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

Abstract

The application relates to an air conditioner control method and device, an intelligent air conditioner and a storage medium. The method comprises the following steps: when the object feature of a to-be-tested object is detected, acquiring the environment parameter of an indoor environment and starting a biological radar to emit a pulse signal to the to-be-tested object in the indoor environment; analyzing a reflected echo signal based on the pulse signal emitted to the to-be-tested object to determine the physiological parameter of the to-be-tested object, wherein the physiological parameter of the to-be-tested object can reflect the body state of the to-be-tested object; and adjusting the operation parameter of an intelligent air conditioner in the indoor environment according to the physiological parameter of the to-be-tested object and the environment parameter of the indoor environment, so that the environment state of the environment where the to-be-tested object is located is adjusted according to the body state of the to-be-tested object, and the best indoor environment meeting the body state of the to-be-tested object is provided for the to-be-tested object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioners, and in particular to an air conditioner control method and device, an intelligent air conditioner, and a storage medium. BACKGROUND

[0002] With the rapid development of social economy and the improvement of people's living standards, more and more users begin to pay attention to their own and their elders' health problems. Traditional health detection methods have great limitations, need manual cooperation, and cannot be continuously detected for a long time. There are many health monitoring devices on the market at present, such as sphygmomanometers, blood glucose meters, and thermometers, but these health monitoring devices only have a single monitoring function and cannot intervene in the environmental state of the user. SUMMARY

[0003] In order to solve the technical problem that the existing health monitoring device cannot intervene in the environmental state of the user, the present application provides an air conditioner control method and device, an intelligent air conditioner, and a storage medium.

[0004] In a first aspect, the present application provides an air conditioner control method, comprising:

[0005] When an object feature of a to-be-detected object is detected, an environment parameter of an indoor environment is acquired and a biological radar is started to emit a pulse signal to the to-be-detected object in the indoor environment;

[0006] An echo signal corresponding to the pulse signal is received by the biological radar;

[0007] A physiological parameter of the to-be-detected object is obtained by analyzing the echo signal;

[0008] According to the environment parameter and the physiological parameter of the to-be-detected object, an operating parameter of an intelligent air conditioner in the indoor environment is adjusted.

[0009] In a second aspect, the present application provides an air conditioner control device, comprising:

[0010] An acquisition module is configured to acquire an environment parameter of an indoor environment and start a biological radar to emit a pulse signal to a to-be-detected object in the indoor environment when an object feature of the to-be-detected object is detected;

[0011] A receiving module is configured to receive an echo signal corresponding to the pulse signal by the biological radar;

[0012] An analysis module is configured to obtain a physiological parameter of the to-be-detected object by analyzing the echo signal;

[0013] A control module is configured to adjust an operating parameter of an intelligent air conditioner in the indoor environment according to the environment parameter and the physiological parameter of the to-be-detected object.

[0014] In a third aspect, the present application provides an intelligent air conditioner, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0015] When an object feature of a to-be-tested object is detected, an environment parameter of an indoor environment is acquired, and a biological radar is started to emit a pulse signal to the to-be-tested object in the indoor environment;

[0016] The biological radar receives a corresponding echo signal of the pulse signal;

[0017] The echo signal is analyzed to obtain a physiological parameter of the to-be-tested object;

[0018] According to the environment parameter and the physiological parameter of the to-be-tested object, an operating parameter of the intelligent air conditioner in the indoor environment is adjusted.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0020] When an object feature of a to-be-tested object is detected, an environment parameter of an indoor environment is acquired, and a biological radar is started to emit a pulse signal to the to-be-tested object in the indoor environment;

[0021] The biological radar receives a corresponding echo signal of the pulse signal;

[0022] The echo signal is analyzed to obtain a physiological parameter of the to-be-tested object;

[0023] According to the environment parameter and the physiological parameter of the to-be-tested object, an operating parameter of the intelligent air conditioner in the indoor environment is adjusted.

[0024] Based on the above air conditioner control method, when an object feature of a to-be-tested object is detected, an environment parameter of an indoor environment is acquired, and a biological radar is started to emit a pulse signal to the to-be-tested object in the indoor environment, the physiological parameter of the to-be-tested object is determined based on an echo signal reflected after the pulse signal is emitted to the to-be-tested object, the physiological parameter of the to-be-tested object can reflect the physical state of the to-be-tested object, and the operating parameter of the intelligent air conditioner in the indoor environment is adjusted according to the physiological parameter of the to-be-tested object and the environment parameter of the indoor environment, so as to adjust the environment state of the environment in which the to-be-tested object is located according to the physical state of the to-be-tested object, and provide the to-be-tested object with the best indoor environment meeting the physical state of the to-be-tested object. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application.

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0027] Figure 1 An application environment diagram of the air conditioner control method in an embodiment;

[0028] Figure 2 A signal transmission flowchart in a biological radar in an embodiment;

[0029] Figure 3 A flowchart of the air conditioner control method in an embodiment;

[0030] Figure 4 A flowchart of the air conditioner control method in an embodiment;

[0031] Figure 5 A phase change diagram of the echo signal in an embodiment;

[0032] Figure 6 A structural block diagram of the air conditioner control device in an embodiment;

[0033] Figure 7 An internal structure diagram of the intelligent air conditioner in an embodiment. DETAILED DESCRIPTION

[0034] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort belong to the scope of protection of the present application.

