A control method and device of an air conditioning apparatus, the air conditioning apparatus, and a storage medium

Abstract

The application discloses a kind of air conditioning equipment control method, device, air conditioning equipment and storage medium, the method includes: according to the target indoor activity position information of air conditioning equipment in a period of history, utilize machine learning algorithm, the space of room where air conditioning equipment is divided into n regions;According to the target indoor activity position information of current air conditioning equipment, determine the region where target in the n regions of air conditioning equipment in the room where air conditioning equipment is located is currently located, and it is recorded as the current region of air conditioning equipment;According to the current region of air conditioning equipment, call the current scene control parameter of air conditioning equipment, so that air conditioning equipment operates according to the current scene control parameter of air conditioning equipment.The scheme, by making the running parameter of air conditioning equipment in the room where user is located can follow the actual activity of user and adjust, meet the demand of air conditioning equipment to the environment of room where it is located and the individualized scene control of user, improve user experience and comfort.

Description

Technical Field

[0001] This invention belongs to the field of smart home technology, specifically relating to a control method, device, air conditioning equipment, and storage medium for an air conditioning device, and particularly to a control method, device, air conditioning equipment, and storage medium for an air conditioning device in a smart home air conditioning system based on a millimeter-wave sensor. Background Technology

[0002] With the development of IoT technology and artificial intelligence, smart home systems have received widespread attention. However, in the control of smart home systems, the control methods for air conditioning systems in related solutions cannot meet the needs of sensing the environment of multiple rooms and controlling personalized scenes, thus failing to improve user experience and comfort.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The purpose of this invention is to provide a control method, device, air conditioning equipment, and storage medium for air conditioning equipment, in order to solve the problem that the control methods of air conditioning systems in related solutions cannot meet the needs of sensing the environment of multiple rooms and controlling personalized scenarios, thus failing to improve user experience and comfort. By combining millimeter-wave sensors and machine learning algorithms, the operating parameters of the air conditioning equipment in the user's room can be adjusted according to the user's actual activities, thereby meeting the needs of the air conditioning equipment to sense the environment of its own room and control personalized scenarios for the user, and improving user experience and comfort.

[0005] This invention provides a control method for an air conditioning device, wherein a millimeter-wave sensor is installed on the air conditioning device; the control method includes: targeting a user in the room where the air conditioning device is located, acquiring target indoor activity location information of the target in the room where the air conditioning device is located, denoted as the target indoor activity location information of the air conditioning device; wherein the target indoor activity location information of the air conditioning device is detected by the millimeter-wave sensor on the air conditioning device; based on the target indoor activity location information of the air conditioning device over a historical period, using a machine learning algorithm, dividing the space of the room where the air conditioning device is located into n regions, denoted as the n regions of the air conditioning device, where n is a positive integer; wherein each of the n regions of the air conditioning device corresponds to a scenario in which the target in the room where the air conditioning device is located uses the air conditioning device; acquiring a historical period of time... The operating parameters for each of the n areas of the air conditioner, input from the external control terminal of the air conditioner within a given time period, are denoted as the scene control parameters for each of the n areas of the air conditioner. During the current operation of the air conditioner, based on the current target indoor activity location information of the air conditioner, the current location of the target in the room where the air conditioner is located is determined within the n areas of the air conditioner, and is denoted as the current area of ​​the air conditioner. Based on the current area of ​​the air conditioner, the scene control parameters corresponding to the current area of ​​the air conditioner are retrieved from the scene control parameters of each of the n areas of the air conditioner, and are denoted as the current scene control parameters of the air conditioner. The current operating parameters of the air conditioner are adjusted to the current scene control parameters of the air conditioner so that the air conditioner operates according to the current scene control parameters of the air conditioner.

[0006] In some embodiments, the target indoor activity location information of the air conditioning device includes: the distance between the target and the air conditioning device, the angle of the target relative to the air conditioning device, and the target's moving speed; based on the target indoor activity location information of the air conditioning device over a historical period, a machine learning algorithm is used to divide the space of the room where the air conditioning device is located into n regions, denoted as the n regions of the air conditioning device, including: analyzing the target indoor activity location information of the air conditioning device over a historical period using a machine learning algorithm to determine the target's indoor activity location in the room where the air conditioning device is located, denoted as the indoor activity location of the air conditioning device; and determining the target's moving speed and activity frequency in the room where the air conditioning device is located at the target's indoor activity location; based on the target's indoor activity locations in the room where the air conditioning device is located at the n indoor activity locations of the air conditioning device, and the target's moving speed and activity frequency in the room where the air conditioning device is located at the target's indoor activity locations, the n indoor activity locations of the air conditioning device are correspondingly divided into n regions of the space of the room where the air conditioning device is located, denoted as the n regions of the air conditioning device.

[0007] In some embodiments, based on the target's indoor activity positions in the room where the air conditioning unit is located (n locations within the air conditioning unit), and the target's movement speed and activity frequency in those locations, the space of the room is divided into n regions, denoted as the n regions of the air conditioning unit. This includes: setting n categories for the target's regions in the room; setting n different sets of parameter values ​​as centroids; in the n different sets of parameters, each set includes a set movement speed for each category and a set activity frequency for that category; and dividing the space of the room where the air conditioning unit is located into n regions. The movement speed of the target in the room where the air conditioner is located is compared with the set movement speed of the n categories in the center of mass to obtain the movement speed comparison result; the absolute value of the difference between the activity frequency of the target in the room where the air conditioner is located in the n indoor activity positions of the air conditioner and the set activity frequency of the n categories in the center of mass is compared with the set frequency difference threshold of the n categories to obtain the activity frequency comparison result; according to the interval to which the movement speed comparison result and the activity frequency comparison result belong, the space of the room where the air conditioner is located is divided into n regions corresponding to the n indoor activity positions of the air conditioner, which are denoted as the n regions of the air conditioner.

[0008] In some embodiments, the category of the target area in the room where the air conditioning unit is located includes at least one of the following: activity area, unoccupied area, rest area, and study area. Based on the movement speed comparison result and the activity frequency comparison result, the n indoor activity locations of the air conditioning unit are correspondingly divided into n areas within the room where the air conditioning unit is located, denoted as the n areas of the air conditioning unit. This includes: if the movement speed comparison result belongs to the movement speed range of the activity area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then one indoor activity location among the n indoor activity locations of the air conditioning unit corresponding to the movement speed comparison result and the activity frequency comparison result is determined as the category of the activity area; if the movement speed comparison result belongs to the movement speed range of the unoccupied area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then the indoor activity location corresponding to the movement speed comparison result and the activity frequency comparison result is determined as the category of the activity area; if the movement speed comparison result belongs to the movement speed range of the unoccupied area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then the indoor activity location corresponding to the movement speed comparison result and the activity frequency comparison result belongs to the activity frequency range of the activity area. Within the active frequency range of the uninhabited area, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result among the n indoor activity locations of the air conditioning equipment is determined as the category of the uninhabited area; if the moving speed comparison result belongs to the moving speed range of the rest area and the active frequency comparison result belongs to the active frequency range of the rest area, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result among the n indoor activity locations of the air conditioning equipment is determined as the category of the rest area; if the moving speed comparison result belongs to the moving speed range of the learning area and the active frequency comparison result belongs to the active frequency range of the learning area, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result among the n indoor activity locations of the air conditioning equipment is determined as the category of the learning area.

[0009] In some embodiments, the n zones of the air conditioning device include at least one of the following: an activity zone, an unoccupied zone, a rest zone, and a study zone; the scene control parameters for each of the n zones of the air conditioning device include at least one of the following: the wind speed of the indoor fan of the air conditioning device, the frequency of the compressor of the air conditioning device, and the air outlet mode of the air conditioning device; based on the current zone of the air conditioning device, the scene control parameters corresponding to the current zone of the air conditioning device are called from the scene control parameters of each of the n zones of the air conditioning device, and denoted as the current scene control parameters of the air conditioning device, including: if the current zone of the air conditioning device is an activity zone, then in the current scene control parameters of the air conditioning device called from the scene control parameters of each of the n zones of the air conditioning device, the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in a preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in a preset frequency range, and the air outlet mode of the air conditioning device is an air outlet mode that follows the person; if the current zone of the air conditioning device is an unoccupied zone, then the scene control parameters of each of the n zones of the air conditioning device are called from the scene control parameters of each of the n zones of the air conditioning device. In the scene control parameters, the current scene control parameters of the air conditioner obtained from the call are as follows: the indoor fan speed of the air conditioner is a preset minimum value, the compressor frequency of the air conditioner is a preset minimum value, and the air outlet mode of the air conditioner is to avoid uninhabited areas. If the current area of ​​the air conditioner is a rest area, then in the current scene control parameters of the air conditioner obtained from the scene control parameters of each of the n areas of the air conditioner, the indoor fan speed of the air conditioner is the minimum speed within a preset speed range, the compressor frequency of the air conditioner is a quiet frequency within a preset frequency range, and the air outlet mode of the air conditioner is to avoid people. If the current area of ​​the air conditioner is a learning area, then in the current scene control parameters of the air conditioner obtained from the scene control parameters of each of the n areas of the air conditioner, the indoor fan speed of the air conditioner is a quiet speed within a preset speed range, the compressor frequency of the air conditioner is a quiet frequency within a preset frequency range, and the air outlet mode of the air conditioner is to prevent direct cold air blowing.

[0010] In some implementations, the method further includes: acquiring usage habit data of the target in the room where the air conditioning unit is located; optimizing the n regions of the air conditioning unit that have been divided based on the usage habit data of the target in the room where the air conditioning unit is located, to obtain new n regions of the air conditioning unit; and / or, optimizing the current scene control parameters of the air conditioning unit corresponding to the corresponding region for the n regions of the air conditioning unit that have been divided or for the new n regions of the air conditioning unit based on the usage habit data of the target in the room where the air conditioning unit is located, to obtain new current scene control parameters of the air conditioning unit corresponding to the corresponding region.

[0011] In some embodiments, the air conditioning device is any one of the air conditioning devices in a home air conditioning system; the communication network of the air conditioning system is formed by self-organizing the air conditioning devices among the one or more air conditioning devices in the home air conditioning system; the control method of the air conditioning device further includes: obtaining a reminder instruction for a set event in a corresponding area of ​​n areas of the air conditioning device input by an external control terminal of the air conditioning device; determining whether a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device based on the target indoor activity location information of the air conditioning device; if it is determined that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device, then causing the air conditioning device itself to issue a reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device, sending the reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and sending the reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device to a preset client.

[0012] In conjunction with the above method, another aspect of the present invention provides a control device for an air conditioning unit, wherein a millimeter-wave sensor is installed on the air conditioning unit; the control device for the air conditioning unit includes: an acquisition unit configured to acquire, with a user in the room where the air conditioning unit is located as the target, target indoor activity location information of the target in the room where the air conditioning unit is located, denoted as the target indoor activity location information of the air conditioning unit; wherein the target indoor activity location information of the air conditioning unit is detected by the millimeter-wave sensor on the air conditioning unit; a control unit configured to, based on the target indoor activity location information of the air conditioning unit over a historical period, use a machine learning algorithm to divide the space of the room where the air conditioning unit is located into n regions, denoted as the n regions of the air conditioning unit, where n is a positive integer; wherein each of the n regions of the air conditioning unit corresponds to a scenario in which the target in the room where the air conditioning unit is located uses the air conditioning unit; the control unit is further configured to acquire historical data... The operating parameters for each of the n areas of the air conditioning device, input from the external control terminal of the air conditioning device over a certain period of time, are denoted as the scene control parameters for each of the n areas of the air conditioning device. The control unit is further configured to, during the current operation of the air conditioning device, determine the current location of the target in the room where the air conditioning device is located within the n areas of the air conditioning device based on the current target indoor activity location information of the air conditioning device; this is denoted as the current area of ​​the air conditioning device. The control unit is also configured to, based on the current area of ​​the air conditioning device, retrieve the scene control parameters corresponding to the current area from the scene control parameters of each of the n areas of the air conditioning device; this is denoted as the current scene control parameters of the air conditioning device. The control unit is further configured to adjust the current operating parameters of the air conditioning device to the current scene control parameters of the air conditioning device, so that the air conditioning device operates according to the current scene control parameters.

