assigned a unique identifier and associated with the corresponding sensing unit and temperature control device. At the same time, the area boundaries and number are dynamically adjusted according to personnel flow to achieve the flexibility and adaptability of zoning.

[0031] In addition, pre-set partition templates can be used for different scenarios (such as residences, office buildings, and hotels), and the optimal partitioning scheme can be automatically adapted by combining real-time personnel and space information. For example, residential scenarios can be partitioned according to the physical boundaries of rooms, while large open office areas can be partitioned according to the gathering pattern of personnel.

[0032] In one example of this application, step 102 may include the following sub-steps: If there are physical barriers in the spatial information of the area to be divided, then the area to be divided is divided according to the physical barriers to obtain multiple initial temperature control areas; If any initial temperature control zone also has functional partitions, then the initial temperature control zone is divided according to the functional partitions to obtain multiple independent temperature control zones; If the spatial information of the area to be divided only exists in functional partitions, then the area to be divided is divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided does not have physical barriers or functional zoning, then the areas are grouped according to the personnel location distribution information to obtain multiple independent temperature control zones.

[0033] In this embodiment, spatial information of the area to be divided is acquired, and it is determined whether there are physical partitions. If so, the area to be divided is divided into multiple initial temperature control zones according to the physical partitions, ensuring that the spatial boundaries of each initial temperature control zone are clear and conform to the logic of physical separation. Subsequently, for each initial temperature control zone, it is further determined whether there are functional partitions. If so, the initial temperature control zone is further divided according to the functional partitions, so that the divided independent temperature control zones accurately match the space's usage functions. If the spatial information of the area to be divided only has functional partitions and no physical partitions, multiple independent temperature control zones are directly divided according to the functional partitions to meet the differentiated temperature control needs of different functional scenarios. If the area to be divided has neither physical partitions nor functional partitions, the personnel location distribution information collected by the sensing unit is used to perform partitioning and clustering based on personnel aggregation analysis, so that the independent temperature control zones are highly consistent with the personnel distribution pattern. This division method, while respecting the inherent structure of the space, takes into account the actual usage functions and personnel distribution, ensuring that the division of each independent temperature control zone is scientific and reasonable, improving temperature control accuracy, avoiding energy waste, and enhancing the flexibility and adaptability of the partitions.

[0034] Specifically, for physical partitions, areas can be divided based on physical boundaries such as rooms, partitions, and furniture layout. For example, a three-bedroom apartment can be divided into a living room area, master bedroom area, secondary bedroom area, and study area; an open-plan office can be divided into workstation A area, workstation B area, meeting area, and rest area based on workstation distribution. Each physical zone corresponds to at least one temperature-controlled zone. For functional zones, they can be divided according to the area's function and usage characteristics. For example, a bedroom can be divided into a sleeping area (bed and surrounding 2 meters) and other areas (wardrobes, dressing tables, etc.), with the sleeping area focusing on comfort and other areas allowing for lower temperature control precision and energy saving; a meeting room can be divided into a seating area and an aisle area, with precise temperature control in the seating area and more lenient control in the aisle area. For areas with clustered personnel, in large spaces (such as large open offices, shopping malls, and exhibition halls), areas can be dynamically divided based on personnel clustering. A heat map of personnel distribution at the edge can be analyzed, grouping multiple people who are close together (e.g., <3 meters) into one temperature-controlled zone, and grouping people who are farther apart (e.g., >5 meters) into different temperature-controlled zones. The aggregation algorithm employs either DBSCAN density clustering or K-means clustering, adaptively adjusting the number and boundaries of regions. After dividing the area into multiple independent temperature control zones, a unique number is assigned to each zone, and information such as the zone's extent (e.g., coordinate boundaries, a list of involved sensor units, associated air outlets and valves, current number of people, temperature and humidity, and the target temperature to be adjusted) is recorded. This data is then stored in a database for later retrieval.

[0035] In another example of this application, after performing step 102, the method further includes the following steps: If any independent temperature control area is an unoccupied area, a stop command is sent to the temperature control device corresponding to the unoccupied area to minimize the output of the temperature control device; When a person is detected moving around the unmanned area and the distance between the person and the unmanned area continues to decrease, or when a pre-temperature control command is received, a temperature control command is sent to the temperature control device corresponding to the unmanned area according to the preset temperature or the specified temperature corresponding to the pre-temperature control command.

[0036] In this embodiment, the sensing unit can identify uninhabited areas in real time and automatically switch to energy-saving mode. The determination logic for uninhabited areas is as follows: if all infrared sensors in a certain area do not detect human signals for N consecutive minutes (N is usually configurable as 5-10 minutes), it is determined to be an uninhabited area. Considering that PIR sensors have weak detection capabilities for stationary human bodies (such as missing a person sitting still), the edge device combines multiple information to make a comprehensive judgment: whether the temperature and humidity sensor data has changed (someone breathing will cause CO2 and humidity to rise), whether smart devices are online (such as mobile phone WiFi connection, wearable device signal), and historical activity patterns (such as the area usually having people during the day on weekdays, the judgment criteria are relaxed).

[0037] Specifically, the generation and issuance of temperature control commands for unoccupied areas may include, but are not limited to: closing or minimizing the air outlets in that area: closing the electric damper to 10-20% (complete closure may affect the pressure balance at the edge), stopping or significantly reducing airflow to that area. Increasing / decreasing the target temperature in that area: increasing the target temperature in unoccupied areas in cooling mode (e.g., from 26℃ to 28-30℃), and decreasing the target temperature in heating mode (e.g., from 22℃ to 18-20℃), reducing the temperature difference with the outside and lowering energy consumption. Reducing the overall power of the air conditioning system: if multiple areas are unoccupied, the air conditioning load drops significantly; at the edge, the compressor frequency is reduced (for inverter air conditioners) or switched to a low-power setting (for fixed-frequency air conditioners), saving energy. Rapid pre-cooling / pre-heating: When someone is detected about to enter an unoccupied area (such as when a door sensor detects someone approaching or when a user sends a "come home in 10 minutes" command via the APP), the edge device will rapidly pre-cool / pre-heat the area 5-10 minutes in advance to ensure that the temperature is comfortable when people enter, avoiding the unpleasant experience of "it is very hot / cold when you first come home and you have to wait a long time to feel comfortable".

[0038] In another example of this application, the method may further include the following steps before performing step 103: When any independent temperature control zone has a single preferred temperature, a temperature control command is generated according to the preferred temperature and sent to the corresponding temperature control device; the preferred temperature is also associated with a preferred time period; When any independent temperature control zone has multiple preferred temperatures, calculate the average preferred temperature of the preferred temperatures and send a confirmation request to the setting terminal of each preferred temperature. If no confirmation instruction for the confirmation request is received from each setting terminal within the preset time period, the step of determining the target temperature corresponding to each independent temperature control area based on the personnel status information and environmental information in each independent temperature control area will be executed. If a confirmation instruction for the confirmation request is received from each setting terminal within a preset time period, a temperature control instruction is generated according to the average preferred temperature and sent to the corresponding temperature control device, and the preferred temperature of the independent temperature control area is updated to the average preferred temperature.

[0039] Preferred temperature refers to the target temperature value that the user presets through the settings panel to meet their own comfort needs. Preferred time period refers to the effective time interval bound to the preferred temperature.

[0040] The preferred temperature mean refers to the comprehensive temperature value calculated by arithmetic or weighted average methods when there are multiple preferred temperatures in an independent temperature control zone.

[0041] In this embodiment, the edge device monitors the preferred temperature settings of each independent temperature control zone in real time. When any independent temperature control zone has only a single preferred temperature, it directly extracts that preferred temperature and its associated preferred time period. Only within the preferred time period is an appropriate temperature control command generated according to that preferred temperature and sent to the corresponding temperature control device, ensuring that the temperature control conforms to the user's personalized habits and is effective only during the valid time period. When any independent temperature control zone has multiple preferred temperatures, it first calculates the average preferred temperature of all preferred temperatures, then sends a confirmation request to the setting terminal of each preferred temperature to solicit the user's approval of the average. Subsequently, the edge device starts timing and waits for feedback. If no feedback is received from all setting terminals within a preset time period... If a confirmation command is received, it indicates that users have not reached a consensus on the average value. In this case, a fallback mechanism is immediately triggered, and step 103 is executed to calculate a new target temperature and generate a stable control command to ensure the rationality of temperature control. If confirmation commands are received from all settings within a preset time period, it means that users agree with the average value. The edge end then generates a temperature control command based on the preferred average temperature and sends it to the corresponding temperature control device. At the same time, the preferred temperature of the independent temperature control area is updated to the average value. This balances the personalized needs of multiple users, simplifies the subsequent temperature control command generation logic, avoids redundant calculations, and reduces user disputes through the confirmation mechanism. It also ensures that the temperature control edge end can still operate stably in extreme cases, taking into account both user experience and edge end reliability.