[0035] Figure 1 An application environment diagram of the air conditioner control method in an embodiment. Refer to Figure 1The air conditioner control method is applied to an air conditioner control system. The air conditioner control system comprises a terminal 110 and an intelligent air conditioner 120. The terminal 110 and the intelligent air conditioner 120 are connected through a network. The terminal 110 can remotely control the intelligent air conditioner 120, and the intelligent air conditioner 120 can also remotely report prompt information to the terminal 110. The terminal 110 can be a desktop terminal or a mobile terminal, and the mobile terminal can be at least one of a mobile phone, a tablet computer, a notebook computer, etc. The intelligent air conditioner 120 is provided with a biological radar, or the intelligent air conditioner 120 and the biological radar 130 are independently provided, but the intelligent air conditioner 120 and the biological radar 130 are in communication connection. When the intelligent air conditioner 120 and the biological radar 130 are independently provided, the biological radar 130 is arranged in a user key activity area in an indoor environment. The biological radar 130 emits an electromagnetic wave pulse signal in a starting state. When a user exists in a space where the intelligent air conditioner 120 is located, a pulse signal reaches a user's chest surface to generate a return signal which is received by the biological radar 130. Since heartbeat and respiration are closely related to chest fluctuation, chest fluctuation will cause a slow time domain periodic signal to exist in the return signal. By analyzing and processing the return signal, a heartbeat signal and a respiration signal can be extracted from the return signal.

[0036] As shown in Figure 2 The biological radar 130 specifically comprises a transmitting antenna 138, a receiving antenna 135, a switch component 134, a power amplifier 137, a delay unit 136, a low-noise amplifier 133, an analog-to-digital converter 132, and a control unit 131. A first end of the control unit 131 is electrically connected to an upper computer 121 in the intelligent air conditioner 120. A second end of the control unit 131 is connected to the transmitting antenna 138 through the power amplifier 137. The second end of the control unit 131 is also connected to a third end of the control unit 131 through the delay unit 136. A fourth end of the control unit 131 is connected to the receiving antenna 135 through the analog-to-digital converter 132, the low-noise amplifier 133, and the switch component 134 in sequence.

[0037] The control unit 131 in the biological radar 130 outputs an electromagnetic wave signal and obtains a pulse signal after amplification by the power amplifier 137. Then, the pulse signal is emitted by the transmitting antenna 138. The receiving antenna 135 receives a return signal and transmits the return signal to the control unit 131 through amplification processing of the low-noise amplifier 133 and analog-to-digital conversion processing of the analog-to-digital converter 132 in sequence. The control unit 131 transmits the processed return signal to the upper computer 121 in the intelligent air conditioner 120 for analysis processing, so as to obtain physiological parameters of the user from the return signal.

[0038] In one embodiment, Figure 3 is a flowchart of an air conditioner control method in one embodiment. Referring to Figure 3The application provides an air conditioner control method. The embodiment mainly takes the air conditioner control method applied to the intelligent air conditioner 120 in the above Figure 1 as an example, and the air conditioner control method specifically comprises the following steps:

[0039] In step S210, when the object features of the to-be-detected object are detected, the environmental parameters of the indoor environment are acquired, and the biological radar 130 is started to emit pulse signals to the to-be-detected object in the indoor environment.

[0040] Specifically, the intelligent air conditioner 120 is further provided with a sound collecting device, a camera and various sensors. The sound collecting device is used to collect the voice features of the user, the camera is used to collect the body features of the to-be-detected object, and the body features specifically include facial features, eye features and gesture features, that is, the object features include voice features, facial features, eye features and gesture features. When the object features are collected, the identity of the to-be-detected object can be determined, and the environmental parameters of the environment where the to-be-detected object is located can be collected and the physiological parameters of the to-be-detected object can be collected by emitting pulse signals to the to-be-detected object, so that the biological radar 130 of the intelligent air conditioner 120 is not started to emit electromagnetic wave pulse signals when there is no user in the indoor environment, and the energy consumption of the intelligent air conditioner 120 is saved. Different types of sensors are used to collect the environmental parameters of the indoor environment, and the environmental parameters specifically include temperature, humidity, air pressure, gas concentration and the like. The biological radar 130 emits electromagnetic wave pulse signals to the to-be-detected object in the indoor environment, which is used to detect the chest fluctuation of the to-be-detected object. The to-be-detected object can be the user or the pet of the user.

[0041] In step S220, the echo signal corresponding to the pulse signal is received by the biological radar 130.

[0042] Specifically, the electromagnetic wave echo signal reflected by the to-be-detected object after the pulse signal hits the to-be-detected object is received by the biological radar 130. The echo signal is used to indicate the chest fluctuation state of the to-be-detected object, and the chest fluctuation state can reflect the heart rate and the breathing rate.

[0043] In step S230, the physiological parameters of the to-be-detected object are obtained by analyzing the echo signal.

[0044] Specifically, since the heartbeat and the breathing are closely related to the chest fluctuation, the chest fluctuation will cause a slow time domain periodic signal in the echo signal. The heartbeat signal and the breathing signal can be extracted from the echo signal by analyzing and processing the echo signal. The heart rate parameters of the to-be-detected object can be determined according to the heartbeat signal, and the breathing parameters of the to-be-detected object can be determined according to the breathing signal. That is, the physiological parameters specifically include the breathing parameters and the heart rate parameters.