[0013] In some embodiments, the target indoor activity location information of the air conditioning device includes: the distance between the target and the air conditioning device, the angle of the target relative to the air conditioning device, and the target's moving speed; the control unit, based on the target indoor activity location information of the air conditioning device over a historical period, uses a machine learning algorithm to divide the space of the room where the air conditioning device is located into n regions, denoted as the n regions of the air conditioning device, including: analyzing the target indoor activity location information of the air conditioning device over a historical period using a machine learning algorithm to determine the target's indoor activity location in the room where the air conditioning device is located, denoted as the indoor activity location of the air conditioning device; and determining the target's moving speed and activity frequency in the room where the air conditioning device is located at the target's indoor activity location; and based on the target's indoor activity locations in the room where the air conditioning device is located at the n indoor activity locations of the air conditioning device, and the target's moving speed and activity frequency in the room where the air conditioning device is located at the target's indoor activity locations, correspondingly dividing the space of the room where the air conditioning device is located into n regions, denoted as the n regions of the air conditioning device.

[0014] In some embodiments, the control unit, based on the target's indoor activity positions in the room where the air conditioning unit is located (n locations within the air conditioning unit), and the target's movement speed and activity frequency at those locations, divides the space of the room into n regions, denoted as the n regions of the air conditioning unit. This includes: setting n categories for the target's regions in the room; setting n different sets of parameter values ​​as centroids; in the n different sets of parameters, each set includes a set movement speed for each category and a set activity frequency for that category; and dividing the space into n regions. The movement speed of the target in the room at the n indoor locations of the air conditioning units is compared with the set movement speeds of the n categories in the center of mass to obtain a movement speed comparison result; the absolute value of the difference between the activity frequency of the target in the room at the n indoor locations of the air conditioning units and the set activity frequency of the n categories in the center of mass is compared with the set frequency difference thresholds of the n categories to obtain an activity frequency comparison result; based on the movement speed comparison result and the activity frequency comparison result, the indoor locations of the n indoor locations of the air conditioning units are correspondingly divided into n regions in the space of the room where the air conditioning units are located, denoted as the n regions of the air conditioning units.

[0015] In some embodiments, the category of the target area in the room where the air conditioning unit is located includes at least one of the following: an activity area category, an unoccupied area category, a rest area category, and a study area category; the control unit, based on the movement speed comparison result and the activity frequency comparison result, divides the space of the room where the air conditioning unit is located into n areas corresponding to the n indoor activity positions of the air conditioning unit, denoted as the n areas of the air conditioning unit, including: if the movement speed comparison result belongs to the movement speed range of the activity area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then determine that one indoor activity position among the n indoor activity positions of the air conditioning unit corresponding to the movement speed comparison result and the activity frequency comparison result is the category of the activity area; if the movement speed comparison result belongs to the movement speed range of the unoccupied area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then determine that one indoor activity position among the n indoor activity positions of the air conditioning unit corresponding to the movement speed comparison result and the activity frequency comparison result is the category of the activity area; if the movement speed comparison result belongs to the movement speed range of the unoccupied area and the activity frequency comparison result belongs to the activity frequency range of the activity area, then determine that one indoor activity position among the n indoor activity positions of the air conditioning unit corresponding to the movement speed comparison result and the activity frequency comparison result is the category of the activity area; If the comparison result belongs to the active frequency range of the uninhabited area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the comparison result of the movement speed and the comparison result of the active frequency is determined as the category of the uninhabited area; if the comparison result of the movement speed belongs to the movement speed range of the rest area and the comparison result of the active frequency belongs to the active frequency range of the rest area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the comparison result of the movement speed and the comparison result of the active frequency is determined as the category of the rest area; if the comparison result of the movement speed belongs to the movement speed range of the learning area and the comparison result of the active frequency belongs to the active frequency range of the learning area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the comparison result of the movement speed and the comparison result of the active frequency is determined as the category of the learning area.

[0016] In some embodiments, the n zones of the air conditioning device include at least one of the following: an activity zone, an unoccupied zone, a rest zone, and a study zone; the scene control parameters for each of the n zones of the air conditioning device include at least one of the following: the wind speed of the indoor fan of the air conditioning device, the frequency of the compressor of the air conditioning device, and the air outlet mode of the air conditioning device; the control unit, based on the current zone of the air conditioning device, retrieves scene control parameters corresponding to the current zone of the air conditioning device from the scene control parameters of each of the n zones of the air conditioning device, denoted as the current scene control parameters of the air conditioning device, including: if the current zone of the air conditioning device is an activity zone, then in the current scene control parameters of the air conditioning device retrieved from the scene control parameters of each of the n zones of the air conditioning device, the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in a preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in a preset frequency range, and the air outlet mode of the air conditioning device is an air outlet mode that follows the user; if the current zone of the air conditioning device is an unoccupied zone, then in the current scene control parameters of the air conditioning device retrieved from the scene control parameters of each of the n zones of the air conditioning device, the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in a preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in a preset frequency range, and the air outlet mode of the air outlet of the air conditioning device is an air outlet mode that follows the user; if the current zone of the air conditioning device is an unoccupied zone, then in the current scene control parameters of the air conditioning device, the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in a preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in a preset frequency range, and the air outlet mode of the air outlet of the air conditioning device is an air outlet mode that follows the user; if the current zone of the air conditioning device is an unoccupied zone, then in the current scene In the scene control parameters of each area, the current scene control parameters of the air conditioning device obtained from the call are as follows: the indoor fan speed of the air conditioning device is a preset minimum value, the compressor frequency of the air conditioning device is a preset minimum value, and the air outlet mode of the air conditioning device is to avoid uninhabited areas. If the current area of ​​the air conditioning device is a rest area, then in the current scene control parameters of the air conditioning device obtained from the scene control parameters of each of the n areas of the air conditioning device, the indoor fan speed of the air conditioning device is the minimum speed within a preset speed range. The compressor frequency of the device is a quiet frequency within a preset frequency range, and the air outlet of the air conditioner is designed to avoid people by direct airflow. If the current area of ​​the air conditioner is a learning area, the current scene control parameters of the air conditioner are retrieved from the scene control parameters of each of the n areas of the air conditioner. In this case, the indoor fan speed of the air conditioner is a quiet speed within a preset wind speed range, the compressor frequency of the air conditioner is a quiet frequency within a preset frequency range, and the air outlet of the air conditioner is designed to prevent direct cold air blowing.

[0017] In some embodiments, the system further includes: the acquisition unit is further configured to acquire user habit data of a target in the room where the air conditioning unit is located; the control unit is further configured to optimize the n regions of the air conditioning unit that have been divided according to the user habit data of the target in the room where the air conditioning unit is located, to obtain new n regions of the air conditioning unit; and / or, the control unit is further configured to optimize the current scene control parameters of the air conditioning unit corresponding to the corresponding region for the n regions of the air conditioning unit that have been divided or for the new n regions of the air conditioning unit, according to the user habit data of the target in the room where the air conditioning unit is located, to obtain new current scene control parameters of the air conditioning unit corresponding to the corresponding region.

[0018] In some embodiments, the air conditioning device is any one of the air conditioning devices in a home air conditioning system; the communication network of the air conditioning system is formed by self-organizing the one or more air conditioning devices in the home air conditioning system; the control method of the air conditioning device further includes: the acquisition unit is further configured to acquire a reminder instruction for a set event in a corresponding area of ​​n areas of the air conditioning device, input by an external control terminal of the air conditioning device; the control unit is further configured to determine whether a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device based on the target indoor activity location information of the air conditioning device; the control unit is further configured to, if it is determined that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device, cause the air conditioning device itself to issue a reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device, send the reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and send the reminder message that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device to a preset client.

[0019] In conjunction with the above-described device, the present invention further provides an air conditioning device, comprising: a control device for the air conditioning device described above.

[0020] In conjunction with the above method, the present invention further provides a storage medium comprising a stored program, wherein, when the program is executed, the device on which the storage medium is located controls the air conditioning equipment control method described above.

[0021] Therefore, the solution of this invention involves installing a millimeter-wave sensor on each air conditioner in a home air conditioning system. This sensor identifies the activity of a target (i.e., the user) in the room where the air conditioner is located. Based on the identified activity, a machine learning algorithm is used to divide the space of the room into more than one region. The operating parameters of the air conditioner are then set for each region within the room, according to the user's needs. During actual operation, the millimeter-wave sensor identifies the user's current region within the room and adjusts the operating parameters to correspond to that region. This allows the air conditioner's operating parameters to adjust in accordance with the user's actual activities. By combining millimeter-wave sensors and machine learning algorithms, the operating parameters of the air conditioner in the user's room can be adjusted to reflect the user's activities, satisfying the air conditioner's need for environmental awareness and personalized scene control, thus improving user experience and comfort.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an embodiment of the control method for an air conditioning device according to the present invention;

[0025] Figure 2 This is a flowchart illustrating an embodiment of the method of the present invention, which divides the space of the room where the air conditioning equipment is located into n regions.

[0026] Figure 3 This is a schematic flowchart of an embodiment of the method of the present invention, which divides the indoor activity positions of n air conditioning devices into n areas corresponding to the space of the room where the air conditioning devices are located;

[0027] Figure 4 This is a flowchart illustrating an embodiment of the method of the present invention for optimizing a divided region or optimizing scene control parameters corresponding to a divided region by incorporating user habit data of the target.

[0028] Figure 5This is a flowchart illustrating an embodiment of the method of the present invention that provides reminders for set events in corresponding areas of n areas of the air conditioning equipment;

[0029] Figure 6 This is a schematic diagram of the structure of a control device for an air conditioning system according to an embodiment of the present invention;

[0030] Figure 7 This is a flowchart illustrating an embodiment of a control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention, specifically a flowchart illustrating the process of dividing areas and setting different scene functions.

[0031] Figure 8 This is a schematic diagram showing the detection results of a millimeter-wave radar detecting the distance, angle, and velocity of a target.

[0032] Referring to the accompanying drawings, the reference numerals in the embodiments of the present invention are as follows:

[0033] 102 - Acquisition unit; 104 - Control unit. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. 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.

[0035] Considering that in the control of smart home systems, the control methods of air conditioning systems in relevant solutions involve global air supply, and the air conditioner only regulates the environment without personalized function settings or intelligent air supply control for different areas.

[0036] For example, user activity patterns vary across different areas of a room. Some areas may be considered frequently used, while others are rarely visited and can be considered inactive. However, the air conditioning control methods in related solutions cannot identify frequently used and inactive areas based on user activity levels in different room zones. They also cannot intelligently direct airflow to active areas and shield inactive areas from drafts. Furthermore, they cannot trigger different settings based on different scenarios for different activity areas. For instance, if a user is in a study area, the air conditioning fan speed should be at its lowest to avoid noise interference with studying.

