Equipment control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an equipment control method and device, electronic equipment and a storage medium, and relates to the technical field of equipment control, and the method comprises the steps: obtaining a signal parameter corresponding to a communication signal between communication equipment and intelligent equipment; feature extraction is carried out according to the signal parameters, and user activity features used for representing human motion are obtained; predicting the motion of the user according to the activity characteristics of the user to obtain a motion prediction result of the user; acquiring a device limitation parameter corresponding to the intelligent device, generating an instruction according to the motion prediction result and the device limitation parameter, and acquiring a following instruction for the intelligent device; the following instruction is sent to the intelligent device, the following instruction is used for indicating the intelligent device to execute the corresponding following operation, and the corresponding user activity characteristics are extracted from the communication parameters, so that the stability of data processing can be ensured in a complex environment, the device can be controlled based on stable and reliable data, and the user experience is improved. And the stability and reliability of equipment control are ensured.

Description

Technical Field

[0001] The present invention relates to the field of equipment control technology, and in particular to a equipment control method, an equipment control device, an electronic device and a computer-readable storage medium. Background Art

[0002] With the rapid development of smart devices and IoT technology, more and more application scenarios require smart devices to respond to users in real time, such as home robots, surveillance cameras, and medical care equipment. However, most smart device following systems rely on visual, infrared, or ultrasonic sensors for detection, which are easily affected by occlusion, light changes, etc. in complex environments, which reduces the stability and reliability of device control. If more sensors are added to smart devices to improve detection accuracy, it will bring additional costs. Summary of the invention

[0003] The embodiments of the present invention provide a device control method, an apparatus, an electronic device and a computer-readable storage medium to solve or partially solve the problems of low reliability, poor stability and high cost in device control.

[0004] An embodiment of the present invention discloses a device control method, which is applied to a control device, wherein the control device is communicatively connected with at least two communication devices and at least one intelligent device, and the method includes:

[0005] Acquire a signal parameter corresponding to a communication signal between the communication device and the smart device;

[0006] Perform feature extraction based on the signal parameters to obtain user activity features for characterizing human body motion;

[0007] Predicting the user's movement according to the user activity characteristics to obtain the user's movement prediction result;

[0008] Acquire device restriction parameters corresponding to the smart device, and generate instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device;

[0009] The follow-up instruction is sent to the smart device, where the follow-up instruction is used to instruct the smart device to perform a corresponding follow-up operation.

[0010] In some feasible implementations, extracting features according to the signal parameters to obtain user activity features for characterizing human motion includes:

[0011] Extracting distance features and motion features from the signal parameters;

[0012] The distance feature is used to characterize the relative distance between the user and the smart device, and the motion feature is used to characterize the relative position and motion direction of the user.

[0013] In some feasible implementations, extracting the signal strength and channel status information from the signal parameters includes:

[0014] Extracting signal strength and channel state information corresponding to the communication signal from the signal parameters, wherein the channel state information includes at least amplitudes and phases of multiple subcarriers;

[0015] Normalizing the signal intensity to obtain a corresponding normalized value;

[0016] Acquire the transmission power corresponding to the communication device, and use the transmission power and the normalized value to perform calculation to obtain the distance feature corresponding to the signal strength;

[0017] Calculating a phase difference between phases at adjacent time points, and obtaining a position mapping relationship for the phase difference, wherein the position mapping relationship is a mapping relationship between different phase differences and a relative position of a user;

[0018] Determine the relative position of the user by using the phase difference and the position mapping relationship;

[0019] The amplitude difference between the amplitudes at adjacent time points is calculated, the change trend of the amplitude difference and the phase difference is determined, and the movement direction of the user is determined based on the change trend.

[0020] In some feasible implementations, predicting the user's movement according to the user activity feature to obtain the user's movement prediction result includes:

[0021] The movement of the user is predicted using the distance feature, the relative position, and the movement direction to obtain a movement prediction result of the user.

[0022] In some feasible implementations, the motion prediction result includes at least a motion state and a motion trajectory, and the using the distance feature, the relative position, and the motion direction to predict the user's motion to obtain the user's motion prediction result includes:

[0023] The distance feature, the relative position and the movement direction are input into a movement prediction model to predict the movement of the user, thereby obtaining the movement state and movement trajectory of the user.

[0024] In some feasible implementations, the generating of instructions according to the motion prediction result and the device limitation parameter to obtain a follow-up instruction for the smart device includes:

[0025] Calculating following information of the smart device according to the motion trajectory and the relative position, wherein the following information at least includes a following position, a first turning angle, and a following direction;

[0026] Calculating a first following distance of the smart device according to the following position and the following direction;

[0027] adjusting the first steering angle to a second steering angle matching the device limitation parameter, and adjusting the first following distance to a second following distance matching the device limitation parameter;

[0028] A following instruction corresponding to the second steering angle and the second following distance is generated.

[0029] In some feasible implementations, the device limitation parameters include at least a maximum steering angle of the smart device and a distance between the smart device and the user, and adjusting the first steering angle to a second steering angle matching the device limitation parameters, and adjusting the first moving distance to a second moving distance matching the device limitation parameters, includes:

[0030] adjusting the first steering angle to a second steering angle matching the maximum steering angle;

[0031] The first following distance is adjusted to a second following distance matching the holding distance.

[0032] The embodiment of the present invention further discloses a device control apparatus, which is applied to a control device, wherein the control device is communicatively connected with at least two communication devices and at least one intelligent device, and the apparatus comprises:

[0033] A signal acquisition module, used to acquire signal parameters corresponding to the communication signal between the communication device and the smart device;

[0034] A feature extraction module, used to extract features according to the signal parameters to obtain user activity features for characterizing human body movements;

[0035] A prediction module, used to predict the user's movement according to the user's activity characteristics, and obtain the user's movement prediction result;

[0036] An instruction generation module, used to obtain device restriction parameters corresponding to the smart device, and generate instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device;

[0037] The control module is used to send the follow-up instruction to the smart device, and the follow-up instruction is used to instruct the smart device to perform a corresponding follow-up operation.

