Equipment control methods, devices, electronic equipment and storage media

Abstract

This invention provides a device control method, apparatus, electronic device, and storage medium, relating to the field of device control technology. The method includes: acquiring signal parameters corresponding to communication signals between a communication device and a smart device; extracting features based on the signal parameters to obtain user activity features characterizing human movement; predicting the user's movement based on the user activity features to obtain a user movement prediction result; acquiring device limitation parameters corresponding to the smart device, and generating instructions based on the movement prediction result and the device limitation parameters to obtain a follow instruction for the smart device; and sending the follow instruction to the smart device, wherein the follow instruction is used to instruct the smart device to perform a corresponding follow operation. By extracting corresponding user activity features from the communication parameters, the stability of data processing can be ensured in complex environments, so as to control the device based on stable and reliable data, thereby ensuring the stability and reliability of device control.

Description

Technical Field

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

[0002] With the rapid development of smart devices and IoT technologies, an increasing number of 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 tracking systems rely on vision, infrared, or ultrasonic sensors for detection. In complex environments, these sensors are easily affected by obstructions and changes in lighting, which can reduce the stability and reliability of device control. Adding more sensors to smart devices to improve detection accuracy would incur additional costs. Summary of the Invention

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

[0004] This invention discloses a device control method applied to a control device, wherein the control device is communicatively connected to at least two communication devices and at least one smart device, the method comprising:

[0005] Obtain the signal parameters corresponding to the communication signal between the communication device and the smart device;

[0006] Based on the signal parameters, feature extraction is performed to obtain user activity features that characterize human movement.

[0007] Based on the user activity characteristics, predict the user's movement to obtain the user's movement prediction result;

[0008] Obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow instruction for the smart device;

[0009] The follow instruction is sent to the smart device, and the follow instruction is used to instruct the smart device to perform the corresponding follow operation.

[0010] In some feasible implementations, the step of extracting features based on the signal parameters to obtain user activity features characterizing human movement includes:

[0011] Extract distance 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 user's relative position and direction of motion.

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

[0014] The signal strength and channel status information corresponding to the communication signal are extracted from the signal parameters, wherein the channel status information includes at least the amplitude and phase of multiple subcarriers;

[0015] The signal strength is normalized to obtain the corresponding normalized value;

[0016] The transmission power corresponding to the communication device is obtained, and the distance feature corresponding to the signal strength is obtained by using the transmission power and the normalized value.

[0017] Calculate the phase difference between adjacent time points and obtain the position mapping relationship for the phase difference, wherein the position mapping relationship is the mapping relationship between different phase differences and the user's relative position;

[0018] The user's relative position is determined by the phase difference and the position mapping relationship.

[0019] Calculate the amplitude difference between adjacent time points, determine the changing trend of the amplitude difference and the phase difference, and determine the user's motion direction based on the changing trend.

[0020] In some feasible implementations, the step of predicting the user's movement based on the user activity characteristics to obtain the user's movement prediction result includes:

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

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

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

[0024] In some feasible implementations, the step of generating instructions based on the motion prediction results and the device limitation parameters to obtain follow instructions for the smart device includes:

[0025] Based on the motion trajectory and the relative position, the following information of the smart device is calculated, and the following information includes at least the following position, the first turning angle, and the following direction;

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

[0027] The first steering angle is adjusted to a second steering angle that matches the equipment limitation parameters, and the first following distance is adjusted to a second following distance that matches the equipment limitation parameters;

[0028] Generate a follow command corresponding to the second steering angle and the second follow distance.

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

[0030] The first steering angle is adjusted to a second steering angle that matches the maximum steering angle.

[0031] The first following distance is adjusted to a second following distance that matches the maintaining distance.

[0032] This invention also discloses a control device for a device, applied to a control device, wherein the control device is communicatively connected to at least two communication devices and at least one smart device, and the device includes:

[0033] The signal acquisition module is used to acquire signal parameters corresponding to the communication signals between the communication device and the smart device;

[0034] The feature extraction module is used to extract features based on the signal parameters to obtain user activity features that characterize human movement;

[0035] The prediction module is used to predict the user's movement based on the user's activity characteristics and obtain the user's movement prediction result;

[0036] The instruction generation module is used to obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow instruction for the smart device.

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

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

[0039] Extract distance 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 user's relative position and direction of motion.

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

[0042] The signal strength and channel status information corresponding to the communication signal are extracted from the signal parameters, wherein the channel status information includes at least the amplitude and phase of multiple subcarriers;

[0043] The signal strength is normalized to obtain the corresponding normalized value;

[0044] The transmission power corresponding to the communication device is obtained, and the distance feature corresponding to the signal strength is obtained by using the transmission power and the normalized value.