[0037] It is evident that the control methods for air conditioning systems in existing solutions cannot meet the needs of sensing multiple room environments and personalized scene control. Therefore, a new control method for air conditioning systems is required. This invention proposes a control method for an air conditioning system, specifically a control method for a smart home air conditioning system based on millimeter-wave sensors. The method uses millimeter-wave sensors to identify the location information of target indoor activities within a room and combines this with machine learning algorithms to self-learn and divide the room into different zones. A mobile application is then used to set different scene functions for each zone, allowing the corresponding air conditioning devices in the home air conditioning system to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensors. This addresses the problem that existing control methods for air conditioning systems cannot meet the needs of sensing multiple room environments and personalized scene control, thereby improving user experience and comfort.

[0038] According to an embodiment of the present invention, a control method for an air conditioning device is provided, such as... Figure 1 The diagram shows a flow chart of an embodiment of the method of the present invention. A millimeter-wave sensor is provided in the air conditioning equipment; as shown... Figure 1 As shown, the control method for the air conditioning equipment of the present invention includes steps S110 to S160.

[0039] In step S110, targeting the user in the room where the air conditioning unit is located, the target indoor activity location information of the target in the room where the air conditioning unit is located is obtained and recorded as the target indoor activity location information of the air conditioning unit; wherein, the target indoor activity location information of the air conditioning unit is detected by a millimeter-wave sensor on the air conditioning unit. Specifically, Figure 7 This is a flowchart illustrating an embodiment of a control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention. Specifically, it is a flowchart illustrating the process of dividing areas and setting different scene functions. Figure 7 As shown, the present invention discloses a control method for a smart home air conditioning system based on millimeter-wave sensors, comprising: Step 2, for each air conditioning unit in the home air conditioning system, each air conditioning unit is equipped with a millimeter-wave sensor (such as a millimeter-wave radar sensor); through the millimeter-wave sensor equipped in each air conditioning unit, the indoor environmental target location status information of the room where the air conditioning unit is located can be identified, and then Step 3 is executed. Each air conditioning unit in the home air conditioning system is equipped with a millimeter-wave sensor, which can send and receive millimeter-wave signals and identify the target indoor activity location information in the room.

[0040] In some embodiments, the target indoor activity location information of the air conditioning device includes: the distance between the target and the air conditioning device, the angle of the target relative to the air conditioning device, and the target's moving speed. Specifically, Figure 8This is a schematic diagram illustrating the detection results of a millimeter-wave radar, showing the range, angle, and velocity information of a target. Figure 8 As shown, the indoor location information of the target within the room includes: the target's distance, angle, and speed of movement. The target is as follows: Figure 8 In the example shown, the target marked by the white dot is precisely located within the room through accurate identification by a millimeter-wave sensor. This target could be a user within the room.

[0041] The present invention achieves target location within a room by using a millimeter-wave sensor to identify target location information. Specifically, the technique of using a millimeter-wave sensor to identify target location information includes the following process:

[0042] ① Transmitting millimeter-wave signals: The transmitter of the millimeter-wave sensor first transmits millimeter-wave signals, which propagate in the indoor environment and are reflected when they encounter the human body.

[0043] ② Receiving echo signals: The receiver of the millimeter-wave sensor receives echo signals reflected by the human body. These echo signals carry information about the human body's position, speed, and direction of movement.

[0044] ③ Analyzing minute movements: By analyzing the received echo signals, millimeter-wave sensors can detect minute movements of the human body, such as breathing, micro-movements, or walking. These minute movements cause changes in the phase and frequency of the echo signals.

[0045] ④ Doppler effect: Millimeter-wave sensors utilize the Doppler effect, which states that the frequency of the echo signal changes when a person moves. By analyzing the frequency changes of the echo signal, the speed and direction of the person's movement can be inferred.

[0046] ⑤ Positioning and tracking: Millimeter-wave sensors can use the received echo signals to calculate the position and movement trajectory of a human body through information such as Doppler frequency shift and phase change.

[0047] ⑥ Algorithm processing: The millimeter-wave sensor's processing system uses signal processing algorithms, such as power spectrum analysis, phase difference measurement, and Doppler frequency shift calculation, to analyze the position and motion information in the echo signal.

[0048] ⑦ Location recognition: By processing and analyzing signals from multiple millimeter-wave sensors, the air conditioning system can identify and locate the position information of different people in the room.

[0049] In step S120, based on the target indoor activity location information of the air conditioning device over a historical period, a machine learning algorithm is used to divide the space of the room where the air conditioning device is located into n regions, denoted as the n regions of the air conditioning device, where n is a positive integer; wherein, each of the n regions of the air conditioning device corresponds to a scenario in which the target in the room where the air conditioning device is located uses the air conditioning device.

[0050] In some implementations, the specific process of dividing the space of the room where the air conditioning equipment is located into n areas, denoted as the n areas of the air conditioning equipment, based on the target indoor activity location information of the air conditioning equipment over a historical period of time in step S120 using a machine learning algorithm is described in the following exemplary description.

[0051] The following is combined with Figure 2 The illustrated flowchart shows an embodiment of the method of the present invention in which the space of the room where the air conditioning equipment is located is divided into n areas. The specific process of dividing the space of the room where the air conditioning equipment is located into n areas in step S120 is further explained, including steps S210 to S220.

[0052] Step S210: Based on the target indoor activity location information of the air conditioning equipment over a historical period, analyze it using a machine learning algorithm to determine the target's indoor activity location in the room where the air conditioning equipment is located, and record it as the indoor activity location of the air conditioning equipment; and determine the moving speed and activity frequency of the target in the room where the air conditioning equipment is located at the indoor activity location of the air conditioning equipment.

[0053] Step S220: Based on the target's indoor activity positions in the room where the air conditioning equipment is located, and the target's movement speed and activity frequency in the room where the air conditioning equipment is located, the indoor activity positions of the n air conditioning equipment are correspondingly divided into n areas in the space of the room where the air conditioning equipment is located, denoted as the n areas of the air conditioning equipment.

[0054] Specifically, such as Figure 7As shown, the control method of a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: Step 3, self-learning area division: Combining machine learning algorithms, based on the target indoor activity location information and activity level identified by the millimeter-wave sensor, the control system of the air conditioning system can automatically learn and divide different areas. For example, by analyzing the activity frequency and speed of the target, the control system of the air conditioning system can divide a certain area into an activity area, a rest area, or an uninhabited area. Here, activity level refers to the frequency of user activity in a certain area, that is, whether users are frequently active in a certain area; for example, in the living room sofa area, the user activity frequency in this area is relatively high, i.e., high activity level. Activity level is obtained through statistics of user activity information, such as counting the number of times a user is in a certain area within a certain period of time as the user's activity level.

[0055] In some embodiments, the specific process of dividing the space of the room where the air conditioner is located into n areas, denoted as the n areas of the air conditioner, according to the target's indoor activity positions in the room where the air conditioner is located and the target's movement speed and activity frequency in the indoor activity positions of the air conditioner, is described in the following exemplary description.

[0056] The following is combined with Figure 3 The illustrated flowchart shows an embodiment of the method of the present invention in which the indoor activity positions of the n air conditioning devices are correspondingly divided into n areas in the space of the room where the air conditioning devices are located. The specific process of dividing the indoor activity positions of the n air conditioning devices into n areas in step S220 is further explained, including steps S310 to S340.

[0057] Step S310: The target area in the room where the air conditioning unit is located is categorized into n categories, and n different sets of parameter values ​​are set as centroids. In each of the n different sets of parameters, the set parameter includes the set movement speed for each category and the set activity frequency for that category, such as the movement speed v for category a. a This represents the activity frequency f of class a. a .

[0058] Step S320: The moving speed of the target in the room where the air conditioning equipment is located at the n indoor activity positions of the air conditioning equipment is compared with the set moving speed of the n categories in the center of mass, and the moving speed comparison result is obtained.

[0059] Step S330: The absolute value of the difference between the active frequency of the target at n indoor activity positions of the air conditioning equipment in the room where the air conditioning equipment is located and the set active frequency of n categories in the centroid is compared with the set frequency difference threshold of n categories respectively to obtain the active frequency comparison result.

[0060] Step S340: Based on the comparison results of the moving speed and the comparison results of the active frequency, the indoor activity positions of the n air conditioning devices are correspondingly divided into n areas in the space of the room where the air conditioning devices are located, denoted as the n areas of the air conditioning devices.

[0061] Specifically, such as Figure 7 As shown, the control method of a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: in step 3, using a deep learning algorithm for more precise spatial division, which can be achieved by using an unsupervised machine learning algorithm to adaptively identify and divide different regions. The machine learning algorithm utilizes the K-means clustering optimization algorithm, classifying by designing a K value, and designing different values ​​for the centroids based on parameters such as movement speed V and activity frequency f, where the activity frequency f is the number of activities within a certain time period; then, the target movement speed and activity frequency collected by the millimeter-wave sensor are compared with the centroids to determine different classifications. The detailed algorithm design is as follows:

[0062] Step 31: Classify the target area of ​​the design into 3 categories by determining 3 different parameter values ​​(v a ,f a ), (v b ,f b ), (v c ,f c ) is the center of mass. Where, v a This represents the movement speed of class A, f. a This represents the activity frequency of class A; v b This represents the movement speed of class B, f. b This represents the activity frequency of class b; v c This represents the movement speed of class C, f. c This represents the activity frequency of class C, and .

[0063] Step 32, the dataset collected by the millimeter-wave sensor is (v i ,f i ), i = 1, 2, 3, 4... Where, v i This represents the moving speed of the data collection process, f. i This indicates the frequency of data collection activity.

[0064] Step 33: Compare the moving velocity and centroid of the data collected by the millimeter-wave sensor.

[0065] .

[0066] Step 34: Further judgment is made based on the activity frequency. If If so, then the dataset belongs to class a. Similarly, if If so, then the dataset belongs to class b; if If the K value represents the number of classes, then the dataset belongs to class c. For example, if K=3, then it is divided into 3 classes: class a, class b, and class c. Where d... a d represents the frequency difference threshold for class a. b Represents the frequency difference threshold for class b, d c This represents the frequency difference threshold for class C.

[0067] Step 35: Finally, the region classification is achieved.

[0068] In some implementations, the category of the target area in the room where the air conditioning unit is located includes at least one of the following: an activity area, an unoccupied area, a rest area, and a study area.

[0069] In step S340, based on the range of the moving speed comparison result and the active frequency comparison result, the indoor activity positions of the n air conditioning devices are correspondingly divided into n areas in the space of the room where the air conditioning devices are located, denoted as the n areas of the air conditioning devices, including any of the following division scenarios:

[0070] The first classification scenario: If the moving speed comparison result belongs to the moving speed range of the activity area and the active frequency comparison result belongs to the active frequency range of the activity area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the activity area.

[0071] The second classification scenario: If the moving speed comparison result belongs to the moving speed range of the uninhabited area and the active frequency comparison result belongs to the active frequency range of the uninhabited area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the uninhabited area.

[0072] The third classification scenario: If the moving speed comparison result belongs to the moving speed range of the rest area and the active frequency comparison result belongs to the active frequency range of the rest area, then among the n indoor activity positions of the air conditioning equipment, the indoor activity position corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the rest area.