[0038] In some feasible implementations, the feature extraction module is specifically used to:

[0039] Extracting distance features and motion features from the signal parameters;

[0040] The distance feature is used to characterize the relative distance between the user and the smart device, and the motion feature is used to characterize the relative position and motion direction of the user.

[0041] In some feasible implementations, the feature extraction module is specifically used to:

[0042] Extracting signal strength and channel state information corresponding to the communication signal from the signal parameters, wherein the channel state information includes at least amplitudes and phases of multiple subcarriers;

[0043] Normalizing the signal intensity to obtain a corresponding normalized value;

[0044] Acquire the transmission power corresponding to the communication device, and use the transmission power and the normalized value to perform calculation to obtain the distance feature corresponding to the signal strength;

[0045] Calculating a phase difference between phases at adjacent time points, and obtaining a position mapping relationship for the phase difference, wherein the position mapping relationship is a mapping relationship between different phase differences and a relative position of a user;

[0046] Determine the relative position of the user by using the phase difference and the position mapping relationship;

[0047] The amplitude difference between the amplitudes at adjacent time points is calculated, the change trend of the amplitude difference and the phase difference is determined, and the movement direction of the user is determined based on the change trend.

[0048] In some feasible implementations, the prediction module is specifically used to:

[0049] The movement of the user is predicted using the distance feature, the relative position, and the movement direction to obtain a movement prediction result of the user.

[0050] In some feasible implementations, the motion prediction result includes at least a motion state and a motion trajectory, and the using the distance feature, the relative position, and the motion direction to predict the user's motion to obtain the user's motion prediction result includes:

[0051] The distance feature, the relative position and the movement direction are input into a movement prediction model to predict the movement of the user, thereby obtaining the movement state and movement trajectory of the user.

[0052] In some feasible implementations, the instruction generation module is specifically used to:

[0053] Calculating following information of the smart device according to the motion trajectory and the relative position, wherein the following information at least includes a following position, a first turning angle, and a following direction;

[0054] Calculating a first following distance of the smart device according to the following position and the following direction;

[0055] adjusting the first steering angle to a second steering angle matching the device limitation parameter, and adjusting the first following distance to a second following distance matching the device limitation parameter;

[0056] A following instruction corresponding to the second steering angle and the second following distance is generated.

[0057] In some feasible implementations, the device limitation parameters include at least a maximum steering angle of the smart device and a distance between the smart device and the user, and the instruction generation module is specifically used to:

[0058] adjusting the first steering angle to a second steering angle matching the maximum steering angle;

[0059] The first following distance is adjusted to a second following distance matching the holding distance.

[0060] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0061] The memory is used to store computer programs;

[0062] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0063] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0064] The embodiments of the present invention include the following advantages:

[0065] In an embodiment of the present invention, it can be applied to a control device, and the control device can be connected to at least two communication devices and at least one smart device. In the process of controlling the smart device to follow the user, the control device can obtain the signal parameters corresponding to the communication signal between the communication device and the smart device, and then perform feature extraction based on the signal parameters to obtain user activity features for characterizing human body movements, and predict the user's movement based on the user activity features to obtain the user's movement prediction results, and then obtain the device restriction parameters corresponding to the smart device, and generate instructions based on the movement prediction results and the device limitation parameters to obtain the following instructions for the smart device, and send the following instructions to the smart device, the following instructions are used to instruct the smart device to perform the corresponding following operation, and by extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in a complex environment, so that the device can be controlled based on stable and reliable data, and the stability and reliability of the device control can be guaranteed, and the control device can generate corresponding following instructions based on real-time data, which ensures the real-time nature of the smart device following the human body movement, and at the same time, based on the existing communication equipment, there is no need to add additional equipment, which can effectively reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flowchart of a method for controlling a device provided in an embodiment of the present invention;

[0067] Figure 2 is a schematic diagram of the structure of device communication provided in an embodiment of the present invention;

[0068] Figure 3 is a structural block diagram of a control device of a device provided in an embodiment of the present invention;

[0069] Figure 4 It is a block diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0071] As an example, most smart device following systems rely on visual, infrared or ultrasonic sensors, which are easily affected by factors such as occlusion and light changes in complex environments, and have problems with insufficient stability and reliability.

[0072] In this regard, in the present invention, it is applied to a control device, and the control device can be connected to at least two communication devices and at least one smart device for communication. In the process of controlling the smart device to follow the user, the control device can obtain the signal parameters corresponding to the communication signal between the communication device and the smart device, and then perform feature extraction based on the signal parameters to obtain user activity features for characterizing human body movements, and predict the user's movements based on the user activity features to obtain the user's movement prediction results, and then obtain the device restriction parameters corresponding to the smart device, and generate instructions based on the movement prediction results and the device limitation parameters to obtain the following instructions for the smart device, and send the following instructions to the smart device. The following instructions are used to instruct the smart device to perform the corresponding following operation. By extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in a complex environment, so that the device can be controlled based on stable and reliable data, and the stability and reliability of the device control can be guaranteed. The control device can generate corresponding following instructions based on real-time data, which ensures the real-time nature of the smart device following the human body's movements. At the same time, based on the existing communication equipment, there is no need to add additional equipment, which can effectively reduce costs.

