Intelligent action recognition method and apparatus in wi-fi environment

Abstract

The present application discloses an intelligent action recognition method and apparatus in a Wi-Fi environment. The method comprises: detecting human action information in a target environment within a preset target time period, wherein the target environment is a Wi-Fi environment; on the basis of the human action information, determining target change information in the target environment, wherein the target change information comprises CSI change information of the target environment within the preset target time period; and on the basis of the target change information, determining an action recognition result corresponding to the target environment. Thus, implementation of the present application enables recognition of human actions in a Wi-Fi environment, thereby facilitating improvement of the accuracy and reliability of obtained human action recognition results, and also facilitating improvement of the intelligence and efficiency of human action recognition.

Description

A method and device for intelligent motion recognition in a Wi-Fi environment Technical Field

[0001] This application relates to the field of intelligent recognition technology, and in particular to a method and apparatus for intelligent action recognition in a Wi-Fi environment. Background Technology

[0002] With the continuous development of science and technology, most current technologies for human body perception rely on radar to detect human movement and gestures. However, using radar perception technology increases manufacturing and usage costs in everyday products. Wi-Fi, a wireless network communication technology, allows electronic devices to connect to the network wirelessly and has become an indispensable part of daily life. Wi-Fi CSI (Channel State Information) is key data describing the characteristics of a wireless channel, including information such as signal amplitude, phase, and propagation delay during transmission. It allows for statistical comparison of received Wi-Fi CSI data over a certain period in a Wi-Fi environment to obtain recognition results. However, current technologies for human body movement recognition via Wi-Fi clients can only identify specific actions, resulting in low accuracy and intelligence. Therefore, providing a new intelligent action recognition method in a Wi-Fi environment to improve the intelligence of human body movement recognition and thus enhance its accuracy and reliability is crucial. Summary of the Invention

[0003] This application provides a method and apparatus for intelligent motion recognition in a Wi-Fi environment, which can recognize human motion in a Wi-Fi environment, thereby improving the accuracy and reliability of the human motion recognition results, as well as the intelligence and efficiency of human motion recognition.

[0004] To address the aforementioned technical problems, the first aspect of this application discloses a method for intelligent action recognition in a Wi-Fi environment, the method comprising:

[0005] Detect human motion information in a target environment within a preset target time period, wherein the target environment is a Wi-Fi environment;

[0006] Based on the human motion information, target change information in the target environment is determined; wherein, the target change information includes CSI change information of the target environment within the preset target time period;

[0007] Based on the target change information, determine the action recognition result corresponding to the target environment.

[0008] As an optional implementation, in the first aspect of this application, the method further includes:

[0009] Based on the action recognition results, the device to be controlled in the target environment is determined;

[0010] Based on the action recognition result and the device to be controlled, a device control command corresponding to the device to be controlled is generated, and the device to be controlled is controlled to perform an operation that matches the device control command;

[0011] The step of generating a device control command corresponding to the device under control based on the action recognition result and the device under control includes:

[0012] Based on the action recognition result and the pre-determined action mapping relationship, the target instruction action that matches the action recognition result is determined from the pre-determined action mapping relationship;

[0013] Based on the target instruction action and the device to be controlled, determine the device operation parameters corresponding to the device to be controlled, and generate the device control instruction corresponding to the device to be controlled according to the device operation parameters.

[0014] As an optional implementation, in the first aspect of this application, determining the target change information in the target environment based on the human motion information includes:

[0015] Based on the human motion information, channel state information within the preset target time period is determined, wherein the channel state information includes one or more of amplitude state information and phase state information;

[0016] Perform data processing operations on the channel state information to obtain target state information, and generate target change information under the target environment based on the target state information;

[0017] The data processing operations include one or more of the following: filtering and denoising operations, and normalization operations.

[0018] As an optional implementation, in the first aspect of this application, generating target change information in the target environment based on the target state information includes:

[0019] Extract time-domain feature information from the target state information, and extract frequency-domain feature information from the target state information;

[0020] A feature fusion operation is performed on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result;

[0021] Based on the feature fusion results, target change information in the target environment is generated.

[0022] As an optional implementation, in the first aspect of this application, determining the action recognition result corresponding to the target environment based on the target change information includes:

[0023] Based on the target change information, determine the motion characteristic information corresponding to the target change information, wherein the motion characteristic information includes one or more of the motion duration information, motion amplitude information, and motion cycle information corresponding to the human motion information;

[0024] Extract the target characteristic features from the action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain the cluster analysis results;

[0025] Based on the clustering analysis results, the action recognition results corresponding to the target environment are determined.

[0026] As an optional implementation, in the first aspect of this application, before determining the target change information in the target environment based on the human motion information, the method further includes:

[0027] Extract motion feature information from the human motion information, wherein the motion feature information includes one or more of the following: motion cycle feature information, motion duration feature information, motion amplitude feature information, and motion speed feature information corresponding to the human motion information;

[0028] Determine whether the action feature information meets the preset action recognition conditions;

[0029] When it is determined that the action feature information meets the preset action recognition conditions, the operation of determining the target change information in the target environment based on the human action information is triggered.

[0030] When it is determined that the action feature information does not meet the preset action recognition conditions, action processing parameters in the target environment are generated based on the action feature information and the preset action recognition conditions. Then, a matching information processing operation is performed on the target environment based on the action processing parameters to update the human action information. The operation of extracting action feature information from the human action information and determining whether the action feature information meets the preset action recognition conditions are triggered again.