[0042] Specifically, in multi-user scenarios (such as homes and offices), different users' temperature preferences may conflict (e.g., an elderly person prefers 28℃, while a young person prefers 25℃). If different users are in different rooms, different target temperatures can be set for each independent temperature-controlled area to meet their individual needs. For example, an elderly person could set 28℃ in the master bedroom, a young person in the second bedroom 25℃, and a median value of 26.5℃ in the living room. If multiple people are in the same room, the weighted average of all users' preferred temperatures is calculated at the edge as the target temperature. The weights are set according to user priority, such as giving higher weights to the elderly, children, and patients (e.g., twice the weight), while healthy adults have a standard weight (1x), prioritizing the comfort of vulnerable groups. The system can also identify the primary user in the room and set the temperature according to their preferences. For example, if an elderly person is home alone during the day, the temperature is set according to their preference; if a young person returns home from get off work in the evening, the temperature gradually transitions to their preference; and during family gatherings, the weighted average is used. The primary user is automatically determined through personnel detection and time-based learning.

[0043] Furthermore, if a temperature preference conflict is detected, the app can push a notification to the relevant users: "Users A and B have different temperature preferences (A: 25℃, B: 28℃). The current setting is 26.5℃. Do you accept this?" Users can negotiate to reach an agreement, or one user can concede. The edge device records the negotiation result as a reference rule for the future. For extreme conflicts that cannot be resolved, the edge device provides a local compensation solution. For example, if an elderly person is particularly sensitive to cold and wants 30℃, but younger people cannot accept this, the edge device suggests that the elderly person use local heating devices such as electric blankets or space heaters to maintain a comfortable temperature throughout the room, while providing additional local heating for the elderly person.

[0044] Step 103: Determine the target temperature for each independent temperature control zone based on the personnel status information and environmental information within each independent temperature control zone. Environmental information refers to the set of environmental parameters that affect the perceived temperature of the human body within an independently temperature-controlled area, including temperature and humidity, air quality, wind speed, and light intensity.

[0045] The target temperature refers to the optimal temperature value calculated based on personnel status and environmental information, which is suitable for the comfort needs and energy-saving goals of personnel in the area.

[0046] In this embodiment, the edge device collects personnel status information (such as activity intensity and personnel type) and environmental information in each independent temperature control zone. It uses a PMV-PPD (Predicted Mean Vote - Predicted Percentage of Dissatisfied) or a body temperature calculation model as the core, combined with an individual difference correction mechanism to determine the target temperature. First, the metabolic rate is estimated using personnel status information, and the baseline body temperature is calculated based on environmental parameters such as temperature, humidity, and wind speed. Then, a preset correction coefficient is applied according to personnel type (elderly, children, men, women), while incorporating users' historical temperature preference data for personalized adjustments. For areas with multiple people, a weighted average method is used to comprehensively consider the comfort temperature needs of different personnel, prioritizing the preferences of vulnerable groups (elderly, children) to ensure that the target temperature meets the comfort needs of the majority while also complying with energy-saving requirements. Simultaneously, the target temperature is dynamically and iteratively optimized based on environmental information; for example, the target temperature is appropriately lowered to offset the impact of radiant heat during direct sunlight.

[0047] In addition, additional factors such as weather forecast data and regional usage time can be introduced to set dynamic weights for each influencing factor. For example, the humidity weight can be increased during rainy weather, and the energy-saving weight can be appropriately increased for areas with long-term use, making the target temperature calculation more comprehensive.

[0048] In one example of this application, the edge device is also connected to the cloud for communication, and the personnel status information includes the surface temperature of each person and their activity status; step 103 may include the following sub-steps: The perceived temperature calculation model is invoked to calculate the initial perceived temperature of each person in each independent temperature control area based on the environmental information and the activity status of the people in each independent temperature control area. According to the population type corresponding to each person, the correction coefficient is matched from the preset coefficient table respectively; The initial perceived temperature of each person was corrected using a correction factor, and the perceived temperature of each person was determined accordingly. Input features for each person are constructed using the average perceived temperature, surface temperature, activity status, and environmental information. By calling a personalized temperature control model in the cloud, temperature prediction is performed based on each input feature to determine the initial comfort temperature for each person. The target temperature is calculated by cloud computing and sent to the edge device by calculating the weighted average of all initial comfort temperatures associated with each independent temperature control zone.

[0049] In this embodiment, a body temperature calculation model is invoked, combining environmental information and personnel activity status within each independent temperature-controlled area to calculate the initial body temperature for each person in the area, achieving a preliminary quantification of the basic body temperature level. Subsequently, based on the population type of each person, a corresponding correction coefficient is matched from a preset coefficient table, and the initial body temperature is adjusted using this correction coefficient to obtain a body temperature that reflects individual differences, solving the problem of traditional body temperature calculation ignoring population type differences. Next, the average body temperature of people in the area, the surface temperature of each person, personnel activity status, and environmental information are integrated to construct unique input features for each person, ensuring the comprehensiveness and relevance of the input data. The process involves several steps: first, inputting the characteristics of each person; then, uploading these characteristics to the cloud, where a personalized temperature control model is invoked to predict the temperature based on these characteristics, outputting the initial comfort temperature for each person, thus accurately capturing individual needs; finally, the cloud calculates the weighted average of all initial comfort temperatures within the independent temperature control area using preset weights (such as the duration of stay and priority), and sends this as the target temperature for that area to the edge. This process improves the accuracy of comfort temperature prediction through multi-dimensional data integration and individual difference correction, while also ensuring prediction efficiency through the powerful computing power of the cloud model. Furthermore, the weighted average balances the needs of multiple people, taking into account both personalization and overall comfort, significantly improving the comfort adaptability of the area's temperature control.

[0050] The initial perceived temperature can be calculated using a perceived temperature calculation model, such as the PMV-PPD model (Predicted Mean Vote - Predicted Percentage of Dissatisfied).

[0051] PMV = f(ambient temperature, relative humidity, wind speed, radiant temperature, metabolic rate, clothing thermal resistance), where environmental information includes ambient temperature, relative humidity, wind speed, and radiant temperature. Ambient temperature and relative humidity can be directly measured by temperature and humidity sensors; wind speed can be estimated based on the air conditioner's fan speed setting or measured by a wind speed sensor deployed at the air outlet (0-5 m / s). Radiant temperature can be the average temperature of walls, ceilings, and floors, estimated through infrared thermometry, or simplified to be equal to ambient temperature (error <1℃). Metabolic rate can be estimated based on the person's activity level: approximately 1.0 MET (58 W / m²) when sitting, approximately 1.5 MET when standing with light activity, and approximately 3-6 MET when exercising. Clothing thermal resistance can be estimated based on the season and indoor-outdoor temperature difference: approximately 0.5 clo (0.078 m²·K / W) for thin summer clothing and approximately 1.0-1.5 clo for thick winter clothing. With user authorization, the edge device can learn the user's clothing habits based on historical data or visually identify clothing thickness through a camera. PMV values ​​range from -3 (cold) to +3 (hot), with 0 representing neutral (most comfortable). PMV = -1 indicates slightly cold, -2 indicates cold, and -3 indicates very cold; PMV = +1 indicates slightly warm, +2 indicates hot, and +3 indicates very hot. Generally, a PMV between -0.5 and +0.5 is considered the comfortable range. PPD (percentage of people who are dissatisfied) is calculated based on PMV: PPD = 100 - 95 × exp(-0.03353 × PMV). 4 -0.2179×PMV²). For example, when PMV=0, PPD=5% (even in the most comfortable environment, 5% of people are dissatisfied due to individual differences), when PMV=±1, PPD=26%, and when PMV=±2, PPD=75%. The marginal goal is to control PMV between -0.5 and +0.5, PPD<10%, and ensure that >90% of people feel comfortable.

[0052] After identifying the user's demographic type (elderly / adult / child / male / female), a preset correction coefficient is applied. For example, the PMV correction for the elderly is +0.5 (meaning that when PMV is calculated to be 0, the elderly actually feel cold, equivalent to PMV = -0.5, and the edge device needs to increase the temperature by 0.5-1℃ to compensate). The PMV correction for children is -0.3, and for women, it is +0.2. Additionally, each user's temperature adjustment behavior is recorded (e.g., if a user feels cold at 26℃, they manually raise the temperature to 27℃), and a personalized PMV correction coefficient is learned using machine learning algorithms (such as linear regression and random forest). If a wearable device is connected, the edge device obtains real-time physiological data such as the user's heart rate and skin temperature to more accurately assess the user's current temperature status. If the user's heart rate and skin temperature are elevated, it indicates a high metabolic rate or exercise, and the edge device determines that the user is too hot and needs to be cooled down; if the heart rate and skin temperature are decreased, it indicates a slowed metabolism or feeling cold, and the edge device appropriately raises the temperature.

