Energy-saving air conditioner controller with millimeter wave human body induction unit

Abstract

The invention discloses an energy-saving air conditioner controller containing a millimeter wave human body induction unit. The energy-saving air conditioner controller comprises a millimeter wave radar sensor module, a microprocessor unit, an air conditioner control interface module and a dynamic threshold algorithm module. A 60-64GHz millimeter wave radar is used for detecting the existence state and the movement track of a human body in real time, an environment parameter acquisition module is combined to realize multivariable cooperative control, and a machine learning algorithm is adopted to predict user behaviors and automatically adjust the operation mode of the air conditioner. The problems that traditional infrared induction is prone to being interfered, and a camera violates privacy are solved, the functions of partitioned precise temperature control, abnormal behavior alarm and the like can be achieved, and the actually measured energy saving rate exceeds 35%.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent air conditioner control, and is particularly applicable to the cross-application of human presence detection and air conditioner energy conservation control realized by millimeter-wave radar, and can be extended to smart home, commercial building and medical monitoring scenarios. Background Art

[0002] Existing air conditioner control systems have significant technical deficiencies in human body sensing and energy conservation optimization, which are mainly reflected in insufficient detection accuracy, poor environmental adaptability and potential privacy and security risks.

[0003] 1. Limitations of Traditional Passive Infrared Sensors (PIRs)

[0004] PIR sensors rely on changes in the infrared radiation emitted by the human body for detection and can only identify moving targets (such as walking or large movements), but cannot effectively sense stationary or slightly moving states (such as breathing during sitting or sleeping). For example, in an office scenario, when a user is stationary at work for a long time, the PIR sensor will misjudge as no one present, causing the air conditioner to automatically turn off and resulting in a decrease in comfort. In addition, PIRs are susceptible to environmental temperature interference. When the temperature difference between indoors and outdoors is small (such as in spring and autumn), their detection sensitivity is significantly reduced, and the false alarm rate can reach more than 30%.

[0005] 2. Defects of Vision Camera Solutions

[0006] Although camera-based solutions can achieve human body posture detection through image recognition, there are two major problems:

[0007] Privacy risk: Continuous video monitoring raises users' concerns about privacy leakage, especially in private places such as bedrooms and bathrooms where it is difficult to apply;

[0008] Environmental dependence: In low light (such as at night), strong backlighting or the presence of obstacles (such as curtains), the recognition accuracy drops sharply. Experimental data shows that the detection success rate based on the YOLO algorithm in a dark environment is less than 60%.

[0009] 3. Deficiencies of Ultrasonic and Wi-Fi Sensing

[0010] Ultrasonic sensors detect the human body through reflected waves, but are easily interfered by air conditioner airflows, curtain swings, etc., with a positioning error of up to ±0.5 meters, which cannot meet the requirements of precise air supply. While the solution based on Wi-Fi Channel State Information (CSI) can penetrate walls, it has high power consumption (>5W), and in a complex environment with obvious multipath effects, the human body's micro-motion signals will be submerged by noise, and the Signal-to-Noise Ratio (SNR) is often lower than 10dB.

[0011] The current market urgently needs a solution that can:

[0012] High-precision detection of static / micromotion human body (breathing frequency ≥ 0.1 Hz);

[0013] Penetrate common obstacles (such as glass, thin walls) without invading privacy;

[0014] An air conditioner control scheme that dynamically integrates environmental data to achieve adaptive energy-saving control.

[0015] The millimeter-wave radar technology of the present invention effectively fills this technical gap through 60 GHz band signal processing and multi-sensor fusion. Summary of the Invention

[0016] The technical problem to be solved by the present invention is to provide an energy-saving air conditioner controller containing a millimeter-wave human body sensing unit, which realizes high-precision human body state detection through a 60-64 GHz millimeter-wave radar, and combines environmental parameter perception and intelligent algorithms to achieve precise control and energy-saving optimization of the air conditioner.

[0017] The present invention is realized through the following scheme: An energy-saving air conditioner controller containing a millimeter-wave human body sensing unit, including:

[0018] A millimeter-wave radar sensor module for real-time detection of the presence state and movement trajectory of the human body in the room;

[0019] A microprocessor unit, connected to the millimeter-wave radar sensor module, for analyzing human activity data and generating control instructions;

[0020] An air conditioner control interface module, connected to the microprocessor unit, for transmitting control instructions to the air conditioner main unit;

[0021] A dynamic threshold algorithm module for adaptively adjusting the sensing sensitivity according to environmental parameters and human activity intensity.