[0045] Calculate the phase difference between adjacent time points and obtain the position mapping relationship for the phase difference, wherein the position mapping relationship is the mapping relationship between different phase differences and the user's relative position;

[0046] The user's relative position is determined by the phase difference and the position mapping relationship.

[0047] Calculate the amplitude difference between adjacent time points, determine the changing trend of the amplitude difference and the phase difference, and determine the user's motion direction based on the changing trend.

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

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

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

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

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

[0053] Based on the motion trajectory and the relative position, the following information of the smart device is calculated, and the following information includes at least the following position, the first turning angle, and the following direction;

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

[0055] The first steering angle is adjusted to a second steering angle that matches the equipment limitation parameters, and the first following distance is adjusted to a second following distance that matches the equipment limitation parameters;

[0056] Generate a follow command corresponding to the second steering angle and the second follow distance.

[0057] In some feasible implementations, the device limiting parameters include at least the maximum turning angle of the smart device and the holding distance between the device and the user, and the instruction generation module is specifically used for:

[0058] The first steering angle is adjusted to a second steering angle that matches the maximum steering angle.

[0059] The first following distance is adjusted to a second following distance that matches the maintaining distance.

[0060] This invention also discloses an electronic device, including 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;

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

[0062] When the processor executes a program stored in the memory, it implements the method described in the embodiments of the present invention.

[0063] This invention also discloses a computer-readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described in this invention.

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

[0065] In this embodiment of the invention, it can be applied to a control device. The control device can communicate with at least two communication devices and at least one smart device. During the process of controlling the smart device to follow a user, the control device can acquire signal parameters corresponding to the communication signals between the communication devices and the smart device. Then, it performs feature extraction based on the signal parameters to obtain user activity features that characterize human movement, and predicts the user's movement based on the user activity features to obtain the user's movement prediction result. Then, it acquires the device limitation parameters corresponding to the smart device, and generates instructions based on the movement prediction result and the device limitation parameters to obtain a follow instruction for the smart device. The follow instruction is sent to the smart device to instruct the smart device to perform the corresponding follow operation. By extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in complex environments, so as to control the device based on stable and reliable data, ensuring the stability and reliability of device control. The control device can generate corresponding follow instructions based on real-time data, ensuring the real-time performance of the smart device following human movement. At the same time, based on existing communication devices, no additional devices need to be added, which can effectively reduce costs. Attached Figure Description

[0066] Figure 1 This is a flowchart of the steps of a device control method provided in an embodiment of the present invention;

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

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

[0069] Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0070] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be 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 vision, infrared, or ultrasonic sensors, which are susceptible to factors such as obstruction and changes in light in complex environments, resulting in insufficient stability and reliability.

[0072] In this invention, a control device is applied that can communicate with at least two communication devices and at least one smart device. During the process of controlling the smart device to follow a user, the control device can acquire signal parameters corresponding to the communication signals between the communication devices and the smart device. Then, feature extraction is performed based on the signal parameters to obtain user activity features characterizing human movement. Based on these user activity features, the user's movement is predicted to obtain the user's movement prediction result. Next, the device limitation parameters corresponding to the smart device are acquired, and instructions are generated based on the movement prediction result and the device limitation parameters to obtain a follow instruction for the smart device. This follow instruction is then sent to the smart device, instructing it to perform the corresponding follow operation. By extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in complex environments, enabling stable and reliable data for device control. This ensures the stability and reliability of device control. Furthermore, the control device can generate corresponding follow instructions based on real-time data, guaranteeing the real-time performance of the smart device following human movement. Simultaneously, based on existing communication devices, no additional equipment is required, effectively reducing costs.

[0073] Reference Figure 1 The diagram illustrates a flowchart of a device control method provided in an embodiment of the present invention. This method is applied to a control device that is communicatively connected to at least two communication devices and at least one intelligent device. Specifically, the method may include the following steps:

[0074] Step 101: Obtain the signal parameters corresponding to the communication signal between the communication device and the smart device;

[0075] In this embodiment of the invention, the control device can be a server or a user terminal, etc. The control device can communicate with at least two communication devices and at least one smart device. The communication devices can be devices capable of connecting to a wireless network and using wireless technology for data communication. The smart device can be a specific execution device. In a device-following scenario, the smart device transmits corresponding device data to the control device by accessing the network provided by the communication devices, so that the control device can control the smart device based on the received device data. For example, the communication device can be a WiFi device, the smart device can be a surveillance camera, and the control device can be a user terminal. In this case, the surveillance camera can transmit corresponding monitoring data to the user terminal based on the wireless network provided by the WiFi device, and the user terminal can control the surveillance camera based on the wireless network provided by the WiFi device. This invention does not impose any limitations on this.