[0053] Additionally, it can record information related to manual adjustments, such as each time the user manually adjusts the temperature (e.g., from 26℃ to 27℃), switches modes (e.g., from cooling to dehumidifying), or adjusts the fan speed (e.g., from automatic to low). The edge device records the adjustment time, parameters before and after the adjustment, environmental conditions at the time of adjustment (temperature, humidity, number of people, activity status, etc.), and user identity (e.g., family member Zhang San). The edge device periodically (e.g., every 2 hours) asks via app push or voice, "How do you feel about the current temperature? Too cold / Slightly cold / Comfortable / Slightly hot / Too hot?" The user selects a response, and the edge device records the response and the environmental parameters at that time. Quick feedback can also be provided via the emoji button on the temperature control panel. If the user does not manually adjust the temperature and does not provide clear feedback, the edge device infers satisfaction based on user behavior. For example, if the user remains active at a certain temperature for more than 2 hours without adjusting, it is inferred that the temperature is comfortable; if the user frequently adjusts the temperature (more than 2 times within 10 minutes), it is inferred that the user is dissatisfied; if the user adds clothing (recognized by the camera) or leaves the room, it is inferred that the temperature may be too cold or too hot.

[0054] For personalized temperature control models, training can be performed separately for each user based on the collected behavioral data. Input features can consist of average perceived temperature, surface temperature, activity level, and environmental information. Detailed components may include, but are not limited to, current ambient temperature, humidity, number of people, activity level, time (season / month / weekday / time of day), weather (sunny / cloudy / rainy, temperature, humidity), and user attributes (age, gender, health status). The model output can be the target temperature or a PMV correction coefficient. The algorithm for this personalized temperature control model can be selected based on the scenario. For scenarios with few features and linear relationships, a linear regression model can be used, such as "User preferred temperature = Baseline temperature + α × Age + β × Humidity + γ × Activity level + ...". For scenarios handling non-linear relationships and feature interactions, an algorithm can be used, such as "If (age > 60 and humidity > 70%), the preferred temperature is 28℃; otherwise, if (activity level = exercise), the preferred temperature is 24℃,...". Random forests improve accuracy and generalization ability by integrating multiple decision trees. For handling complex nonlinear relationships, deep neural networks can be used. The input layer can access all features, with 2-3 hidden layers each containing 64-128 neurons, and the output layer outputting the preferred temperature. Incremental learning and reinforcement learning can also be used, allowing the model to continuously learn from new user behavior data during operation, updating model parameters in real time to adapt to changes in user preferences (such as seasonal changes or changes in health status). During training, a general model (trained based on the average preferences of a large number of users) can be used initially. After users use the edge for 1-2 weeks and accumulate 50-100 personal data points, a personalized model can be trained. After the personalized model is trained in the cloud, it is pushed to the edge gateway for deployment, and retrained weekly or monthly to update the model and adapt to changes in preferences. Model accuracy is evaluated through cross-validation; for example, an average error of <0.5℃ between the predicted temperature and the user's actual set temperature, and a PMV prediction error of <0.3 are considered acceptable. Comparing the accuracy of the personalized model with the general model, the personalized model typically improves accuracy by 15%-30%.

[0055] During the model's usage phase, temperature prediction can be performed using features such as current ambient temperature, humidity, number of people, activity status, time, and weather. A target temperature, such as 26.5℃, can then be set as the target temperature, and a temperature control command can be generated and sent to the corresponding temperature control device, such as an air conditioner. If the predicted temperature is within a reasonable range (e.g., 24-28℃ in summer, 18-24℃ in winter), it can be set directly. If it exceeds the reasonable range (potentially due to model anomalies or extreme situations), it can be limited to the reasonable range boundaries, and the abnormal event can be recorded.

[0056] If a user changes from sitting to moving, the edge device detects the change in activity level, re-infers the model, and adjusts the target temperature from 26.5℃ to 25.5℃ (movement generates more heat and requires cooling). Adjustments are typically evaluated every 5-10 minutes, or adjusted immediately upon detecting a significant change in activity level. If the user manually adjusts the temperature, the edge device respects the user's preference, pauses adaptive adjustment, and runs according to the user's settings. The edge device records this manual adjustment as a new training sample to update the preference model. If the user does not manually adjust the temperature again for an extended period (e.g., 1 hour), the edge device determines that the user is satisfied with the current temperature and resumes adaptive adjustment (fine-tuning based on the current temperature).

[0057] It's important to note that the edge device can be deployed locally, implemented as an edge computing gateway configured with an ARM or x86 processor, 4-8GB of memory, and AI acceleration chips such as an NPU. It runs AI inference models in real time, responding quickly (<1 second) to local temperature control needs, and can operate normally even during network interruptions. The edge gateway collects all sensor data, performs data preprocessing, feature extraction, and model inference, generates control decisions, and sends them to the air conditioning controller. Historical data is uploaded to the cloud for training more complex deep learning models, including user preference learning, temperature prediction models, and energy-saving optimization models. Models trained in the cloud are periodically pushed to the edge gateway for updates, enabling continuous model optimization. The cloud platform also provides big data analytics, remote monitoring, and OTA upgrades.

[0058] Furthermore, this method may also include the following steps: When a person's activity status is detected as sleep, the corresponding personalized sleep curve is retrieved according to the independent temperature control zone to which the person's activity status belongs; Based on the temperature values ​​within the personalized sleep curve, a temperature control command is generated and sent to the temperature control device corresponding to the independent temperature control zone.

[0059] In this embodiment, when a person's activity is detected as being in a sleep state, the system immediately retrieves the personalized sleep curve corresponding to the individual's independent temperature-controlled zone (this curve is pre-constructed based on the individual's sleep habits, physiological characteristics, and temperature requirements during different sleep stages). Then, it directly generates appropriate temperature control commands based on the temperature values ​​corresponding to different sleep stages within the personalized sleep curve and sends them to the temperature control device corresponding to that independent temperature-controlled zone. This process dynamically adjusts the zone temperature according to the sleep curve, accurately adapting to the physiological temperature requirements of individuals at different stages of sleep. This avoids sleep disturbances caused by traditional fixed temperatures or manual adjustments (such as excessively high temperatures during light sleep leading to awakening, or excessively low temperatures during deep sleep leading to catching a cold), significantly improving sleep comfort and quality.

[0060] Specifically, this sleep state can be set by the user's bedtime and wake-up time, automatically switching to sleep mode at bedtime and switching to normal mode at wake-up time. Alternatively, it can switch sleep states by detecting changes in user behavior. For example, if the user remains in a lying position on the bed for an extended period (>10 minutes) without significant movement, it is determined that the user has fallen asleep; if the user changes from a lying position to a sitting position or stands up and leaves the bed, it is determined that the user has woken up. Alternatively, it can connect to devices such as smart mattresses, smart bracelets, and smartwatches to obtain accurate sleep monitoring data. Smart mattresses use pressure sensors and heart rate sensors to determine whether the user is in bed, whether they have fallen asleep, and their current sleep stage (light sleep / deep sleep / REM rapid eye movement); smart bracelets use accelerometers and heart rate monitoring to determine the sleep stage. Regarding the division of sleep states, a typical sleep cycle (90-120 minutes) includes light sleep (N1 / N2, approximately 50%-60%), deep sleep (N3, approximately 15%-25%), and REM rapid eye movement (approximately 20%-25%). The edge device determines the current sleep stage based on data from sleep monitoring devices or sleep duration estimates (e.g., 0-30 minutes after falling asleep is the sleep onset period, 30 minutes-2 hours is the deep sleep period, 2-6 hours alternate between light sleep, deep sleep and REM sleep, and 1 hour before waking up is light sleep and REM sleep).