[0031] As an optional implementation, in the first aspect of this application, after detecting human motion information in the target environment within a preset target time period, the method further includes:

[0032] Based on all the detected human motion information, determine the number of users in the target environment, and determine whether the number of users is greater than or equal to a preset number threshold;

[0033] When it is determined that the number of users is greater than or equal to the preset number threshold, the user information of each user included in the target environment is determined, and the identification priority of each user is determined based on the user information of each user.

[0034] Based on the identification priority, a target user is determined from all the users, and an update operation is performed on the human motion information according to the action information corresponding to the target user to obtain the updated human motion information.

[0035] A second aspect of this application discloses a device for intelligent action recognition in a Wi-Fi environment, the device comprising:

[0036] The detection module is used to detect human motion information in a target environment within a preset target time period, wherein the target environment is a Wi-Fi environment;

[0037] The first determining module is used to determine target change information in the target environment based on the human motion information; wherein the target change information includes CSI change information of the target environment within a preset target time period;

[0038] The second determining module is used to determine the action recognition result corresponding to the target environment based on the target change information.

[0039] As an optional implementation, in a second aspect of this application, the second determining module is further configured to determine the device to be controlled in the target environment based on the action recognition result;

[0040] The device further includes:

[0041] The first generation module is used to generate a device control command corresponding to the device under control based on the action recognition result and the device under control.

[0042] The control module is used to control the device under control to perform operating operations that match the device control commands;

[0043] The specific method by which the first generation module generates the device control command corresponding to the device under control based on the action recognition result and the device under control includes:

[0044] Based on the action recognition result and the pre-determined action mapping relationship, the target instruction action that matches the action recognition result is determined from the pre-determined action mapping relationship;

[0045] Based on the target instruction action and the device to be controlled, determine the device operation parameters corresponding to the device to be controlled, and generate the device control instruction corresponding to the device to be controlled according to the device operation parameters.

[0046] As an optional implementation, in the second aspect of this application, the specific method by which the first determining module determines the target change information in the target environment based on the human motion information includes:

[0047] Based on the human motion information, channel state information within the preset target time period is determined, wherein the channel state information includes one or more of amplitude state information and phase state information;

[0048] Perform data processing operations on the channel state information to obtain target state information, and generate target change information under the target environment based on the target state information;

[0049] The data processing operations include one or more of the following: filtering and denoising operations, and normalization operations.

[0050] As an optional implementation, in the second aspect of this application, the specific method by which the first determining module generates target change information in the target environment based on the target state information includes:

[0051] Extract time-domain feature information from the target state information, and extract frequency-domain feature information from the target state information;

[0052] A feature fusion operation is performed on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result;

[0053] Based on the feature fusion results, target change information in the target environment is generated.

[0054] As an optional implementation, in the second aspect of this application, the specific method by which the second determining module determines the action recognition result corresponding to the target environment based on the target change information includes:

[0055] Based on the target change information, determine the motion characteristic information corresponding to the target change information, wherein the motion characteristic information includes one or more of the motion duration information, motion amplitude information, and motion cycle information corresponding to the human motion information;

[0056] Extract the target characteristic features from the action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain the cluster analysis results;

[0057] Based on the clustering analysis results, the action recognition results corresponding to the target environment are determined.

[0058] As an optional implementation, in a second aspect of this application, the apparatus further includes:

[0059] The extraction module is used to extract motion feature information from the human motion information before the first determining module determines the target change information in the target environment based on the human motion information. The motion feature information includes one or more of the following: motion cycle feature information, motion duration feature information, motion amplitude feature information, and motion speed feature information corresponding to the human motion information.

[0060] The first judgment module is used to determine whether the action feature information meets the preset action recognition conditions; when it is determined that the action feature information meets the preset action recognition conditions, the first determination module is triggered to perform the operation of determining the target change information in the target environment based on the human action information.

[0061] The second generation module is used to generate action processing parameters in the target environment based on the action feature information and the preset action recognition conditions when the first judgment module determines that the action feature information does not meet the preset action recognition conditions. The module then performs matching information processing operations on the target environment based on the action processing parameters to update the human action information and re-triggers the extraction module to perform the operation of extracting action feature information from the human action information and triggers the first judgment module to determine whether the action feature information meets the preset action recognition conditions.

[0062] As an optional implementation, in a second aspect of this application, the second determining module is further configured to determine the number of users in the target environment based on all the detected human motion information in the target environment after the detection module detects human motion information in the target environment within a preset target time period.

[0063] The device further includes:

[0064] The second judgment module is used to determine whether the number of users is greater than or equal to a preset number threshold.

[0065] The second determining module is further configured to, when the second determining module determines that the number of users is greater than or equal to a preset number threshold, determine the user information of each user included in the target environment, and determine the identification priority of each user based on the user information of each user; and determine the target user from all the users based on the identification priority;

[0066] The update module is used to perform an update operation on the human motion information based on the motion information corresponding to the target user, so as to obtain the updated human motion information.

[0067] A third aspect of this application discloses another action intelligent recognition method apparatus in a Wi-Fi environment, the apparatus comprising:

[0068] Memory containing executable program code;

[0069] A processor coupled to the memory;

[0070] The processor calls the executable program code stored in the memory to execute the action intelligent recognition method in the Wi-Fi environment disclosed in the first aspect of this application.

[0071] The fourth aspect of this application discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the action intelligent recognition method in a Wi-Fi environment disclosed in the first aspect of this application.