[0045] Step S240, adjusting the operation parameter of the intelligent air conditioner 120 in the indoor environment according to the environmental parameter and the physiological parameter of the to-be-measured object.

[0046] Specifically, the physiological parameter of the to-be-measured object can understand the body state thereof, and the intelligent air conditioner 120 can adjust the environmental parameter on the basis of the environmental state suitable for the body state of the to-be-measured object, that is, change the current environmental parameter into the environmental parameter suitable for the body state of the to-be-measured object by adjusting the operation parameter of the intelligent air conditioner 120, so as to intervene in the environmental state in which the to-be-measured object is located and provide a living environment suitable for the body state of the to-be-measured object.

[0047] In one embodiment, as shown in Figure 4 The analyzing the echo signal to obtain the physiological parameter of the to-be-measured object comprises:

[0048] decomposing the echo signal into a plurality of component signals of different frequencies;

[0049] determining the signal type of each of the component signals, wherein the signal type comprises a noise signal and a valid signal;

[0050] grouping each of the component signals with the signal type of the valid signal into a reconstructed signal;

[0051] separating the reconstructed signal into a heartbeat signal and a breathing signal;

[0052] determining the heartbeat parameter and the breathing parameter of the to-be-measured object based on the heartbeat signal and the breathing signal respectively, wherein the physiological parameter comprises the heartbeat parameter and the breathing parameter.

[0053] Specifically, the echo signal is split into component signals of different frequency bands, that is, the high-frequency part and the low-frequency part of the echo signal are preliminarily separated, the signal type of each component signal is determined, the signal type comprises a noise signal and a valid signal, the noise signal refers to that the noise proportion in the component signal is large and the useful signal proportion is small, and the valid signal refers to that the noise proportion in the component signal is small and the useful signal proportion is large. Specifically, the signal-to-noise ratio of each component signal can be determined, the signal type of the component signal with the signal-to-noise ratio higher than or equal to a signal-to-noise ratio threshold is determined as the valid signal, and the signal type of the component signal with the signal-to-noise ratio lower than the signal-to-noise ratio threshold is determined as the noise signal.

[0054] The noise signal is filtered, that is, each valid signal is recombined to obtain a reconstructed signal, and the reconstructed signal is filtered and separated based on the LMS algorithm to obtain a heartbeat signal and a breathing signal.

[0055] For example Figure 5As shown, by continuously sending pulse signals to the object to be detected, the heartbeat signal of the object to be detected is detected in real time. For the surface of a stationary object, the phase of the echo pulse received by the receiver of the biological radar 130 remains unchanged, that is, the biological radar 130 transmits a pulse signal to a stationary object, and the phase of the received echo signal remains unchanged. The biological radar 130 transmits a pulse signal to the surface of a periodically moving object, and the change of the echo signal pulse received by the receiver of the biological radar 130 over time is periodic. When detecting vital signs, respiration or heartbeat will cause the chest to fluctuate, and the chest fluctuation will affect the radar echo pulse. By analyzing the continuously received echo pulses, the heartbeat parameters and respiration parameters can be extracted.

[0056] In one embodiment, the step of decomposing the echo signal into a plurality of component signals of different frequencies comprises:

[0057] Based on a cubic spline interpolation function, each maximum point in the original signal is connected to form an upper envelope signal, and each minimum point in the original signal is connected to form a lower envelope signal, wherein the original signal comprises the echo signal;

[0058] The average value between the upper envelope signal and the lower envelope signal is determined as an envelope average signal;

[0059] When a difference signal between the original signal and the envelope average signal satisfies a preset function condition, and the average value between a local maximum value corresponding to any time in the upper envelope signal and a local minimum value corresponding to the corresponding time in the lower envelope signal is zero, the difference signal is taken as an Nth component signal, wherein N indicates the number of outer loops in which the difference signal satisfies the preset function condition, and the preset function condition comprises that the number difference between the number of local extreme points and the number of corresponding local zero-crossing points in the difference signal is less than a preset number;

[0060] The remaining signal after the original signal is subtracted by the Nth component signal is taken as a new original signal, and the steps of connecting each maximum point in the original signal to form an upper envelope signal based on a cubic spline interpolation function and connecting each minimum point in the original signal to form a lower envelope signal are executed until the signal decomposition is stopped when an Mth component signal is obtained by circulation, and M is a positive integer greater than N.

[0061] Specifically, the envelope average signal is denoted as wherein N is used to indicate the number of outer loops for calculating the envelope average, and the original signal is denoted as Therefore, the difference signal is When the number difference between the number of local extreme points in the difference signal and the number of local zero-crossing points in the same local range is less than a preset number, it indicates that the difference signal satisfies the preset function condition, the preset function condition is an intrinsic mode function (IMF) condition, and the preset number is 1, so as to limit the number of local extreme points in the difference signal to be the same as or at most multiplied by one of the number of local zero-crossing points in the same local range.

[0062] When the difference signal satisfies the preset function condition and the average value between the local maximum value of any moment in the upper envelope signal and the local minimum value of the corresponding moment in the lower envelope signal is zero, the difference signal is taken as an Nth order component signal (IMF component signal), and the Nth order component signal is denoted as The residual signal obtained by subtracting the difference signal from the original signal is taken as a new original signal to continue the above steps of determining the upper envelope signal and the lower envelope signal until judging whether the new difference signal satisfies the preset function condition.