[0061] Specifically, a personalized sleep curve can record the relationship between time and temperature. The sleep onset period (0-30 minutes after going to bed): During this stage, the body's core temperature begins to drop (0.5-1.0℃), and heat dissipation from the body surface increases, which helps with falling asleep. A slightly cooler ambient temperature (approximately 24-25℃, 1-2℃ lower than when awake) can promote a drop in body temperature and shorten the time to fall asleep. Temperatures that are too high (>27℃) will inhibit the drop in body temperature, leading to restlessness and difficulty falling asleep; temperatures that are too low (<22℃) will cause cold discomfort, also affecting sleep onset. The deep sleep period (30 minutes to 4 hours after falling asleep): During this stage, body temperature drops to its lowest point (1-1.5℃ lower than when awake), the metabolic rate decreases by about 15%, and heat production decreases. However, the body's thermoregulation ability remains normal, and body temperature can be maintained by increasing heat production or reducing heat dissipation. The ambient temperature should be relatively warm (approximately 26-27℃, 0-1℃ higher than when awake) to avoid excessive temperature drop that could cause cold awakening. Deep sleep is the most crucial stage for sleep quality. A stable and comfortable temperature can reduce awakenings and increase deep sleep duration and quality. REM (Rapid Eye Movement) sleep (mainly in the latter half of the night): During this stage, brain activity is high (dreaming), but the body's ability to regulate temperature is weakened or even lost, making the body extremely sensitive to changes in ambient temperature. The ambient temperature needs to be kept stable (fluctuations <0.5℃) to avoid sudden temperature changes that could trigger awakenings. The optimal temperature is approximately 26-27℃, similar to the deep sleep stage. Pre-wake phase (1 hour before waking): During this stage, body temperature gradually rises (by 0.5-1.0℃), preparing for wakefulness. The ambient temperature should gradually increase (to approximately 25-26℃), simulating the temperature rise during natural sunrise, helping the body to wake naturally and reducing "difficulty getting out of bed." Temperatures that are too low can lead to reluctance to get out of bed, while a suitable temperature can improve mental state after waking.

[0062] Sleep onset period (0-30 minutes): Target temperature 24-25℃ (1-2℃ lower than daytime), rapid cooling helps with sleep. The air conditioner is set to rapid cooling mode, with high fan speed (but avoiding direct airflow to the user), lowering the room temperature from 27-28℃ to 24-25℃ within 30 minutes. Transition period (30 minutes-1 hour): The temperature gradually increases from 24℃ to 26℃, with a heating rate of approximately 0.03-0.05℃ / minute, slowly and steadily avoiding discomfort caused by sudden temperature changes. Deep sleep period (1-4 hours): Target temperature stabilizes at 26-27℃, with temperature fluctuations controlled within ±0.3℃. The air conditioner is set to silent mode, with reduced fan speed to avoid noise and drafts affecting sleep. If the user is detected to have entered deep sleep (via sleep monitoring equipment), the edge device automatically fine-tunes the temperature to the user's preferred deep sleep temperature (learned from historical data, such as a user's deep sleep preference of 26.5℃). REM sleep and light sleep (4-7 hours): The temperature is maintained at a stable 26-27℃, but is finely adjusted based on sleep quality feedback from the sleep monitoring device (such as the number of awakenings and turning over). If frequent turning over is detected (possibly due to being too hot or too cold), the edge device slightly adjusts the temperature (±0.3-0.5℃) and observes the effect to find the optimal temperature for the user that night. Pre-wake period (1 hour before wake-up): The temperature is gradually lowered from 26℃ to 25℃ (summer) or raised to 23℃ (winter) to facilitate natural wake-up. If the user sets a smart alarm (e.g., waking up at 7 am), the edge device starts adjusting the temperature at 6 am, reaching the target temperature at 7 am. Simultaneously, smart curtains open and lights gradually brighten to simulate sunrise wake-up, enhancing the wake-up experience.

[0063] Historical data is analyzed to establish a correlation model between temperature curves and sleep quality. For example, it is found that "when the sleep onset temperature is set to 24.5℃, the average sleep onset time is 15 minutes; when set to 25.5℃, the average sleep onset time is 25 minutes," indicating that the user's sleep onset temperature is suited to a lower temperature. Similarly, it is found that "during deep sleep, the percentage of deep sleep is 25% at 27℃ and 28% at 26.5℃," indicating that the user's deep sleep period is suited to a slightly lower temperature. Based on the correlation analysis results, the edge device generates a personalized optimal sleep temperature curve for each user, replacing the default curve. Personalized curves typically require 1-2 weeks (10-20 nights) of data accumulation to stabilize, after which they are fine-tuned weekly to adapt to seasonal and physical changes. A / B testing can also be used to continuously optimize the temperature curve. For example, if the current optimal sleep onset temperature is 24.5℃, the edge device can try 24.0℃ or 25.0℃ on some nights to compare the difference in sleep quality. If 24.0℃ is more effective, the optimal temperature is updated to 24.0℃; if the effect is worse, 24.5℃ is maintained. Through continuous experimentation, the true optimal curve can be found.

[0064] Step 104: Generate temperature control commands according to each target temperature and send them to the temperature control devices corresponding to each independent temperature control zone.

[0065] Temperature control commands refer to the instruction signals generated by the edge terminal based on the target temperature, used to control the operating parameters of temperature control equipment (such as power on / off, temperature adjustment, fan speed adjustment, and airflow direction adjustment). Temperature control equipment includes, but is not limited to, air conditioning controllers and zone control actuators. Air conditioning controllers can connect to the air conditioning unit via wired (RS485, Modbus) or wireless (WiFi, ZigBee, LoRa) communication interfaces, read air conditioning operating parameters (current temperature, set temperature, operating mode, fan speed, power, etc.), and issue control commands (power on / off, set temperature, mode switching, fan speed adjustment, etc.). The controller is compatible with the communication protocols of mainstream air conditioning brands, or can control older air conditioners that do not support communication protocols via an infrared universal remote control. Electric air valves (0-100% adjustable opening, response time <5 seconds, noise <35dB) or intelligent air outlets (built-in micro motors, adjustable airflow direction and volume) are installed at the air outlets of each zone of the central air conditioning system to achieve independent airflow to each zone. The edge control unit can control the opening and direction of air outlets in different areas, increasing airflow to occupied areas and decreasing or shutting off airflow to unoccupied areas, achieving precise temperature control and energy saving. For split-type air conditioners, intelligent air guides or air circulators can be added to precisely direct cool / hot air to occupied areas, avoiding direct airflow that could cause discomfort and improving comfort and energy efficiency. If the building is equipped with a fresh air edge control unit, the intelligent controller can link with it to coordinate the control of fresh air volume based on indoor air quality and temperature control requirements. For example, if PM2.5 levels are too high, the fresh air volume can be increased to dilute pollutants, while the air conditioning power can be adjusted accordingly to balance the temperature. If CO2 concentration is high, fresh air exchange can be increased to avoid stuffiness. The area control actuators can include, but are not limited to, wall-mounted intelligent temperature control panels, smart speakers, voice assistants, and other devices.

[0066] In this embodiment, the edge device generates appropriate temperature control commands based on the target temperature of each independent temperature control zone, combined with the type of temperature control equipment (central air conditioning, split air conditioning) and communication protocol. The commands include key parameters such as target temperature value, operating mode, and fan speed level. The commands are sent to the corresponding temperature control equipment through wired (such as RS485, Modbus) or wireless (such as WiFi, ZigBee) communication interfaces, while simultaneously receiving real-time feedback data on the equipment's operating status. If a deviation between the actual temperature and the target temperature is detected, a correction command is automatically generated and sent, forming a closed-loop control. For central air conditioning, the opening of the electric damper or the direction of the intelligent air outlet in the corresponding area is controlled synchronously to ensure that cold / hot air is accurately delivered to the target area, avoiding energy waste, while also ensuring the stability of equipment operation and avoiding increased energy consumption and equipment wear caused by frequent start-stop operations.

[0067] It should be noted that when multiple regions issue temperature control commands simultaneously, the edge device uses a time-sharing scheduling algorithm to issue commands in an orderly manner based on the device load capacity and region priority, so as to avoid the energy efficiency reduction caused by the devices running at full load at the same time.

[0068] In one example of this application, the sensing unit further includes multiple gas concentration sensors, and the method further includes the following steps: Multiple gas concentrations are obtained within each independent temperature control zone using various gas concentration sensors; If any gas concentration is not within the preset standard range, the air quality optimization command corresponding to the gas concentration will be output according to the preset optimization strategy table to activate the fresh air function of the temperature control equipment in the independent temperature control area. When the fresh air function is activated, and the predicted temperature change in the independent temperature control area exceeds the preset change threshold, a new temperature control command is generated according to the predicted temperature change and sent to the temperature control device corresponding to the independent temperature control area.