[0072] Compared with the prior art, the embodiments of this application have the following beneficial effects:

[0073] In this embodiment, human motion information in a target environment within a preset target time period is detected, wherein the target environment is a Wi-Fi environment; based on the human motion information, target change information in the target environment is determined; wherein the target change information includes CSI change information of the target environment within the preset target time period; and based on the target change information, the motion recognition result corresponding to the target environment is determined. It is evident that implementing this application enables human motion recognition even in a Wi-Fi environment, which is beneficial for improving the accuracy and reliability of the obtained human motion recognition results, and also for improving the intelligence and efficiency of human motion recognition. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 is a schematic diagram of a motion intelligent recognition scenario in a Wi-Fi environment disclosed in an embodiment of this application;

[0076] Figure 2 is a flowchart illustrating an intelligent action recognition method in a Wi-Fi environment disclosed in an embodiment of this application;

[0077] Figure 3 is a flowchart illustrating another intelligent action recognition method in a Wi-Fi environment disclosed in an embodiment of this application;

[0078] Figure 4 is a schematic diagram of the structure of a motion intelligent recognition device in a Wi-Fi environment disclosed in an embodiment of this application;

[0079] Figure 5 is a schematic diagram of another motion intelligent recognition device in a Wi-Fi environment disclosed in an embodiment of this application;

[0080] Figure 6 is a schematic diagram of the structure of another motion intelligent recognition device in a Wi-Fi environment disclosed in an embodiment of this application. Detailed Implementation

[0081] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0082] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0083] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0084] This application discloses a method and apparatus for intelligent motion recognition in a Wi-Fi environment, which can recognize human motions in a Wi-Fi environment, thereby improving the accuracy and reliability of the human motion recognition results, as well as the intelligence and efficiency of human motion recognition. These will be described in detail below.

[0085] To better understand the action intelligent recognition method and apparatus in a Wi-Fi environment disclosed in this application, the scenario of the action intelligent recognition method in a Wi-Fi environment is first described. Specifically, the action intelligent recognition scenario in a Wi-Fi environment can be as shown in Figure 1, which is a schematic diagram of the action intelligent recognition scenario in a Wi-Fi environment disclosed in this application. As shown in Figure 1, the action intelligent recognition scenario in a Wi-Fi environment can be a scenario within the coverage area of ​​a Wi-Fi signal, and this scenario includes a router, a client device, and a human body performing an operation. The action intelligent recognition scenario in a Wi-Fi environment can be a home scenario, or a shopping mall scenario, a company scenario, etc., and the embodiments of this application do not specifically limit it. It should be noted that the intelligent action recognition method in a Wi-Fi environment can detect human action information in a target environment within a preset target time period, where the target environment is a Wi-Fi environment; based on the human action information, determine target change information in the target environment; where the target change information includes CSI change information of the target environment within the preset target time period; and determine the action recognition result corresponding to the target environment based on the target change information. This method can recognize actions using existing Wi-Fi signals and devices without the need for additional sensors, which is beneficial to improving the intelligence and efficiency of user action recognition and obtaining action recognition results, as well as improving the accuracy and reliability of user action recognition.

[0086] Optionally, based on the action recognition results, the device to be controlled in the target environment is determined. Corresponding device control commands are generated based on the action recognition results and the device to be controlled, and the device to be controlled executes matching operations. Specifically, the matching target command action is determined based on the action recognition results and action mapping relationship. Based on the target command action and the device to be controlled, the corresponding device operation parameters are determined, and then the corresponding device control commands are generated. This allows for intuitive body movements to control smart devices in a Wi-Fi environment. Users can control devices from a certain distance without physical contact, improving the intelligence and efficiency of device control. Furthermore, by using pre-set action mapping relationships, recognized actions are quickly converted into device control commands, reducing operation steps and time, further improving device control efficiency. The device to be controlled can sense user actions via Wi-Fi signals, enabling more intelligent interaction. Accurate action recognition and pre-set action mapping relationships reduce the possibility of misoperation, improving the accuracy and reliability of device control and further enhancing stability. This achieves intelligent, convenient, and personalized device control, ultimately improving the intelligence, efficiency, and accuracy of user control of the device.

[0087] Optionally, based on human motion information, channel state information within a preset target time period is determined, and data processing operations are performed to obtain target state information. Target change information under the target environment is generated based on this target state information. This can remove noise and interference from CSI data through filtering operations, retaining signal features related to human motion, and normalizing the amplitude and phase of CSI data to a fixed range. This eliminates differences under different device or environmental conditions, making the data more stable and consistent, thus improving data stability and accuracy. This, in turn, improves the accuracy and reliability of motion recognition, and also enhances signal quality and motion recognition accuracy. Furthermore, by utilizing existing Wi-Fi devices and signals, no additional sensors or hardware are required, reducing system hardware costs. This also improves the accuracy and reliability of recognizing user actions in the target environment, further enhancing the intelligence, efficiency, and convenience of user control of smart devices within the target environment.