[0076] In one example, refer to Figure 2This illustration shows a schematic diagram of the device communication structure provided in an embodiment of the present invention. At least two communication devices and at least one smart device can be deployed in the target area. The communication devices can send and receive corresponding wireless communication signals, and the smart device can also receive and send corresponding wireless communication signals. The smart device accesses the wireless network through the communication devices and establishes a corresponding communication connection with the control device. The communication devices can be WiFi routers with wireless communication capabilities or smart devices with WiFi modules, etc.; the smart device can be a device equipped with a WiFi receiving module for receiving WiFi signals from the environment, and the smart device can also have the ability to perform corresponding physical actions, such as movement and rotation, etc., which are not limited in this invention.

[0077] It should be noted that, in order to ensure comprehensive signal coverage, the locations of communication equipment can be rationally arranged. The corresponding arrangement process is as follows:

[0078] 1. Environmental Analysis

[0079] Before deploying WiFi devices, a preliminary analysis of the target environment is necessary, including: space size and structure: measuring the area and height of the space, identifying the shape of the room, walls, doors, windows, and other fixed obstacles (such as furniture, walls, etc.). Main activity areas: determining the main areas of user activity; these areas are typically the key areas that the system needs to monitor.

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

[0081] The number and type of WiFi devices required are determined based on the size and shape of the space.

[0082] 3. Layout Design Principles

[0083] When deploying WiFi devices, the following principles should be followed to avoid signal blind spots: ① Centralized deployment: Place WiFi devices in the center of the activity area to maximize signal coverage; ② Height deployment: Install devices at higher locations (such as above walls) to reduce the impact of signal obstacles and increase signal propagation distance; ③ Obstacle avoidance: Ensure WiFi devices are kept a certain distance from walls, furniture, and other obstacles. Avoid placing devices in areas where the signal will be severely attenuated, such as near metal objects or in completely enclosed spaces; ④ Device spacing: If using multiple WiFi devices, maintain appropriate spacing. A distance of 10 to 30 meters between devices is generally recommended, adjusted according to signal strength and coverage area; ⑤ Cross-coverage: By appropriately overlapping the coverage areas of each WiFi device, ensure signal reception in any location. Overlapping signal coverage areas improve signal stability and data transmission speed.

[0084] 4. Signal Testing and Optimization

[0085] After the initial setup, conduct on-site signal testing to ensure the effectiveness of WiFi signal coverage:

[0086] Signal strength test: Use WiFi analysis tools (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: Identify signal blind spots and analyze possible causes (such as obstacles, improper equipment placement, etc.).

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

[0089] In this embodiment of the invention, during the process of the control device controlling the smart device to follow the user's actions and perform corresponding following operations, the control device can acquire signal parameters corresponding to the communication signals between the communication device and the smart device in real time. 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. Therefore, the control device can collect the signal parameters corresponding to these communication signals in order to control the smart device to perform corresponding device following operations based on the signal parameters. The signal parameters can be used to describe the signal state of the communication signals.

[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 identify the presence and activity of objects (such as people) by analyzing the reflection of WiFi signals during their propagation. For example, by monitoring changes in WiFi signals, it can determine whether a person is in a room and even identify their actions (such as walking, sitting, etc.).

[0091] Step 102: Perform feature extraction based on the signal parameters to obtain user activity features that characterize human movement;

[0092] In this embodiment of the invention, after the control device collects the signal parameters corresponding to the communication signal between the communication device and the smart device, it can extract features from the signal parameters to obtain user activity features that characterize human movement. Thus, the user's actions can be detected through the signal parameters, so as to generate corresponding control commands based on the detection results and control the smart device to follow the user's actions and perform corresponding follow operations.

[0093] In some feasible implementations, the control device can extract distance and motion features from signal parameters. The distance features characterize the relative distance between the user and the smart device, while the motion features characterize the user's relative position and direction of movement.

