Control method and device of Internet of Things equipment, equipment, air conditioner, medium and product

Abstract

The invention provides a control method, device and equipment of Internet of Things equipment, an air conditioner, a medium and a product, and the method comprises the steps: transmitting a wireless transmission signal to an equipment environment where the preset Internet of Things equipment is located, and receiving a wireless reflection signal returned from the equipment environment; determining user feature information corresponding to the equipment environment according to the wireless reflection signal; and according to the user feature information, adjusting operation parameters of the Internet of Things equipment. According to one embodiment of the invention, the user feature information in the equipment environment is monitored by receiving the wireless reflection signal in real time, and the operation parameters of the Internet of Things equipment are changed according to the user feature information, so that the operation parameters are matched with the user feature information, the inherent wireless signal resources of the Internet of Things equipment are effectively utilized, the hardware cost does not need to be increased, and the cost is reduced. The problem that the computer vision technology easily relates to user privacy and the requirement for light intensity is avoided, and the problem that the special sensor technology is high in manufacturing cost is also avoided.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular to a control method, device, control device, air conditioner, medium and product of an Internet of Things device. Specifically, it relates to a control method, device, control device, air conditioner, computer-readable storage medium and computer program product of an Internet of Things device. Background Art

[0002] As people's demand for intelligent control and human-computer interaction increases, human behavior sensing technology has become a hot research direction in the field of the Internet of Things. If human behavior sensing technology can be applied in the field of the Internet of Things, it can greatly simplify the way users control devices. For example, users can start the IoT devices around them by waving their hands.

[0003] However, the human behavior perception technology in the related technology cannot be applied to the field of Internet of Things. This is because the existing human behavior perception technology is based on computer vision technology or dedicated sensor technology. Computer vision technology is limited by user privacy and light conditions and cannot be applied in Internet of Things devices. In addition, there are blind spots in camera detection. Even if computer vision technology is applied to Internet of Things devices, human perception can only be achieved within a specific area within the camera's line of sight, which affects user use. Although dedicated sensor technology can achieve fine-grained behavior perception, the cost of deploying dedicated sensors is extremely expensive, and it is inconvenient to install dedicated sensors. Therefore, dedicated sensor technology is difficult to be widely used. Summary of the invention

[0004] The main purpose of the present invention is to overcome the defects of the above-mentioned related technologies and provide a control method, device, equipment, air conditioner, medium and product for Internet of Things devices to solve the problem that human behavior perception technology in the related technologies cannot be applied to the field of Internet of Things.

[0005] On one hand, the present invention provides a control method for an Internet of Things device, comprising: sending a wireless transmission signal to a device environment where a preset Internet of Things device is located, and receiving a wireless reflection signal returned from the device environment; determining user characteristic information corresponding to the device environment based on the wireless reflection signal; and adjusting the operating parameters of the Internet of Things device based on the user characteristic information.

[0006] Optionally, the characteristic parameters in the user characteristic information include: the number of users; determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: for each of the wireless reflection signals, extracting signal reception strength and channel state information from the wireless reflection signal; extracting signal domain feature sequences from the signal reception strength and the channel state information respectively; and determining the number of users in the device environment based on the signal domain feature sequences corresponding to a plurality of the wireless reflection signals by using a pre-trained user quantity perception model.

[0007] Optionally, the characteristic parameters in the user characteristic information include: user location; determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: extracting signal reception strength and channel state information in each of the wireless reflection signals; for each of the wireless reflection signals, identifying a reference position corresponding to the signal reception strength and channel state information of the wireless reflection signal in a user signal fingerprint library corresponding to the Internet of Things device, and determining the reference position as the user location in the device environment; wherein the user signal fingerprint library corresponding to the Internet of Things device is used to pre-record: wireless signal characteristics corresponding to multiple reference positions in the device environment; the wireless signal characteristics include at least: reference signal reception strength and reference channel state information.

[0008] Optionally, in the user signal fingerprint library corresponding to the Internet of Things device, identifying the reference position corresponding to the signal reception strength and channel state information of the wireless reflected signal, and determining the reference position as the user position in the device environment, includes: splicing the signal reception strength and channel state information of the wireless reflected signal into a spliced ​​signal information; wherein, in the user signal fingerprint library, the reference signal reception strength and reference channel state information corresponding to the same reference position are a spliced ​​reference signal information; in the user signal fingerprint library, determining a preset number of reference signal information that are closest to the spliced ​​signal information corresponding to the wireless reflected signal; in the case of determining one reference signal information, determining the reference position corresponding to the determined reference signal information as the user position; in the case of determining multiple reference signal information, determining the reference position corresponding to the reference signal information that is closest to the spliced ​​signal information as the user position; or, among the reference positions corresponding to multiple reference signal information, determining the reference position located at the center as the user position.

[0009] Optionally, the characteristic parameters in the user characteristic information include: user status; determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: extracting channel status information in each of the wireless reflection signals and extracting dynamic status information from the channel status information; using a pre-trained motion state perception model, determining the user status in the device environment according to the dynamic status information corresponding to multiple wireless reflection signals.

[0010] Optionally, adjusting the operating parameters of the Internet of Things device according to the user characteristic information includes: identifying the changed characteristic parameters in the user characteristic information determined this time according to the user characteristic information determined last time; when the changed characteristic parameter is the number of users, adjusting the operating power parameter of the Internet of Things device according to the number of users; when the changed characteristic parameter is the user position, adjusting the operating direction parameter of the Internet of Things device according to the user position; when the changed characteristic parameter is the user status, adjusting the operating power parameter and / or operating mode parameter of the Internet of Things device according to the user status.