[0073] Reference Figure 1 , shows a flowchart of a control method for a device provided in an embodiment of the present invention, which is applied to a control device, wherein the control device is communicatively connected with at least two communication devices and at least one intelligent device, and specifically may include the following steps:

[0074] Step 101, obtaining a signal parameter corresponding to a communication signal between the communication device and the smart device;

[0075] In an embodiment of the present invention, the control device may be a server or a user terminal, etc. The control device may be connected to at least two communication devices and at least one intelligent device for communication. The communication device may be a device that can connect to a wireless network and use wireless technology for data communication, and the intelligent device may be a specific execution device. In a device following scenario, the intelligent device transmits corresponding device data to the control device by accessing the network provided by the communication device, so that the control device controls the intelligent device based on the received device data. For example, the communication device may be a WiFi device, the intelligent device may be a surveillance camera, and the control device may be a user terminal. The surveillance camera may transmit corresponding surveillance data to the user terminal based on the wireless network provided by the WiFi device, and the user terminal may control the surveillance camera based on the wireless network provided by the WiFi device, etc. The present invention does not limit this.

[0076] In one example, referring to Figure 2, showing a schematic diagram of the structure of device communication provided in an embodiment of the present invention, at least two communication devices and at least one intelligent device can be deployed in the target area. Among them, the communication device can send and receive corresponding wireless communication signals, and the intelligent device can also receive and send corresponding wireless communication signals. The intelligent device accesses the wireless network through the communication device and establishes a corresponding communication connection with the control device. Among them, the communication device can be a WiFi router with wireless communication function or an intelligent device with a WiFi module, etc.; the intelligent device can be a device equipped with a WiFi receiving module for receiving WiFi signals in the environment, and the intelligent device can also have the ability to perform corresponding physical actions, such as moving, rotating, etc., which is not limited by the present invention.

[0077] It should be noted that in order to ensure comprehensive signal coverage, the location of the communication equipment can be reasonably arranged, and the corresponding arrangement process can be as follows:

[0078] 1. Environmental analysis

[0079] Before deploying WiFi equipment, it is necessary to conduct a preliminary analysis of the target environment, such as the size and structure of the space: measure the area and height of the space, identify the shape of the room, walls, doors and windows, and other fixed obstacles (such as furniture, walls, etc.). Main activity areas: determine the main areas of user activities, which are usually the key areas that the tracking system needs to monitor.

[0080] 2. Selection of the number and type of WiFi devices

[0081] Determine the number and type of WiFi devices you need based on the size and shape of your space.

[0082] 3. Layout design principles

[0083] When making specific layouts, the following principles should be followed to avoid signal blind spots: ① Centralized layout: Try to place the WiFi device in the center of the active area to maximize the signal coverage area; ② High deployment: Install the device in a higher position (such as above the wall) to reduce the impact of signal obstacles and increase the signal propagation distance; ③ Avoid obstacles: Ensure that the WiFi device is kept at a certain distance from obstacles such as walls and furniture. Avoid placing the device in areas where the signal will be severely attenuated, such as near metal objects or in completely enclosed spaces; ④ Device spacing: If multiple WiFi devices are used, appropriate spacing should be maintained. It is generally recommended that the distance between devices be between 10 and 30 meters, adjusted according to the signal strength and coverage of the device; ⑤ Cross coverage: Ensure that the signal can be received at any location by appropriately overlapping the coverage areas of each WiFi device. The overlapping areas of signal coverage can improve signal stability and data transmission speed.

[0084] 4.Signal testing and optimization

[0085] After the initial deployment, conduct an on-site signal test to ensure the effectiveness of WiFi signal coverage:

[0086] Signal strength test: Use a WiFi analysis tool (such as WiFi Analyzer, NetSpot, etc.) to measure the signal strength and quality in different areas. Record the signal strength (RSSI value) and interference.

[0087] Blind spot identification: Determine the signal blind spot and analyze the possible causes (such as obstacles, improper equipment location, etc.).

[0088] Adjustment and optimization: Based on the test results, adjust the position and angle of the WiFi device to ensure that blind spots disappear and the signal coverage is as uniform as possible.

[0089] In an embodiment of the present invention, when the control device controls the smart device to follow the user's actions to perform a corresponding follow-up operation, the control device can obtain in real time the signal parameters corresponding to the communication signal between the communication device and the smart device. Wherein, the communication device is constantly sending and receiving corresponding communication signals, and correspondingly, the smart device is also constantly sending and receiving corresponding communication signals. Then, the control device can collect the signal parameters corresponding to these communication signals, so as to control the smart device to perform a corresponding device follow-up operation based on the signal parameters. Wherein, the signal parameters can be used to describe the signal state of the communication signal.

[0090] It should be noted that WiFi Sensing uses the propagation characteristics of WiFi signals to obtain environmental information and perform object detection. Specifically, it can analyze the reflection of objects (such as people) during the propagation of WiFi signals to identify their existence and activities. For example, by monitoring the changes in WiFi signals, it can determine whether a person is in the room and even identify their actions (such as walking, sitting down, etc.).

[0091] Step 102, performing feature extraction according to the signal parameters to obtain user activity features for characterizing human body motion;

[0092] In an embodiment of the present invention, after the control device collects the signal parameters corresponding to the communication signal between the communication device and the smart device, the signal parameters can be feature extracted to obtain user activity features for characterizing human body movements, so that the user's actions can be detected through the signal parameters, so as to generate corresponding control instructions based on the detection results, and control the smart device to follow the user's actions to perform corresponding follow-up operations.

[0093] In some feasible implementations, the control device may extract distance features and motion features from the signal parameters, wherein the distance feature is used to characterize the relative distance between the user and the smart device, and the motion feature is used to characterize the relative position and motion direction of the user.