[0063] For example, the current original signal is , the envelope average signal is , and the difference signal is When the difference signal satisfies the preset function condition and the average value of the local extreme values in the upper envelope signal and the lower envelope signal is zero, the is taken as a first order component signal, and the first order component signal is denoted as The first order residual signal is obtained by subtracting the first order component signal from the original signal The first order residual signal is taken as a new original signal to re-circulate the steps of determining the upper envelope signal and the lower envelope signal until the residual signal is obtained. In this way, the , …, , that is, until the Mth order component signal is obtained by circulation, and the Mth order component signal is the simplest signal that cannot be further decomposed. That is, the signal decomposition cycle is stopped when the above process is circulated to the point where the component signal cannot be extracted, and in summary, the original signal is decomposed into

[0064]

[0065] After the original signal is decomposed into a finite number of component signals by the EMD algorithm, each component signal is a single component signal and contains different time characteristic scales, and the time characteristic scale of the component signal gradually increases with the increase of the cycle order (IMF order), and the frequency scale contained in the component signal gradually decreases with the increase of the cycle order.

[0066] EMD algorithm is based on the time scale characteristics of the signal itself to carry out signal decomposition, without pre-setting any base function, which is essentially different from the Fourier decomposition and wavelet decomposition method based on the prior harmonic base function and wavelet base function. Due to such characteristics, EMD algorithm can be applied to any type of signal decomposition in theory, thus having very obvious advantages in processing non-stationary and nonlinear data, being suitable for analyzing nonlinear and non-stationary signal sequences, and having a higher signal-to-noise ratio. Therefore, EMD algorithm is suitable for various complex scenes under the home state. The key of the algorithm is to decompose the complex signal into a finite number of IMF components, and each IMF component contains the local characteristic signal of different time scales of the original signal. Empirical mode decomposition method can make non-stationary data stationary, and then obtain the time-frequency spectrum by Hilbert transform to obtain the frequency with physical meaning. Compared with short-time Fourier transform and wavelet decomposition, the decomposition is based on the local characteristics of the time scale of the signal sequence, so it has adaptability.

[0067] In one embodiment, after determining the mean value between the upper envelope signal and the lower envelope signal as an envelope mean value signal, the method further comprises:

[0068] When the difference signal between the original signal and the envelope mean value signal does not satisfy the preset function condition, performing the inner loop step of connecting each maximum point in the original signal to form an upper envelope signal based on a cubic spline interpolation function, connecting each minimum point in the original signal to form a lower envelope signal, and accumulating the number of inner loop steps performed by the inner loop step, and when the number of inner loop steps reaches a preset number, taking the difference signal corresponding to the number of inner loop steps as an Nth component signal.

[0069] Specifically, when the difference signal between the original signal and the envelope mean value signal does not satisfy the preset function condition, the difference signal is taken as a new original signal for inner loop, that is, the above steps of determining the upper envelope signal, the lower envelope signal, the envelope mean value signal, and judging whether the new difference signal satisfies the preset function condition are performed on the difference signal as a new original signal. If the inner loop satisfies the preset function condition, the Nth component signal is output according to the previous embodiment, and the number of inner loop steps does not interfere with the number of outer loop steps. Here, N is the corresponding order of the component signal obtained by the last outer loop plus one, that is, the order of the component signal is still determined by the number of outer loop steps. For example, the current original signal is , the envelope mean value signal is , the difference signal is , and the difference signal does not satisfy the preset function condition, then The inner loop is performed as a new original signal, that is, the difference signal is taken as the original signal to determine the upper envelope signal, the lower envelope signal and the envelope mean signal, and it is judged whether the new difference signal meets the preset function condition, until the new difference signal meets the preset function condition, and then the first-order component signal is output based on the outer loop number 1, and the first-order component signal at this time is denoted as , wherein k(t) is used to indicate the inner loop number, if the updated difference signal still cannot meet the preset function condition when the inner loop number reaches the preset number, the difference signal corresponding to the inner loop number is taken as the first-order component signal, that is, the first-order component signal at this time is denoted as , and s(t) is used to indicate the preset number, and the inner loop is limited to a finite loop by the preset number.

[0070] In an embodiment, the signal type of each of the component signals is determined, including:

[0071] When no local signal matching the preset frequency signal is found in each of the component signals, the autocorrelation function value corresponding to each of the component signals is determined, wherein the preset frequency signal includes a preset heartbeat signal and a preset respiratory signal;

[0072] The signal type of each of the component signals is determined based on the autocorrelation function value corresponding to each of the component signals.

[0073] Specifically, each component signal is matched with the preset frequency signal to determine whether there is a heartbeat signal or a respiratory signal in the component signal, if no local signal matching the preset frequency signal is found in each of the component signals, the autocorrelation function value corresponding to each of the component signals is calculated by the autocorrelation function, and since the autocorrelation function values of the component signals with a large proportion of useful signals and the component signals with a large proportion of noise signals are quite different, each of the component signals can be effectively distinguished according to the autocorrelation function value, that is, the signal type of each of the component signals is determined as a useful signal or a noise signal.