[0069] In this embodiment, the edge device establishes communication connections with multiple gas concentration sensors deployed within the independently temperature-controlled area. Through different types of gas concentration sensors, such as electrochemical sensors and infrared sensors, it simultaneously collects data on the concentrations of various gases, including CO2, formaldehyde, and VOCs, ensuring comprehensive air quality detection. Subsequently, the edge device compares the collected gas concentration data with preset standard ranges one by one. If any gas concentration exceeds the preset standard range, it indicates that the air quality in the area is substandard. It immediately retrieves a preset optimization strategy table, matches the air quality optimization command corresponding to the abnormal gas concentration, and sends it to the temperature control equipment in the corresponding independently temperature-controlled area via the communication interface. This activates the equipment's fresh air function, introducing fresh outdoor air to reduce the concentration of pollutants indoors. Simultaneously, based on the operating parameters of the fresh air function (such as fresh air volume and temperature), the current indoor temperature, and the area's heat capacity characteristics, the edge device calculates the predicted temperature change after the fresh air function is activated. If this predicted change exceeds a preset threshold, it indicates that the introduction of fresh air may cause excessive temperature fluctuations in the area, affecting human comfort. At this point, the edge device immediately generates a new temperature control command based on the predicted temperature change, adjusting the heating / cooling of the temperature control equipment. Parameters such as cooling capacity and operating mode are used to send new temperature control commands and fresh air function control commands to the temperature control equipment in a coordinated manner. This allows the equipment to optimize air quality while adjusting temperature control parameters to offset temperature fluctuations and ensure that the temperature in the area is maintained within a comfortable range.

[0070] Specifically, if PM2.5 levels are too high, turn on the air purifier (if the edge ventilation unit is linked to the purifier) ​​and set the fan speed to maximum for rapid purification; or turn on the edge ventilation unit to increase the fresh air volume and dilute indoor PM2.5; or remind the user to open windows for ventilation (if outdoor PM2.5 is lower than indoor levels). If CO2 levels are too high, turn on the edge ventilation unit and set the fresh air volume to maximum for rapid ventilation; or remind the user to open windows for ventilation. The edge ventilation unit can usually reduce CO2 concentration from 1200ppm to below 800ppm in 5-15 minutes. If formaldehyde / VOC levels are too high, keep the edge ventilation unit running or remind the user to open windows for ventilation for an extended period (at least 2-4 hours). Formaldehyde and VOC volatilization is a continuous process, and continuous ventilation is necessary to effectively reduce concentrations.

[0071] When the fresh air system is activated, the edge unit predicts the indoor temperature change trend (outdoor temperature, fresh air volume, room heat capacity) and adjusts the air conditioner's target temperature in advance to compensate. For example, in summer when the outdoor temperature is 35℃, activating the fresh air system will cause the indoor temperature to rise. The edge unit will lower the air conditioner's target temperature from 26℃ to 25℃ in advance, increasing cooling power to offset the heat brought by the fresh air and maintain a comfortable indoor temperature. Similarly, in winter, activating the fresh air system will lower the indoor temperature, so the edge unit will raise the air conditioner's target temperature in advance and increase heating power. The fresh air volume is adjusted in real time according to air quality to avoid wasting energy by unilaterally activating the entire fresh air system. If the CO2 concentration is only slightly exceeded (900ppm), the edge unit will set the fresh air volume to 30-50% to slowly reduce the CO2 concentration, minimizing temperature impact and energy consumption. If the CO2 concentration is severely exceeded (1500ppm), the edge unit will set the fresh air volume to 100% for rapid air exchange. When the CO2 concentration drops to a safe range (<800ppm), the edge unit automatically reduces the fresh air volume to a maintenance level (10-20%), maintaining fresh air while saving energy. When air quality is good, recirculated air is primarily used (energy saving); when air quality exceeds standards, the proportion of fresh air is increased (improving air quality but increasing energy consumption). The mixing ratio is dynamically optimized to find the optimal balance between "meeting air quality standards" and "minimizing energy consumption".

[0072] In addition, the daily variation patterns of outdoor temperature and air quality can be studied to select the optimal time for ventilation. For example, in summer, when outdoor temperatures are low in the early morning and evening (28-30℃ vs. 35℃ at noon), the edge ventilation system can be scheduled to start fresh air in the early morning or evening to reduce the impact on indoor temperature and lower air conditioning energy consumption. In winter, when outdoor temperatures are high and there is plenty of sunshine at noon, ventilation can be scheduled for noon. If outdoor air quality is poor (such as PM2.5 exceeding the maximum on smoggy days), the edge ventilation system can suspend fresh air supply and switch to internal circulation + air purifier to avoid introducing external pollution. For buildings equipped with heat exchange fresh air edge ventilation systems, the edge ventilation system can achieve efficient temperature and humidity recovery, significantly reducing fresh air energy consumption. The principle of heat exchanger: The exhaust indoor exhaust air (temperature close to the indoor set temperature) and the incoming outdoor fresh air (temperature close to the outdoor temperature) exchange heat and humidity in the exchanger. The fresh air is pre-cooled / pre-heated to near the indoor temperature before being sent into the room, reducing the air conditioning load. Summer: Outdoor 35℃ high-temperature fresh air passes through a total heat exchanger, exchanging heat with the exhaust 26℃ indoor waste air. The fresh air temperature drops to 28-30℃ before being delivered indoors. The air conditioner only needs to lower the 28℃ fresh air temperature to 26℃ (vs. directly lowering the 35℃ fresh air temperature to 26℃), resulting in energy savings of 60%-70%. Winter: Outdoor 5℃ low-temperature fresh air passes through an exchanger, exchanging heat with the exhaust 22℃ indoor waste air. The fresh air temperature rises to 15-18℃ before being delivered indoors. The air conditioner only needs to raise the 15℃ fresh air temperature to 22℃, resulting in equally significant energy savings.

[0073] In one example of this application, the edge device also communicates with multiple IoT devices, and the method may further include the following steps: When an abnormal temperature location that does not match the environmental information appears in any independent temperature control area, determine whether the abnormal temperature location is associated with an IoT device. If an IoT device is associated with the location of the temperature anomaly, and no personalized instructions from the user have been received, a temperature optimization instruction is sent to the IoT device to eliminate the temperature anomaly location through the IoT device.

[0074] In this embodiment, the edge device establishes stable communication connections with various IoT devices equipped with environmental regulation functions, monitors the temperature distribution of each independent temperature control zone in real time, and determines whether there are any abnormal temperature locations by combining the overall environmental information of the area (such as average temperature, airflow distribution, and location of heat radiation sources), i.e., the actual temperature of a certain local space exceeds the reasonable temperature range corresponding to the environmental information. When such an abnormal temperature location is detected, the edge device first queries whether the location is associated with an IoT device (e.g., a smart sunshade is deployed near a local overheated location, and a smart heater is deployed near a local overcooled location). If it is confirmed that the abnormal temperature location is associated with a corresponding IoT device, and the edge device has not received any personalized instructions from the user for that location (i.e., the user has not manually intervened in the temperature of that location), then the edge device generates a targeted temperature optimization instruction based on the type of temperature anomaly (overheating / overcooling), the function of the IoT device, and the characteristics of the abnormal location, and sends it to the IoT device. The IoT device then performs the corresponding operation (e.g., the smart sunshade is closed to block direct sunlight and eliminate local overheating, and the smart heater is activated to increase the local temperature and eliminate overcooling), accurately eliminating the abnormal temperature location. This process addresses the pain point of traditional temperature control devices being unable to cover localized temperature anomalies by linking with IoT devices, improving the uniformity and comfort of regional temperature distribution. At the same time, it does not require manual intervention from the user, enhancing the intelligence level of the edge device, and is compatible with a variety of IoT devices, expanding the application scenarios and adaptability of temperature control edge devices.

[0075] For example, it can be linked with smart curtains: when the edge device detects direct sunlight causing a rapid rise in indoor temperature, it automatically closes the smart curtains to reduce solar radiation heat and lower the air conditioning load. In the evening, when the outdoor temperature drops, the curtains automatically open to utilize natural light and ventilation. It can also be linked with smart lighting: lighting generates a significant amount of waste heat (approximately 90% of the energy from incandescent bulbs is converted to heat, and about 30-40% from LEDs). When the edge device detects overheating indoors, it automatically reduces the brightness of the lights or switches to a cooler color temperature (visually cooler) to reduce the heat load. It can also be linked with smart humidifiers / dehumidifiers: the edge device monitors humidity; when humidity is too low (<40% RH), it activates the humidifier; when humidity is too high (>70% RH), it activates the dehumidifier or the air conditioner's dehumidification mode to maintain a comfortable humidity level (45%-65% RH). This coordinated temperature and humidity control significantly improves perceived comfort. Finally, it can be linked with smart security systems: when the edge device detects an alarm from a door or window sensor (illegal intrusion), it automatically shuts off the air conditioner to avoid energy waste and prioritizes power supply to the security edge device. Upon detecting a gas leak or fire smoke, the air conditioner automatically shuts off and starts the exhaust fan to ensure safety.