[0094] In practical implementation, the control device can extract the signal strength and channel status information corresponding to the communication signal from the signal parameters. The channel status information includes at least the amplitude and phase of multiple subcarriers. The signal strength is normalized to obtain the corresponding normalized value. Then, the transmission power of the communication device is obtained, and the distance characteristics corresponding to the signal strength are calculated using the transmission power and the normalized value. Secondly, the control device can calculate the phase difference between adjacent time points and obtain the position mapping relationship based on the phase difference. The position mapping relationship is the mapping relationship between different phase differences and the user's relative position. The user's relative position is determined using the phase difference and position mapping relationship. Then, the amplitude difference between adjacent time points is calculated to determine the changing trend of the amplitude difference and phase difference. Based on the changing trend, the user's movement direction is determined. Thus, the user's action can be detected through the signal parameters, so that corresponding control commands can be generated based on the detection results to control the intelligent device to follow the user's actions and perform corresponding following operations.

[0095] Signal strength refers to the strength of the signal received by the communication device or smart device, typically measured in dBm. Signal strength can be used to determine the distance characteristics relative to the user, i.e., the distance between the user and the smart device. Channel state information can include the amplitude and phase of multiple subcarriers. Amplitude and phase describe the signal attenuation and phase changes during transmission. Based on channel state information, the user's relative position and direction of movement in the environment can be determined. Furthermore, transmit power can refer to the power at which the communication device or smart device transmits communication signals; this invention does not limit this.

[0096] For distance characteristics, after acquiring the signal strength, the control device recognizes that signal strength is typically a negative value; a smaller value indicates a weaker signal, and a larger value indicates a stronger signal. Therefore, the control device can first normalize the signal strength, mapping its value to the range of 0-1. For example, this can be achieved using the normalized formula: 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. This normalization of the signal strength facilitates 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, the distance feature is obtained. This distance feature represents the relative distance between the user and the smart device.

[0098] Optionally, for the calculation of relative distance, the control device can perform the calculation 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 exponent. Thus, 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] Regarding relative position and direction of movement, after acquiring channel state information, the control device can deduce the user's relative position and direction of movement 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 user's position, and the direction of the phase change can be used to indicate the user's direction of movement. Specifically, the control device can first calculate the phase difference and amplitude difference between adjacent time points (or adjacent subcarriers). For example, the phase difference can be calculated using the phase calculation formula: phase_diff = phase(t2) - phase(t1), and the amplitude difference can be calculated using the amplitude calculation formula: amplitude_diff = amplitude(t2) - amplitude(t1), etc. This invention does not limit this.

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

[0101] In one example, assume that three Wi-Fi access points (AP1, AP2, AP3) are located in the three corners of the room. For user location 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 determined to be Δφ13_measured through corresponding calculation methods, 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 in 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 in the upper left position of the room. Thus, through the mapping relationship between phase difference and position, the control device can accurately determine the user's relative position, thereby achieving precise positioning.

[0102] Furthermore, assuming a smart home environment with multiple Wi-Fi access points (APs) distributed across different rooms, smart devices can receive Wi-Fi signals from these access points. During the process of a control device guiding a smart device to follow another device, the control device can further track the changing trends of Δφ and ΔA over a period of time. Assume that in the past few seconds, Δφ changed from Δφ_left to Δφ_center, and ΔA changed from A_left to A_center. The corresponding trend analysis is as follows: Phase difference change: If the phase difference is decreasing, it indicates that the user is moving towards the access point; Amplitude difference change: If the amplitude difference is increasing, it indicates that the user is getting closer to the access point. The control device can combine the changes in phase difference and amplitude difference to determine the user's direction of movement. 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 indicates that the user is moving away from the access point. Thus, the control device can not only determine the user's relative position using phase difference and amplitude difference, but also determine the user's direction of movement by monitoring the changing trends of these parameters.

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

[0104] Step 103: Predict the user's movement based on the user activity characteristics to obtain the user's movement prediction result;

[0105] In this embodiment of the invention, the control device can predict the user's movement based on the user's activity characteristics, obtain the user's movement prediction result within a certain time period, and then control the intelligent device to follow based on the movement prediction result. In a specific implementation, the control device can use distance features, relative position, and movement direction to predict the user's movement and obtain the user's movement prediction result.

[0106] The motion prediction results include at least the motion state and the motion trajectory. The control device can then use this to predict the user's motion by inputting distance features, relative position, and motion direction into the motion prediction model, thereby obtaining the user's motion state and motion trajectory.

[0107] It should be noted that motion prediction models can be models obtained by collecting user activity features and training them based on those features. These models then predict user activities. For example, recurrent neural networks or long short-term memory networks can be trained to capture user movement patterns in time series data and predict future movement directions.

[0108] In one example, within a smart home environment, multiple Wi-Fi access points (APs) are installed, covering different areas throughout the house. Smart devices can receive Wi-Fi signals emitted by these access points. Control devices (such as a smart home control center) analyze the phase difference, amplitude difference, and other characteristics of the Wi-Fi signals to predict the user's movement status and trajectory. 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 position: By analyzing the phase and amplitude differences between different access points, the control device can determine the user's relative position. For example, the user may be located in the living room, bedroom, or kitchen.