[0074] In an embodiment, the signal type of each of the component signals is determined, including:

[0075] When a local signal matching the preset frequency signal is found in the target component signal, the signal type of the target component signal is determined according to the proportion of the local signal matching the preset frequency signal in the target component signal.

[0076] Specifically, if the local signal matching the preset frequency signal is found in the target component signal, the proportion of the local signal matching the preset frequency signal in the target component signal is determined, so that the proportion of the effective signal in the target component signal can be determined, and it is determined whether the target component signal contains more noise signals or more effective signals. If the proportion of the local signal matching the preset frequency signal in the target component signal is greater than or equal to the preset effective proportion, it is determined that the target component signal contains more effective signals, and the signal type of the target component signal is determined as the effective signal. If the proportion of the local signal matching the preset frequency signal in the target component signal is less than the preset effective proportion, it is determined that the target component signal contains less effective signals, and the signal type of the target component signal is determined as the noise signal.

[0077] In one embodiment, the physiological parameter further includes a distance parameter, and the analyzing the echo signal to obtain the physiological parameter of the to-be-measured object comprises:

[0078] Based on the transmission and reception time length of the echo signal and a preset signal propagation speed, a distance parameter of the to-be-measured object to the biological radar 130 is determined.

[0079] Specifically, the transmission and reception time length of the echo signal is used to indicate the time length between the transmission time of the pulse signal and the reception time of the echo signal, and the transmission and reception time length is denoted as The preset signal propagation speed refers to the propagation speed of electromagnetic waves in air, and the preset signal propagation speed is denoted as v. Therefore, the distance parameter of the to-be-measured object to the biological radar 130 is S=(v ). The distance parameter can reflect the distance between the to-be-measured object and the intelligent air conditioner 120, and the exhaust volume of the intelligent air conditioner 120 can be increased or decreased in combination with the adaptability of the position of the to-be-measured object when the operating parameters of the intelligent air conditioner 120 are adjusted subsequently. The pulse width of the pulse signal transmitted by the ultra-wideband biological radar 130 is in the nanosecond or picosecond level, and the waveform rising edge is extremely steep and not easy to be disturbed, so the distance of the to-be-measured object to the biological radar 130 can be accurately detected from the echo pulse.

[0080] In one embodiment, after the analyzing the echo signal to obtain the physiological parameter of the to-be-measured object, the method further comprises:

[0081] The physiological parameter of the to-be-measured object is saved to a database, and the physiological parameter of the to-be-measured object and / or the analysis and prediction result corresponding to the physiological parameter are output through a preset channel.

[0082] Specifically, the preset approach specifically includes at least one of voice broadcast, LED display screen, network transmission, etc. to output the physiological parameters of the to-be-tested object. Specifically, the physiological parameters of the to-be-tested object can be broadcasted through the speaker arranged on the intelligent air conditioner 120, so as to inform the user in the indoor environment to know the physiological parameters of the to-be-tested object. And / or, the physiological parameters of the to-be-tested object can be displayed on the LED display screen arranged on the intelligent air conditioner 120, so that the user in the indoor environment can visually watch the physiological parameters of the to-be-tested object. And / or, the physiological parameters of the to-be-tested object can be transmitted to the terminal bound with the intelligent air conditioner 120 through the communication module of the intelligent air conditioner 120, so that the user holding the terminal can remotely know the physiological parameters of the to-be-tested object in the indoor environment.

[0083] The physiological parameters of the to-be-tested object or all the physiological parameters of the to-be-tested object within a preset time period can also be analyzed and predicted in real time or periodically by the host computer 121 in the intelligent air conditioner 120, to determine the physical health state of the to-be-tested object, and the analysis and prediction results based on the physiological parameters are output to the terminal bound with the intelligent air conditioner 120, that is, the user holding the terminal can be timely or periodically informed to know the physical health state or the physical change of the to-be-tested object. The user holding the terminal can be specifically the to-be-tested object himself / herself, a guardian or a medical staff of the to-be-tested object.

[0084] In one embodiment, the adjusting the operation parameters of the intelligent air conditioner 120 in the indoor environment according to the environmental parameters and the physiological parameters of the to-be-tested object comprises:

[0085] acquiring the motion state of the to-be-tested object;

[0086] inputting the physiological parameters, the environmental parameters and the motion state into a preset learning model to output a target air conditioner operation mode corresponding to the to-be-tested object;

[0087] adjusting the operation parameters of the intelligent air conditioner 120 in the indoor environment according to the target air conditioner operation mode.

[0088] Specifically, the acquiring the motion state of the to-be-tested object can be specifically determined by the camera arranged on the intelligent air conditioner 120 to collect the collected image containing the to-be-tested object, that is, the motion state of the to-be-tested object is determined according to the posture of the to-be-tested object in the collected image, or the motion state of the to-be-tested object is determined by the posture of the to-be-tested object in the collected image combined with the breathing parameter and the heartbeat parameter in the physiological parameters, for example, the to-be-tested object in the collected image assumes a standing leg-lifting posture, but the breathing parameter value indicates that the breathing is relatively slow, and the heartbeat parameter value belongs to the normal heart rate range, so that the motion state of the to-be-tested object can be accurately determined as light exercise.