[0022] The millimeter-wave radar sensor module operates at a frequency of 60-64 GHz, with a detection range covering 5-15 meters and the ability to penetrate non-metallic obstacles.

[0023] The human body presence state includes three modes: static, micromotion, and walking. The microprocessor unit adjusts the air conditioner operating power in different levels according to different modes.

[0024] It also includes:

[0025] An environmental parameter acquisition module for real-time monitoring of indoor temperature, humidity, and light intensity;

[0026] The microprocessor unit realizes multi-variable collaborative control by integrating human activity data and environmental parameters.

[0027] When it is detected that the human body is static for more than a preset time threshold and the environmental temperature is within the comfortable range, it automatically switches to the low-power standby mode.

[0028] The dynamic threshold algorithm module includes:

[0029] A human behavior prediction sub-module based on machine learning, which is used to learn the user's activity pattern and predict the air conditioner usage demand.

[0030] It also includes:

[0031] A zoning control module, which realizes the grid zoning management of the indoor space through multiple millimeter-wave radar sensors and independently controls the air supply parameters of each area.

[0032] The air conditioner control interface module supports at least one communication protocol among infrared, Wi-Fi, and Zigbee.

[0033] It also includes:

[0034] An abnormal behavior alarm module, which triggers a warning signal when a human body fall or long-term static inactivity is detected.

[0035] An energy-saving air conditioner control method applying the above controller, which establishes an indoor human body dynamic thermal map through millimeter-wave radar scanning;

[0036] Combines historical usage data and environmental parameters to generate an optimal energy efficiency control strategy;

[0037] Automatically adjusts the air supply angle and wind speed according to the real-time position of the human body.

[0038] The beneficial effects of the present invention are:

[0039] 1. The energy-saving air conditioner controller containing a millimeter-wave human sensing unit of the present invention can identify micro-motions above 0.1 Hz (such as breathing) through a 60 GHz millimeter-wave radar, achieving a 99.2% detection accuracy rate for static personnel, and completely solving the problem that traditional PIR sensors cannot detect sitting still;

[0040] 2. The energy-saving air conditioner controller containing a millimeter-wave human sensing unit of the present invention, through dynamic zoning control and environmental compensation algorithms, the measured energy-saving rate in the office scenario reaches 38% - 42%, and the energy-saving in large spaces such as shopping malls exceeds 33%, saving more than 36,000 yuan in electricity bills annually (for a 2000㎡ standard office area);

[0041] 3. The energy-saving air conditioner controller containing a millimeter-wave human sensing unit of the present invention uses non-contact detection, which can avoid privacy leakage, and realizes safety guardianship through fall detection (response < 1 second) and breathing monitoring, with a false alarm rate < 0.5%, which is safe and reliable;

[0042] 4. The energy-saving air conditioner controller of the present invention containing a millimeter-wave human body sensing unit can penetrate 10 cm of non-metallic obstacles, and the false operation rate approaches 0% under interference such as direct sunlight and strong wind. The MTBF of the device reaches 50,000 hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system architecture diagram of an energy-saving air conditioner controller of the present invention containing a millimeter-wave human body sensing unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following is a further description of the present invention in conjunction with Figure 1 However, the protection scope of the present invention is not limited to the described content.

[0045] For clarity, not all features of the actual embodiments are described. In the following description, well-known functions and structures are not described in detail because they would obscure the present invention with unnecessary details. It should be considered that in the development of any actual embodiment, a large number of implementation details must be made to achieve the specific goals of the developer, for example, changing from one embodiment to another according to the relevant system or business limitations. In addition, it should be considered that such development work may be complex and time-consuming, but it is only routine work for those skilled in the art.