[0094] In a specific implementation, the control device can extract the signal strength and channel status information corresponding to the communication signal from the signal parameters, where the channel status information includes at least the amplitude and phase of multiple subcarriers, and normalize the signal strength to obtain the corresponding normalized value, and then obtain the corresponding transmit power of the communication device, and use the transmit power and the normalized value for calculation to obtain the distance feature corresponding to the signal strength; secondly, the control device can calculate the phase difference between the phases at adjacent time points, and obtain the position mapping relationship for the phase difference, which is the mapping relationship between different phase differences and the relative position of the user, and use the phase difference and position mapping relationship to determine the relative position of the user, and then calculate the amplitude difference between the amplitudes at adjacent time points, determine the changing trends of the amplitude difference and the phase difference, and determine the user's movement direction based on the changing trends, so that the user's action can be detected through the signal parameters, so as to generate corresponding control instructions based on the detection results, and control the smart device to follow the user's action to perform corresponding follow-up operations.

[0095] Among them, the signal strength can represent the strength of the signal received by the communication device or smart device, which can usually be expressed in dBm. The distance characteristics corresponding to the user can be determined by the signal strength, that is, the distance between the user and the smart device can be determined; the channel state information can include the amplitude and phase of multiple subcarriers, and the amplitude and phase can be used to describe the attenuation and phase change of the signal during the transmission process. Based on the channel state information, the relative position and movement direction of the user in the environment can be determined. In addition, the transmission power can be the power when the communication device and the smart device transmit the communication signal, and the present invention is not limited to this.

[0096] For the distance feature, after the control device obtains the signal strength, since the signal strength is usually a negative value, the smaller the value, the weaker the signal, and the larger the value, the stronger the signal. In this regard, the control device can first normalize the signal strength to map its value to the range of 0-1. For example, the corresponding normalization formula can be used: Normalized_RSSI = (RSSI-min_RSSI) / (max_RSSI-min_RSSI), where min_RSSI and max_RSSI are the minimum and maximum values ​​of the signal strength, respectively. The signal strength is normalized to facilitate better data analysis.

[0097] After normalizing the signal strength, the control device can use the signal strength to analyze the distance between the user and the smart device by obtaining the transmission power and the corresponding path loss index, and then combining the transmission power, the normalized signal strength, and the path loss index to calculate and obtain the distance feature. The distance feature is used to characterize the relative distance between the user and the smart device.

[0098] Optionally, for the calculation process of the relative distance, the control device can perform calculations based on a preset calculation formula or model, for example, the distance calculation formula can be: distance = 10^((TxPower-RSSI) / (10*n)), where TxPower can be the transmission power and n can be the path loss index, so that the control device can identify the relative distance between the user and the smart device based on the signal strength, so as to better control the smart device to follow the user.

[0099] For the relative position and direction of movement, after acquiring the channel state information, the control device can deduce the relative position and direction of movement of the user by analyzing the amplitude and phase changes of the channel state information. For example, a subcarrier with a large phase change may correspond to a large change in the position of the user, and the direction of the phase change can be used to indicate the direction of movement of the user, etc. Specifically, the control device can first calculate the phase difference and amplitude difference between adjacent time points (or adjacent subcarriers), such as calculating the corresponding phase difference through the phase calculation formula: phase_diff = phase (t2) - phase (t1), and calculating the corresponding amplitude difference through the amplitude calculation formula: amplitude_diff = amplitude (t2) - amplitude (t1), etc., and the present invention is not limited to this.

[0100] After obtaining the phase difference and the amplitude difference, the control device can further obtain a position mapping relationship for the phase difference. The position mapping relationship is a mapping relationship between different phase differences and the user's relative position. The control device can use the phase difference and position mapping relationship to determine the user's relative position, as well as determine the changing trends of the amplitude difference and the phase difference, and determine the user's movement direction based on the changing trends.

[0101] In one example, assume that three Wi-Fi access points (AP1, AP2, AP3) in a room are located at three corners of the room. For user position mapping: at the center of the room, the phase difference between AP1 and AP2 is Δφ12_center, and the phase difference between AP1 and AP3 is Δφ13_center; at the top left of the room, the phase difference between AP1 and AP2 is Δφ12_left_top, and the phase difference between AP1 and AP3 is Δφ13_left_top. Specifically, when the phase difference between AP1 and AP2 is determined to be Δφ12_measured, and the phase difference between AP1 and AP3 is Δφ13_measured through a corresponding calculation method, the control device compares Δφ12_measured and Δφ13_measured with a predefined position mapping relationship. If Δφ12_measured≈Δφ12_center and Δφ13_measured≈Δφ13_center, the control device determines that the user is located at the center of the room; if Δφ12_measured≈Δφ12_left_top and Δφ13_measured≈Δφ13_left_top, the control device determines that the user is located at the upper left position of the room. Therefore, through the mapping relationship between the phase difference and the position, the control device can accurately determine the relative position of the user, thereby achieving precise positioning.

[0102] In addition, suppose that in a smart home environment, multiple Wi-Fi access points (APs) are arranged, and these access points are distributed in different rooms. Smart devices can receive Wi-Fi signals from these access points. In the process of the control device controlling the smart device to follow, the control device can further track the change trend of Δφ and ΔA over a period of time. Assume that in the past few seconds, Δφ changes from Δφ_left to Δφ_center, and ΔA changes from A_left to A_center. The corresponding change trend analysis is as follows: Phase difference change: If the phase difference is decreasing, it means that the user is moving towards the access point; Amplitude difference change: If the amplitude difference is increasing, it means that the user is closer to the access point. The control device can determine the user's movement direction by combining the changes in phase difference and amplitude difference. For example, if Δφ decreases and ΔA increases, the system determines that the user is approaching the access point. If Δφ increases and ΔA decreases, it means that the user is moving away from the access point. Therefore, the control device can not only determine the user's relative position by using the phase difference and amplitude difference, but also determine the user's movement direction by monitoring the change trends of these parameters.