[0011] On the other hand, the present invention provides a control device for an Internet of Things device, comprising: a transceiver unit, used to send a wireless transmission signal to a device environment where a preset Internet of Things device is located, and to receive a wireless reflection signal returned from the device environment; a determination unit, used to determine user characteristic information corresponding to the device environment based on the wireless reflection signal; and a control unit, used to adjust the operating parameters of the Internet of Things device based on the user characteristic information.

[0012] Optionally, the characteristic parameters in the user characteristic information include: the number of users; the determination unit, determining the user characteristic information corresponding to the device environment based on the wireless reflection signal, includes: for each of the wireless reflection signals, extracting the signal reception strength and the channel state information from the wireless reflection signal; and, respectively extracting the signal domain feature sequence from the signal reception strength and the channel state information; and determining the number of users in the device environment according to the signal domain feature sequences corresponding to the multiple wireless reflection signals using a pre-trained user number perception model.

[0013] Optionally, the characteristic parameters in the user characteristic information include: user location; the determination unit, determining the user characteristic information corresponding to the device environment based on the wireless reflection signal, includes: extracting signal reception strength and channel state information in each of the wireless reflection signals; for each of the wireless reflection signals, identifying a reference position corresponding to the signal reception strength and channel state information of the wireless reflection signal in a user signal fingerprint library corresponding to the Internet of Things device, and determining the reference position as the user location in the device environment; wherein the user signal fingerprint library corresponding to the Internet of Things device is used to pre-record: wireless signal characteristics corresponding to multiple reference positions in the device environment; the wireless signal characteristics include at least: reference signal reception strength and reference channel state information.

[0014] Optionally, the determination unit identifies, in a user signal fingerprint library corresponding to the Internet of Things device, a reference position corresponding to the signal reception strength and channel state information of the wireless reflected signal, and determines the reference position as the user position in the device environment, including: splicing the signal reception strength and channel state information of the wireless reflected signal into a spliced ​​signal information; wherein, in the user signal fingerprint library, the reference signal reception strength and reference channel state information corresponding to the same reference position are a spliced ​​reference signal information; in the user signal fingerprint library, determining a preset number of reference signal information that are closest to the spliced ​​signal information corresponding to the wireless reflected signal; in the case of determining one reference signal information, determining the reference position corresponding to the determined reference signal information as the user position; in the case of determining multiple reference signal information, determining the reference position corresponding to the reference signal information that is closest to the spliced ​​signal information as the user position; or, among the reference positions corresponding to multiple reference signal information, determining the reference position located at the center as the user position.

[0015] Optionally, the characteristic parameters in the user characteristic information include: user status; the determination unit determines the user characteristic information corresponding to the device environment based on the wireless reflection signal, including: extracting channel state information in each of the wireless reflection signals and extracting dynamic state information from the channel state information; using a pre-trained motion state perception model, determining the user status in the device environment according to the dynamic state information corresponding to multiple wireless reflection signals.

[0016] Optionally, the control unit adjusts the operating parameters of the Internet of Things device according to the user characteristic information, including: identifying the changed characteristic parameters in the user characteristic information determined this time according to the user characteristic information determined last time; when the changed characteristic parameter is the number of users, adjusting the working power parameter of the Internet of Things device according to the number of users; when the changed characteristic parameter is the user position, adjusting the working direction parameter of the Internet of Things device according to the user position; when the changed characteristic parameter is the user status, adjusting the working power parameter and / or working mode parameter of the Internet of Things device according to the user status.

[0017] In another aspect of the present invention, a control device for an Internet of Things device comprises: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus, wherein the processor is configured to: execute a control program for the Internet of Things device stored in the memory to implement any of the control methods for the Internet of Things device described above.

[0018] In another aspect of the present invention, an air conditioner is provided, wherein the air conditioner comprises the control device of the above-mentioned Internet of Things device. The air conditioner is an air conditioner with a humidification function.

[0019] Another aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed to implement any of the above-mentioned control methods for the Internet of Things device.

[0020] In yet another aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any of the aforementioned methods are implemented.

[0021] According to the technical solution of the present invention, a wireless transmission signal can be sent to a device environment where a preset Internet of Things device is located, and a wireless reflection signal returned from the device environment can be received; based on the wireless reflection signal, user characteristic information corresponding to the device environment can be determined; based on the user characteristic information, the operating parameters of the Internet of Things device can be adjusted.

[0022] According to the technical solution of the present invention, the user characteristic information in the device environment is monitored by receiving the wireless reflection signal in real time, and the operating parameters of the Internet of Things device are changed according to the user characteristic information to match the operating parameters with the user characteristic information. The inherent wireless signal resources of the Internet of Things device are effectively utilized without increasing the hardware cost, avoiding the problem that computer vision technology easily involves user privacy and light intensity requirements, and also avoiding the high cost of dedicated sensor technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 is a flow chart of a control method for an Internet of Things device according to an embodiment of the present invention;

[0025] Figure 2 A control schematic diagram of an Internet of Things device according to an embodiment of the present invention;

[0026] Figure 3 is a structural block diagram of a control device for an Internet of Things device according to an embodiment of the present invention;

[0027] Figure 4 4 is a structural block diagram of a control device for an Internet of Things device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] An embodiment of the present invention provides a control method for an Internet of Things device. Figure 1 FIG. 1 is a flow chart of a control method for an Internet of Things device according to an embodiment of the present invention.