[0088] Optionally, the number of users in the target environment is determined based on all detected human motion information. It is then determined whether the number of users is greater than or equal to a preset threshold. If so, the user information of each user in the target environment is determined, and the recognition priority of each user is determined. Based on the recognition priority, the target user is identified, and the human motion information is updated according to the motion information corresponding to the target user to obtain updated human motion information. This method can accurately determine the number of users in the target environment by detecting all human motion information, and can identify whether multiple users exist in the environment. This provides the user command data basis for subsequent control of the device. By determining the recognition priority of each user, the motion information of important users can be processed first, which is beneficial to improving user experience. In addition to its ease of use and comfort, the system can provide personalized services based on user priorities. It updates human motion information based on the target user's corresponding action information, ensuring the system's real-time performance and accuracy. This allows for timely reflection of the target user's latest action status, reducing misjudgments or delays caused by changes in user actions. This improves the intelligence and accuracy of user control over the device. Furthermore, by dynamically updating human motion information, it ensures the real-time and accuracy of device control, enabling smooth real-time interaction. This enhances the intelligence and efficiency of human motion recognition within the target environment, as well as the accuracy and reliability of generated recognition results. Ultimately, it also improves the intelligence, efficiency, and convenience of user control over smart devices within the target environment.

[0089] For example, as shown in Figure 1, the Wi-Fi signal coverage area includes routers and client devices. The recognition result of the action is used to indicate that the human body is waving its arm back and forth quickly within the coverage area. This generates control commands to increase the air conditioner's fan speed and decrease its temperature, thereby controlling the air conditioner to increase its fan speed and decrease its temperature.

[0090] It should be noted that the scenario architecture shown in Figure 1 is only to illustrate the scenario used by the action intelligent recognition method in a Wi-Fi environment. The router, client device and human body involved are only schematic illustrations. The specific structure / size / shape / location / installation method, etc. can be adapted to the actual scenario. The scenario architecture shown in Figure 1 is not limited in this respect.

[0091] The above describes the scenarios in which the intelligent motion recognition method is applied in a Wi-Fi environment. The following is a detailed description of the intelligent motion recognition method and device in a Wi-Fi environment.

[0092] Example 1

[0093] Please refer to Figure 2, which is a flowchart illustrating a motion intelligent recognition method in a Wi-Fi environment disclosed in an embodiment of this application. The motion intelligent recognition method in a Wi-Fi environment described in Figure 2 can be applied to a motion intelligent recognition device in a Wi-Fi environment, or it can be applied to a Wi-Fi port; this embodiment of the application does not limit its application. As shown in Figure 1, the motion intelligent recognition method in a Wi-Fi environment may include the following operations:

[0094] 101. Detect human motion information in the target environment within a preset target time period.

[0095] In this embodiment of the application, the target environment is a Wi-Fi environment. That is, the target environment is an area covered by Wi-Fi signals.

[0096] In this embodiment of the application, the preset target time period can optionally be 3 seconds, 5 seconds or 10 seconds, and this embodiment of the application does not make a specific limitation.

[0097] In this embodiment of the application, optionally, the human motion information may include human motion information corresponding to each user in the target environment, or it may only include human motion information corresponding to a specific user in the target environment. Further optionally, the human motion information may also include gesture motion information of each user in the target environment.

[0098] 102. Based on human motion information, determine target change information in the target environment.

[0099] In this embodiment of the application, the target change information includes the CSI change information of the target environment within a preset target time period.

[0100] In this embodiment, optionally, the CSI change information refers to Wi-Fi CSI (Channel State Information), which is key data describing the characteristics of a wireless channel. It includes information such as the amplitude, phase, and propagation delay of the signal during transmission, and is an important set of parameters describing the characteristics of a wireless channel in wireless communication. It contains various key information about the wireless signal during transmission, reflecting the signal propagation characteristics under specific environments. CSI change information refers to the changes in CSI parameters within a specific time period due to environmental factors or the movement of objects (such as a human body). Specifically, the amplitude in the CSI change information represents the signal strength, reflecting the attenuation or enhancement of the signal during transmission. Changes in amplitude can be caused by factors such as signal propagation distance, obstacle obstruction, and multipath effects. The phase represents the signal phase shift, reflecting the time delay or phase change of the signal during propagation. Phase changes can be caused by factors such as the length of the signal propagation path and multipath effects. The propagation delay is the propagation time of the signal from the transmitter to the receiver. Changes in delay can be caused by changes in the signal propagation path (such as the movement of objects).

[0101] 103. Based on the target change information, determine the action recognition result corresponding to the target environment.

[0102] In this embodiment of the application, optionally, the action recognition result corresponding to the target environment may include the action recognition result corresponding to the user contained in the target environment.

[0103] In this embodiment of the application, optionally, for example, the action recognition result corresponding to the target environment may include the back-and-forth waving motion made by the user in the target environment within 3 seconds.

[0104] In this embodiment of the application, optionally, body movement recognition in a Wi-Fi environment can theoretically be achieved by waving and cutting the Wi-Fi signal (i.e., electromagnetic wave) propagating in the air through a certain part of the human body (such as the arm), thereby changing the CSI of the Wi-Fi received by the client. The receiving end can then identify the corresponding action made by the human body based on the CSI of the Wi-Fi signal received over a period of time and through a professional algorithm.

[0105] As can be seen, the intelligent action recognition method in the Wi-Fi environment described in Figure 1 can detect human action information in the target environment within a preset target time period, determine target change information in the target environment based on the human action information, and determine the corresponding action recognition result in the target environment based on the target change information. It can directly and intelligently recognize user actions in the Wi-Fi environment, and can recognize actions using existing Wi-Fi signals and devices without additional sensors. This is beneficial to improving the intelligence and efficiency of user action recognition and obtaining action recognition results, as well as improving the accuracy and reliability of user action recognition.