[0089] The preset learning model is an intelligent model trained in advance by machine learning. The preset learning model establishes a corresponding relationship between the user and different air conditioner operation modes under different physiological parameters, different environmental parameters, and different motion states. The preset learning model is mainly based on the sampling data in the database for model training, and the database stores a large amount of physiological parameters, environmental parameters, motion states, and air conditioner operation modes. The physiological parameters include heartbeat parameters, breathing parameters, and distance parameters. The environmental parameters include temperature, humidity, gas concentration, and air pressure. The motion states include aerobic exercise, anaerobic exercise, light exercise, and non-exercise posture, including lying and sitting. Therefore, the physiological parameters, environmental parameters, and motion states of the to-be-tested object are input into the preset learning model as input parameters, and the corresponding target air conditioner operation mode is output. The intelligent air conditioner 120 adjusts the operation parameters according to the target air conditioner operation mode. For example, when the physiological parameters indicate that the health of the to-be-tested object is low, the target air conditioner operation mode is soft wind sweeping, which avoids the discomfort of direct wind blowing on the to-be-tested object. The intelligent air conditioner 120 operates according to the target air conditioner operation mode to provide a comfortable indoor environment for the to-be-tested object.

[0090] The above air conditioner control method can achieve high-precision, real-time, long-term, and non-interfering physiological parameter monitoring without close contact with the user. The intelligent air conditioner 120 involved in the above air conditioner control method has a simple hardware structure and is easy to use. It can also provide all-around, multi-target, and non-binding user health monitoring services. It can provide physical monitoring services for users of different age groups and control the air conditioner according to the monitored physiological parameters. Different air conditioner control modes are used for users with different health states to provide personalized air conditioner control services and provide the best indoor environment suitable for the user's physical condition. In addition, the above air conditioner control method can also prevent the occurrence of emergency diseases, predict and remind relevant personnel in advance based on the user's physiological parameters, and help to prolong the healthy life of the elderly when the to-be-tested object is an elderly person.

[0091] Figure 3 And Figure 4 is a flowchart of an air conditioner control method in an embodiment. It should be understood that although Figure 3 and Figure 4 the steps in the flowchart are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 3 and Figure 4At least one of the steps in the method can comprise a plurality of sub-steps or stages which are not necessarily performed at the same time but can be performed at different times, and the order of the sub-steps or stages is not necessarily sequential but can be performed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0092] In one embodiment, as shown in Figure 6 An air conditioner control device is provided, comprising:

[0093] The acquisition module 310 is configured to acquire an environmental parameter of an indoor environment and start the biological radar 130 to emit a pulse signal to a to-be-detected object in the indoor environment when detecting an object feature of the to-be-detected object.

[0094] The receiving module 320 is configured to receive, by the biological radar 130, a return signal corresponding to the pulse signal.

[0095] The analysis module 330 is configured to analyze the return signal to obtain a physiological parameter of the to-be-detected object.

[0096] The control module 340 is configured to adjust an operating parameter of the intelligent air conditioner 120 in the indoor environment according to the environmental parameter and the physiological parameter of the to-be-detected object.

[0097] In one embodiment, the analysis module 330 is further configured to:

[0098] decompose the return signal into a plurality of component signals of different frequencies;

[0099] determine a signal type of each of the component signals, wherein the signal type comprises a noise signal and a valid signal;

[0100] compose a reconstructed signal from each of the component signals of which the signal type is a valid signal;

[0101] separate the reconstructed signal into a heartbeat signal and a breathing signal;

[0102] determine a heartbeat parameter and a breathing parameter of the to-be-detected object based on the heartbeat signal and the breathing signal, respectively, wherein the physiological parameter comprises the heartbeat parameter and the breathing parameter.

[0103] In one embodiment, the analysis module 330 is further configured to:

[0104] connect each maximum point in an original signal to form an upper envelope signal and connect each minimum point in the original signal to form a lower envelope signal based on a cubic spline interpolation function, wherein the original signal comprises the return signal.

[0105] determining a mean value between the upper envelope signal and the lower envelope signal as an envelope mean value signal;

[0106] when a difference signal between the original signal and the envelope mean value signal satisfies a preset function condition, and a mean value between a local maximum value corresponding to any moment in the upper envelope signal and a local minimum value corresponding to a corresponding moment in the lower envelope signal is zero, taking the difference signal as an Nth-order component signal, wherein N indicates an outer loop number of times that the difference signal satisfies the preset function condition, and the preset function condition includes that a number difference between a number of local extreme points and a number of corresponding local zero-crossing points in the difference signal is less than a preset number;

[0107] taking a residual signal obtained by subtracting the Nth-order component signal from the original signal as a new original signal, and performing the steps of connecting each maximum value point in the original signal by a line to form an upper envelope signal and connecting each minimum value point in the original signal to form a lower envelope signal based on the cubic spline interpolation function until a stop signal is obtained when an Mth-order component signal is obtained in a loop, M being a positive integer greater than N.

[0108] In an embodiment, the analysis module 330 is further configured to:

[0109] when the difference signal between the original signal and the envelope mean value signal does not satisfy the preset function condition, performing an inner loop step of connecting each maximum value point in the original signal by a line to form an upper envelope signal and connecting each minimum value point in the original signal to form a lower envelope signal based on the cubic spline interpolation function, taking the difference signal as a new original signal, and accumulating an inner loop number of times that the inner loop step is performed, and taking the difference signal corresponding to the inner loop number of times as an Nth-order component signal when the inner loop number of times reaches a preset number.