[0031] Step S110, sending a wireless transmission signal to a device environment where a preset IoT device is located, and receiving a wireless reflection signal returned from the device environment.

[0032] Types of IoT devices include, but are not limited to: air conditioners, humidifiers, humidifying air conditioners, cold fans, and warm fans.

[0033] The device environment refers to the place where the IoT device is set up. For example, the device environment is the indoor environment where the IoT device is located.

[0034] The wireless transmission signal refers to the wireless signal emitted by the communication module built into the IoT device. The types of communication modules include but are not limited to: Wi-Fi modules. Accordingly, the types of wireless transmission signals include but are not limited to: Wi-Fi signals.

[0035] Wireless reflection signals refer to wireless signals reflected by people or objects in the device environment. The types of wireless reflection signals are the same as those of the detected transmission signals.

[0036] Step S120: determining user characteristic information corresponding to the device environment according to the wireless reflected signal.

[0037] User feature information is used to reflect the user behavior patterns and space usage status in the device environment. For example, user feature information includes: number of users, user location and / or user status.

[0038] Specifically, the received signal strength (RSSI) and channel state information (CSI) can be extracted from the wireless reflection signal; the received signal strength and the channel state information are preprocessed respectively; and the user characteristic information corresponding to the device environment is determined based on the preprocessed received signal strength and the channel state information.

[0039] The signal reception strength and channel state information are wireless signal characteristics of the wireless reflection signal. The signal reception strength and channel state information can be regarded as two independent signals. Among them, the signal reception strength is a signal strength indicator, which is suitable for quickly estimating signal quality. The channel state information is used to provide detailed channel information, which is suitable for more detailed analysis. In the actual environment, the signal may be affected by many factors, such as obstacles, interference sources, etc. By combining the two data, the signal reception strength and channel state information, the limitations of a single data source can be compensated, thereby improving the robustness and accuracy of the system.

[0040] Step S130: adjusting the operating parameters of the IoT device according to the user characteristic information.

[0041] According to the user characteristic information, the current operating parameters of the IoT device are adjusted to operating parameters that match the user characteristic information.

[0042] In one embodiment of the present invention, a wireless transmission signal is sent to the device environment where the preset IoT device is located, and a wireless reflection signal returned from the device environment is received; user characteristic information corresponding to the device environment is determined based on the wireless reflection signal; and the operating parameters of the IoT device are adjusted based on the user characteristic information. One embodiment of the present invention monitors the user characteristic information in the device environment by receiving the wireless reflection signal in real time, and changes the operating parameters of the IoT device based on the user characteristic information so that the operating parameters match the user characteristic information, effectively utilizing the inherent wireless signal resources of the IoT device without increasing the hardware cost, avoiding the problem that computer vision technology easily involves user privacy and light intensity requirements, and also avoiding the high cost of dedicated sensor technology.

[0043] Combine the following Figure 2 The control schematic diagram of the Internet of Things device shown in the figure further describes the control method of the Internet of Things device of the present invention.

[0044] In one embodiment of the present invention, an Internet of Things device that needs to be controlled using human behavior perception can be deployed in a preset device environment; the Internet of Things device has a built-in wireless signal transceiver. For example: a humidifying air conditioner with a Wi-Fi module is deployed in an indoor environment. The control method of an Internet of Things device of an embodiment of the present invention can be implemented on an Internet of Things device or on a preset control device. The control device is connected to the Internet of Things device, and under the control of the control device, transmits a wireless transmission signal, receives a wireless reflection signal, and reports the information of the wireless reflection signal to the control device. The control device is, for example: a home host device.

[0045] Control the wireless signal transceiver in the IoT device to transmit the wireless transmission signal to the device environment and receive the wireless reflection signal reflected back from the device environment. After the wireless transmission signal is sent, it may touch static objects due to scattering, refraction, and reflection. Static objects such as furniture and walls may also touch the human body due to scattering, refraction, and reflection. Therefore, multiple wireless reflection signals will be reflected back in the device environment one after another. These wireless reflection signals may be reflected back by static objects in the device environment or by the human body. Therefore, it is necessary to analyze each received wireless reflection signal to determine the user feature information in the device environment.

[0046] The wireless signal characteristics of each wireless reflection signal include signal reception strength and channel state information. The signal reception strength and channel state information can be extracted from each wireless reflection signal.

[0047] In order to analyze the user characteristic information more accurately, the signal reception strength and the channel state information can be preprocessed respectively. The signal reception strength and the channel state information can be regarded as two signals, and the preprocessing is used to remove abnormal values ​​and noise in the signal.

[0048] The preprocessing includes but is not limited to: outlier removal processing, interpolation processing, filtering processing and averaging processing.

[0049] Outlier removal processing includes: using the preset Hampel detection algorithm to identify abnormal outliers in each wireless signal feature. Outliers refer to wireless signal features that deviate from other wireless signal features. Outliers may be caused by environmental interference or other factors in the data collection process. Removing these outliers helps improve the accuracy of subsequent analysis. For example: when the RSSI of a signal is much larger than that of other signals, it can be removed.

[0050] Interpolation processing includes: in the process of receiving wireless reflection signals, due to delay or other factors, the wireless reflection signals may be non-uniformly distributed in time. To solve this problem, linear interpolation can be performed on the wireless signal characteristics to make the wireless signal characteristics uniform in time intervals, which helps to ensure the consistency and accuracy of subsequent analysis.