[0106] Example 2

[0107] Please refer to Figure 3, which is a flowchart illustrating another intelligent motion recognition method in a Wi-Fi environment disclosed in this application. The intelligent motion recognition method in a Wi-Fi environment described in Figure 3 can be applied to an intelligent motion recognition device in a Wi-Fi environment, or it can be applied to a Wi-Fi port; this application does not limit its application. As shown in Figure 3, the intelligent motion recognition method in a Wi-Fi environment may include the following operations:

[0108] 201. Detect human motion information in the target environment within a preset target time period.

[0109] 202. Based on human motion information, determine target change information in the target environment.

[0110] 203. Based on the target change information, determine the action recognition result corresponding to the target environment.

[0111] For detailed descriptions of steps 201-203 in this embodiment, please refer to the other descriptions of steps 101-103 in Embodiment 1. These descriptions will not be repeated in this embodiment.

[0112] 204. Based on the action recognition results, identify the devices to be controlled in the target environment.

[0113] In this embodiment of the application, the number of devices to be controlled in the target environment may be one or more, and this embodiment of the application does not make a specific limitation.

[0114] In this embodiment of the application, optionally, the device to be controlled in the target environment can be a client smart device connected to Wi-Fi in the target environment. Further optionally, the device to be controlled in the target environment can be one or more of a smart air conditioner, a smart TV, a smart fan, etc.

[0115] In this embodiment of the application, optionally, the above-mentioned determination of the device to be controlled in the target environment based on the action recognition result may include:

[0116] Analyze the action recognition results to obtain action analysis results, which include action meaning information corresponding to the action recognition results;

[0117] Based on the action analysis results, determine the user demand information of the action users corresponding to the action recognition results. The action users corresponding to the action recognition results include the users who made the action recognition results.

[0118] Based on user needs information, identify the controllable devices in the target environment that match the user needs information.

[0119] In this embodiment of the application, optionally, for example, the action recognition result is used to indicate that the user is currently fanning himself. The resulting action analysis result indicates that the user is very hot at this time, thereby determining that the user's demand information is to lower the ambient temperature. At this time, the controllable device in the target environment that matches the user's demand information can be a smart fan and / or a smart air conditioner.

[0120] 205. Based on the action recognition results and the device to be controlled, generate the corresponding device control command for the device to be controlled, and control the device to be controlled to perform the operation that matches the device control command.

[0121] In this embodiment of the application, based on the action recognition result and the device to be controlled, a device control command corresponding to the device to be controlled is generated, including:

[0122] Based on the action recognition results and the pre-determined action mapping relationships, the target instruction action that matches the action recognition results is determined from the pre-determined action mapping relationships;

[0123] Based on the target instruction action and the device to be controlled, determine the corresponding device operation parameters of the device to be controlled, and generate the corresponding device control instruction based on the device operation parameters.

[0124] In this embodiment of the application, optionally, the pre-determined action mapping relationship includes a pre-determined mapping relationship between multiple actions and the device to be controlled, and the device operating parameters corresponding to each device to be controlled.

[0125] In this embodiment of the application, optionally, the above-mentioned determination of the device operation parameters corresponding to the device under control based on the target instruction action and the device under control includes: determining the target control instruction that matches the target instruction action among all device operation instructions corresponding to the device under control, and determining the device operation parameters corresponding to the device under control based on the target control instruction.

[0126] In this embodiment of the application, optionally, for example, if the action recognition result is that the user in the target environment is holding his hands and trembling, then the target instruction action determined in the action mapping relationship is to turn off the air conditioner or increase the temperature of the air conditioner, and the device operation parameters corresponding to the target air conditioner are determined according to the target instruction action and the target air conditioner, thereby generating a device control instruction to turn off the air conditioner or increase the temperature of the air conditioner.

[0127] As can be seen, the intelligent action recognition method in the Wi-Fi environment described in Figure 2 can determine the device to be controlled in the target environment based on the action recognition results. It generates corresponding device control commands based on the action recognition results and the device to be controlled, and controls the device to perform matching operations. Specifically, it determines the matching target command action based on the action recognition results and action mapping relationship, determines the corresponding device operation parameters based on the target command action and the device to be controlled, and then generates the corresponding device control command. This allows users to control intelligent devices through intuitive body movements in the Wi-Fi environment. Users do not need to touch the device; they can control it from a certain distance through actions, which improves the intelligence and efficiency of controlling the device. Furthermore, by using a pre-set action mapping relationship, the recognized actions are quickly converted into device control commands, reducing operation steps and time, further improving device control efficiency. The device to be controlled can sense user actions through Wi-Fi signals, thus achieving more intelligent interaction. Accurate action recognition and pre-set action mapping relationships reduce the possibility of misoperation, improving the accuracy and reliability of controlling the device, and further enhancing the stability of device control. This achieves intelligent, convenient, and personalized device control, and further improves the intelligence, efficiency, and accuracy of user control of the device.

[0128] In an optional embodiment, determining target change information in the target environment based on human motion information includes:

[0129] Based on human motion information, channel state information within a preset target time period is determined, wherein the channel state information includes one or more of amplitude state information and phase state information.

[0130] Perform data processing operations on the channel state information to obtain the target state information, and generate target change information under the target environment based on the target state information;

[0131] The data processing operations include one or more of the following: filtering and denoising operations, and normalization operations.

[0132] In this optional embodiment, the above-mentioned determination of channel state information within a preset target time period based on human motion information may include:

[0133] Based on human motion information, status data is collected by Wi-Fi client devices within a preset target time period, and channel status information is generated based on the status data.

[0134] The Wi-Fi client can be a router or a smart device; the status data can include one or more of the following: signal amplitude status data and signal phase status data.