[0073] The fourth classification scenario: If the moving speed comparison result belongs to the moving speed range of the learning region and the active frequency comparison result belongs to the active frequency range of the learning region, then among the n indoor activity positions of the air conditioning equipment, the indoor activity position corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the learning region.

[0074] Specifically, such as Figure 7 As shown, the control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: an intelligent area division strategy provided in step 3 to more accurately divide different areas, such as activity areas, unoccupied areas, and rest areas. For specific area division strategies, please refer to the following exemplary description.

[0075] Activity Zone: This zone is defined as an area with high activity levels and a certain activity speed. If targets frequently and continuously appear within a certain area over a 20-minute period, it indicates high activity in this zone. Activity level refers to activity frequency, the number of times an object moves within a given time period. Activity speed is greater than 2 m / s. By analyzing the target activity frequency and speed identified by millimeter-wave sensors, the air conditioning system can determine this area as an activity zone, suitable for scenarios requiring higher operating power from the air conditioning equipment. Figure 8 As shown, you can see the target's movement speed in the output.

[0076] Resting Area: This area is defined as a region where the target remains continuously and moves only slightly without significant movement. It indicates that the human body maintains slight movements within this specific area, but does not leave the area or exhibit any obvious movement or departure. For example, the target remains continuously present within a certain location range for 30 minutes; the speed of the slight movements is close to a few centimeters per second (e.g., 10 cm / s).

[0077] Imagine a person watching TV in a living room. They sit on the sofa, making subtle movements such as picking up the remote, pressing buttons, and adjusting their posture. Although they are making these minute movements, they don't get up from the sofa and leave the sofa area; instead, they remain on the sofa watching TV. In this case, their position on the sofa can be considered a state of "a continuously existing target with minute movements, but without significant speed of movement."

[0078] By observing the continuous presence of a target identified by a millimeter-wave sensor at a specific location and combining this with information about micro-movements, the air conditioning system can determine that the area is a resting area, suitable for scenarios that require maintaining a comfortable temperature but do not require excessive energy consumption.

[0079] Uninhabited area: This area is designated as a region where a target exists, exhibiting a certain speed of movement, but for a short period of time. For example, a human's movement speed is 1.1 m / s - 1.5 m / s (i.e., a person's walking speed is 1.1 m / s - 1.5 m / s), and the time spent in this area is less than 1 second. In other words, if a target walks through this area, the millimeter-wave radar detects the target's movement speed and identifies it as a person walking through the area. If the person's movement speed is within the walking speed range, it is determined that a person has walked through the area.

[0080] By analyzing the presence time and movement speed of targets identified by millimeter-wave sensors at specific locations, the air conditioning system can determine that the area is an uninhabited zone, making it suitable for scenarios where it is necessary to temporarily shut down or reduce the operating power of air conditioning equipment.

[0081] In step S130, the operating parameters for each of the n areas of the air conditioner, input from the external control terminal of the air conditioner over a historical period, are obtained and recorded as the scene control parameters for each of the n areas of the air conditioner. The external control terminal of the air conditioner may be a remote control for the air conditioner, a mobile app for the air conditioner, etc.

[0082] In step S140, during the current operation of the air conditioning equipment, based on the current target indoor activity location information of the air conditioning equipment, the current location of the target in the room where the air conditioning equipment is located is determined among the n areas of the air conditioning equipment, and is denoted as the current area of ​​the air conditioning equipment.

[0083] In step S150, based on the current region of the air conditioning device, the scene control parameters corresponding to the current region of the air conditioning device are called from the scene control parameters of each of the n regions of the air conditioning device, and recorded as the current scene control parameters of the air conditioning device.

[0084] In some embodiments, the n areas of the air conditioning device include at least one of the following: an activity area, an unoccupied area, a rest area, and a study area; the scene control parameters for each of the n areas of the air conditioning device include at least one of the following: the wind speed of the indoor fan of the air conditioning device, the frequency of the compressor of the air conditioning device, and the air outlet mode of the air conditioning device.

[0085] Based on the current region of the air conditioning device, the scene control parameter corresponding to the current region of the air conditioning device is called from the scene control parameters of each of the n regions of the air conditioning device, and is denoted as the current scene control parameter of the air conditioning device, including any of the following calling scenarios:

[0086] The first calling scenario: If the current area of ​​the air conditioning device is an active area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are called, in which the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in the preset frequency range, and the air outlet of the air conditioning device is the air outlet mode of following the person.

[0087] The second scenario: If the current area of ​​the air conditioning device is an uninhabited area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are obtained, and the wind speed of the indoor fan of the air conditioning device is a preset minimum value, the frequency of the compressor of the air conditioning device is a preset minimum value, and the air outlet of the air conditioning device is set to avoid supplying air to uninhabited areas (i.e., no air is supplied to uninhabited areas).

[0088] The third scenario: If the current area of ​​the air conditioning unit is a rest area, then from the scene control parameters of each of the n areas of the air conditioning unit, the current scene control parameters of the air conditioning unit are retrieved, where the indoor fan speed is the minimum speed within a preset speed range, the compressor frequency is a silent frequency within a preset frequency range, and the air outlet mode is an airflow pattern that avoids people. The compressor frequency can be set to a non-noise frequency, i.e., the compressor operating frequency that does not produce noise.

[0089] The fourth calling scenario: If the current area of ​​the air conditioning device is a learning area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are called, in which the wind speed of the indoor fan of the air conditioning device is the silent wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the silent frequency in the preset frequency range, and the air outlet of the air conditioning device is the air outlet mode of the anti-cold air direct blowing mode.

[0090] Specifically, such as Figure 7 As shown, the control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes:

[0091] Step 4, Scene Setting and Triggering: Users can define different scenes. For example, users can set scenes for different areas of each room through a mobile application and set specific control parameters for the air conditioning equipment for each scene.

[0092] Step 5: When the air conditioner detects that the environment meets certain scene conditions through the millimeter-wave sensor, it automatically triggers the corresponding scene function. Different scene settings are triggered in different areas. For example, in the living room sofa area, the air conditioner's operating parameters are set to maximum fan speed; in the study area, the air conditioner's operating parameters are set to minimum fan speed.

[0093] In practice, the identified and divided areas of each room can be uploaded to a cloud server. The mobile application communicates with the cloud server, displaying the divided areas of each room. Users can then use the app to set the operating parameters of the air conditioning units in different areas to achieve personalized settings. This allows the air conditioning units to perform different scene functions in different areas and under different conditions. For example, if the user is in the sofa area, the living room air conditioning unit will perform the function parameters set for that area; if the user is in the study area at the desk, the study room air conditioning unit will operate in silent mode. Of course, if the air conditioning unit is a voice-activated unit, it can also be selected to play ambient sound for studying.

[0094] In step S160, the current operating parameters of the air conditioning device are adjusted to the current scene control parameters of the air conditioning device, so that the air conditioning device operates according to the current scene control parameters of the air conditioning device, thereby meeting the current usage needs of the target in the room where the air conditioning device is located, and improving the user experience and comfort of the target in the room where the air conditioning device is located.

[0095] The present invention provides a control method for a smart home air conditioning system based on millimeter-wave sensors. The method uses millimeter-wave sensors to identify the location information of target indoor activities within a room and combines this with machine learning algorithms to self-learn and divide the room into different zones. A mobile application is then used to set different scene functions for each zone, allowing the corresponding air conditioning devices in the home air conditioning system to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensors. This solves the problem that related solutions cannot meet the needs of sensing multiple room environments and providing personalized scene control, thus improving user experience and comfort.

[0096] In some embodiments, the control method for the air conditioning equipment described in the present invention further includes: optimizing the divided areas based on the target's usage habit data or optimizing the scene control parameters corresponding to the divided areas.

[0097] The following is combined with Figure 4The flowchart shown is a schematic diagram of an embodiment of the method of the present invention, which optimizes the divided area or the scene control parameters corresponding to the divided area by combining the target's usage habit data. It further illustrates the specific process of optimizing the divided area or the scene control parameters corresponding to the divided area by combining the target's usage habit data, including steps S410 to S430.

[0098] Step S410: Obtain the usage habit data of the target in the room where the air conditioning equipment is located.

[0099] Step S420: Based on the usage habit data of the target in the room where the air conditioning unit is located, optimize the n areas of the air conditioning unit that have been divided to obtain new n areas of the air conditioning unit. And / or,

[0100] Step S430: Based on the usage habit data of the target in the room where the air conditioning equipment is located, optimize the current scene control parameters of the air conditioning equipment corresponding to the n areas of the air conditioning equipment that have been divided or the n new areas of the air conditioning equipment, and obtain the new current scene control parameters of the air conditioning equipment corresponding to the corresponding areas.

[0101] Specifically, in the solution of the present invention, after step 5, it may further include: step 6, intelligent optimization. That is, the solution of the present invention also utilizes machine learning algorithms to analyze and learn user behavior patterns, thereby automatically setting control rules, or utilizes deep learning algorithms to perform more precise spatial division, so as to achieve intelligent optimization and thereby improve the intelligence level of the home air conditioning system.

[0102] The system collects user behavior data within the home environment, preprocesses the data to extract feature values ​​(time, distance, angle, movement speed, activity trajectory), and then uses artificial intelligence learning algorithms to analyze and learn to identify user behavior, resulting in more detailed category divisions. The air conditioner then adjusts its operating parameters according to the user's behavior in that area. For example, after collecting data on the user's frequent exercise in a certain area of ​​the home environment, the air conditioner automatically identifies this area as an exercise zone. Based on the user's exercise habits, the air conditioner executes appropriate operating parameters for that specific scenario at that time and in that specific area. For instance, the airflow pattern in that area can be adjusted to avoid people, preventing the user from catching a cold after sweating.

[0103] In some embodiments, the air conditioning control method of the present invention refers to any one air conditioning unit in a household air conditioning system; among the one or more air conditioning units in the household air conditioning system, a self-organizing network is formed to create a communication network for the air conditioning system. Specifically, as shown... Figure 7As shown, the control method of a smart home air conditioning system based on millimeter-wave sensors of the present invention further includes: step 1, self-organizing network between each air conditioning device in the home air conditioning system, so that each air conditioning device in the home air conditioning system can be interconnected, and then step 2 is executed.

[0104] In step 1, the air conditioning units in the home air conditioning system are interconnected through a local mesh network (i.e., wireless mesh network) on each unit, forming a self-organizing network via wireless communication modules. This networking method enables interconnection and interoperability between the various air conditioning units in the home air conditioning system.

[0105] The local mesh (wireless mesh network) networking method for each air conditioner unit is a network topology based on wireless communication technology. It connects multiple air conditioner units to form a self-organizing network, where the units can communicate and transmit data. The specific process of connecting all air conditioner units in a home air conditioning system via wireless communication modules to form a self-organizing network is as follows:

[0106] ① Air Conditioner Connection: In a home environment, multiple air conditioners can be connected to the same network via a wireless communication module (such as a Bluetooth module).

[0107] ② Self-organizing network: In the initial state, each air conditioner automatically establishes a connection with other nearby air conditioners and forms a self-organizing network.

[0108] ③ Node Communication: In a mesh network, each air conditioner can act as a data transmission node. Different air conditioners can communicate directly or relay data between other air conditioners.

[0109] ④ Data transmission: Different air conditioners can transmit data directly to each other or by relaying other air conditioning devices. Data can be transmitted along the network path until it reaches the target air conditioning device.