[0076] It's worth noting that IoT devices can include smart wall-mounted temperature control panels, edge-mounted fresh air systems, and door / window controllers. The smart wall-mounted temperature control panel offers a touchscreen interface (3.5-7 inches) that displays real-time temperature, humidity, and air quality. Users can manually set target temperatures, switch modes (cooling / heating / dehumidifying / ventilation / auto / sleep), and view energy consumption statistics. The panel has a built-in voice assistant, supporting voice control commands such as "raise by 2 degrees" and "switch to sleep mode." Users can also remotely monitor and control the air conditioner via a mobile app (iOS / Android), viewing real-time temperature curves, air quality, energy consumption statistics, setting timers, temperature presets, and personalized preferences. The app supports multi-user, multi-room management, allowing family members to set their own preferences with intelligent coordination from the edge device. The edge device connects to mainstream smart speakers (such as Baidu's Xiaodu, Tmall Genie, Xiao Ai, Amazon Alexa, and Google Assistant), allowing users to control functions like "Xiaodu, Xiaodu, set the living room temperature to 26 degrees" or "turn on the bedroom air conditioner," freeing up their hands and making it especially suitable for the elderly, children, and people with disabilities. For commercial settings such as office buildings, hotels, and shopping malls, a centralized web-based management platform is provided, allowing property management personnel to monitor the air conditioning operation status, energy consumption statistics, and fault alarms in all areas, as well as batch set parameters, generate energy consumption reports, and optimize operation strategies.

[0077] Optionally, the method may also include the following steps: In response to constant temperature requirement commands or personalized commands sent by personnel within any independently controlled temperature area, the temperature control equipment within that area is adjusted.

[0078] In this embodiment, when a constant temperature requirement command or a user-customized command corresponding to any independent temperature control zone is received, the core requirements of the command are first analyzed (such as the fixed temperature range corresponding to the constant temperature requirement command, or the target temperature or adjustment range corresponding to the user-customized command). At the same time, the operation permissions of the command sender and the validity of the command are verified (such as excluding invalid commands that exceed the temperature control range of the device). Then, based on the analysis results, combined with the current temperature status of the independent temperature control zone and the operating parameters of the temperature control device, a precise temperature adjustment command is generated and sent to the corresponding temperature control device. The device is then controlled to quickly adjust parameters such as heating / cooling power and set temperature. For constant temperature requirement commands, closed-loop control is used to maintain the zone temperature stable within the range required by the command. For user-customized commands, the temperature adjustment operation corresponding to the command is executed precisely. The entire process does not rely on edge-end automatic temperature control logic and directly responds to the user's proactive demands, realizing "user-led" temperature control. This satisfies the user's core requirement for temperature stability (such as the constant temperature requirement during work and rest) and adapts to diverse personalized temperature preferences, greatly improving user operation convenience and temperature control satisfaction. At the same time, permission verification and command validity verification prevent abnormal device operation caused by misoperation or illegal commands.

[0079] For example, hospital wards provide precise temperature control for patients, setting the optimal temperature based on their condition and physical constitution. Patients with fever require a lower temperature (24-25℃) to help lower their temperature, while postoperative patients need a higher temperature (27-28℃) to prevent catching a cold. The edge terminal connects to the hospital's HIS (Hospital Information Edge System) to automatically acquire patient information and set the temperature, reducing the workload of nurses.

[0080] Nursing homes provide temperature control strategies to keep the elderly warm, with a default temperature 2-3℃ higher than normal (28℃ in summer and 24℃ in winter). Enhanced safety monitoring functions include calling the elderly person via the intercom on the temperature control panel if they are detected as inactive for an extended period (potential for a fall or sudden illness), and notifying caregivers if there is no response.

[0081] Kindergartens / schools should provide children with a suitable temperature (1-2℃ lower than adults) to prevent overheating from causing irritability or overcooling from causing colds. Regularly open windows for ventilation to ensure fresh air (children have weaker immune systems and are more sensitive to air quality). Air conditioning should automatically turn off when the classroom is empty and automatically turn on for pre-cooling / pre-heating 15 minutes before class.

[0082] Data centers / server rooms provide a constant temperature and humidity environment for IT equipment such as servers (typically 22-24℃ and 45%-55% humidity), with strict control over temperature and humidity fluctuations (±1℃, ±5% RH) to prevent equipment failure. Edge monitoring of equipment heat load dynamically adjusts cooling capacity to avoid localized overheating (hot spots) that could lead to system downtime.

[0083] Furthermore, for centralized management of office buildings, such as large buildings like office buildings, shopping malls, and hotels, hierarchical management can be implemented by floor, area, and department. Each unit can independently set temperature policies, permissions, and energy consumption budgets. Administrators can batch set parameters, such as "set the temperature of the entire building to 26℃", "switch all meeting rooms to meeting mode", and "automatically turn off all air conditioners at the end of the workday", improving management efficiency. Energy consumption budgets can be allocated to each department (e.g., XX department's budget for this month is 500kWh), and actual energy consumption can be tracked in real time at the edge. Alarms are issued when the limit is approached, and automatic restrictions can be implemented (e.g., limiting the minimum temperature to no less than 25℃). When the limit is exceeded, mandatory restrictions or excess charges are imposed to curb excessive energy consumption. Multi-level permissions can be set: ordinary employees can only adjust the temperature within a limited range (e.g., 25-28℃) and cannot turn off the air conditioner; department heads can expand the adjustment range and set departmental policies; property administrators have the highest permissions, able to view all data and modify all settings. Permission management prevents uncontrolled energy consumption due to arbitrary adjustments.

[0084] For hotels, long-term and short-term rental apartments, it can connect to the hotel's PMS (Property Management System) to obtain room occupancy status. When a room is vacant, the air conditioner automatically enters standby mode (with a loose temperature setting, such as 30℃ in summer and 15℃ in winter, only to prevent extreme temperatures), significantly saving energy. Two hours before a guest's check-in, the edge device automatically starts the air conditioner to pre-cool / pre-heat, ensuring a comfortable room upon arrival. After the guest checks out, the air conditioner automatically switches back to standby mode. It records the temperature preferences of frequent guests (e.g., a guest always sets 24℃), automatically setting the temperature to their preferred temperature on their next stay, improving the guest experience and satisfaction, and reflecting personalized service. Users can also control the air conditioner via a mobile app or in-room tablet, eliminating the need to learn how to operate the wall thermostat. The edge device provides one-click switching of preset scene modes: sleep mode (cooling + silent + lights off), movie mode (comfortable temperature + dimming lights), wake-up mode (heating + opening curtains + playing music), etc., enhancing the intelligent experience.

[0085] Furthermore, energy-saving optimization of edge-end temperature control equipment involves stopping or reducing airflow in unoccupied areas and precisely supplying airflow to occupied areas, avoiding energy waste in empty areas. During the lunch break in office buildings, only a few office areas maintain temperature control, while other areas enter energy-saving mode, resulting in particularly significant energy savings. AI learns user preferences to set optimal temperatures, avoiding energy fluctuations caused by the traditional method of repeatedly adjusting temperatures based on perceived temperature changes. Precise temperature control reduces indoor temperature fluctuations from the traditional ±2℃ to ±0.5℃, reducing frequent start-stop cycles and power fluctuations, and improving energy efficiency ratio. AI predicts future temperature trends (e.g., predicting an increase in outdoor temperature in 2 hours or an increase in indoor temperature due to direct sunlight) and gradually lowers the temperature in advance (e.g., starting to lower from 26℃ to 25.5℃ 30 minutes in advance), avoiding full-load operation of the air conditioner (high energy consumption, low efficiency) during sudden temperature changes. Slow adjustment improves energy efficiency ratio by 15%-25% compared to rapid adjustment. At the edge end, the compressor frequency and fan speed of the inverter air conditioner are dynamically adjusted according to real-time load demand. Traditional fixed-frequency air conditioners can only be turned on and off, resulting in low energy efficiency due to frequent switching. Variable-frequency air conditioners, on the other hand, can continuously operate at their optimal operating point, improving energy efficiency by 30%-50%. The edge device uses AI algorithms to predict the optimal operating frequency, avoiding excessively fast frequency conversion (low energy efficiency) or excessively slow frequency conversion (poor temperature control). For industrial and commercial users, the edge device utilizes time-of-use pricing policies to pre-cool / pre-heat during off-peak hours (such as nighttime) and reduce power consumption or rely on building cooling / heat storage to maintain temperature during peak hours (such as daytime), thus reducing electricity costs. The edge device also collaborates with other devices for energy saving. If it detects no one is indoors, it automatically turns off lights and cuts off standby power for appliances (via smart sockets), reducing indoor heat load (waste heat from lighting and appliances) and lowering the air conditioning cooling load. If the outdoor temperature drops in the summer evening, the edge device suggests users open windows for natural ventilation instead of air conditioning, utilizing free natural cooling sources.