[0051] Filtering includes: using a preset noise reduction filter to perform noise reduction on wireless signal features. Of course, the device environment includes not only static objects and human bodies, but also wireless reflection signals reflected by static objects and human bodies have different frequency ranges, such as: the frequency range of walking is 20-80Hz, and the frequency of breathing is 0.1-0.5Hz. The frequency range corresponding to the human body can be obtained through existing research or testing. After obtaining the frequency range corresponding to the human body, a filter can be used to filter the wireless signal features within the frequency range corresponding to the human body.

[0052] The averaging process includes: if there is a large noise in the wireless signal feature of the wireless reflection signal, the influence of the noise can be reduced by averaging the wireless signal feature of the wireless reflection signal.

[0053] After preprocessing, each wireless signal feature may be analyzed to form user feature information, wherein the feature parameters in the user feature information include: the number of users, the location of users and / or the status of users.

[0054] The number of users refers to the number of users within the working range of the IoT device.

[0055] User location refers to the user's location within the working range of the IoT device.

[0056] User status refers to the user status within the working range of the IoT device. For example, is the user walking fast or slowly? Is the user sleeping or walking?

[0057] The working range of the IoT device refers to the coverage of the communication module of the IoT device, and the coverage can be set according to the requirements.

[0058] In one embodiment of the present invention, signal reception strength and channel state information can be extracted from wireless signal features, and features for determining the number of users, features for determining user locations, and features for determining user states are respectively extracted from the preprocessed signal reception strength and channel state information, and the features for determining the number of users, the features for determining user locations, and the features for determining user states are input into a pre-trained deep learning model (deep learning algorithm); wherein the deep learning model determines the number of users, user locations, and user states in the device environment based on the features for determining the number of users, the features for determining user locations, and the features for determining user states; and the number of users, user locations, and user states output by the deep learning model are obtained.

[0059] In another embodiment of the present invention, the signal reception strength and channel state information can be extracted from the wireless signal features, and the features for determining the number of users, the features for determining the user positions, and the features for determining the user states are respectively extracted from the pre-processed signal reception strength and channel state information, and the number of users, the user positions, and the user states are respectively determined using different determination methods. Of course, a pre-trained model can also be used to complete the steps of using different determination methods to respectively determine the number of users, the user positions, and the user states. That is, different determination methods are respectively executed using different layers in the model.

[0060] The following describes the process of determining the number of users, the location of users, and the status of users using different determination methods.

[0061] When determining the number of users corresponding to the device environment according to the wireless reflection signal, for each wireless reflection signal, the signal reception strength and channel state information can be extracted from the wireless reflection signal; and, from the signal reception strength and the channel state information, the signal domain feature sequence can be extracted respectively to obtain the signal domain feature sequence corresponding to the wireless reflection signal (i.e., the signal domain feature sequence corresponding to the combined signal reception strength and the signal domain feature corresponding to the channel state information); using a pre-trained user quantity perception model, the number of users in the device environment can be determined according to the signal domain feature sequences corresponding to the multiple wireless reflection signals. Alternatively, for each wireless reflection signal, the signal reception strength and channel state information can be extracted from the wireless reflection signal; from the signal reception strength and the channel state information, the signal domain feature sequence can be extracted respectively to obtain the signal domain feature sequence corresponding to the wireless reflection signal; the signal domain feature sequences corresponding to the multiple wireless reflection signals can be fused to obtain a signal domain fusion feature sequence; using a pre-trained user quantity perception model, the number of users in the device environment can be determined according to the signal domain fusion feature sequence.

[0062] Wireless reflection signals refer to signals captured from the device environment. These signals include the impact of user activities in the device environment on wireless signals.

[0063] The signal domain feature sequence is used to reflect the changes in the signal in different dimensions, which helps to identify the impact of different numbers of users on the signal.

[0064] The signal domain feature sequence includes multiple types of signal domain features arranged in a preset order.

[0065] Types of signal domain features include, but are not limited to, time domain features, subcarrier domain features, frequency domain features, and time-frequency domain features. Examples of time domain features include mean, variance, and standard deviation. Examples of subcarrier domain features include amplitude and phase of subcarriers. Examples of frequency domain features include spectrum density and spectrum peak. Examples of time-frequency domain features include short-time Fourier transform (STFT) features and discrete wavelet transform (DWT) features.

[0066] Furthermore, for each of the wireless reflection signals, signal reception strength and channel state information are extracted from the wireless reflection signal; the signal reception strength and the channel state information are preprocessed respectively, and signal domain feature sequences are extracted respectively from the signal reception strength and the channel state information after the preprocessing.

[0067] Furthermore, the signal domain feature sequences corresponding to the fusion of the multiple wireless reflection signals can be the average value calculation of the same signal domain features of the multiple wireless reflection signals. That is to say, the signal domain fusion feature sequence also includes multiple types of signal domain features arranged in a preset order, and each signal domain feature is the average value of the signal domain features corresponding to the multiple wireless reflection signals. A single signal domain feature sequence may be affected by factors such as noise and interference. After fusing multiple signal domain feature sequences, the multiple signal domain feature sequences can complement each other to improve the accuracy and reliability of feature extraction.

[0068] Furthermore, the user quantity perception model is a pre-trained lightweight model. The lightweight model is trained to have the ability to determine the number of users in the device environment based on the signal domain fusion feature sequence. Among them, the types of lightweight models include but are not limited to: Mobile Net model. The use of lightweight models can reduce the amount of calculations and parameters, so that the control method of the Internet of Things device of the present invention can be deployed on a hardware platform with limited computing resources.