[0135] In this optional embodiment, the filtering and denoising operation may include one or more of low-pass filtering, high-pass filtering, and band-pass filtering; wherein, low-pass filtering is used to remove high-frequency noise and retain low-frequency signal components; high-pass filtering is used to remove low-frequency interference and retain high-frequency signal components; and band-pass filtering is used to combine low-pass and high-pass filtering to retain signal components within a specific frequency range.

[0136] In this optional embodiment, the normalization operation may be to adjust the amplitude and phase of the CSI data to a standardized range to facilitate subsequent analysis and processing; further, the normalization operation may include one or more of amplitude normalization and phase normalization.

[0137] In this optional embodiment, the target state information may optionally include one or more of amplitude state change information and phase state change information within a preset target time period; wherein, the target change information may include the target state information.

[0138] As can be seen, implementing this optional embodiment can determine the channel state information within a preset target time period based on human motion information and perform data processing operations to obtain target state information. Based on the target state information, target change information in the target environment can be generated. It can remove noise and interference from CSI data through filtering operations, retain signal features related to human motion, and normalize the amplitude and phase of CSI data to a fixed range. This can eliminate differences under different device or environmental conditions, making the data more stable and consistent, which is beneficial to improving the stability and accuracy of the data. This, in turn, is beneficial to improving the accuracy and reliability of motion recognition. It can also improve signal quality and the accuracy of motion recognition. Furthermore, by using existing Wi-Fi devices and signals, no additional sensors or hardware are required, thus reducing the hardware cost of the system. At the same time, it is beneficial to improve the accuracy and reliability of recognizing user actions in the target environment, and further beneficial to improving the intelligence, efficiency, and convenience of users controlling smart devices in the target environment.

[0139] In another optional embodiment, target change information under the target environment is generated based on the target state information, including:

[0140] Extract time-domain features from the target state information, and extract frequency-domain features from the target state information;

[0141] A feature fusion operation is performed on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result;

[0142] Based on the feature fusion results, target change information in the target environment is generated.

[0143] In this optional embodiment, optional time-domain feature information refers to features directly extracted from the time series. These features can reflect the signal's change characteristics over time. Time-domain features are typically used to describe the signal's amplitude, phase, periodicity, and other characteristics. Frequency-domain feature information refers to features extracted after the signal is converted from the time domain to the frequency domain through Fourier transform. These features can reflect the signal's frequency characteristics. Frequency-domain features are typically used to describe the signal's frequency distribution, main frequency components, etc.

[0144] In this optional embodiment, the above-described feature fusion operation on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result may include:

[0145] A feature combination operation is performed on the time-domain and frequency-domain feature information to obtain feature vector information, and a feature fusion result is generated based on the feature vector information.

[0146] In this optional embodiment, the feature fusion result can be used to describe signal changes in the target environment, wherein the signal changes may include changes in Wi-Fi-CSI signals.

[0147] In this optional embodiment, the target change information may optionally include all feature fusion results.

[0148] In this optional embodiment, the above-mentioned generation of target change information in the target environment based on the feature fusion result may include:

[0149] The feature fusion results are input into a preset change information analysis model to obtain analysis output results, and the target change information under the target environment is determined based on the analysis output results.

[0150] As can be seen, implementing this optional embodiment can extract time-domain and frequency-domain feature information from the target state information, perform feature fusion operations on the time-domain and frequency-domain feature information to obtain feature fusion results, and generate target change information in the target environment based on the feature fusion results. It can provide multi-dimensional information of the signal through time-domain and frequency-domain features, and can more accurately capture the features of human actions. By fusing time-domain and frequency-domain features, it can reduce the misjudgment that may be caused by a single feature. Furthermore, through feature fusion, multiple features can be merged into a comprehensive feature vector, reducing the dimensionality of features, which is beneficial to improving the efficiency of data processing. The optimized feature vector can be processed and analyzed faster, thereby improving the real-time performance of human action recognition and control of the controlled device in the target environment. This allows the device to respond to human actions faster, which in turn improves the accuracy and reliability of the action recognition results, as well as the intelligence and efficiency of the action recognition results. Furthermore, it also helps to improve the intelligence, efficiency, and convenience of users controlling intelligent devices in the target environment.

[0151] In another optional embodiment, based on the target change information, the action recognition result corresponding to the target environment is determined, including:

[0152] Based on the target change information, determine the action characteristic information corresponding to the target change information. The action characteristic information includes one or more of the following: action duration information, action amplitude information, and action cycle information corresponding to human action information.

[0153] Extract target characteristic features from action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain cluster analysis results;

[0154] Based on the cluster analysis results, the action recognition results corresponding to the target environment are determined.

[0155] In this optional embodiment, the process of determining the motion characteristic information corresponding to the target change information based on the target change information may include:

[0156] Based on the target change information, identify the key change information in the target change information. The key change information may include one or more of the following: change information on the duration of the action, change information on the amplitude of the action, and change information on the cycle of the action.

[0157] Based on all the identified key change information, determine the motion characteristic information corresponding to the target change information.

[0158] In this optional embodiment, the preset clustering algorithm may optionally include one or more of the K-Means algorithm, DBSCAN algorithm, or hierarchical clustering algorithm.

[0159] In this optional embodiment, the clustering analysis results may optionally include the action category corresponding to the action characteristic information.