[0110] In an embodiment, the analysis module 330 is further configured to:

[0111] when no local signal matching a preset frequency signal is found in each of the component signals, determining an autocorrelation function value corresponding to each of the component signals, wherein the preset frequency signal includes a preset heartbeat signal and a preset respiration signal;

[0112] determining a signal type of each of the component signals based on the autocorrelation function value corresponding to each of the component signals.

[0113] In an embodiment, the analysis module 330 is further configured to:

[0114] When a local signal matching the preset frequency signal is found in the target component signal, the signal type of the target component signal is determined according to the proportion of the local signal matching the preset frequency signal in the target component signal.

[0115] In one embodiment, the parsing module 330 is further configured to:

[0116] Based on the transmission and reception duration of the echo signal and the preset signal propagation speed, the distance parameters from the object under test to the bio-radar 130 are determined.

[0117] In one embodiment, the apparatus further includes a processing module for:

[0118] The physiological parameters of the subject under test are saved to the database and output through a preset method.

[0119] In one embodiment, the control module 340 is further configured to:

[0120] Obtain the motion state of the object under test;

[0121] The physiological parameters, environmental parameters, and motion state are input into a preset learning model, and the target air conditioning operation mode corresponding to the test object is output.

[0122] Adjust the operating parameters of the smart air conditioner 120 in the indoor environment according to the target air conditioner operating mode.

[0123] Figure 7 An internal structural diagram of a computer device, specifically a smart air conditioner 120, is shown in one embodiment. Figure 7 As shown, the intelligent air conditioner 120 includes a processor, a memory, a network interface, an input device, and a display screen connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium of the intelligent air conditioner 120 stores an operating system and may also store a computer program. When the processor executes the computer program, it enables the processor to implement an air conditioning control method. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the air conditioning control method. The display screen of the intelligent air conditioner 120 can be an LCD screen or an e-ink screen. The input device of the intelligent air conditioner 120 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad located on the casing of the intelligent air conditioner 120, or an external keyboard, touchpad, or mouse, etc.

[0124] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the intelligent air conditioner 120 to which the scheme of the present application is applied. The specific intelligent air conditioner 120 can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0125] In one embodiment, the air conditioner control device provided by the present application can be implemented in the form of a computer program that can run on the intelligent air conditioner 120 as shown in the figure. Figure 7 The memory of the intelligent air conditioner 120 can store various program modules that make up the air conditioner control device, such as the acquisition module 310, the receiving module 320, the analysis module 330, and the control module 340 shown in the figure. Figure 6 The computer program composed of various program modules enables the processor to perform the steps in the air conditioner control method of each embodiment of the present application described in the specification.

[0126] Figure 7 The intelligent air conditioner 120 can execute the acquisition module 310 in the air conditioner control device to acquire the environmental parameters of the indoor environment and start the biological radar 130 to emit pulse signals to the object to be detected in the indoor environment when the object characteristics of the object to be detected are detected, as shown in the figure. Figure 6 The intelligent air conditioner 120 can execute the receiving module 320 to receive the echo signal corresponding to the pulse signal through the biological radar 130. The intelligent air conditioner 120 can execute the analysis module 330 to analyze the echo signal to obtain the physiological parameters of the object to be detected. The intelligent air conditioner 120 can execute the control module 340 to adjust the operating parameters of the intelligent air conditioner 120 in the indoor environment according to the environmental parameters and the physiological parameters of the object to be detected.

[0127] In one embodiment, an intelligent air conditioner 120 is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method described in any of the above embodiments.

[0128] In one embodiment, a computer readable storage medium is provided, which stores a computer program that is executed by a processor to implement the method described in any of the above embodiments.

[0129] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0130] It should be noted that the relational terms herein such as "first" and "second" and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprising", "including", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by an indefinite article "a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus including the defined element.

[0131] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An air conditioning control method, characterized in that, The method includes: When the object characteristics of the object to be tested are detected, the environmental parameters of the indoor environment are acquired and the bio-radar is activated to transmit pulse signals to the object to be tested in the indoor environment; The echo signal corresponding to the pulse signal is received by the bio-radar; The physiological parameters of the subject under test are obtained by analyzing the echo signal; Based on the environmental parameters and the physiological parameters of the object under test, adjust the operating parameters of the intelligent air conditioner in the indoor environment; The step of analyzing the echo signal to obtain the physiological parameters of the subject includes: The echo signal is decomposed into multiple component signals of different frequencies; Determine the signal type of each component signal, wherein the signal type includes noise signals and valid signals; The reconstructed signal is composed of the component signals that are of the valid signal type. The reconstructed signal is separated into a heartbeat signal and a respiratory signal; The heart rate parameters and respiratory parameters of the subject to be tested are determined based on the heart rate signal and the respiratory signal, respectively, wherein the physiological parameters include the heart rate parameters and the respiratory parameters; The step of determining the signal type of each component signal includes: When no local signal matching the preset frequency signal is found in any of the component signals, the autocorrelation function value corresponding to each component signal is determined, wherein the preset frequency signal includes a preset heartbeat signal and a preset respiratory signal; the signal type of each component signal is determined based on the autocorrelation function value corresponding to each component signal. When a local signal matching the preset frequency signal is found in the target component signal, the signal type of the target component signal is determined according to the proportion of the local signal matching the preset frequency signal in the target component signal.