[0069] Furthermore, when training the user quantity perception model, wireless reflection signals under different numbers of users can be collected in advance and preprocessed, and signal domain feature sequence extraction and user quantity determination can be performed. The actual number of users is taken as the true value, and the signal domain feature sequence is taken as the input data of the user quantity perception model to train the model and verify the performance of the user quantity perception model. Among them, the user quantity perception model can be trained by the gradient descent method until the user quantity perception model converges.

[0070] When determining the user position corresponding to the device environment based on the wireless reflection signal, the signal reception strength and channel state information can be extracted from each received wireless reflection signal; for each wireless reflection signal, the reference position corresponding to the signal reception strength and channel state information of the wireless reflection signal is identified in the user signal fingerprint library corresponding to the Internet of Things device, and the reference position is determined as the user position in the device environment; wherein the user signal fingerprint library corresponding to the Internet of Things device pre-records: the reference positions corresponding to multiple reference users in the device environment, and the reference signal reception strength and reference channel state information extracted from the wireless reflection signal returned by each reference user.

[0071] The user information fingerprint library is a pre-constructed database that can record the wireless signal characteristics corresponding to each reference position. The wireless signal characteristics include at least: signal reception strength and channel state information. Among them, the user signal fingerprint library corresponding to the IoT device pre-records: the reference positions corresponding to multiple reference users in the device environment and the reference signal reception strength and reference channel state information extracted from the wireless reflection signal returned by each reference user. Further, for each IoT device, a coordinate system corresponding to the device environment can be set, and each coordinate position in the coordinate system is a reference position. The reference user can walk or rest in the device environment, and then after transmitting a wireless transmission signal to the device environment, the reference signal reception strength and reference channel state information corresponding to different coordinate positions can be collected. The reference signal reception strength and reference channel state information corresponding to different coordinate positions can be pre-processed respectively, and the reference signal reception strength and reference channel state information after pre-processing are stored corresponding to the corresponding coordinate position to form a user signal fingerprint library.

[0072] Furthermore, in each of the received wireless reflection signals, the signal reception strength and the channel state information are extracted, and for each wireless reflection signal, the signal reception strength and the channel state information corresponding to the wireless reflection signal can be preprocessed respectively; in the user signal fingerprint library, the reference position corresponding to the signal reception strength and the channel state information after preprocessing is identified, and the reference position is determined as the user position in the device environment.

[0073] Further, identifying the reference position corresponding to the signal reception strength and channel state information of the wireless reflected signal includes: splicing the signal reception strength and channel state information of the wireless reflected signal into a spliced ​​signal information; wherein, in the user signal fingerprint library, the reference signal reception strength and reference channel state information corresponding to the same reference position are a spliced ​​reference signal information; in the user signal fingerprint library, determining a preset number of reference signal information that are closest to the spliced ​​signal information corresponding to the wireless reflected signal; in the case of determining one reference signal information, determining the reference position corresponding to the determined reference signal information as the user position; in the case of determining multiple reference signal information, determining the reference position corresponding to the reference signal information that is closest to the spliced ​​signal information as the user position; or, among the reference positions corresponding to multiple reference signal information, determining the reference position located at the center as the user position.

[0074] Furthermore, the signal reception strength and channel state information of the wireless reflected signal are spliced ​​into a spliced ​​signal information, which may be formed by composing the signal reception strength and channel state information of the wireless reflected signal into a sequence in a preset order to form a spliced ​​signal information.

[0075] Furthermore, the reference signal reception strength and reference channel state information corresponding to the same reference position are a concatenated reference signal information, and the reference signal reception strength and the reference channel state information may be combined into a sequence in the same order to form the reference signal information.

[0076] Furthermore, a preset nearest neighbor algorithm (K-Nearest Neighbor, referred to as KNN) can be used to determine a preset number of reference signal information that is closest to the spliced ​​signal information corresponding to the wireless reflection signal in the user signal fingerprint library. The preset number can be an empirical value or a value obtained through experiments. Distance measurement methods include but are not limited to: Euclidean distance, Manhattan distance or cosine similarity.

[0077] Further, among the reference positions corresponding to the multiple reference signal information, the reference position located at the center position is determined as the user position. The multiple reference positions can be set in a circle that can accommodate the multiple reference positions and has the smallest area, and the reference position closest to the center of the circle is used as the reference position of the center position, and the parameter position of the center position is determined as the user position.

[0078] When determining the user state corresponding to the device environment based on the wireless reflection signal, channel state information can be extracted from each of the wireless reflection signals and dynamic state information can be extracted from the channel state information; using a pre-trained motion state perception model, the user state in the device environment can be determined based on the dynamic state information corresponding to each of the multiple wireless reflection signals.

[0079] Dynamic state information refers to the signal segment corresponding to the human body movement in the channel state information.

[0080] The user status is used to reflect the user's behavior status in the device environment. The user status includes but is not limited to: movement speed and activity type. Movement speed is used to reflect the speed of the user's movement. Activity type is used to reflect the type of user activity, such as resting, running, jumping, etc.

[0081] The motion state perception model is trained to determine the user state in the device environment according to the channel state information respectively corresponding to the plurality of wireless reflection signals.

[0082] Furthermore, in each of the wireless reflection signals, channel state information is extracted, the channel state information is preprocessed, and dynamic state information is extracted from the preprocessed channel state information, thereby obtaining the dynamic state information corresponding to the wireless reflection signal.