[0112] Direction of movement: By monitoring the changing trends of phase difference and amplitude difference, the control device can determine the user's direction of movement. 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. Construct a motion prediction model

[0114] Suppose the control device uses a machine learning-based motion prediction model, which can take multiple features as input and output the user's motion state and trajectory. The model's input features include:

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

[0116] The model's output includes:

[0117] Movement state: for example, being at rest, 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 the control device takes to predict motion:

[0121] 1) Initial state

[0122] User's initial location: Living room;

[0123] Distance characteristics: The user is 3 meters away from AP1 and 4 meters away from AP2;

[0124] Relative location: Living room;

[0125] Direction of motion: 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] The phase difference Δφ was detected to change from Δφ_living_room to Δφ_hallway, and the amplitude difference ΔA was detected to change 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 location: The user moves from the living room to the hallway.

[0132] Direction of movement: The phase difference is decreasing while the amplitude difference is increasing, indicating that the user is moving towards the access point.

[0133] 4) Input into the model for prediction

[0134] The control device inputs the updated distance features, relative position, and direction of motion into the motion prediction model. The model outputs the prediction result as follows:

[0135] Movement state: Walking

[0136] Movement trajectory: From the living room through the hallway, predicted to enter the kitchen.

[0137] 5) Continuous forecasting

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

[0139] Ultimately, the model predicts that the user will enter the kitchen and continue to be active within it.

[0140] 4. Specific example data

[0141] Table 1 below shows the feature inputs and model prediction results:

[0142]

[0143] Table 1

[0144] Step 104: Obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow instruction for the smart device;

[0145] After determining the motion prediction results for the user through the above process, the control device can further obtain the corresponding device limitation parameters of the smart device. Then, based on the motion prediction results and device limitation parameters, it generates instructions to obtain follow instructions for the smart device. Based on the follow instructions, the control device can follow the user's activities and perform corresponding follow operations. In the process of generating follow instructions, by combining the device limitation parameters, the limitations of the device itself are fully considered, improving the rationality of the follow instructions and ensuring that the smart device can accurately and effectively execute the follow instructions.

[0146] In some feasible implementations, the control device can calculate the following information of the intelligent device based on the motion trajectory and relative position. The following information includes at least the following position, the first turning angle, and the following direction. Then, according to the following position and the following direction, the first following distance of the intelligent device is calculated. The first turning angle is then adjusted to a second turning angle that matches the device's limiting parameters, and the first following distance is adjusted to a second following distance that matches the device's limiting parameters. This generates a following command corresponding to the second turning angle and the second following distance. In the process of generating the following command, by combining the device's limiting parameters, the limitations of the device itself are fully considered, improving the rationality of the following command and ensuring that the intelligent device can accurately and effectively execute the following command.

[0147] The device limiting parameters include at least the maximum steering angle of the smart device and the holding distance between the device and the user. The control device can adjust the first steering angle to a second steering angle that matches the maximum steering angle, and adjust the first following distance to a second following distance that matches the holding distance.

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

[0149] 1. Obtain user exercise information

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

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

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

[0153] Follow location: Smart vacuum cleaners need to follow the user's location. For example, if a user moves from the living room to the hallway, the smart vacuum cleaner needs to calculate its following position in the hallway.

[0154] First turning angle: The directional angle that the smart vacuum cleaner needs to adjust. For example, the smart vacuum cleaner needs to turn from its current location (living room) into the hallway.

[0155] Follow direction: The direction of movement that the smart vacuum cleaner needs to follow. For example, from the living room towards the hallway.

[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 movement trajectory and relative position. For example, the smart vacuum cleaner needs to maintain a position 1 meter behind the user.

[0158] 4. Adjust follow information

[0159] Equipment limitations:

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

[0161] Maintain distance: The optimal distance between a smart vacuum cleaner and the user is 1.5 meters.

[0162] Adjust the steering angle:

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

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

[0165] Adjust the following distance:

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

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

[0168] 5. Generate follow instructions

[0169] Follow command: The control device generates a follow command, which includes a second steering angle and a second follow distance, to ensure that the smart vacuum cleaner can accurately execute the follow operation.

[0170] Instruction content:

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

[0172] Through the above process, in the process of generating follow instructions, by combining the device's limiting parameters and fully considering the device's own limitations, the rationality of the follow instructions is improved, ensuring that the intelligent device can accurately and effectively execute the follow instructions.