[0160] In this optional embodiment, the above-mentioned determination of the action recognition result corresponding to the target environment based on the clustering analysis result may include:

[0161] Based on the cluster analysis results, the target action category corresponding to the action characteristic information is determined, and based on the target action category and action characteristic information, the action recognition result that matches the action characteristic information is determined in the action set corresponding to the target action category.

[0162] As can be seen, implementing this optional embodiment can determine the corresponding action characteristic information based on the target change information, extract the target characteristic features from the action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain the cluster analysis results. Based on the cluster analysis results, the action recognition result corresponding to the target environment is determined. By extracting various action characteristic information such as action duration, action amplitude, and action cycle, it can comprehensively describe the characteristics of human actions from multiple perspectives. Through multi-dimensional feature analysis, it can more accurately identify different types of actions, which is beneficial to improving the accuracy and reliability of subsequent user action recognition. Furthermore, the clustering algorithm can automatically separate similar action features. Categorization allows for more accurate identification of specific actions within a target environment. Cluster analysis maps complex and diverse action features to predefined action categories, further improving the accuracy and reliability of identification. Extracting action characteristic information (such as duration, amplitude, and period) is a relatively simple and efficient process, directly reflecting the key characteristics of the action. Furthermore, clustering algorithms typically have high computational efficiency, enabling rapid processing of large amounts of feature data to generate clustering results. This enhances the intelligence and efficiency of subsequent user action identification, and further improves the intelligence, efficiency, and convenience of user control of smart devices within the target environment.

[0163] In yet another optional embodiment, before determining target change information in the target environment based on human motion information, the method further includes:

[0164] Extract motion feature information from human motion information, wherein the motion feature information includes one or more of the following: motion cycle feature information, motion duration feature information, motion amplitude feature information, and motion speed feature information corresponding to human motion information.

[0165] Determine whether the action feature information meets the preset action recognition conditions;

[0166] When the action feature information is determined to meet the preset action recognition conditions, the operation of determining the target change information in the target environment based on human action information is triggered.

[0167] When it is determined that the action feature information does not meet the preset action recognition conditions, action processing parameters in the target environment are generated based on the action feature information and the preset action recognition conditions. Then, the target environment is subjected to matching information processing operations based on the action processing parameters to update the human action information. The operation of extracting action feature information from the human action information and determining whether the action feature information meets the preset action recognition conditions are triggered again.

[0168] In this optional embodiment, the extracted motion cycle feature information can be determined by analyzing the frequency components of the CSI using Fourier transform; the extracted motion duration feature information can be obtained by analyzing the time series of the CSI, for example, by calculating the time period during which the signal amplitude or phase change exceeds a certain threshold; the extracted motion amplitude feature information can be obtained by calculating the maximum amplitude change of the CSI, for example, by calculating the difference between the maximum and minimum values ​​of the signal amplitude; and the extracted motion speed feature information can be determined by calculating the rate of change of the CSI to determine the motion speed, for example, by calculating the rate of change of the signal amplitude or phase.

[0169] In this optional embodiment, the determination of whether the action feature information meets the preset action recognition conditions may include:

[0170] Determine whether the motion cycle feature information is used to indicate that the motion cycle is greater than or equal to a preset cycle threshold;

[0171] When it is determined that the motion cycle feature information is used to represent a motion cycle greater than or equal to a preset cycle threshold, the motion feature information is determined to meet the preset motion recognition conditions; when it is determined that the motion cycle feature information is used to represent a motion cycle less than the preset cycle threshold, the motion feature information is determined not to meet the preset motion recognition conditions; and / or,

[0172] Determine whether the action duration feature information is used to indicate that the action duration is greater than or equal to a preset duration threshold;

[0173] When the action duration feature information is determined to indicate an action duration greater than or equal to a preset duration threshold, the action feature information is determined to meet the preset action recognition conditions; when the action duration feature information is determined to indicate an action duration less than the preset duration threshold, the action feature information is determined to not meet the preset action recognition conditions; and / or,

[0174] Determine whether the motion amplitude feature information is used to indicate that the motion amplitude is greater than or equal to a preset amplitude threshold;

[0175] When it is determined that the motion amplitude feature information is used to indicate that the motion amplitude is greater than or equal to a preset amplitude threshold, the motion feature information is determined to meet the preset motion recognition conditions; when it is determined that the motion amplitude feature information is used to indicate that the motion amplitude is less than a preset amplitude threshold, the motion feature information is determined to not meet the preset motion recognition conditions.

[0176] In this optional embodiment, the action processing parameters in the target environment may optionally include one or more of the following: sampling duration processing parameters, sampling frequency processing parameters, and sampling range processing parameters.

[0177] In this optional embodiment, the aforementioned process of performing matching information processing operations on the target environment based on motion processing parameters to update human motion information may include: performing information sampling and adjustment operations on the target environment according to the motion processing parameters to obtain information processing results, and updating human motion information based on the information processing results. For example, the information processing operations may include adjusting the sampling time and adjusting the sampling frequency, performing information sampling processing operations in the target environment based on the adjusted sampling time and sampling frequency to obtain information processing results, and updating human motion information based on the information processing results.