[0103] Through the above process, the control device can extract user activity features for characterizing user activities from the signal parameters, such as relative distance, relative position, and movement direction, so as to further predict the user's activity status based on the user activity features.

[0104] Step 103, predicting the user's movement according to the user activity characteristics to obtain the user's movement prediction result;

[0105] In an embodiment of the present invention, the control device can predict the user's movement according to the user's activity characteristics, and obtain the user's movement prediction result within a certain period of time, so as to control the smart device to follow based on the movement prediction result. In a specific implementation, the control device can predict the user's movement using distance characteristics, relative position, and movement direction to obtain the user's movement prediction result.

[0106] Among them, the motion prediction result at least includes the motion state and the motion trajectory. The control device can predict the user's motion by inputting the distance feature, relative position and motion direction into the motion prediction model to obtain the user's motion state and motion trajectory.

[0107] It should be noted that the motion prediction model can be a model obtained by collecting user activity features and training a corresponding model based on the user activity features, and the user's activities are predicted through the motion prediction model. For example, a recurrent neural network or a long short-term memory network can be trained so that the model can capture the user's motion pattern in the time series and predict the user's future motion direction.

[0108] In one example, in a smart home environment, multiple Wi-Fi access points (APs) are installed to cover different areas of the house. Smart devices can receive Wi-Fi signals from these access points. Control devices (such as smart home control centers) predict the user's motion state and trajectory by analyzing the phase difference, amplitude difference, and other characteristics of the Wi-Fi signal. Specifically:

[0109] 1. Signal feature extraction

[0110] Distance characteristics: By measuring the signal strength (RSSI) and phase difference (Δφ), the control device can estimate the distance between the smart device and each Wi-Fi access point.

[0111] Relative location: By analyzing the phase and amplitude differences between different access points, the control device can determine the relative location of the user. For example, the user may be in the living room, bedroom, or kitchen, etc.

[0112] Movement direction: By monitoring the changing trends of phase difference and amplitude difference, the control device can determine the user's movement direction. For example, a decrease in phase difference and an increase in amplitude difference may mean that the user is moving towards a certain access point.

[0113] 2. Build a motion prediction model

[0114] Assume that the control device uses a machine learning-based motion prediction model that can input multiple features and output the user's motion state and trajectory. The input features of the model include:

[0115] Distance characteristics (the distance between the user and each access point); relative position (the room or area the user is currently in); movement direction (based on the changing trend of phase difference and amplitude difference).

[0116] The outputs of the model include:

[0117] Movement status: for example, still, walking, running, etc.

[0118] Movement trajectory: For example, the trajectory of a user moving from the living room to the kitchen.

[0119] 3. Motion prediction process

[0120] Assuming a user is walking from the living room to the kitchen, here are the steps to control how the device predicts motion:

[0121] 1) Initial state

[0122] User initial location: living room;

[0123] Distance characteristics: The distance between the user and AP1 is 3 meters, and the distance between the user and AP2 is 4 meters;

[0124] Relative location: living room;

[0125] Movement direction: The initial state is stationary.

[0126] 2) Monitoring signal changes

[0127] The control device continuously monitors the phase difference and amplitude difference of the Wi-Fi signal.

[0128] It is detected that the phase difference Δφ changes from Δφ_living_room to Δφ_hallway, and the amplitude difference ΔA changes from A_living_room to A_hallway.

[0129] 3) Feature Update

[0130] Distance characteristics: The distance between the user and AP1 becomes 2.5 meters, and the distance between the user and AP2 becomes 3.5 meters.

[0131] Relative positions: The user moves from the living room to the hallway.

[0132] Movement direction: The phase difference is decreasing and the amplitude difference is increasing, indicating that the user is moving toward the access point.

[0133] 4) Input model for prediction

[0134] The control device inputs the updated distance features, relative position and movement direction into the motion prediction model. The model output prediction results can be:

[0135] Movement status: walking

[0136] Movement trajectory: From the living room through the corridor, it is predicted to enter the kitchen

[0137] 5) Continuous forecasting

[0138] The control device continues to monitor signal changes, update input features, and continuously obtain prediction results from the model.

[0139] Eventually, the model predicts that the user will enter the kitchen and continue to move around in the kitchen.

[0140] 4. Specific example data

[0141] As shown in Table 1 below, the feature input and model prediction results are shown:

[0142]

[0143] Table 1

[0144] Step 104, obtaining device restriction parameters corresponding to the smart device, and generating instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device;

[0145] After determining the motion prediction result for the user through the above process, the control device can further obtain the device limitation parameters corresponding to the smart device, and then generate instructions according to the motion prediction result and the device limitation parameters to obtain follow-up instructions for the smart device, so as to control the smart device to follow the user's activities and perform corresponding follow-up operations based on the follow-up instructions. In the process of generating follow-up instructions, by combining the device limitation parameters of the device, the limitations of the device itself are fully considered, the rationality of the follow-up instructions is improved, and it is ensured that the smart device can execute the follow-up instructions accurately and effectively.

[0146] In some feasible implementations, the control device can calculate the following information of the smart device based on the motion trajectory and relative position, where the following information includes at least the following position, the first steering angle, and the following direction. Then, according to the following position and the following direction, the first following distance of the smart device is calculated. Then, the first steering angle is adjusted to a second steering angle that matches the device limitation parameters, and the first following distance is adjusted to a second following distance that matches the device limitation parameters, thereby generating a following instruction corresponding to the second steering angle and the second following distance. In the process of generating the following instruction, by combining the device limitation parameters of the device, the limitations of the device itself are fully considered, the rationality of the following instruction is improved, and it is ensured that the smart device can accurately and effectively execute the following instruction.

[0147] Among them, the device limitation parameters include at least the maximum steering angle of the smart device and the maintained distance between the smart device and the user, and the control device can adjust the first steering angle to a second steering angle matching the maximum steering angle, and adjust the first following distance to a second following distance matching the maintained distance.