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

[0174] In practical implementation, after the control device generates the corresponding follow command, it can send the follow command to the smart device so that the smart device can execute the corresponding follow operation based on the follow command. By extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in complex environments, so that the device can be controlled based on stable and reliable data, ensuring the stability and reliability of device control. The control device can also generate the corresponding follow command based on real-time data, ensuring the real-time performance of the smart device in following human movement. At the same time, based on existing communication equipment, no additional equipment needs to be added, which can effectively reduce costs.

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

[0176] In this embodiment of the invention, it can be applied to a control device. The control device can communicate with at least two communication devices and at least one smart device. During the process of controlling the smart device to follow a user, the control device can acquire signal parameters corresponding to the communication signals between the communication devices and the smart device. Then, it performs feature extraction based on the signal parameters to obtain user activity features that characterize human movement, and predicts the user's movement based on the user activity features to obtain the user's movement prediction result. Then, it acquires the device limitation parameters corresponding to the smart device, and generates instructions based on the movement prediction result and the device limitation parameters to obtain a follow instruction for the smart device. The follow instruction is sent to the smart device to instruct the smart device to perform the corresponding follow operation. By extracting the corresponding user activity features from the communication parameters, the stability of data processing can be guaranteed in complex environments, so as to control the device based on stable and reliable data, ensuring the stability and reliability of device control. The control device can generate corresponding follow instructions based on real-time data, ensuring the real-time performance of the smart device following human movement. At the same time, based on existing communication devices, no additional devices need to be added, which can effectively reduce costs.

[0177] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples provide exemplary descriptions:

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

[0179] Step 1: Environment Deployment

[0180] WiFi Device Deployment: Deploy several WiFi devices within 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 area coverage, the WiFi devices need to be strategically placed to avoid signal blind spots. It is recommended to use 2 to 3 devices to ensure accurate positioning.

[0181] Smart device installation: Smart devices (such as robots or cameras) need to be equipped with a WiFi receiver module to receive WiFi signals from the environment. These devices should also be capable of performing physical actions, such as moving or rotating.

[0182] Step 2: WiFi signal acquisition

[0183] Signal Acquisition Module: WiFi devices and smart devices continuously send and receive WiFi signals. The system collects key parameters of these signals, including RSSI (Received Signal Strength) and CSI (Channel Status Information). These parameters change as a person 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: The acquired WiFi signals undergo preprocessing, including filtering and noise reduction, to minimize the impact of external interference. Low-pass filters can be used to remove high-frequency noise, or Kalman filters can be used for data smoothing.

[0187] Feature extraction: Extracting motion-related features from the preprocessed WiFi signal. For example, RSSI (intensity) changes can reflect the distance of human movement, while CSI phase changes can reveal the specific location and direction of movement. Comprehensive analysis of signals from multiple WiFi devices can further improve positioning accuracy.

[0188] Step 4: Motion Trajectory Prediction

[0189] Machine learning models: These models use machine learning algorithms to analyze extracted signal features and predict human motion trajectories. Commonly used models include RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory Networks), which can capture motion patterns in time series and predict future motion directions.

[0190] Model training: Data collection is required during system deployment, and this data is used to train the model. Supervised learning can be performed using labeled motion datasets to optimize prediction accuracy.

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

[0192] Step 5: Intelligent Device Control and Multi-Device Collaboration

[0193] Equipment control module: Based on the motion trajectory prediction results, the system generates corresponding control commands and transmits them to the intelligent device. The device then performs corresponding physical actions according to the commands, such as adjusting the robot's position or rotating the camera lens.

[0194] Action execution: To ensure smooth operation and timely response of equipment actions, control commands need to take into account the physical constraints of the equipment (such as maximum speed, turning radius, etc.) and perform appropriate motion planning.

[0195] Feedback mechanism: The system can use sensor feedback (such as position information) to correct the device's actions and 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 from multiple devices, using algorithms such as weighted averaging or Bayesian fusion to improve positioning accuracy.

[0197] Device switching: Over a wide area, 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 accurately tracks and whether the response is timely). This feedback data can be used to update and optimize machine learning models to adapt to constantly changing environments and user behaviors.

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

[0201] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0202] Reference Figure 3 This diagram illustrates a structural block diagram of a control device for a device according to an embodiment of the present invention. The control device is applied to a control device and is communicatively connected to at least two communication devices and at least one intelligent device. Specifically, it may include the following modules:

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

[0204] Feature extraction module 302 is used to extract features based on the signal parameters to obtain user activity features that characterize human movement;

[0205] Prediction module 303 is used to predict the user's movement based on the user activity characteristics and obtain the user's movement prediction result;

[0206] The instruction generation module 304 is used to obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow 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 the corresponding follow operation.