[0110] ⑤ Dynamic adjustment: Mesh networks are dynamic. If air conditioning devices are added or removed, the network will automatically adjust, and the air conditioning devices will re-establish connections to maintain network connectivity.

[0111] In the solution of the present invention, the control method of the air conditioning equipment further includes: a process of reminding the corresponding area of ​​the setting event in the n areas of the air conditioning equipment.

[0112] The following is combined with Figure 5The flowchart shown is a schematic diagram of an embodiment of the method of the present invention for reminding the corresponding setting events of the corresponding areas in the n areas of the air conditioning device. The specific process of reminding the corresponding setting events of the n areas of the air conditioning device is further explained, including: steps S510 to S530.

[0113] Step S510: Obtain reminder instructions for set events in corresponding areas of the n areas of the air conditioning equipment, input from the external control terminal of the air conditioning equipment. These set events include events such as a child appearing in an uninhabited area, an elderly person falling in any area, etc.

[0114] Step S520: Based on the target indoor activity location information of the air conditioning equipment, determine whether a set event has occurred in the corresponding area of ​​the n areas of the air conditioning equipment.

[0115] Step S530: If it is determined that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, then the air conditioning device itself sends a reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to a preset client.

[0116] Specifically, in the solution of this invention, each air conditioner in the home can subscribe to scene events set by the user. For example, when the user sets an unoccupied area scene, the air conditioner will determine, based on the identification results of the millimeter-wave sensor, that the target has been in an unoccupied area for an extended period of time in a specific location. Once this scene event is triggered, the air conditioner will send a broadcast alert to all air conditioners in the home to remind family members to check the situation in time and prevent incidents such as elderly family members falling.

[0117] For example, if a user sets a scenario via a mobile app for an unoccupied area: a target is located anywhere in the area and moves at a low speed (a few centimeters per second) for a certain period of time (e.g., 5 minutes), an alarm will be triggered. For instance, if an elderly person falls while walking in an unoccupied area, and the air conditioner detects that a target has been present in the area for a certain period without movement, the air conditioner will immediately send a broadcast alarm to all air conditioning devices in the home and push a reminder message to the family members' mobile apps.

[0118] This invention utilizes millimeter-wave sensors to identify the location of target indoor activities within a room, and combines this with machine learning algorithms to self-learn and divide the room into different zones based on activity levels. A mobile application allows users to set different scenes for these zones, automatically triggering different scene functions based on the environmental target state. This enables corresponding air conditioning units in the home air conditioning system to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensors, improving user experience and comfort. Through the application of millimeter-wave sensors and machine learning algorithms, corresponding air conditioning units in the home air conditioning system can accurately sense and identify the location of target indoor activities in different rooms, and adaptively identify and divide different zones based on the target's activity level. Specifically, it self-learns and divides different room activity zones based on the target's activity level. Users can set different scenes corresponding to different room activity zones through the mobile application, triggering different function settings based on different zones, achieving zone scene control, and thus realizing personalized air conditioning control, providing a more comfortable indoor environment that meets individual needs. Furthermore, this invention also addresses home safety and comfort issues through scene event subscription and intelligent optimization functions for corresponding air conditioning units in the home air conditioning system, providing a more intelligent, convenient, and safer air conditioning system control solution, enhancing home safety and comfort.

[0119] The technical solution of this embodiment involves installing a millimeter-wave sensor on each air conditioner in a home air conditioning system. This sensor identifies the activity of a target (i.e., the user) in the room where the air conditioner is located. Based on the identified activity, a machine learning algorithm is used to divide the room into multiple zones. Operating parameters for each zone are then set according to the user's needs. During actual operation, the millimeter-wave sensor continues to identify the user's current zone within the room and adjusts the air conditioner's operating parameters to match those zone. This allows the air conditioner's operating parameters to adapt to the user's actual activities. By combining millimeter-wave sensors and machine learning algorithms, the operating parameters of the air conditioner in the user's room can adjust to reflect their activities, satisfying the air conditioner's need for environmental awareness and personalized scene control, thus improving user experience and comfort.

[0120] According to an embodiment of the present invention, a control device for an air conditioning device corresponding to a control method for an air conditioning device is also provided. See also Figure 6The diagram shows a structural schematic of an embodiment of the device of the present invention. A millimeter-wave sensor is provided in the air conditioning equipment; as shown... Figure 6 As shown, the control device of the air conditioning equipment includes: an acquisition unit 102 and a control unit 104.

[0121] The acquisition unit 102 is configured to acquire the target indoor activity location information of the user in the room where the air conditioning unit is located, denoted as the target indoor activity location information of the air conditioning unit; wherein the target indoor activity location information of the air conditioning unit is detected by a millimeter-wave sensor on the air conditioning unit. The specific functions and processing of this acquisition unit 102 are described in step S110. Specifically, Figure 7 This is a flowchart illustrating an embodiment of a control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention. Specifically, it is a flowchart illustrating the process of dividing areas and setting different scene functions. Figure 7 As shown, the present invention discloses a control method for a smart home air conditioning system based on millimeter-wave sensors, comprising: Step 2, for each air conditioning unit in the home air conditioning system, each air conditioning unit is equipped with a millimeter-wave sensor (such as a millimeter-wave radar sensor); through the millimeter-wave sensor equipped in each air conditioning unit, the indoor environmental target location status information of the room where the air conditioning unit is located can be identified, and then Step 3 is executed. Each air conditioning unit in the home air conditioning system is equipped with a millimeter-wave sensor, which can send and receive millimeter-wave signals and identify the target indoor activity location information in the room.

[0122] In some embodiments, the target indoor activity location information of the air conditioning device includes: the distance between the target and the air conditioning device, the angle of the target relative to the air conditioning device, and the target's moving speed. Specifically, Figure 8 This is a schematic diagram illustrating the detection results of a millimeter-wave radar, showing the range, angle, and velocity information of a target. Figure 8 As shown, the indoor location information of the target within the room includes: the target's distance, angle, and speed of movement. The target is as follows: Figure 8 In the example shown, the target marked by the white dot is precisely located within the room through accurate identification by a millimeter-wave sensor. This target could be a user within the room.

[0123] The present invention achieves target location within a room by using a millimeter-wave sensor to identify target location information. Specifically, the technique of using a millimeter-wave sensor to identify target location information includes the following process:

[0124] ① Transmitting millimeter-wave signals: The transmitter of the millimeter-wave sensor first transmits millimeter-wave signals, which propagate in the indoor environment and are reflected when they encounter the human body.

[0125] ② Receiving echo signals: The receiver of the millimeter-wave sensor receives echo signals reflected by the human body. These echo signals carry information about the human body's position, speed, and direction of movement.

[0126] ③ Analyzing minute movements: By analyzing the received echo signals, millimeter-wave sensors can detect minute movements of the human body, such as breathing, micro-movements, or walking. These minute movements cause changes in the phase and frequency of the echo signals.

[0127] ④ Doppler effect: Millimeter-wave sensors utilize the Doppler effect, which states that the frequency of the echo signal changes when a person moves. By analyzing the frequency changes of the echo signal, the speed and direction of the person's movement can be inferred.

[0128] ⑤ Positioning and tracking: Millimeter-wave sensors can use the received echo signals to calculate the position and movement trajectory of a human body through information such as Doppler frequency shift and phase change.

[0129] ⑥ Algorithm processing: The millimeter-wave sensor's processing system uses signal processing algorithms, such as power spectrum analysis, phase difference measurement, and Doppler frequency shift calculation, to analyze the position and motion information in the echo signal.

[0130] ⑦ Location recognition: By processing and analyzing signals from multiple millimeter-wave sensors, the air conditioning system can identify and locate the position information of different people in the room.

[0131] The control unit 104 is configured to divide the space of the room where the air conditioner is located into n regions, denoted as the n regions of the air conditioner, based on the target indoor activity location information of the air conditioner over a historical period, using a machine learning algorithm; where n is a positive integer; and each of the n regions of the air conditioner corresponds to a scenario in which the target in the room uses the air conditioner. The specific functions and processing of this control unit 104 are described in step S120.

[0132] In some embodiments, the control unit 104, based on the target indoor activity location information of the air conditioning equipment over a historical period, uses a machine learning algorithm to divide the space of the room where the air conditioning equipment is located into n regions, denoted as the n regions of the air conditioning equipment, including:

[0133] The control unit 104 is further configured to analyze the target indoor activity location information of the air conditioning equipment over a historical period using a machine learning algorithm to determine the target's indoor activity location in the room where the air conditioning equipment is located, and record this as the indoor activity location of the air conditioning equipment; and to determine the target's movement speed and activity frequency at the indoor activity location of the air conditioning equipment. The specific functions and processing of this control unit 104 are further described in step S210.

[0134] The control unit 104 is further configured to divide the space of the room containing the air conditioning equipment into n areas, denoted as the n areas of the air conditioning equipment, based on the target's indoor activity positions in the room containing the air conditioning equipment and the target's movement speed and activity frequency in those positions. The specific functions and processing of this control unit 104 are further described in step S220.

[0135] Specifically, such as Figure 7 As shown, the control method of a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: Step 3, self-learning area division: Combining machine learning algorithms, based on the target indoor activity location information and activity level identified by the millimeter-wave sensor, the control system of the air conditioning system can automatically learn and divide different areas. For example, by analyzing the activity frequency and speed of the target, the control system of the air conditioning system can divide a certain area into an activity area, a rest area, or an uninhabited area. Here, activity level refers to the frequency of user activity in a certain area, that is, whether users are frequently active in a certain area; for example, in the living room sofa area, the user activity frequency in this area is relatively high, i.e., high activity level. Activity level is obtained through statistics of user activity information, such as counting the number of times a user is in a certain area within a certain period of time as the user's activity level.

[0136] In some embodiments, the control unit 104, based on the target's indoor activity positions in the room where the air conditioning unit is located (n locations within the air conditioning unit) and the target's movement speed and activity frequency at those locations, divides the space of the room into n regions, denoted as the n regions of the air conditioning unit, corresponding to the n indoor activity positions of the target in the room where the air conditioning unit is located.

[0137] The control unit 104 is further configured to classify the target area in the room where the air conditioning unit is located into n categories, and to set n different sets of parameter values ​​as centroids; in the n different sets of parameters, each set of parameters includes a set moving speed for each category and a set active frequency for each category, such as the moving speed v for category a.a This represents the activity frequency f of class a. a For details on the specific functions and processing of the control unit 104, please refer to step S310.

[0138] The control unit 104 is further configured to compare the moving speed of the target in the room where the air conditioning unit is located at n indoor positions of the air conditioning unit with the set moving speeds of n categories in the center of mass, and obtain the moving speed comparison results. The specific functions and processing of this control unit 104 are further described in step S320.

[0139] The control unit 104 is further configured to compare the absolute values ​​of the differences between the active frequencies of the target at n indoor locations of the air conditioning unit in the room where the air conditioning unit is located and the set active frequencies of n categories in the centroid with set frequency difference thresholds for the n categories, and obtain an active frequency comparison result. The specific functions and processing of this control unit 104 are further described in step S330.

[0140] The control unit 104 is further configured to divide the indoor activity positions of the n air conditioning devices into n areas based on the range of the moving speed comparison result and the active frequency comparison result, denoted as the n areas of the air conditioning devices. The specific functions and processing of this control unit 104 are further described in step S340.