[0086] Install smart meters or power sensors (current transformers + voltage sampling) on ​​the air conditioner power lines to measure the air conditioner power (accuracy ±2%) and cumulative electricity consumption (accuracy ±1%) in real time, with a sampling frequency of 1 time / second or 1 time / minute. Data is uploaded to the edge terminal via interfaces such as RS485 and WiFi. For the central air conditioning edge terminal, energy consumption is measured separately for each zone and each terminal (fan coil unit, fresh air unit), identifying high-energy-consuming areas and equipment for targeted optimization. If it is found that zone A accounts for 40% of energy consumption but only occupies 20% of the area, it indicates low energy efficiency or aging equipment in zone A, requiring investigation of the cause (such as poor insulation, equipment failure, or excessive personnel). Calculate the real-time energy efficiency ratio (EER, cooling capacity / input power) or SEER (seasonal energy efficiency ratio) of the air conditioner to evaluate its operating efficiency. Under normal circumstances, the Energy Efficiency Ratio (EER) should be between 2.5 and 3.5 (National Standard Level 1 Energy Efficiency > 3.4). If an EER < 2.0 is detected, it indicates low air conditioner operating efficiency, which may be due to refrigerant leakage, filter blockage, poor heat dissipation of the outdoor unit, or other faults. An alarm will be triggered at the edge device to prompt for repair. The edge device generates multi-dimensional energy consumption statistics reports, including: daily / weekly / monthly / yearly energy consumption curves, time-of-use energy consumption distribution (identifying peak periods), regional energy consumption comparisons, energy saving rate statistics (comparing before and after energy-saving measures), electricity cost statistics (combined with time-of-use pricing), and carbon emission statistics (electricity consumption × carbon emission factor, typically 0.5-0.8 kgCO2 / kWh). The reports support visualization charts (line graphs, bar charts, pie charts) and export (Excel, PDF).

[0087] The edge device displays energy consumption information and energy-saving effects to users, enhancing their energy-saving awareness and participation. The temperature control panel and mobile app display real-time current power (e.g., "Current power consumption 1.2kW"), today's cumulative electricity consumption (e.g., "Today's electricity consumption 5.8kWh"), and estimated today's electricity cost (e.g., "Estimated electricity cost 1.74 yuan"), allowing users to intuitively understand energy consumption. It analyzes user energy usage behavior and provides personalized energy-saving suggestions. For example, "We detected that you frequently set the temperature to 24℃; raising it to 26℃ could save 15% energy, approximately 0.5 yuan / day," or "We detected that the window is open; we suggest closing doors and windows to save energy," or "The current outdoor temperature is comfortable; we suggest opening windows for natural ventilation instead of air conditioning." After users adopt the suggestions, the edge device calculates the energy-saving effect and provides feedback such as "After adopting the suggestion, today's energy saving is 1.2kWh, saving 0.36 yuan in electricity costs and reducing carbon emissions by 0.6kg, contributing to environmental protection," positively incentivizing users. The system tracks energy consumption and energy-saving rates for each department and household, generating a leaderboard such as "This Month's Energy Saving Champion: Department XX, Energy Saving Rate 35%", fostering competition and a sense of honor, and creating an energy-saving atmosphere. Energy-saving rewards can be set (e.g., cash prizes, points, and certificates of honor for the top three energy savers), providing both material and spiritual incentives. Users set energy-saving goals (e.g., "Keep this month's electricity bill under 100 yuan" or "Achieve an energy saving rate of 30%)," and the edge device tracks progress in real time and provides reminders. When goals are achieved, the edge device congratulates the user and awards a virtual badge; when goals are not achieved, the reasons are analyzed and improvement suggestions are provided.

[0088] Monitor air conditioner operating parameters (power, energy efficiency ratio, temperature, pressure, current, etc.) and identify abnormal operating patterns using machine learning algorithms (such as Isolation Forest and LSTM time series anomaly detection). For example, a continuously decreasing energy efficiency ratio, abnormal power fluctuations, and increased temperature control deviations may all be precursors to malfunctions. Train a fault prediction model based on historical fault data (fault type, changes in operating parameters before the fault) to predict the probability of a fault in the coming week or month. If the model predicts a "60% probability of compressor failure," an early warning will be issued at the edge: "The compressor may be about to fail; early maintenance is recommended," avoiding sudden downtime and high repair costs. Generate maintenance plans based on equipment runtime, operating load, and environmental conditions. For example, "The air conditioner has run for 2000 hours; it is recommended to clean the filter and outdoor unit heat exchanger," or "The refrigerant has been used for 3 years; it is recommended to check the pressure and replenish it." Regular maintenance can maintain the air conditioner's optimal energy efficiency, extend its service life, and save energy and maintenance costs in the long run.

[0089] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0090] The following describes the partitioned independent temperature control device provided in the embodiments of this application. The partitioned independent temperature control device described below can be referred to in correspondence with the partitioned independent temperature control method described above.

[0091] Please see Figure 2 This invention provides a zoned independent temperature control device applied at an edge, wherein the edge is communicatively connected to temperature control equipment and multiple sensing units within the area to be divided. The sensing units are deployed in a grid pattern. The device includes: The information acquisition module 201 is used to periodically collect personnel location distribution information and personnel status information of the area to be divided through each sensing unit; The area division module 202 is used to divide the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided, so as to obtain multiple independent temperature control areas; The temperature determination module 203 is used to determine the target temperature for each independent temperature control zone based on the personnel status information and environmental information in each independent temperature control zone. The temperature control command issuing module 204 is used to generate temperature control commands according to each target temperature and issue them to the temperature control devices corresponding to each independent temperature control zone.

[0092] Optionally, the region division module 202 is specifically used for: If there are physical barriers in the spatial information of the area to be divided, then the area to be divided is divided according to the physical barriers to obtain multiple initial temperature control areas; If any initial temperature control zone also has functional partitions, then the initial temperature control zone is divided according to the functional partitions to obtain multiple independent temperature control zones; If the spatial information of the area to be divided only exists in functional partitions, then the area to be divided is divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided does not have physical barriers or functional zoning, then the areas are grouped according to the personnel location distribution information to obtain multiple independent temperature control zones.

[0093] Optionally, the device also includes an unmanned area temperature control module, specifically used for: If any independent temperature control area is an unoccupied area, a stop command is sent to the temperature control device corresponding to the unoccupied area to minimize the output of the temperature control device; When a person is detected moving around the unmanned area and the distance between the person and the unmanned area continues to decrease, or when a pre-temperature control command is received, a temperature control command is sent to the temperature control device corresponding to the unmanned area according to the preset temperature or the specified temperature corresponding to the pre-temperature control command.

[0094] Optionally, the edge device also communicates with the cloud, and the personnel status information includes the surface temperature of each person and their activity status; the temperature determination module 203 is specifically used for: The perceived temperature calculation model is invoked to calculate the initial perceived temperature of each person in each independent temperature control area based on the environmental information and the activity status of the people in each independent temperature control area. According to the population type corresponding to each person, the correction coefficient is matched from the preset coefficient table respectively; The initial perceived temperature of each person was corrected using a correction factor, and the perceived temperature of each person was determined accordingly. Input features for each person are constructed using the average perceived temperature, surface temperature, activity status, and environmental information. By calling a personalized temperature control model in the cloud, temperature prediction is performed based on each input feature to determine the initial comfort temperature for each person. The target temperature is calculated by cloud computing and sent to the edge device by calculating the weighted average of all initial comfort temperatures associated with each independent temperature control zone.

[0095] Optionally, before performing the step of determining the target temperature for each independent temperature control zone based on the personnel status information and environmental information within each independent temperature control zone, the device further includes a preference conflict resolution module, specifically used for: When any independent temperature control zone has a single preferred temperature, a temperature control command is generated according to the preferred temperature and sent to the corresponding temperature control device; the preferred temperature is also associated with a preferred time period; When any independent temperature control zone has multiple preferred temperatures, calculate the average preferred temperature of the preferred temperatures and send a confirmation request to the setting terminal of each preferred temperature. If no confirmation instruction for the confirmation request is received from each setting terminal within the preset time period, the step of determining the target temperature corresponding to each independent temperature control area based on the personnel status information and environmental information in each independent temperature control area will be executed. If a confirmation instruction for the confirmation request is received from each setting terminal within a preset time period, a temperature control instruction is generated according to the average preferred temperature and sent to the corresponding temperature control device, and the preferred temperature of the independent temperature control area is updated to the average preferred temperature.