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

[0209] Extract distance 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 user's relative position and direction of motion.

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

[0212] The signal strength and channel status information corresponding to the communication signal are extracted from the signal parameters, wherein the channel status information includes at least the amplitude and phase of multiple subcarriers;

[0213] The signal strength is normalized to obtain the corresponding normalized value;

[0214] The transmission power corresponding to the communication device is obtained, and the distance feature corresponding to the signal strength is obtained by using the transmission power and the normalized value.

[0215] Calculate the phase difference between adjacent time points and obtain the position mapping relationship for the phase difference, wherein the position mapping relationship is the mapping relationship between different phase differences and the user's relative position;

[0216] The user's relative position is determined by the phase difference and the position mapping relationship.

[0217] Calculate the amplitude difference between adjacent time points, determine the changing trend of the amplitude difference and the phase difference, and determine the user's motion direction based on the changing trend.

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

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

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

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

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

[0223] Based on the motion trajectory and the relative position, the following information of the smart device is calculated, and the following information includes at least the following position, the first turning angle, and the following direction;

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

[0225] The first steering angle is adjusted to a second steering angle that matches the equipment limitation parameters, and the first following distance is adjusted to a second following distance that matches the equipment limitation parameters;

[0226] Generate a follow command corresponding to the second steering angle and the second follow distance.

[0227] In some feasible implementations, the device limiting parameters include at least the maximum turning angle of the smart device and the holding distance between the device and the user, and the instruction generation module 304 is specifically used for:

[0228] The first steering angle is adjusted to a second steering angle that matches the maximum steering angle.

[0229] The first following distance is adjusted to a second following distance that matches the maintaining distance.

[0230] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0231] In addition, this invention also 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, it implements the various processes of the control method embodiments of the above-described device and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0232] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the control method embodiments of the above-described device, achieving the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be 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, components such as: 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 understand that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, mobile phones, tablet computers, laptop computers, PDAs, in-vehicle terminals, wearable devices, and pedometers.

[0235] It should be understood that, in this embodiment of the invention, the radio frequency unit 401 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink data from the base station and processes it with the processor 410; additionally, it transmits uplink data to the base station. Typically, 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. Furthermore, the radio frequency unit 401 can also communicate with networks and other devices through a wireless communication system.

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

[0237] The audio output unit 403 can convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into audio signals and output them as sound. Furthermore, the audio output unit 403 can also provide audio output related to specific functions performed by the electronic device 400 (e.g., call signal reception sound, message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, and a receiver, etc.

[0238] Input unit 404 is used to receive audio or video signals. Input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image data of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The processed image frames can be displayed on display unit 406. The image frames processed by GPU 4041 can be stored in memory 409 (or other storage medium) or transmitted via radio frequency unit 401 or network module 402. Microphone 4042 can receive sound and process such sound into audio data. The processed audio data can be converted into a format that can be transmitted to a mobile communication base station via radio frequency unit 401 in 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. The ambient light sensor can adjust the brightness of the display panel 4061 according to the ambient light level, and the proximity sensor can turn off the display panel 4061 and / or backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used to identify the posture of the electronic device (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. The sensor 405 may also include a fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be described in detail 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] User input unit 407 can be used to receive input numerical or character information, and generate key signal inputs related to user settings and function control of electronic devices. Specifically, user input unit 407 includes a touch panel 4071 and other input devices 4072. Touch panel 4071, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near touch panel 4071). Touch panel 4071 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 410, which receives and executes commands from the processor 410. In addition, touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. Besides touch panel 4071, user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here.

[0242] Furthermore, the touch panel 4071 can cover the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it transmits the information to the processor 410 to determine the type of touch event. Subsequently, the processor 410 provides corresponding visual output on the display panel 4061 according to the type of touch event. It is understood that in one embodiment, the touch panel 4071 and the display panel 4061 are implemented as two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device. The specific implementation is not limited here.

[0243] Interface unit 408 serves as an interface for connecting external devices to electronic device 400. For example, external devices may include a wired or wireless headphone 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, and so on. Interface unit 408 can be used to receive input from external devices (e.g., data, power, etc.) and transmit the received input to one or more components within electronic device 400, or it can be used to transmit data between electronic device 400 and external devices.