[0046] The present invention proposes an energy-saving air conditioner control system based on millimeter-wave human body sensing. High-precision human body state detection is achieved through a 60-64 GHz millimeter-wave radar. Combining environmental parameter perception and intelligent algorithms, precise control and energy-saving optimization of the air conditioner are realized. The system mainly includes the following core modules:

[0047] Millimeter-wave radar sensing module: Adopting a frequency-modulated continuous-wave (FMCW) radar to detect human presence, position, motion state (stationary, micro-motion, walking), and vital signs such as breathing frequency in real time;

[0048] Multi-sensor fusion module: Integrating environmental sensors such as temperature, humidity, light, and CO2 to build a multi-dimensional perception network;

[0049] Intelligent decision-making module: Based on a dynamic threshold algorithm of machine learning, realizing human behavior prediction and optimization of air conditioner control strategies;

[0050] Execution control module: Supporting air conditioner control interfaces with multiple protocols (infrared / Wi-Fi / Zigbee) to achieve zoned air supply and precise adjustment of wind speed / temperature.

[0051] 1. Millimeter-wave human body state recognition technology

[0052] Micro-motion detection algorithm: Analyze the breathing signal through the Doppler frequency shift of 0.1 - 0.5 Hz, and use the Adaptive Clutter Suppression (ACS) algorithm to eliminate environmental noise, with a detection sensitivity of ±0.05 Hz.

[0053] Example: When the user is sitting still, the system can recognize the 0.2 Hz fluctuations of the chest and abdomen and separate them from background vibrations (such as the swing of a fan).

[0054] Multi-target tracking: Based on the DBSCAN clustering algorithm and Kalman filtering, achieve trajectory tracking in a multi-person scenario, with a spatial resolution of ≤10 cm.

[0055] Application scenario: In a meeting room, it can distinguish two adjacent seated people and independently record their activity status.

[0056] 2. Dynamic energy-saving control strategy

[0057] The hierarchical temperature control logic is shown in Table 1:

[0058]

[0059]

[0060] Adopt the fuzzy PID control algorithm to dynamically compensate for the influence of light and humidity:

[0061]

[0062] Where ΔT is the temperature difference caused by the light intensity (measured as a 0.8 °C increase per 1000 lux).

[0063] 3. Privacy protection design

[0064] Signal anonymization processing:

[0065] The original radar data completes feature extraction at the edge end, and only uploads the encrypted control instructions (such as "1 person is stationary in Area A"), meeting the requirements of GDPR.

[0066] Penetration optimization:

[0067] 60 GHz millimeter waves can penetrate 5 cm thick non-metallic materials (such as gypsum board) to achieve detection through walls, while avoiding visual exposure.

[0068] Example 1

[0069] Used for intelligent zoning temperature control in the office.

[0070] System configuration

[0071] 1. Hardware deployment

[0072] Millimeter-wave radar: 4 TI IWR6843ISK modules (60.5 - 63.5 GHz), angular resolution 5°, installation height 3.2 m

[0073] Air-conditioning terminal: Daikin VRV system, with a Zigbee slave control module (CC2530) added to each air outlet

[0074] Environmental sensor: Bosch BME280 (temperature and humidity) + TSL2561 (light), arranged in groups of 1 every 5 m

[0075] 2. Space division

[0076] The 48㎡ office is divided into 6 grids of 2m×4m, and each grid corresponds to 1 independent air valve.

[0077] Workflow

[0078] 1. Human detection stage

[0079] The radar scans at a frequency of 10Hz and extracts through the FFT algorithm:

[0080] Stationary personnel: Respiratory micro-motion signal (Doppler frequency shift of 0.2 - 0.5Hz)

[0081] Walking personnel: Periodic gait characteristics of 3 - 8Hz

[0082] 2. Control strategy generation

[0083] Dynamic threshold adjustment is shown in Table 2:

[0084]

[0085] Calculation of air supply parameters:

[0086]

[0087] Where P is the air valve opening, N_active is the number of active people, and A_grid is the grid area. 3. Execution example scenario: 2 people are detected stationary in Area A (keyboard tapping generates 0.4Hz micro-motion), and the light intensity is 950lux. Actions:

[0088] Close the air valves in Areas B - E;

[0089] Supply air in Area A at 26.5℃ (basic setting 26℃ + light compensation 0.5℃);

[0090] Set the wind speed to 2m / s (calculated based on the area of 2×4 = 8㎡ and 4㎡ per person).

[0091] The measured data is shown in Table 3:

[0092]

[0093] Example 2

[0094] Used for temperature control in the elderly home safety monitoring scenario.

[0095] System configuration

[0096] Sensor network

[0097] Main radar: Infineon BGT60LTR11AIP (60 GHz, integrated FMCW algorithm).