[0141] Specifically, such as Figure 7 As shown, the control method of a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: in step 3, using a deep learning algorithm for more precise spatial division, which can be achieved by using an unsupervised machine learning algorithm to adaptively identify and divide different regions. The machine learning algorithm utilizes the K-means clustering optimization algorithm, classifying by designing a K value, and designing different values ​​for the centroids based on parameters such as movement speed V and activity frequency f, where the activity frequency f is the number of activities within a certain time period; then, the target movement speed and activity frequency collected by the millimeter-wave sensor are compared with the centroids to determine different classifications. The detailed algorithm design is as follows:

[0142] Step 31: Classify the target area of ​​the design into 3 categories by determining 3 different parameter values ​​(v a ,f a ), (v b ,f b ), (v c ,f c ) is the center of mass. Where, v a This represents the movement speed of class A, f. a This represents the activity frequency of class A; vb This represents the movement speed of class B, f. b This represents the activity frequency of class b; v c This represents the movement speed of class C, f. c This represents the activity frequency of class C, and .

[0143] Step 32, the dataset collected by the millimeter-wave sensor is (v i ,f i ), i = 1, 2, 3, 4... Where, v i This represents the moving speed of the data collection process, f. i This indicates the frequency of data collection activity.

[0144] Step 33: Compare the moving velocity and centroid of the data collected by the millimeter-wave sensor.

[0145] .

[0146] Step 34: Further judgment is made based on the activity frequency. If If so, then the dataset belongs to class a. Similarly, if If so, then the dataset belongs to class b; if If the K value represents the number of classes, then the dataset belongs to class c. For example, if K=3, then it is divided into 3 classes: class a, class b, and class c. Where d... a d represents the frequency difference threshold for class a. b Represents the frequency difference threshold for class b, d c This represents the frequency difference threshold for class C.

[0147] Step 35: Finally, the region classification is achieved.

[0148] In some implementations, the category of the target area in the room where the air conditioning unit is located includes at least one of the following: an activity area, an unoccupied area, a rest area, and a study area.

[0149] The control unit 104, based on the range of the moving speed comparison result and the active frequency comparison result, divides the indoor activity positions of the n air conditioning devices into n regions corresponding to the space of the room where the air conditioning devices are located, denoted as the n regions of the air conditioning devices, including any of the following division scenarios:

[0150] The first classification scenario: The control unit 104 is further configured to determine, if the moving speed comparison result belongs to the moving speed range of the activity area and the active frequency comparison result belongs to the active frequency range of the activity area, then determine one of the n indoor activity positions of the air conditioning equipment that corresponds to the moving speed comparison result and the active frequency comparison result as the category of the activity area.

[0151] The second classification scenario: The control unit 104 is further configured to determine, if the moving speed comparison result belongs to the moving speed range of the uninhabited area and the active frequency comparison result belongs to the active frequency range of the uninhabited area, then determine one of the indoor activity positions of the n indoor activity positions of the air conditioning equipment that corresponds to the moving speed comparison result and the active frequency comparison result as the category of the uninhabited area.

[0152] The third classification scenario: The control unit 104 is further configured to determine, if the moving speed comparison result belongs to the moving speed range of the rest area and the active frequency comparison result belongs to the active frequency range of the rest area, then determine one of the n indoor activity positions of the air conditioning equipment that corresponds to the moving speed comparison result and the active frequency comparison result as the category of the rest area.

[0153] The fourth classification scenario: The control unit 104 is further configured to determine, if the moving speed comparison result belongs to the moving speed range of the learning region and the active frequency comparison result belongs to the active frequency range of the learning region, then determine one of the n indoor activity positions of the air conditioning equipment that corresponds to the moving speed comparison result and the active frequency comparison result as the category of the learning region.

[0154] Specifically, such as Figure 7 As shown, the control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes: an intelligent area division strategy provided in step 3 to more accurately divide different areas, such as activity areas, unoccupied areas, and rest areas. For specific area division strategies, please refer to the following exemplary description.

[0155] Activity Zone: This zone is defined as an area with high activity levels and a certain activity speed. If targets frequently and continuously appear within a certain area over a 20-minute period, it indicates high activity in this zone. Activity level refers to activity frequency, the number of times an object moves within a given time period. Activity speed is greater than 2 m / s. By analyzing the target activity frequency and speed identified by millimeter-wave sensors, the air conditioning system can determine this area as an activity zone, suitable for scenarios requiring higher operating power from the air conditioning equipment. Figure 8As shown, you can see the target's movement speed in the output.

[0156] Resting Area: This area is defined as a region where the target remains continuously and moves only slightly without significant movement. It indicates that the human body maintains slight movements within this specific area, but does not leave the area or exhibit any obvious movement or departure. For example, the target remains continuously present within a certain location range for 30 minutes; the speed of the slight movements is close to a few centimeters per second (e.g., 10 cm / s).

[0157] Imagine a person watching TV in a living room. They sit on the sofa, making subtle movements such as picking up the remote, pressing buttons, and adjusting their posture. Although they are making these minute movements, they don't get up from the sofa and leave the sofa area; instead, they remain on the sofa watching TV. In this case, their position on the sofa can be considered a state of "a continuously existing target with minute movements, but without significant speed of movement."

[0158] By observing the continuous presence of a target identified by a millimeter-wave sensor at a specific location and combining this with information about micro-movements, the air conditioning system can determine that the area is a resting area, suitable for scenarios that require maintaining a comfortable temperature but do not require excessive energy consumption.

[0159] Uninhabited area: This area is designated as a region where a target exists, exhibiting a certain speed of movement, but for a short period of time. For example, a human's movement speed is 1.1 m / s - 1.5 m / s (i.e., a person's walking speed is 1.1 m / s - 1.5 m / s), and the time spent in this area is less than 1 second. In other words, if a target walks through this area, the millimeter-wave radar detects the target's movement speed and identifies it as a person walking through the area. If the person's movement speed is within the walking speed range, it is determined that a person has walked through the area.

[0160] By analyzing the presence time and movement speed of targets identified by millimeter-wave sensors at specific locations, the air conditioning system can determine that the area is an uninhabited zone, making it suitable for scenarios where it is necessary to temporarily shut down or reduce the operating power of air conditioning equipment.

[0161] The control unit 104 is further configured to acquire operating parameters for each of the n areas of the air conditioning device, input from an external control terminal of the air conditioning device over a historical period, and denoted as scene control parameters for each of the n areas of the air conditioning device. The specific functions and processing of the control unit 104 are further described in step S130. The external control terminal of the air conditioning device may be a remote control for the air conditioning device, a mobile app for the air conditioning device, etc.

[0162] The control unit 104 is further configured to, during the current operation of the air conditioning unit, determine, based on the current target indoor activity location information of the air conditioning unit, the current location of the target in the room where the air conditioning unit is located, among the n areas of the air conditioning unit, and denot this as the current area of ​​the air conditioning unit. The specific functions and processing of this control unit 104 are further described in step S140.

[0163] The control unit 104 is further configured to, based on the current region of the air conditioning device, retrieve the scene control parameters corresponding to the current region of the air conditioning device from the scene control parameters of each of the n regions of the air conditioning device, and record them as the current scene control parameters of the air conditioning device. The specific functions and processing of this control unit 104 are further described in step S150.

[0164] In some embodiments, the n areas of the air conditioning device include at least one of the following: an activity area, an unoccupied area, a rest area, and a study area; the scene control parameters for each of the n areas of the air conditioning device include at least one of the following: the wind speed of the indoor fan of the air conditioning device, the frequency of the compressor of the air conditioning device, and the air outlet mode of the air conditioning device.

[0165] The control unit 104, based on the current region of the air conditioning device, calls the scene control parameters corresponding to the current region of the air conditioning device from the scene control parameters of each of the n regions of the air conditioning device, denoted as the current scene control parameters of the air conditioning device, including any of the following calling scenarios:

[0166] The first calling scenario: The control unit 104 is further configured to, if the current area of ​​the air conditioning device is an active area, then from the scene control parameters of each of the n areas of the air conditioning device, call the current scene control parameters of the air conditioning device, wherein the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in the preset frequency range, and the air outlet of the air conditioning device is an air outlet that follows the person.

[0167] The second calling scenario: The control unit 104 is further configured to, if the current area of ​​the air conditioning device is an uninhabited area, then from the scene control parameters of each of the n areas of the air conditioning device, call the current scene control parameters of the air conditioning device, in which the wind speed of the indoor fan of the air conditioning device is a preset minimum value, the frequency of the compressor of the air conditioning device is a preset minimum value, and the air outlet mode of the air conditioning device is to avoid the uninhabited area for air supply.

[0168] The third calling scenario: The control unit 104 is further configured to, if the current area of ​​the air conditioning device is a rest area, then from the scene control parameters of each of the n areas of the air conditioning device, call the current scene control parameters of the air conditioning device, wherein the wind speed of the indoor fan of the air conditioning device is the minimum wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the silent frequency in the preset frequency range, and the air outlet of the air conditioning device is an air outlet that avoids people.

[0169] The fourth calling scenario: The control unit 104 is further configured to, if the current area of ​​the air conditioning device is a learning area, then from the scene control parameters of each of the n areas of the air conditioning device, call the current scene control parameters of the air conditioning device, wherein the wind speed of the indoor fan of the air conditioning device is a silent wind speed within a preset wind speed range, the frequency of the compressor of the air conditioning device is a silent frequency within a preset frequency range, and the air outlet of the air conditioning device is an air outlet mode that prevents direct cold air blowing.

[0170] Specifically, such as Figure 7 As shown, the control method for a smart home air conditioning system based on a millimeter-wave sensor according to the present invention further includes:

[0171] Step 4, Scene Setting and Triggering: Users can define different scenes. For example, users can set scenes for different areas of each room through a mobile application and set specific control parameters for the air conditioning equipment for each scene.

[0172] Step 5: When the air conditioner detects that the environment meets certain scene conditions through the millimeter-wave sensor, it automatically triggers the corresponding scene function. Different scene settings are triggered in different areas. For example, in the living room sofa area, the air conditioner's operating parameters are set to maximum fan speed; in the study area, the air conditioner's operating parameters are set to minimum fan speed.

[0173] In practice, the identified and divided areas of each room can be uploaded to a cloud server. The mobile application communicates with the cloud server, displaying the divided areas of each room. Users can then use the app to set the operating parameters of the air conditioning units in different areas to achieve personalized settings. This allows the air conditioning units to perform different scene functions in different areas and under different conditions. For example, if the user is in the sofa area, the living room air conditioning unit will perform the function parameters set for that area; if the user is in the study area at the desk, the study room air conditioning unit will operate in silent mode. Of course, if the air conditioning unit is a voice-activated unit, it can also be selected to play ambient sound for studying.

[0174] The control unit 104 is further configured to adjust the current operating parameters of the air conditioning device to the current scene control parameters of the air conditioning device, so that the air conditioning device operates according to the current scene control parameters to meet the current usage needs of the target in the room where the air conditioning device is located, thereby improving the user experience and comfort of the target in the room where the air conditioning device is located. The specific functions and processing of the control unit 104 are further described in step S160.

[0175] The present invention provides a control method for a smart home air conditioning system based on millimeter-wave sensors. The method uses millimeter-wave sensors to identify the location information of target indoor activities within a room and combines this with machine learning algorithms to self-learn and divide the room into different zones. A mobile application is then used to set different scene functions for each zone, allowing the corresponding air conditioning devices in the home air conditioning system to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensors. This solves the problem that related solutions cannot meet the needs of sensing multiple room environments and providing personalized scene control, thus improving user experience and comfort.