[0083] Furthermore, in order to improve the accuracy of the control method of the IoT device, after the channel state information is preprocessed, the channel state information can be processed by signal segmentation to retain the signal segments corresponding to the action. Furthermore, the device environment includes not only static objects and human bodies, but also human bodies with and without action, and the frequency ranges corresponding to action and no action are also different. Since the wireless reflection signal may contain a large number of data points, but not all parts contain useful action information, the channel state information can be divided into smaller segments. When the human body moves, adjacent subcarriers in the channel state information may become correlated. This correlation may be due to the change in signal characteristics caused by the action, resulting in a certain correlation between different frequency components. The channel state information can be decomposed into sub-signals of different frequency bands through wavelet transform, including low-frequency and high-frequency parts, and the signal of the high-frequency part can be used as a signal segment with action.

[0084] Furthermore, when training the motion state perception model, wireless reflection signals under different user states can be collected in advance, channel state information can be extracted from different wireless reflection signals respectively, and dynamic state information can be extracted from the channel state information; the user state is taken as the true value, and the dynamic state information corresponding to the user state is taken as the input information of the motion state perception model, and the performance of the user state predicted by the motion state perception model is trained and verified. Among them, the motion state perception model can be trained by the gradient descent method until the user quantity perception model converges.

[0085] Furthermore, the motion state perception model includes a speed perception sub-model and an activity perception sub-model. The speed perception sub-model and the activity perception sub-model can be jointly trained. After joint training, the speed perception sub-model can determine the movement speed of the human body by quantitatively analyzing the changes in the time series of the channel state information. After joint training, the activity perception sub-model can perform spectral analysis on the channel state information and decompose it into low-frequency components and high-frequency components. Among them, the low-frequency component usually reflects slow posture changes, while the high-frequency component reflects fast movements. The types of activity perception sub-models include but are not limited to: Hidden Markov Model (HMM).

[0086] For example: the carrier is in the 5.825GHz frequency band, and according to the relationship between the frequency and wavelength of electromagnetic waves, the wavelength is about 5.15 cm. If a high-energy frequency band is observed near the frequency of 35-40Hz in the spectrum of walking activities, the Doppler effect formula can be used to infer the speed, and the movement speed v = Δf*λ / 2. If the frequency is 35Hz, then v = 35*0.0515 / 2 = 0.901m / s; if it is 40Hz, then v = 40*0.0515 / 2 = 1.03m / s; through the above calculation, the walking speed range represented by the 35-40Hz frequency component in the walking activity spectrum is approximately 0.9-1.03m / s.

[0087] After transmitting the wireless transmission signal to the device environment, the received multipath wireless reflection signal is divided into a static component and a dynamic component. The static component mainly includes the direct signal and the reflection signal caused by fixed obstacles such as walls. These signals are relatively stable when the device environment remains unchanged. However, the dynamic component is the reflection signal caused by human body movement, which changes with the movement of the human body. One embodiment of the present invention is to determine the number of users and user positions in the device environment through the wireless reflection signal corresponding to the human body, and identify the movement speed and activity type of the human body based on the signal segment when the human body moves.

[0088] After determining the user characteristic information (number of users, user location and user status), the operating parameters of the Internet of Things device can be adjusted according to the user characteristic information.

[0089] The operating parameters include, but are not limited to, operating power parameters, operating direction parameters, and operating mode parameters of the IoT device, wherein the types of the operating power parameters, operating direction parameters, and operating mode parameters may be determined according to the type of the IoT device.

[0090] Working power parameters include but are not limited to: compressor power and fan power.

[0091] The working direction parameter is used to adjust the working area of ​​the IoT device. For example: the air outlet direction parameter of a humidifying air conditioner.

[0092] Working mode parameters include but are not limited to: normal mode, silent mode, and sleep mode.

[0093] Specifically, based on the user characteristic information determined last time, identify the changed characteristic parameters in the user characteristic information determined this time; when the changed characteristic parameter is the number of users, adjust the working power parameter of the Internet of Things device according to the number of users; when the changed characteristic parameter is the user position, adjust the working direction parameter of the Internet of Things device according to the user position; when the changed characteristic parameter is the user status, adjust the working power parameter and / or working mode parameter of the Internet of Things device according to the user status.

[0094] Furthermore, adjusting the working power parameters of the IoT device according to the number of users includes: adjusting the working power parameters of the IoT device to the working power parameters corresponding to the number of users. Among them, multiple user number ranges are pre-set and working power parameters are set corresponding to each user number range. After determining the number of users in the device environment, the IoT device is adjusted to the working power parameters corresponding to the user number range to which the number of users belongs. For example: when the number of users increases, the wind speed of the humidifying air conditioner can be increased to speed up the convection and exchange of air, thereby improving the overall comfort; when it is detected that there is no one in the room, the humidifying air conditioner can be shut down or the operating power can be reduced to reduce energy consumption.

[0095] Furthermore, adjusting the working direction parameters of the IoT device according to the user position includes: adjusting the working direction of the IoT device to the direction facing the user position by adjusting the working direction parameters of the IoT device. When there is only one user position, the working direction of the IoT device is adjusted to the direction facing the user position; when there are multiple user positions, determine whether the coverage range of the working direction of the IoT device can cover all user positions. If it can be fully covered, the working direction of the IoT device is adjusted to the direction facing (i.e., covering) all user positions; if it cannot be fully covered, the working direction of the IoT device is adjusted to the direction facing the most user positions. For example: when there are many people gathered in a corner of the room, the humidifying air conditioner can change the air outlet direction and adjust the local temperature and humidity to prioritize ensuring that the temperature and humidity in this area are within a comfortable range.