2. The method according to claim 1, characterized in that, The step of decomposing the echo signal into multiple component signals of different frequencies includes: The upper envelope signal is formed by connecting the maxima points in the original signal using a cubic spline interpolation function, and the lower envelope signal is formed by connecting the minima points in the original signal. The original signal includes the echo signal. The mean between the upper envelope signal and the lower envelope signal is determined as the envelope mean signal; When the difference signal between the original signal and the envelope mean signal satisfies a preset function condition, and the mean between the local maximum value at any time in the upper envelope signal and the local minimum value at the corresponding time in the lower envelope signal is zero, the difference signal is taken as the Nth order component signal, where N indicates the number of outer loops in which the difference signal satisfies the preset function condition. The preset function condition includes that the difference between the number of local extreme points and the number of corresponding local zero crossing points in the difference signal is less than a preset number. The remaining signal after subtracting the Nth-order component signal from the original signal is taken as the new original signal. The steps of connecting the maxima points in the original signal to form the upper envelope signal and connecting the minima points in the original signal to form the lower envelope signal are performed based on the cubic spline interpolation function until the Mth-order component signal is obtained, at which point the signal decomposition stops. M is a positive integer greater than N.

3. The method according to claim 2, characterized in that, After determining the mean between the upper envelope signal and the lower envelope signal as the envelope mean signal, the method further includes: When the difference signal between the original signal and the envelope mean signal does not satisfy the preset function condition, the difference signal is used as the new original signal to perform the inner loop step of connecting the maximum points in the original signal to form the upper envelope signal and connecting the minimum points in the original signal to form the lower envelope signal based on the cubic spline interpolation function, and the number of inner loops performed by the inner loop step is accumulated. When the number of inner loops reaches the preset number, the difference signal corresponding to the number of inner loops is used as the Nth order component signal.

4. The method according to claim 1, characterized in that, The physiological parameters also include distance parameters. The process of analyzing the echo signal to obtain the physiological parameters of the subject includes: Based on the transmission and reception duration of the echo signal and the preset signal propagation speed, the distance parameters from the object under test to the bio-radar are determined.

5. The method according to claim 1, characterized in that, After obtaining the physiological parameters of the subject by analyzing the echo signal, the method further includes: The physiological parameters of the subject under test are saved to the database and output through a preset method.

6. The method according to claim 5, characterized in that, The step of adjusting the operating parameters of the intelligent air conditioner in the indoor environment based on the environmental parameters and the physiological parameters of the test subject includes: Obtain the motion state of the object under test; The physiological parameters, environmental parameters, and motion state are input into a preset learning model, and the target air conditioning operation mode corresponding to the test object is output. Adjust the operating parameters of the smart air conditioner in the indoor environment according to the target air conditioner operating mode.

7. An air conditioning control device, characterized in that, The device includes: The acquisition module is used to acquire the environmental parameters of the indoor environment and activate the bio-radar to transmit pulse signals to the object under test in the indoor environment when the object characteristics of the object under test are detected. The receiving module is used to receive the echo signal corresponding to the pulse signal through the bio-radar; The analysis module is used to analyze the echo signal to obtain the physiological parameters of the object under test; The control module is used to adjust the operating parameters of the intelligent air conditioner in the indoor environment according to the environmental parameters and the physiological parameters of the object under test; The step of analyzing the echo signal to obtain the physiological parameters of the subject includes: The echo signal is decomposed into multiple component signals of different frequencies; Determine the signal type of each component signal, wherein the signal type includes noise signals and valid signals; The reconstructed signal is composed of the component signals that are of the valid signal type. The reconstructed signal is separated into a heartbeat signal and a respiratory signal; The heart rate parameters and respiratory parameters of the subject to be tested are determined based on the heart rate signal and the respiratory signal, respectively, wherein the physiological parameters include the heart rate parameters and the respiratory parameters; The step of determining the signal type of each component signal includes: When no local signal matching the preset frequency signal is found in any of the component signals, the autocorrelation function value corresponding to each component signal is determined, wherein the preset frequency signal includes a preset heartbeat signal and a preset respiratory signal; the signal type of each component signal is determined based on the autocorrelation function value corresponding to each component signal. When a local signal matching the preset frequency signal is found in the target component signal, the signal type of the target component signal is determined according to the proportion of the local signal matching the preset frequency signal in the target component signal.

8. A smart air conditioner, characterized in that, The invention includes a bio-radar, a memory, a processor, and a computer program stored in the memory and executable on the processor. The bio-radar is used to transmit pulse signals and receive echo signals corresponding to the pulse signals. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. The intelligent air conditioner according to claim 8, characterized in that, The bio-radar includes a transmitting antenna, a receiving antenna, a switching component, a power amplifier, a delay unit, a low-noise amplifier, an analog-to-digital converter, and a control unit. The first terminal of the control unit is electrically connected to the host computer in the intelligent air conditioner. The second terminal of the control unit is connected to the transmitting antenna via the power amplifier. The second terminal of the control unit is also connected back to the third terminal of the control unit via the delay unit. The fourth terminal of the control unit is connected to the receiving antenna in sequence via the analog-to-digital converter, the low-noise amplifier, and the switching component.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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