[0148] In one example, in a smart home environment, a user moves between different rooms. The smart vacuum cleaner needs to follow the user's movement trajectory to clean. The control device (such as a smart home control center) obtains the user's movement trajectory and relative position through Wi-Fi Sensing technology, and generates corresponding following instructions to ensure that the smart vacuum cleaner can accurately and effectively perform the following task. The specific process can be as follows:

[0149] 1. Obtain user movement information

[0150] Movement trajectory: Assume that the user moves from the living room through the corridor and into the bedroom.

[0151] Relative position: The user's position in the living room, hallway, and bedroom.

[0152] 2. Calculate the following information of smart devices

[0153] Following position: The smart vacuum cleaner needs to follow the user's position. For example, if the user moves from the living room to the corridor, the smart vacuum cleaner needs to calculate its following position in the corridor.

[0154] First turning angle: the direction angle that the smart vacuum cleaner needs to adjust. For example, the smart vacuum cleaner needs to turn from the current position (living room) to the corridor.

[0155] Follow direction: The direction the robot vacuum cleaner needs to follow. For example, from the living room to the corridor.

[0156] 3. Calculate the first following distance

[0157] First following distance: Calculate the initial following distance between the smart vacuum cleaner and the user based on the user's motion trajectory and relative position. For example, the smart vacuum cleaner needs to stay 1 meter behind the user.

[0158] 4. Adjust follow information

[0159] Equipment limitation parameters:

[0160] Maximum steering angle: The maximum steering angle of the smart vacuum cleaner is 45 degrees.

[0161] Keep distance: The best distance between the smart vacuum cleaner and the user is 1.5 meters.

[0162] Adjust the steering angle:

[0163] Assume that the first turning angle is 60 degrees, which exceeds the maximum turning angle (45 degrees) of the intelligent vacuum cleaner.

[0164] The control device adjusts the first steering angle to 45 degrees to obtain a second steering angle.

[0165] Adjust the following distance:

[0166] Assume that the first following distance is 1 meter, which is smaller than the optimal keeping distance (1.5 meters) of the smart vacuum cleaner.

[0167] The control device adjusts the first following distance to 1.5 meters to obtain a second following distance.

[0168] 5. Generate follow-up instructions

[0169] Following instruction: The control device generates a following instruction, which includes a second steering angle and a second following distance, to ensure that the intelligent vacuum cleaner can accurately perform the following operation.

[0170] Instruction content:

[0171] Following position: corridor; second turning angle: 45 degrees; second following distance: 1.5 meters.

[0172] Through the above process, in the process of generating the follow-up instruction, by combining the device limitation parameters of the device, the limitations of the device itself are fully considered, the rationality of the follow-up instruction is improved, and it is ensured that the smart device can execute the follow-up instruction accurately and effectively.

[0173] Step 105: Send the follow instruction to the smart device, where the follow instruction is used to instruct the smart device to perform a corresponding follow operation.

[0174] In a specific implementation, after the control device generates a corresponding follow-up instruction, the follow-up instruction can be sent to the smart device so that the smart device performs the corresponding follow-up operation based on the follow-up instruction. By extracting the corresponding user activity characteristics from the communication parameters, the stability of data processing can be guaranteed in a complex environment, so that the device can be controlled based on stable and reliable data, ensuring the stability and reliability of device control, and the control device can generate corresponding follow-up instructions based on real-time data, ensuring the real-time nature of the smart device following human body movements. At the same time, based on existing communication equipment, there is no need to add additional equipment, which can effectively reduce costs.

[0175] It should be noted that the embodiments of the present invention include but are not limited to the above examples. It is understandable that those skilled in the art can also make settings according to actual needs under the guidance of the ideas of the embodiments of the present invention, and the present invention is not limited to this.

[0176] In an embodiment of the present invention, it can be applied to a control device, and the control device can be connected to at least two communication devices and at least one smart device. In the process of controlling the smart device to follow the user, the control device can obtain the signal parameters corresponding to the communication signal between the communication device and the smart device, and then perform feature extraction based on the signal parameters to obtain user activity features for characterizing human body movements, and predict the user's movement based on the user activity features to obtain the user's movement prediction results, and then obtain the device restriction parameters corresponding to the smart device, and generate instructions based on the movement prediction results and the device limitation parameters to obtain the following instructions for the smart device, and send the following instructions to the smart device, the following instructions are used to instruct the smart device to perform the corresponding following operation, and by extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in a complex environment, so that the device can be controlled based on stable and reliable data, and the stability and reliability of the device control can be guaranteed, and the control device can generate corresponding following instructions based on real-time data, which ensures the real-time nature of the smart device following the human body movement, and at the same time, based on the existing communication equipment, there is no need to add additional equipment, which can effectively reduce costs.

[0177] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples are used for exemplary description:

[0178] As an example, control devices, WiFi devices, and smart devices can form a corresponding control system, through which the follow-up operation of smart devices can be realized. Specifically, the implementation process of the control system may include environment deployment, WiFi signal acquisition, signal processing and feature extraction, motion trajectory prediction, device control and multi-device collaboration, etc. The specific process may be as follows:

[0179] Step 1: Environment deployment

[0180] WiFi device deployment: Deploy several WiFi devices in the target area (such as a home or office). These devices can be existing WiFi routers or smart devices with WiFi modules. To ensure full coverage of the area, the location of WiFi devices needs to be arranged reasonably to avoid signal blind spots. It is recommended to use 2 to 3 devices to ensure positioning accuracy.

[0181] Smart device installation: Smart devices (such as robots or cameras) need to be equipped with WiFi receiving modules to receive WiFi signals in the environment. These devices should also have the ability to perform physical actions, such as moving, rotating, etc.