[0098] Auxiliary sensors: mattress pressure distribution sensor (0.1 m 2 / unit) and infrared thermal imager (FLIR Lepton 3.5).

[0099] Anomaly detection logic:

[0100] 1. Long - time stillness warning

[0101] Judgment conditions:

[0102] Coefficient of variation of respiratory rate < 15% for 2 consecutive hours;

[0103] Central displacement of pressure sensor < 5 cm;

[0104] Thermal imaging shows core body temperature drop > 1.5℃ / h;

[0105] Response actions:

[0106] The air conditioner is switched to 28℃ (to prevent hypothermia);

[0107] Push a level - three alarm to the guardian APP.

[0108] 2. Fall detection

[0109] Feature extraction:

[0110] Sudden height change: 1.7 m → 0.3 m (within 3 seconds);

[0111] Subsequent micro - motion mode: irregular low - frequency tremor.

[0112] False - alarm suppression:

[0113] Exclude pet interference through thermal imaging;

[0114] Compare with the SAR imaging of the metal reflection signal of the wardrobe.

[0115] Performance indicators

[0116] Respiration detection accuracy: ±0.2 times / minute (3 times improvement compared to the UWB solution);

[0117] Fall detection latency: < 1.5 s (meeting AAL standards);

[0118] Privacy protection: Raw data is processed at the edge, and only event codes are uploaded (compliant with GDPR).

[0119] The measured data is as follows:

[0120] 1. Health monitoring accuracy is shown in Table 4

[0121]

[0122] 2. Air conditioner control effect

[0123] Temperature stability: All-night fluctuation is ±0.38°C (±1.5°C for traditional thermostats);

[0124] Response speed: From detection to completion of air conditioner adjustment: 1.2 s;

[0125] Rate of misoperation: 0.2 times per month (mainly from pet interference).

[0126] Although the technical solutions of the present invention have been described in detail and enumerated, it should be understood that for those skilled in the art, making modifications to the above embodiments or adopting equivalent alternative solutions are obvious to those skilled in the art. These modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. An energy-saving air-conditioning controller containing a millimeter-wave human body sensing unit, characterized in that: include: Millimeter-wave radar sensor module, used to detect the presence and movement trajectory of human beings indoors in real time; A microprocessor unit, connected to the millimeter wave radar sensor module, for analyzing human activity data and generating control instructions; An air conditioning control interface module, connected to the microprocessor unit, for transmitting control instructions to the air conditioning host; The dynamic threshold algorithm module is used to adaptively adjust the sensing sensitivity according to environmental parameters and human activity intensity.

2. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: The millimeter-wave radar sensor module operates at a frequency of 60-64 GHz, has a detection range of 5-15 meters, and has the ability to penetrate non-metallic obstacles.

3. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: The human body existence state includes three modes: stillness, slight movement, and walking. The microprocessor unit adjusts the air conditioner operating power in different levels according to different modes.

4. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: Also includes: Environmental parameter acquisition module, used to monitor indoor temperature, humidity and light intensity in real time; The microprocessor unit integrates human activity data and environmental parameters to achieve multivariable coordinated control.

5. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 4, characterized in that: When it detects that a person has been stationary for more than a preset time threshold and the ambient temperature is in a comfortable range, it automatically switches to low-power standby mode.

6. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: The dynamic threshold algorithm module includes: The human behavior prediction submodule based on machine learning is used to learn user activity patterns and predict air conditioning usage needs.

7. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: Also includes: The zoning control module uses multiple millimeter-wave radar sensors to achieve grid-based zoning management of indoor spaces and independently control the air supply parameters of each area.

8. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: The air conditioning control interface module supports at least one communication protocol among infrared, Wi-Fi, and Zigbee.

9. The energy-saving air-conditioning controller with a millimeter-wave human body sensing unit according to claim 1, characterized in that: Also includes: The abnormal behavior alarm module triggers a warning signal when it detects a person falling or being motionless for a long time.

10. An energy-saving air conditioning control method based on the controller according to any one of claims 1 to 9, characterized in that: Establish dynamic thermal map of human body in the room through millimeter wave radar scanning; Combine historical usage data with environmental parameters to generate optimal energy efficiency control strategies; Automatically adjust the air supply angle and wind speed according to the real-time position of the human body.

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