[0176] In some embodiments, the control method for the air conditioning equipment is characterized by further comprising: optimizing the divided areas based on the target's usage habit data or optimizing the scene control parameters corresponding to the divided areas.

[0177] The acquisition unit 102 is further configured to acquire usage habit data of the target in the room where the air conditioning equipment is located. For the specific functions and processing of the acquisition unit 102, please refer to step S410.

[0178] The control unit 104 is further configured to optimize the n zones of the air conditioning unit, which have already been divided, based on the target usage habit data in the room where the air conditioning unit is located, to obtain new n zones of the air conditioning unit. The specific functions and processing of this control unit 104 are further described in step S420. And / or,

[0179] The control unit 104 is further configured to optimize the current scene control parameters of the air conditioning equipment corresponding to the n zones of the air conditioning equipment (either the n zones of the air conditioning equipment already defined or the n zones of the new air conditioning equipment) based on the target usage habit data in the room where the air conditioning equipment is located, thereby obtaining the new current scene control parameters of the air conditioning equipment corresponding to the n zones. The specific functions and processing of this control unit 104 are further described in step S430.

[0180] Specifically, in the solution of the present invention, after step 5, it may further include: step 6, intelligent optimization. That is, the solution of the present invention also utilizes machine learning algorithms to analyze and learn user behavior patterns, thereby automatically setting control rules, or utilizes deep learning algorithms to perform more precise spatial division, so as to achieve intelligent optimization and thereby improve the intelligence level of the home air conditioning system.

[0181] The system collects user behavior data within the home environment, preprocesses the data to extract feature values ​​(time, distance, angle, movement speed, activity trajectory), and then uses artificial intelligence learning algorithms to analyze and learn to identify user behavior, resulting in more detailed category divisions. The air conditioner then adjusts its operating parameters according to the user's behavior in that area. For example, after collecting data on the user's frequent exercise in a certain area of ​​the home environment, the air conditioner automatically identifies this area as an exercise zone. Based on the user's exercise habits, the air conditioner executes appropriate operating parameters for that specific scenario at that time and in that specific area. For instance, the airflow pattern in that area can be adjusted to avoid people, preventing the user from catching a cold after sweating.

[0182] In some embodiments, the air conditioning device is any one of the air conditioning devices in a household air conditioning system; the communication network of the air conditioning system is formed by the self-organizing network between one or more air conditioning devices in the household air conditioning system. Specifically, such as Figure 7 As shown, the control method of a smart home air conditioning system based on millimeter-wave sensors of the present invention further includes: step 1, self-organizing network between each air conditioning device in the home air conditioning system, so that each air conditioning device in the home air conditioning system can be interconnected, and then step 2 is executed.

[0183] In step 1, the air conditioning units in the home air conditioning system are interconnected through a local mesh network (i.e., wireless mesh network) on each unit, forming a self-organizing network via wireless communication modules. This networking method enables interconnection and interoperability between the various air conditioning units in the home air conditioning system.

[0184] The local mesh (wireless mesh network) networking method for each air conditioner unit is a network topology based on wireless communication technology. It connects multiple air conditioner units to form a self-organizing network, where the units can communicate and transmit data. The specific process of connecting all air conditioner units in a home air conditioning system via wireless communication modules to form a self-organizing network is as follows:

[0185] ① Air Conditioner Connection: In a home environment, multiple air conditioners can be connected to the same network via a wireless communication module (such as a Bluetooth module).

[0186] ② Self-organizing network: In the initial state, each air conditioner automatically establishes a connection with other nearby air conditioners and forms a self-organizing network.

[0187] ③ Node Communication: In a mesh network, each air conditioner can act as a data transmission node. Different air conditioners can communicate directly or relay data between other air conditioners.

[0188] ④ Data transmission: Different air conditioners can transmit data directly to each other or by relaying other air conditioning devices. Data can be transmitted along the network path until it reaches the target air conditioning device.

[0189] ⑤ Dynamic adjustment: Mesh networks are dynamic. If air conditioning devices are added or removed, the network will automatically adjust, and the air conditioning devices will re-establish connections to maintain network connectivity.

[0190] The control method for the air conditioning equipment further includes: a process of reminding users of set events for corresponding areas in the n areas of the air conditioning equipment.

[0191] The acquisition unit 102 is further configured to acquire reminder instructions input from the external control terminal of the air conditioning device for set events in corresponding areas of the n areas of the air conditioning device. The specific functions and processing of the acquisition unit 102 are further described in step S510. These set events include events such as a child appearing in an uninhabited area, an elderly person falling in any area, etc.

[0192] The control unit 104 is further configured to determine whether a set event has occurred in a corresponding area among the n areas of the air conditioning unit based on the target indoor activity location information of the air conditioning unit. The specific functions and processing of the control unit 104 are further described in step S520.

[0193] The control unit 104 is further configured to, if it is determined that a set event has occurred in a corresponding area of ​​the n areas of the air conditioning device, cause the air conditioning device itself to issue a reminder message indicating that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, send the reminder message to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and send the reminder message to a preset client. The specific functions and processing of this control unit 104 are further described in step S530.

[0194] Specifically, in the solution of this invention, each air conditioner in the home can subscribe to scene events set by the user. For example, when the user sets an unoccupied area scene, the air conditioner will determine, based on the identification results of the millimeter-wave sensor, that the target has been in an unoccupied area for an extended period of time in a specific location. Once this scene event is triggered, the air conditioner will send a broadcast alert to all air conditioners in the home to remind family members to check the situation in time and prevent incidents such as elderly family members falling.

[0195] For example, if a user sets a scenario via a mobile app for an unoccupied area: a target is located anywhere in the area and moves at a low speed (a few centimeters per second) for a certain period of time (e.g., 5 minutes), an alarm will be triggered. For instance, if an elderly person falls while walking in an unoccupied area, and the air conditioner detects that a target has been present in the area for a certain period without movement, the air conditioner will immediately send a broadcast alarm to all air conditioning devices in the home and push a reminder message to the family members' mobile apps.

[0196] This invention utilizes millimeter-wave sensors to identify the location of target indoor activities within a room, and combines this with machine learning algorithms to self-learn and divide the room into different zones based on activity levels. A mobile application allows users to set different scenes for these zones, automatically triggering different scene functions based on the environmental target state. This enables corresponding air conditioning units in the home air conditioning system to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensors, improving user experience and comfort. Through the application of millimeter-wave sensors and machine learning algorithms, corresponding air conditioning units in the home air conditioning system can accurately sense and identify the location of target indoor activities in different rooms, and adaptively identify and divide different zones based on the target's activity level. Specifically, it self-learns and divides different room activity zones based on the target's activity level. Users can set different scenes corresponding to different room activity zones through the mobile application, triggering different function settings based on different zones, achieving zone scene control, and thus realizing personalized air conditioning control, providing a more comfortable indoor environment that meets individual needs. Furthermore, this invention also addresses home safety and comfort issues through scene event subscription and intelligent optimization functions for corresponding air conditioning units in the home air conditioning system, providing a more intelligent, convenient, and safer air conditioning system control solution, enhancing home safety and comfort.

[0197] Since the processing and functions implemented by the device in this embodiment are basically the same as the embodiments, principles and examples of the aforementioned methods, any details not covered in the description of this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0198] The technical solution of this invention involves installing a millimeter-wave sensor on each air conditioner in a home air conditioning system. This sensor identifies the activity of a target (i.e., the user) in the room where the air conditioner is located. Based on the identified activity, a machine learning algorithm is used to divide the room into multiple zones. Operating parameters for each zone are then set according to the user's needs. During actual operation, the millimeter-wave sensor identifies the user's current zone within the room and adjusts the air conditioner's operating parameters to match those zone. This allows the air conditioner's operating parameters to follow the user's actual activities, triggering different functional settings for different zones. This achieves zone-based scene control, providing personalized air conditioning control and a more comfortable indoor environment tailored to individual needs.

[0199] According to an embodiment of the present invention, an air conditioning device corresponding to a control device for an air conditioning device is also provided. This air conditioning device may include the control device for the air conditioning device described above.

[0200] Since the processing and functions implemented by the air conditioning equipment in this embodiment are basically the same as the embodiments, principles and examples of the aforementioned devices, any details not covered in the description of this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0201] The technical solution of this invention involves installing a millimeter-wave sensor on each air conditioner in a home air conditioning system. This sensor identifies the activity of a target (i.e., the user) in the room where the air conditioner is located. Based on the identified activity, a machine learning algorithm is used to divide the room into multiple zones. Operating parameters for each zone are then set according to the user's needs. During actual operation, the millimeter-wave sensor identifies the user's current zone within the room and adjusts the air conditioner's operating parameters to match those zone. This ensures the air conditioner's operating parameters adapt to the user's actual activities, solving the problem that related solutions cannot meet the needs of sensing multiple room environments and personalized scene control, thus improving user experience and comfort.

[0202] According to an embodiment of the present invention, a storage medium corresponding to a control method for an air conditioning device is also provided. The storage medium includes a stored program, wherein the program controls the device where the storage medium is located to execute the control method for the air conditioning device described above when it is executed.

[0203] Since the processing and functions implemented by the storage medium in this embodiment are basically the same as the embodiments, principles and examples of the aforementioned methods, any details not covered in this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0204] The technical solution of this invention involves installing a millimeter-wave sensor on each air conditioner in a home air conditioning system. This sensor identifies the activity of a target (i.e., the user) in the room where the air conditioner is located. Based on the identified activity, a machine learning algorithm is used to divide the room into multiple zones. Operating parameters for each zone are then set according to the user's needs. During actual operation, the millimeter-wave sensor identifies the user's current zone within the room and adjusts the operating parameters accordingly. This allows the air conditioner to automatically trigger different scene functions based on the environmental conditions identified by the millimeter-wave sensor, improving user experience and comfort.

[0205] In summary, it is readily understood by those skilled in the art that, without conflict, the aforementioned advantageous methods can be freely combined and superimposed.

[0206] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A control method for an air conditioning device, characterized in that, A millimeter-wave sensor is installed in the air conditioning equipment; the control method of the air conditioning equipment includes: Taking the user in the room where the air conditioner is located as the target, the target indoor activity location information of the target in the room where the air conditioner is located is obtained and recorded as the target indoor activity location information of the air conditioner; wherein, the target indoor activity location information of the air conditioner is detected by the millimeter wave sensor on the air conditioner; Based on the target indoor activity location information of the air conditioning equipment over a historical period, a machine learning algorithm is used to divide the space of the room where the air conditioning equipment is located into n regions, denoted as the n regions of the air conditioning equipment, where n is a positive integer. Each of the n regions of the air conditioning equipment corresponds to a scenario in which the target in the room uses the air conditioning equipment. By analyzing the target's activity frequency and movement speed, a certain region is divided into an activity area, a rest area, or an uninhabited area. Different values ​​of movement speed and activity frequency parameters are designed as centroids. The target's movement speed and activity frequency collected by the millimeter-wave sensor are compared with the centroids to determine different classifications. The operating parameters for each of the n areas of the air conditioning equipment, which are input from the external control terminal of the air conditioning equipment over a historical period, are obtained and recorded as the scene control parameters for each of the n areas of the air conditioning equipment. During the current operation of the air conditioning equipment, based on the current target indoor activity location information of the air conditioning equipment, the current location of the target in the room where the air conditioning equipment is located is determined among the n areas of the air conditioning equipment, and is denoted as the current area of ​​the air conditioning equipment; Based on the current region of the air conditioning device, the scene control parameters corresponding to the current region of the air conditioning device are called from the scene control parameters of each of the n regions of the air conditioning device, and are recorded as the current scene control parameters of the air conditioning device; The current operating parameters of the air conditioning equipment are adjusted to the current scene control parameters of the air conditioning equipment, so that the air conditioning equipment operates according to the current scene control parameters.