[0244] The memory 409 can be used to store software programs and various data. The memory 409 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory 409 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0245] The processor 410 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 409, and by calling data stored in the memory 409, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the 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 various components. Preferably, the power supply 411 can be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption 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 document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0249] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0250] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

[0251] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

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

[0253] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

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

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

[0256] If the aforementioned functions are implemented as 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 this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0257] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling a device, characterized in that, Applied to a control device, the control device being communicatively connected to at least two communication devices and at least one smart device, the method includes: Obtain the signal parameters corresponding to the communication signal between the communication device and the smart device; The signal strength and channel status information corresponding to the communication signal are extracted from the signal parameters, wherein the channel status information includes at least the amplitude and phase of multiple subcarriers; The signal strength is normalized to obtain the corresponding normalized value; The transmit power and path loss index corresponding to the communication device are obtained, and the transmit power, the path loss index and the normalized value are used to calculate the distance feature corresponding to the signal strength; the distance feature is used to characterize the relative distance between the user and the smart device. Calculate the phase difference between adjacent time points and obtain the position mapping relationship for the phase difference, wherein the position mapping relationship is the mapping relationship between different phase differences and the user's relative position; The user's relative position is determined by the phase difference and the position mapping relationship. Calculate the amplitude difference between adjacent time points, determine the changing trend of the amplitude difference and the phase difference, and determine the user's motion direction based on the changing trend; The user's movement is predicted using the distance features, the relative position, and the direction of movement to obtain the user's movement prediction result; the movement prediction result includes at least the movement state and the movement trajectory; the movement state includes at least standing, walking, and running. Obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow instruction for the smart device; The follow instruction is sent to the smart device, and the follow instruction is used to instruct the smart device to perform the corresponding follow operation.

2. The method according to claim 1, characterized in that, The process of predicting the user's movement using the distance features, the relative position, and the direction of movement to obtain the user's movement prediction result includes: The distance features, relative position, and direction of movement are input into the motion prediction model to predict the user's motion, thereby obtaining the user's motion state and trajectory.

3. The method according to claim 2, characterized in that, The step of generating instructions based on the motion prediction results and the device limitation parameters to obtain follow instructions for the smart device includes: Based on the motion trajectory and the relative position, the following information of the smart device is calculated, and the following information includes at least the following position, the first turning angle, and the following direction; Calculate the first following distance of the smart device according to the following position and the following direction; The first steering angle is adjusted to a second steering angle that matches the equipment limitation parameters, and the first following distance is adjusted to a second following distance that matches the equipment limitation parameters; Generate a follow command corresponding to the second steering angle and the second follow distance.

4. The method according to claim 3, characterized in that, The device limiting parameters include at least the maximum steering angle of the smart device and the following distance from the user. Adjusting the first steering angle to a second steering angle that matches the device limiting parameters, and adjusting the first following distance to a second following distance that matches the device limiting parameters, includes: The first steering angle is adjusted to a second steering angle that matches the maximum steering angle. The first following distance is adjusted to a second following distance that matches the maintaining distance.

5. A control device for an equipment, characterized in that, Applied to a control device, the control device being communicatively connected to at least two communication devices and at least one intelligent device, the device comprising: The signal acquisition module is used to acquire signal parameters corresponding to the communication signals between the communication device and the smart device; The feature extraction module is used to extract signal strength and channel state information corresponding to the communication signal from the signal parameters. The channel state information includes at least the amplitude and phase of multiple subcarriers. The signal strength is normalized to obtain a corresponding normalized value. The transmission power and path loss index of the communication device are obtained, and the transmission power, path loss index, and normalized value are used to calculate the distance feature corresponding to the signal strength. The distance feature is used to characterize the relative distance between the user and the smart device. The phase difference between adjacent time points is calculated, and a position mapping relationship for the phase difference is obtained. The position mapping relationship is a mapping relationship between different phase differences and the user's relative position. The relative position of the user is determined using the phase difference and the position mapping relationship. The amplitude difference between adjacent time points is calculated, and the changing trend of the amplitude difference and the phase difference is determined. The user's movement direction is determined based on the changing trend. The prediction module is used to predict the user's movement using the distance features, the relative position, and the direction of movement, and to obtain the user's movement prediction result; the movement prediction result includes at least the movement state and the movement trajectory; the movement state includes at least standing, walking, and running. The instruction generation module is used to obtain the device limitation parameters corresponding to the smart device, and generate instructions based on the motion prediction results and the device limitation parameters to obtain a follow instruction for the smart device. The control module is used to send the follow instruction to the smart device, and the follow instruction is used to instruct the smart device to perform the corresponding follow operation.

6. 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; When the processor executes a program stored in the memory, it implements the method as described in any one of claims 1-4.

7. A computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1-4.

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