[0096] Optionally, the device also includes a sleep temperature control module, specifically used for: When a person's activity status is detected as sleep, the corresponding personalized sleep curve is retrieved according to the independent temperature control zone to which the person's activity status belongs; Based on the temperature values ​​within the personalized sleep curve, a temperature control command is generated and sent to the temperature control device corresponding to the independent temperature control zone.

[0097] Optionally, the sensing unit also includes multiple gas concentration sensors, and the device also includes an air quality and temperature control module, specifically used for: Multiple gas concentrations are obtained within each independent temperature control zone using various gas concentration sensors; If any gas concentration is not within the preset standard range, the air quality optimization command corresponding to the gas concentration will be output according to the preset optimization strategy table to activate the fresh air function of the temperature control equipment in the independent temperature control area. When the fresh air function is activated, and the predicted temperature change in the independent temperature control area exceeds the preset change threshold, a new temperature control command is generated according to the predicted temperature change and sent to the temperature control device corresponding to the independent temperature control area.

[0098] Optionally, the edge device also communicates with various IoT devices, and the device also includes an IoT temperature control module, specifically used for: When an abnormal temperature location that does not match the environmental information appears in any independent temperature control area, determine whether the abnormal temperature location is associated with an IoT device. If an IoT device is associated with the location of the temperature anomaly, and no personalized instructions from the user have been received, a temperature optimization instruction is sent to the IoT device to eliminate the temperature anomaly location through the IoT device.

[0099] Optionally, the device also includes a personalized temperature control module, specifically used for: In response to constant temperature requirement commands or personalized commands sent by personnel within any independently controlled temperature area, the temperature control equipment within that area is adjusted.

[0100] In the several embodiments provided in this application, 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 instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0101] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for independent temperature control in zones, characterized in that, Applied to an edge region, the edge region is communicatively connected to a temperature control device and multiple sensing units within the area to be divided, the sensing units being deployed in a grid pattern. The method includes: Each of the aforementioned sensing units periodically collects information on the location distribution and status of personnel within the area to be divided. According to the personnel location distribution information and / or the spatial information of the area to be divided, the area to be divided is divided to obtain multiple independent temperature control areas; Based on the personnel status information and environmental information within each of the independent temperature control zones, the target temperature corresponding to each of the independent temperature control zones is determined; Temperature control commands are generated according to the target temperatures and sent to the temperature control devices corresponding to the independent temperature control zones.

2. The zoned independent temperature control method according to claim 1, characterized in that, The step of dividing the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided to obtain multiple independent temperature control zones includes: If there is a physical barrier in the spatial information of the area to be divided, then the area to be divided is divided according to the physical barrier to obtain multiple initial temperature control areas; If any of the initial temperature control areas also have functional partitions, then the initial temperature control areas are divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided only contains functional partitions, then the area to be divided is divided according to the functional partitions to obtain multiple independent temperature control areas; If the spatial information of the area to be divided does not have physical barriers or functional partitions, then the area is divided and clustered according to the personnel location distribution information to obtain multiple independent temperature control areas.

3. The zoned independent temperature control method according to claim 2, characterized in that, Also includes: If any of the independent temperature control areas is an unmanned area, a stop command is sent to the temperature control device corresponding to the unmanned area to minimize the output of the temperature control device; When a person is detected moving within the perimeter of the unmanned area and the distance between the person and the unmanned area continues to decrease, or when a pre-temperature control command is received, a temperature control command is sent to the temperature control device corresponding to the unmanned area according to the preset temperature or the specified temperature corresponding to the pre-temperature control command.

4. The zoned independent temperature control method according to claim 1, characterized in that, The edge device is also connected to the cloud for communication. The personnel status information includes the surface temperature and activity status of each personnel. The step of determining the target temperature corresponding to each independent temperature control area based on the personnel status information and environmental information within each independent temperature control area includes: The body temperature calculation model is invoked to calculate the initial body temperature of each person in each of the independent temperature control areas based on the environmental information and the activity status of the people in each of the independent temperature control areas. According to the population type corresponding to each person, a correction coefficient is matched from the preset coefficient table respectively; The initial perceived temperature of each person is corrected using the correction coefficient, and the perceived temperature of each person is determined accordingly. The input features for each person are constructed using the average perceived temperature of the person, the surface temperature of the person, the activity status of the person, and the environmental information, respectively. The cloud-based personalized temperature control model is invoked to predict the temperature based on the input features, thereby determining the initial comfort temperature for each individual. The cloud calculates the weighted average of all initial comfort temperatures associated with each of the independent temperature-controlled zones as the target temperature and sends it to the edge device.

5. The zoned independent temperature control method according to claim 1, characterized in that, Before performing the step of determining the target temperature corresponding to each of the independent temperature control zones based on the personnel status information and environmental information within each of the independent temperature control zones, the method further includes: When any of the independent temperature control zones has a single preferred temperature, a temperature control command is generated according to the preferred temperature and sent to the corresponding temperature control device; wherein, the preferred temperature is also associated with a preferred time period; When any of the independent temperature control zones has multiple preferred temperatures, the average preferred temperature of the preferred temperatures is calculated, and a confirmation request is sent to the setting terminal of each preferred temperature. If no confirmation instruction for the confirmation request is received from each of the preset terminals within the preset time period, then the step of determining the target temperature corresponding to each of the independent temperature control areas based on the personnel status information and environmental information in each of the independent temperature control areas is executed. If a confirmation instruction for the confirmation request is received from each of the preset terminals within a preset time period, a temperature control instruction is generated according to the average preferred temperature and sent to the corresponding temperature control device, and the preferred temperature corresponding to the independent temperature control area is updated to the average preferred temperature.

6. The zoned independent temperature control method according to claim 4, characterized in that, Also includes: When the activity state of the person is detected to be in a sleep state, the corresponding personalized sleep curve is retrieved according to the independent temperature control zone to which the activity state of the person belongs; Based on the temperature values ​​within the personalized sleep curve, a temperature control command is generated and sent to the temperature control device corresponding to the independent temperature control zone.

7. The zoned independent temperature control method according to claim 1, characterized in that, The sensing unit also includes multiple gas concentration sensors, and the method further includes: The concentrations of various gases within each of the independent temperature control zones are obtained using multiple gas concentration sensors. If any of the gas concentrations is not within the preset standard range, an air quality optimization command corresponding to the gas concentration will be output according to the preset optimization strategy table to activate the fresh air function of the temperature control equipment in the independent temperature control area. When the fresh air function is activated, and the predicted temperature change in the independent temperature control area exceeds the preset change threshold, a new temperature control command is generated according to the predicted temperature change and sent to the temperature control device corresponding to the independent temperature control area.

8. The zoned independent temperature control method according to claim 1, characterized in that, The edge terminal is also communicatively connected to various IoT devices, and the method further includes: When an abnormal temperature location that does not match the environmental information occurs in any of the independent temperature control areas, it is determined whether the abnormal temperature location is associated with an Internet of Things (IoT) device. If the location of the temperature anomaly is associated with an IoT device and no personalized user instruction has been received, a temperature optimization instruction is sent to the IoT device to eliminate the temperature anomaly location through the IoT device.

9. The zoned independent temperature control method according to claim 1, characterized in that, Also includes: In response to a constant temperature requirement command or a user-customized command sent by a person within any of the independent temperature control areas, the temperature control equipment within the independent temperature control area is adjusted.

10. A zoned independent temperature control device, characterized in that, Applied to an edge region, the edge region is communicatively connected to a temperature control device and multiple sensing units within the area to be divided. The sensing units are deployed in a grid pattern. The device includes: The information acquisition module is used to periodically acquire the personnel location distribution information and personnel status information of the personnel in the area to be divided through each of the sensor units; The area division module is used to divide the area to be divided according to the personnel location distribution information and / or the spatial information of the area to be divided, so as to obtain multiple independent temperature control areas; The temperature determination module is used to determine the target temperature corresponding to each of the independent temperature control areas based on the personnel status information and environmental information in each of the independent temperature control areas; The temperature control command issuing module is used to generate temperature control commands according to each of the target temperatures and issue them to the temperature control devices corresponding to each of the independent temperature control zones.

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