2. The control method for air conditioning equipment according to claim 1, characterized in that, The target indoor activity location information of the air conditioning equipment includes: the distance between the target and the air conditioning equipment, the angle of the target relative to the air conditioning equipment, and the target's moving speed; Based on the target indoor activity location information of the air conditioning equipment over a historical period, a machine learning algorithm is used to divide the space of the room where the air conditioning equipment is located into n regions, denoted as the n regions of the air conditioning equipment, including: Based on the target indoor activity location information of the air conditioning equipment over a historical period, machine learning algorithms are used to analyze and determine the target's indoor activity location in the room where the air conditioning equipment is located, which is recorded as the indoor activity location of the air conditioning equipment; and the moving speed and activity frequency of the target in the room where the air conditioning equipment is located at the indoor activity location of the air conditioning equipment are determined. Based on the target's indoor activity positions in the room where the air conditioning unit is located, and the target's movement speed and activity frequency in the room where the air conditioning unit is located, the indoor activity positions of the n air conditioning units are correspondingly divided into n regions in the space of the room where the air conditioning unit is located, denoted as the n regions of the air conditioning unit.

3. The control method for air conditioning equipment according to claim 2, characterized in that, Based on the target's indoor movement positions within the room where the air conditioning unit is located (n locations), and the target's movement speed and activity frequency within those locations, the space of the room is divided into n areas corresponding to the n indoor movement positions of the air conditioning unit, denoted as the n areas of the air conditioning unit, including: The target area in the room where the air conditioning unit is located is classified into n categories, and n different sets of parameter values ​​are set as centroids; among the n different parameters, each set of parameters includes the set movement speed of each category and the set activity frequency of each category; The moving speed of the target in the room where the air conditioning equipment is located at n indoor positions of the air conditioning equipment is compared with the set moving speed of n categories in the center of mass, and the moving speed comparison result is obtained. The absolute value of the difference between the active frequency of the target at n indoor activity positions of the air conditioning equipment in the room where the air conditioning equipment is located and the set active frequency of n categories in the centroid is compared with the set frequency difference threshold of n categories to obtain the active frequency comparison result. Based on the comparison results of the moving speed and the comparison results of the active frequency, the indoor activity positions of the n air conditioning devices are correspondingly divided into n areas in the space of the room where the air conditioning devices are located, denoted as the n areas of the air conditioning devices.

4. The control method for air conditioning equipment according to claim 3, characterized in that, The category of the target area in the room where the air conditioning equipment is located includes at least one of the following: activity area, unoccupied area, rest area, and study area. Based on the comparison results of the moving speed and the comparison results of the active frequency, the indoor activity locations of the n air conditioning units are correspondingly divided into n regions within the room where the air conditioning units are located, denoted as the n regions of the air conditioning units, including: If the moving speed comparison result belongs to the moving speed range of the activity area and the active frequency comparison result belongs to the active frequency range of the activity area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the activity area; If the moving speed comparison result belongs to the moving speed range of the uninhabited area and the active frequency comparison result belongs to the active frequency range of the uninhabited area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the uninhabited area; If the moving speed comparison result belongs to the moving speed range of the rest area and the active frequency comparison result belongs to the active frequency range of the rest area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the rest area; If the moving speed comparison result belongs to the moving speed range of the learning area and the active frequency comparison result belongs to the active frequency range of the learning area, then among the n indoor activity locations of the air conditioning equipment, the indoor activity location corresponding to the moving speed comparison result and the active frequency comparison result is determined as the category of the learning area.

5. The control method for an air conditioning device according to any one of claims 1 to 4, characterized in that, The n zones of the air conditioning equipment include at least one of the following: activity area, unmanned area, rest area, and study area; the scene control parameters of each of the n zones of the air conditioning equipment include at least one of the following: the wind speed of the indoor fan of the air conditioning equipment, the frequency of the compressor of the air conditioning equipment, and the air outlet mode of the air conditioning equipment. Based on the current region of the air conditioning device, the scene control parameters corresponding to the current region of the air conditioning device are retrieved from the scene control parameters of each of the n regions of the air conditioning device, and are denoted as the current scene control parameters of the air conditioning device, including: If the current area of ​​the air conditioning device is an active area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are retrieved, and the wind speed of the indoor fan of the air conditioning device is the maximum wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the maximum frequency in the preset frequency range, and the air outlet of the air conditioning device is an air outlet that follows the person. If the current area of ​​the air conditioning device is an uninhabited area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are retrieved, and the wind speed of the indoor fan of the air conditioning device is a preset minimum value, the frequency of the compressor of the air conditioning device is a preset minimum value, and the air outlet of the air conditioning device is set to avoid uninhabited areas when supplying air. If the current area of ​​the air conditioning device is a rest area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are called, and the wind speed of the indoor fan of the air conditioning device is the minimum wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the silent frequency in the preset frequency range, and the air outlet of the air conditioning device is the air outlet method of avoiding people. If the current area of ​​the air conditioning device is a learning area, then from the scene control parameters of each of the n areas of the air conditioning device, the current scene control parameters of the air conditioning device are retrieved, and the wind speed of the indoor fan of the air conditioning device is the silent wind speed in the preset wind speed range, the frequency of the compressor of the air conditioning device is the silent frequency in the preset frequency range, and the air outlet of the air conditioning device is the air outlet mode of the anti-cold air direct blowing mode.

6. The control method for an air conditioning device according to any one of claims 1 to 4, characterized in that, Also includes: Obtain usage habit data of the target in the room where the air conditioning unit is located; Based on the usage habit data of the target in the room where the air conditioning equipment is located, the n areas of the air conditioning equipment that have been divided are optimized to obtain new n areas of the air conditioning equipment; And / or, Based on the usage habit data of the target in the room where the air conditioning equipment is located, for the n areas of the air conditioning equipment that have been divided or for the new n areas of the air conditioning equipment, the current scene control parameters of the air conditioning equipment corresponding to the corresponding area are optimized to obtain the new current scene control parameters of the air conditioning equipment corresponding to the corresponding area.

7. The control method for air conditioning equipment according to claim 5, characterized in that, Also includes: Obtain usage habit data of the target in the room where the air conditioning unit is located; Based on the usage habit data of the target in the room where the air conditioning equipment is located, the n areas of the air conditioning equipment that have been divided are optimized to obtain new n areas of the air conditioning equipment; And / or, Based on the usage habit data of the target in the room where the air conditioning equipment is located, for the n areas of the air conditioning equipment that have been divided or for the new n areas of the air conditioning equipment, the current scene control parameters of the air conditioning equipment corresponding to the corresponding area are optimized to obtain the new current scene control parameters of the air conditioning equipment corresponding to the corresponding area.

8. The control method for an air conditioning device according to any one of claims 1 to 4 and 7, characterized in that, The air conditioning device is any one of the air conditioning devices in a household air conditioning system; the communication network of the air conditioning system is formed by self-organizing networks among the one or more air conditioning devices in the household air conditioning system; the control method of the air conditioning device further includes: Obtain reminder instructions for set events in corresponding areas of n areas of the air conditioning equipment, input from the external control terminal of the air conditioning equipment; Based on the target indoor activity location information of the air conditioning equipment, determine whether a set event has occurred in the corresponding area of ​​the n areas of the air conditioning equipment; If it is determined that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, the air conditioning device itself sends a reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to a preset client.

9. The control method for air conditioning equipment according to claim 5, characterized in that, The air conditioning device is any one of the air conditioning devices in a household air conditioning system; the communication network of the air conditioning system is formed by self-organizing networks among the one or more air conditioning devices in the household air conditioning system; the control method of the air conditioning device further includes: Obtain reminder instructions for set events in corresponding areas of n areas of the air conditioning equipment, input from the external control terminal of the air conditioning equipment; Based on the target indoor activity location information of the air conditioning equipment, determine whether a set event has occurred in the corresponding area of ​​the n areas of the air conditioning equipment; If it is determined that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, the air conditioning device itself sends a reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to a preset client.

10. The control method for air conditioning equipment according to claim 6, characterized in that, The air conditioning device is any one of the air conditioning devices in a household air conditioning system; the communication network of the air conditioning system is formed by self-organizing networks among the one or more air conditioning devices in the household air conditioning system; the control method of the air conditioning device further includes: Obtain reminder instructions for set events in corresponding areas of n areas of the air conditioning equipment, input from the external control terminal of the air conditioning equipment; Based on the target indoor activity location information of the air conditioning equipment, determine whether a set event has occurred in the corresponding area of ​​the n areas of the air conditioning equipment; If it is determined that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, the air conditioning device itself sends a reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device, sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to other air conditioning devices in the air conditioning system through the communication network of the air conditioning system, and sends the reminder message that a set event has occurred in the corresponding area of ​​the n areas of the air conditioning device to a preset client.

11. A control device for an air conditioning unit that uses the control method for an air conditioning unit as described in any one of claims 1 to 10 to control the air conditioning unit, characterized in that, A millimeter-wave sensor is installed in the air conditioning equipment; the control device of the air conditioning equipment includes: The acquisition unit is configured to acquire the target indoor activity location information of the user in the room where the air conditioning equipment is located, and record it as the target indoor activity location information of the air conditioning equipment; wherein, the target indoor activity location information of the air conditioning equipment is detected by a millimeter-wave sensor on the air conditioning equipment; The control unit is configured to divide the space of the room where the air conditioner is located into n regions, denoted as the n regions of the air conditioner, based on the target indoor activity location information of the air conditioner over a historical period of time and using a machine learning algorithm; wherein each of the n regions of the air conditioner corresponds to a scenario in which the target in the room where the air conditioner is located uses the air conditioner. The control unit is further configured to acquire the operating parameters for each of the n areas of the air conditioning equipment input by the external control terminal of the air conditioning equipment over a historical period of time, and record them as the scene control parameters for each of the n areas of the air conditioning equipment; The control unit is further configured to, during the current operation of the air conditioning equipment, determine, based on the current target indoor activity location information of the air conditioning equipment, the current location of the target in the room where the air conditioning equipment is located among the n areas of the air conditioning equipment, and denoted as the current area of ​​the air conditioning equipment; The control unit is further configured to, based on the current region of the air conditioning device, retrieve the scene control parameters corresponding to the current region of the air conditioning device from the scene control parameters of each of the n regions of the air conditioning device, and record them as the current scene control parameters of the air conditioning device; The control unit is further configured to adjust the current operating parameters of the air conditioning equipment to the current scene control parameters of the air conditioning equipment, so that the air conditioning equipment operates according to the current scene control parameters of the air conditioning equipment.

12. An air conditioning device, characterized in that, include: The control device for the air conditioning equipment as described in claim 11.

13. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the device containing the storage medium is controlled to perform the control method of the air conditioning device according to any one of claims 1 to 10.

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