[0096] Furthermore, the user status includes: user movement speed and user activity type; adjusting the working power parameters and / or working mode parameters of the Internet of Things device according to the user status includes: when the changed user operating status is the user movement speed, adjusting the working power parameters of the Internet of Things device according to the user movement speed; when the changed user operating status is the user activity type, adjusting the working mode parameters of the Internet of Things device according to the user activity type.

[0097] Among them, multiple motion speed ranges can be pre-set and working power parameters can be set corresponding to each motion speed range; the Internet of Things device is adjusted to the working power parameter corresponding to the motion speed range to which the user's motion speed belongs; among them, if the working power parameter corresponding to the number of users and the working power parameter corresponding to the user's motion speed are different, the priority of the number of users and the priority of the user's motion speed are compared, and the Internet of Things device is adjusted to the working power parameter corresponding to the characteristic parameter with the highest priority.

[0098] Among them, the working mode parameters are set for each user activity type, and the IoT devices are adjusted to the working mode parameters corresponding to the user activity type. For example: when a person is resting, the air conditioner is adjusted to use silent mode or sleep mode to reduce noise and wind speed, maintain the temperature within a more suitable range, and help users sleep and rest better; when a person is active, the air conditioner is controlled to increase the air speed to maintain air circulation.

[0099] An embodiment of the present invention also provides a control device for an Internet of Things device. Figure 3 , which is a structural diagram of a control device for an Internet of Things device according to an embodiment of the present invention.

[0100] The control device of the Internet of Things device includes: a transceiver unit 310, a determination unit 320 and a control unit 330.

[0101] The transceiver unit 310 is used to send a wireless transmission signal to a device environment where a preset IoT device is located, and receive a wireless reflection signal returned from the device environment.

[0102] The determination unit 320 is configured to determine user characteristic information corresponding to the device environment according to the wireless reflected signal.

[0103] The control unit 330 is used to adjust the operating parameters of the Internet of Things device according to the user characteristic information.

[0104] The functions of the device described in one embodiment of the present invention have been described in the above method embodiment, so for details not covered in the description of this embodiment, please refer to the relevant descriptions in the above embodiments, and no further description will be given here.

[0105] An embodiment of the present invention further provides a control device for an Internet of Things device, such as Figure 4 , which is a structural block diagram of a control device for an Internet of Things device according to an embodiment of the present invention.

[0106] The control device of the Internet of Things device includes: a processor 410, a communication interface 420, a memory 430 and a communication bus 440. The processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0107] The memory 430 is used to store computer programs.

[0108] In one embodiment of the present invention, the processor 410, when used to execute the program stored in the memory 430, implements the control method of the Internet of Things device provided by any of the aforementioned method embodiments, including: sending a wireless transmission signal to the device environment where the preset Internet of Things device is located, and receiving a wireless reflection signal returned from the device environment; determining user feature information corresponding to the device environment based on the wireless reflection signal; and adjusting the operating parameters of the Internet of Things device based on the user feature information.

[0109] Among them, the characteristic parameters in the user characteristic information include: the number of users; the determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: for each of the wireless reflection signals, extracting the signal reception strength and channel state information from the wireless reflection signal; extracting signal domain feature sequences from the signal reception strength and the channel state information respectively; and using a pre-trained user quantity perception model to determine the number of users in the device environment according to the signal domain feature sequences corresponding to multiple wireless reflection signals.

[0110] Among them, the characteristic parameters in the user characteristic information include: user location; determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: extracting the signal reception strength and channel state information in each of the wireless reflection signals; for each of the wireless reflection signals, identifying the reference position corresponding to the signal reception strength and channel state information of the wireless reflection signal in the user signal fingerprint library corresponding to the Internet of Things device, and determining the reference position as the user position in the device environment; wherein the user signal fingerprint library corresponding to the Internet of Things device is used to pre-record: wireless signal characteristics corresponding to multiple reference positions in the device environment; the wireless signal characteristics include at least: reference signal reception strength and reference channel state information.

[0111] Among them, in the user signal fingerprint library corresponding to the Internet of Things device, identifying the reference position corresponding to the signal reception strength and channel state information of the wireless reflected signal, and determining the reference position as the user position in the device environment, includes: splicing the signal reception strength and channel state information of the wireless reflected signal into a spliced ​​signal information; wherein, in the user signal fingerprint library, the reference signal reception strength and reference channel state information corresponding to the same reference position are a spliced ​​reference signal information; in the user signal fingerprint library, determining a preset number of reference signal information that is closest to the spliced ​​signal information corresponding to the wireless reflected signal; in the case of determining one reference signal information, determining the reference position corresponding to the determined reference signal information as the user position; in the case of determining multiple reference signal information, determining the reference position corresponding to the reference signal information that is closest to the spliced ​​signal information as the user position; or, in the reference positions corresponding to multiple reference signal information, determining the reference position located at the center as the user position.

[0112] Among them, the characteristic parameters in the user characteristic information include: user status; determining the user characteristic information corresponding to the device environment based on the wireless reflection signal includes: extracting channel state information in each of the wireless reflection signals and extracting dynamic state information from the channel state information; using a pre-trained motion state perception model, determining the user status in the device environment according to the dynamic state information corresponding to multiple wireless reflection signals.

[0113] Among them, adjusting the operating parameters of the Internet of Things device according to the user characteristic information includes: identifying the changed characteristic parameters in the user characteristic information determined this time according to the user characteristic information determined last time; when the changed characteristic parameter is the number of users, adjusting the working power parameter of the Internet of Things device according to the number of users; when the changed characteristic parameter is the user position, adjusting the working direction parameter of the Internet of Things device according to the user position; when the changed characteristic parameter is the user status, adjusting the working power parameter and / or working mode parameter of the Internet of Things device according to the user status.