[0182] Step 2: WiFi signal collection

[0183] Signal acquisition module: WiFi devices and smart devices continuously send and receive WiFi signals, and the system collects key parameters of these signals, including RSSI (received signal strength) and CSI (channel status information). These parameters will change as the human body moves in the environment.

[0184] Data synchronization and storage: The collected WiFi signal data needs to be synchronously transmitted to the central processing unit and stored for subsequent signal processing and analysis.

[0185] Step 3: Signal processing and feature extraction

[0186] Preprocessing: Preprocess the collected WiFi signals, including filtering, denoising and other steps to reduce the impact of external interference. You can use a low-pass filter to remove high-frequency noise, or use a Kalman filter to smooth the data.

[0187] Feature extraction: Extract features related to human motion from pre-processed WiFi signals. For example, changes in RSSI (intensity) can reflect the distance of human movement, while changes in CSI phase can reveal the specific position and direction of human movement. The positioning accuracy can be further improved by comprehensive analysis of signals from multiple WiFi devices.

[0188] Step 4: Motion trajectory prediction

[0189] Machine learning model: Use machine learning algorithms to analyze the extracted signal features and predict the movement trajectory of the human body. Commonly used models include RNN (recurrent neural network) and LSTM (long short-term memory network), which can capture movement patterns in time series and predict future movement directions.

[0190] Model training: Data collection is required when the system is deployed, and the model is trained using this data. Supervised learning can be performed using labeled motion datasets to optimize the accuracy of predictions.

[0191] Real-time prediction: During actual operation, the system uses the trained model to predict the human motion trajectory based on the real-time collected signal data, and outputs the current motion state and the predicted position of the next step.

[0192] Step 5: Smart device control and multi-device collaboration

[0193] Device control module: Based on the motion trajectory prediction results, the system generates corresponding control instructions and transmits them to the smart device. The device performs corresponding physical actions according to the instructions, such as the robot adjusting its position or the camera rotating its lens.

[0194] Action execution: To ensure smooth and timely response of device actions, control instructions need to consider the physical constraints of the device (such as maximum speed, turning radius, etc.) and perform appropriate motion planning.

[0195] Feedback mechanism: The system can correct the device action through sensor feedback (such as position information) to ensure the accuracy of the following process.

[0196] Multi-device collaboration: When a single device cannot cover the entire environment, collaboration between multiple WiFi devices becomes necessary. The system fuses the signal data of multiple devices together and improves positioning accuracy through algorithms such as weighted averaging or Bayesian fusion.

[0197] Device switching: Within a larger range, the system can automatically manage the switching of WiFi devices. When the signal of one device is weak, it switches to another device to ensure the continuity of tracking.

[0198] Step 6: System self-optimization and feedback

[0199] User feedback collection: The system collects performance data through user feedback (such as whether the device follows accurately and whether the response is timely). Feedback data can be used to update and optimize machine learning models to adapt to changing environments and user behaviors.

[0200] Self-learning mechanism: The system improves its performance in different environments by regularly retraining the model or adjusting parameters. This mechanism ensures that the system can adaptively adjust in long-term use to maintain high tracking accuracy.

[0201] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0202] Reference Figure 3 , shows a structural block diagram of a control device of a device provided in an embodiment of the present invention, which is applied to a control device, wherein the control device is communicatively connected with at least two communication devices and at least one intelligent device, and specifically may include the following modules:

[0203] The signal acquisition module 301 is used to acquire signal parameters corresponding to the communication signal between the communication device and the smart device;

[0204] A feature extraction module 302 is used to extract features according to the signal parameters to obtain user activity features for characterizing human body motion;

[0205] A prediction module 303, configured to predict the user's movement according to the user's activity characteristics and obtain a movement prediction result of the user;

[0206] An instruction generation module 304 is used to obtain device restriction parameters corresponding to the smart device, and generate instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device;

[0207] The control module 305 is used to send the follow instruction to the smart device, and the follow instruction is used to instruct the smart device to perform a corresponding follow operation.

[0208] In some feasible implementations, the feature extraction module 302 is specifically used for:

[0209] Extracting distance features and motion features from the signal parameters;

[0210] The distance feature is used to characterize the relative distance between the user and the smart device, and the motion feature is used to characterize the relative position and motion direction of the user.

[0211] In some feasible implementations, the feature extraction module 302 is specifically used for:

[0212] Extracting signal strength and channel state information corresponding to the communication signal from the signal parameters, wherein the channel state information includes at least amplitudes and phases of multiple subcarriers;

[0213] Normalizing the signal intensity to obtain a corresponding normalized value;

[0214] Acquire the transmission power corresponding to the communication device, and use the transmission power and the normalized value to perform calculation to obtain the distance feature corresponding to the signal strength;

[0215] Calculating a phase difference between phases at adjacent time points, and obtaining a position mapping relationship for the phase difference, wherein the position mapping relationship is a mapping relationship between different phase differences and a relative position of a user;

[0216] Determine the relative position of the user by using the phase difference and the position mapping relationship;

[0217] The amplitude difference between the amplitudes at adjacent time points is calculated, the change trend of the amplitude difference and the phase difference is determined, and the movement direction of the user is determined based on the change trend.

[0218] In some feasible implementations, the prediction module 303 is specifically used to:

[0219] The movement of the user is predicted using the distance feature, the relative position, and the movement direction to obtain a movement prediction result of the user.

[0220] In some feasible implementations, the motion prediction result includes at least a motion state and a motion trajectory, and the using the distance feature, the relative position, and the motion direction to predict the user's motion to obtain the user's motion prediction result includes:

[0221] The distance feature, the relative position and the movement direction are input into a movement prediction model to predict the movement of the user, thereby obtaining the movement state and movement trajectory of the user.

[0222] In some feasible implementations, the instruction generation module 304 is specifically used to:

[0223] Calculating following information of the smart device according to the motion trajectory and the relative position, wherein the following information at least includes a following position, a first turning angle, and a following direction;

[0224] Calculating a first following distance of the smart device according to the following position and the following direction;

[0225] adjusting the first steering angle to a second steering angle matching the device limitation parameter, and adjusting the first following distance to a second following distance matching the device limitation parameter;

[0226] A following instruction corresponding to the second steering angle and the second following distance is generated.

[0227] In some feasible implementations, the device limitation parameters include at least a maximum steering angle of the smart device and a distance between the smart device and the user, and the instruction generation module 304 is specifically used to:

[0228] adjusting the first steering angle to a second steering angle matching the maximum steering angle;

[0229] The first following distance is adjusted to a second following distance matching the holding distance.

[0230] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0231] In addition, an embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the control method embodiment of the above-mentioned device are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0232] The 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, each process of the control method embodiment of the above-mentioned device is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0233] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0234] The electronic device 400 includes but is not limited to: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0235] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 401 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0236] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0237] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0238] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0239] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0240] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0241] The user input unit 407 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0242] Further, the touch panel 4071 may be covered on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event, and then the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. It can be understood that in one embodiment, the touch panel 4071 and the display panel 4061 are used as two independent components to implement the input and output functions of the electronic device, but in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to implement the input and output functions of the electronic device, which is not limited here.

[0243] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0244] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0245] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0246] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to each component. Preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0247] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0248] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0249] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0250] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0251] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0252] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0253] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 devices or units, which can be electrical, mechanical or other forms.

[0254] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0255] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0256] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or 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: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0257] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for controlling a device, characterized in that: Applied to a control device, the control device is communicatively connected with at least two communication devices and at least one intelligent device, the method comprising: Acquire a signal parameter corresponding to a communication signal between the communication device and the smart device; Perform feature extraction based on the signal parameters to obtain user activity features for characterizing human body motion; Predicting the user's movement according to the user activity characteristics to obtain the user's movement prediction result; Acquire device restriction parameters corresponding to the smart device, and generate instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device; The follow-up instruction is sent to the smart device, where the follow-up instruction is used to instruct the smart device to perform a corresponding follow-up operation.

2. The method according to claim 1, characterized in that The extracting features according to the signal parameters to obtain user activity features for characterizing human motion includes: Extracting distance features and motion features from the signal parameters; The distance feature is used to characterize the relative distance between the user and the smart device, and the motion feature is used to characterize the relative position and motion direction of the user.

3. The method according to claim 2, characterized in that The extracting the signal strength and channel status information from the signal parameters includes: Extracting signal strength and channel state information corresponding to the communication signal from the signal parameters, wherein the channel state information includes at least amplitudes and phases of multiple subcarriers; Normalizing the signal intensity to obtain a corresponding normalized value; Acquire the transmission power corresponding to the communication device, and use the transmission power and the normalized value to perform calculation to obtain the distance feature corresponding to the signal strength; Calculating a phase difference between phases at adjacent time points, and obtaining a position mapping relationship for the phase difference, wherein the position mapping relationship is a mapping relationship between different phase differences and a relative position of a user; Determine the relative position of the user by using the phase difference and the position mapping relationship; The amplitude difference between the amplitudes at adjacent time points is calculated, the change trend of the amplitude difference and the phase difference is determined, and the movement direction of the user is determined based on the change trend.

4. The method according to claim 3, characterized in that The step of predicting the user's movement according to the user activity characteristics to obtain the user's movement prediction result includes: The movement of the user is predicted using the distance feature, the relative position, and the movement direction to obtain a movement prediction result of the user.

5. The method according to claim 4, characterized in that The motion prediction result includes at least a motion state and a motion trajectory, and the using the distance feature, the relative position, and the motion direction to predict the user's motion to obtain the user's motion prediction result includes: The distance feature, the relative position and the movement direction are input into a movement prediction model to predict the movement of the user, thereby obtaining the movement state and movement trajectory of the user.

6. The method according to claim 5, characterized in that Generating instructions according to the motion prediction result and the device limitation parameter to obtain a follow-up instruction for the smart device includes: Calculating following information of the smart device according to the motion trajectory and the relative position, wherein the following information at least includes a following position, a first turning angle, and a following direction; Calculating a first following distance of the smart device according to the following position and the following direction; adjusting the first steering angle to a second steering angle matching the device limitation parameter, and adjusting the first following distance to a second following distance matching the device limitation parameter; A following instruction corresponding to the second steering angle and the second following distance is generated.

7. The method according to claim 6, characterized in that The device limitation parameters include at least a maximum steering angle of the smart device and a distance between the smart device and the user, and the first steering angle is adjusted to a second steering angle matching the device limitation parameters, and the first moving distance is adjusted to a second moving distance matching the device limitation parameters, including: adjusting the first steering angle to a second steering angle matching the maximum steering angle; The first following distance is adjusted to a second following distance matching the holding distance.

8. A control device for an equipment, characterized in that: Applied to a control device, the control device is communicatively connected with at least two communication devices and at least one intelligent device, the device comprising: A signal acquisition module, used to acquire signal parameters corresponding to the communication signal between the communication device and the smart device; A feature extraction module, used to extract features according to the signal parameters to obtain user activity features for characterizing human body movements; A prediction module, used to predict the user's movement according to the user's activity characteristics, and obtain the user's movement prediction result; An instruction generation module, used to obtain device restriction parameters corresponding to the smart device, and generate instructions according to the motion prediction result and the device restriction parameters to obtain a follow-up instruction for the smart device; The control module is used to send the follow-up instruction to the smart device, and the follow-up instruction is used to instruct the smart device to perform a corresponding follow-up operation.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 7.

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