[0114] An embodiment of the present invention provides an air conditioner, which is specifically an air conditioner with a humidification function, that is, a humidifying air conditioner. The air conditioner includes a control device of the above-mentioned Internet of Things device, and can automatically sense the number of users, user locations and user status in the environment through wireless reflection signals, and automatically adjust the temperature and wind speed of the humidifying air conditioner to achieve energy saving and comfort effects.

[0115] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the control method of the Internet of Things device provided in any of the above method embodiments are implemented. Since the control method of the Internet of Things device has been described in detail above, for the details not detailed in the description of this embodiment, please refer to the relevant description in the above embodiments, and no further description will be given here.

[0116] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the control method of an Internet of Things device provided in any of the aforementioned method embodiments. Since the control method of an Internet of Things device has been described in detail above, for any details not fully described in the description of this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further description will be given here.

[0117] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on a computer-readable medium or transmitted via a computer-readable medium. Other examples and implementations are within the scope and spirit of the present invention and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these. In addition, each functional unit may be integrated into a processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components of the control device may or may not be physical units, that is, they may be located in one place or distributed in multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0121] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. A control method for an Internet of Things device, characterized in that: include: Sending a wireless transmission signal to a device environment where a preset IoT device is located, and receiving a wireless reflection signal returned from the device environment; Determining user characteristic information corresponding to the device environment according to the wireless reflected signal; Adjust the operating parameters of the Internet of Things device according to the user characteristic information.

2. The method according to claim 1, characterized in that: The characteristic parameters in the user characteristic information include: the number of users; The determining, according to the wireless reflected signal, user characteristic information corresponding to the device environment includes: For each of the wireless reflected signals, extracting a signal reception strength and channel state information from the wireless reflected signal; and respectively extracting a signal domain feature sequence from the signal reception strength and the channel state information; The pre-trained user quantity perception model is used to determine the number of users in the device environment according to signal domain feature sequences respectively corresponding to a plurality of the wireless reflection signals.

3. The method according to claim 1, characterized in that The characteristic parameters in the user characteristic information include: user location; The determining, according to the wireless reflected signal, user characteristic information corresponding to the device environment includes: Extracting signal reception strength and channel state information from each of the wireless reflected signals; For each of the wireless reflection signals, identifying a reference position corresponding to the signal reception strength and channel state information of the wireless reflection signal in a user signal fingerprint library corresponding to the IoT device, and determining the reference position as a user position in the device environment; Among them, the user signal fingerprint library corresponding to the Internet of Things device is used to pre-record: wireless signal characteristics corresponding to multiple reference positions in the device environment; the wireless signal characteristics at least include: reference signal reception strength and reference channel state information.

4. The method according to claim 3, characterized in that The step of identifying a reference position corresponding to the signal reception strength and channel state information of the wireless reflected signal in a user signal fingerprint library corresponding to the IoT device, and determining the reference position as a user position in the device environment, comprises: Splicing the signal reception strength and channel state information of the wireless reflected signal into a spliced ​​signal information; wherein, in the user signal fingerprint library, the reference signal reception strength and reference channel state information corresponding to the same reference position are a spliced ​​reference signal information; In the user signal fingerprint library, determining a preset number of reference signal information that are closest to the spliced ​​signal information corresponding to the wireless reflection signal; When a reference signal information is determined, a reference position corresponding to the determined reference signal information is determined as the user position; When multiple reference signal information is determined, the reference position corresponding to the reference signal information closest to the spliced ​​signal information is determined as the user position; or, among the reference positions corresponding to multiple reference signal information, the reference position located at the center is determined as the user position.

5. The method according to claim 1, characterized in that The characteristic parameters in the user characteristic information include: user status; The determining, according to the wireless reflected signal, user characteristic information corresponding to the device environment includes: Extracting channel state information from each of the wireless reflected signals and extracting dynamic state information from the channel state information; The pre-trained motion state perception model is used to determine the user state in the device environment according to the dynamic state information respectively corresponding to the plurality of wireless reflection signals.

6. The method according to claim 1, characterized in that The adjusting the operating parameters of the IoT device according to the user characteristic information includes: According to the user characteristic information determined last time, identifying the characteristic parameters that have changed in the user characteristic information determined this time; When the characteristic parameter that has changed is the number of users, adjusting the operating power parameter of the Internet of Things device according to the number of users; When the characteristic parameter that has changed is the user's location, adjusting the working direction parameter of the Internet of Things device according to the user's location; When the characteristic parameter that changes is a user state, the operating power parameter and / or the operating mode parameter of the Internet of Things device is adjusted according to the user state.

7. A control device for an Internet of Things device, characterized in that: include: A transceiver unit, used to send a wireless transmission signal to a device environment where a preset IoT device is located, and receive a wireless reflection signal returned from the device environment; a determining unit, configured to determine user characteristic information corresponding to the device environment according to the wireless reflected signal; A control unit is used to adjust the operating parameters of the Internet of Things device according to the user characteristic information.

8. A control device for an Internet of Things device, characterized in that: include: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor coupled to the at least one bus; At least one memory connected to the at least one bus, wherein the processor is configured to: execute a control program of the Internet of Things device stored in the memory to implement the control method of the Internet of Things device according to any one of claims 1-6.

9. An air conditioner, characterized in that: The air conditioner includes the control device of the Internet of Things device according to claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed to implement the control method of the Internet of Things device according to any one of claims 1 to 6.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of any one of the methods of claims 1 to 6 when executed by a processor.

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