[0178] As can be seen, implementing this optional embodiment can extract motion feature information from human motion information and determine whether it meets preset motion recognition conditions. If it does, it triggers the operation of determining target change information in the target environment based on human motion information. If it does not meet the conditions, it generates motion processing parameters in the target environment based on the motion feature information and the preset motion recognition conditions. It then performs matching information processing operations on the target environment based on the motion processing parameters to update human motion information, extract motion feature information from human motion information, and determine whether the motion feature information meets the preset motion recognition conditions. By extracting motion feature information (such as motion cycle, duration, amplitude, speed, etc.) and determining whether the preset conditions are met, it can accurately filter out effective actions that meet the motion recognition requirements, thereby improving the accuracy of motion recognition. When the motion feature information does not meet the preset conditions, it can dynamically generate motion processing parameters based on the current motion feature information. By performing corresponding information processing operations, the device can adapt to different action characteristics and environmental conditions through a dynamic adjustment mechanism, enhancing its flexibility. By dynamically adjusting sampling time, sampling frequency, and signal processing parameters, the device can better adapt to different types of action modes. By filtering effective action feature information through preset conditions, further processing of action data that does not meet the requirements can be avoided, thereby reducing unnecessary data processing steps, improving data processing efficiency, and enhancing the intelligence of data processing. By dynamically adjusting action processing parameters and updating human action information in a timely manner, the device can respond to action changes more quickly, achieving more efficient real-time action recognition. This is beneficial for improving the intelligence and efficiency of human action recognition in the target environment, as well as improving the accuracy and reliability of the generated recognition results. Furthermore, it also helps to improve the intelligence, efficiency, and convenience of users controlling intelligent devices in the target environment.

[0179] In yet another optional embodiment, after detecting human motion information in the target environment within a preset target time period, the method further includes:

[0180] Based on all detected human motion information, determine the number of users in the target environment and determine whether the number of users is greater than or equal to a preset threshold.

[0181] When it is determined that the number of users is greater than or equal to a preset threshold, the user information of each user in the target environment is determined, and the identification priority of each user is determined based on the user information of each user.

[0182] Based on the identification priority, the target user is determined from all users, and the human motion information is updated according to the action information corresponding to the target user to obtain the updated human motion information.

[0183] In this optional embodiment, the detected human motion information may include the human motion information of all users existing in the target environment, and then the number of users in the target environment may be determined based on the human motion information of all users.

[0184] In this optional embodiment, it is further possible to terminate the process when it is determined that the number of users is less than a preset threshold.

[0185] In this optional embodiment, the user information of each user may include one or more of the following: age information, identity information, device control permission information, health status information, etc., and this application embodiment does not make specific limitations.

[0186] In this optional embodiment, the determination of each user's identification priority based on each user's user information may include:

[0187] Based on each user's information, determine the device control weight corresponding to each user, and determine the recognition priority of each user based on the device control weight. The higher the device control weight, the higher the user's recognition priority, and the lower the device control weight, the lower the user's recognition priority.

[0188] In this optional embodiment, the above-mentioned determination of the target user from all users based on identification priority can be that the user with the highest identification priority is identified as the target user. For example, if user A is a child with low device control privileges, while user B is an adult woman with high device control privileges, then user A's identification priority is lower than user B's identification priority, and user B is the target user.

[0189] In this optional embodiment, the above-mentioned updating operation on the human motion information based on the motion information corresponding to the target user to obtain the updated human motion information may include: updating the motion information corresponding to the target user to the human motion information, that is, the updated human motion information is the motion information corresponding to the target user.

[0190] As can be seen, implementing this optional embodiment can determine the number of users in the target environment based on all detected human motion information, determine whether the number of users is greater than or equal to a preset threshold, and if so, determine the user information of each user in the target environment and then determine the recognition priority of each user. Based on the recognition priority, the target user is determined, and the human motion information is updated according to the motion information corresponding to the target user to obtain the updated human motion information. This allows for accurate determination of the number of users in the target environment through all detected human motion information, and can identify whether multiple users exist in the environment, thus providing a user command data basis for subsequent device control. By determining the recognition priority of each user, the motion information of important users can be processed first, which is beneficial for improving... This system enhances user convenience and comfort, provides personalized services based on user priorities, and updates human motion information according to the target user's actions, ensuring real-time performance and accuracy. It promptly reflects the target user's latest action status, reducing misjudgments or delays caused by changes in user actions. This improves the intelligence and accuracy of user control over the device. Furthermore, by dynamically updating human motion information, it ensures real-time and accurate device control, enabling smooth real-time interaction. This improves the intelligence and efficiency of human motion recognition within the target environment, as well as the accuracy and reliability of generated recognition results. Ultimately, it enhances the intelligence, efficiency, and convenience of user control of smart devices within the target environment.

[0191] Example 3

[0192] Please refer to Figure 4, which is a schematic diagram of the structure of a motion intelligent recognition device in a Wi-Fi environment disclosed in an embodiment of this application. As shown in Figure 4, the motion intelligent recognition device in a Wi-Fi environment may include:

[0193] The detection module 301 is used to detect human motion information in the target environment within a preset target time period, wherein the target environment is a Wi-Fi environment;

[0194] The first determining module 302 is used to determine target change information in the target environment based on human motion information; wherein, the target change information includes CSI change information of the target environment within a preset target time period;

[0195] The second determining module 303 is used to determine the action recognition result corresponding to the target environment based on the target change information.

[0196] As can be seen, the device described in Figure 4 can detect human motion information in the target environment within a preset target time period, determine target change information in the target environment based on the human motion information, and determine the corresponding action recognition result in the target environment based on the target change information. It can intelligently recognize user actions directly in a Wi-Fi environment, and can recognize actions using existing Wi-Fi signals and devices without additional sensors. This is beneficial to improving the intelligence and efficiency of user action recognition and obtaining action recognition results, as well as improving the accuracy and reliability of user action recognition.

[0197] In an optional embodiment, as shown in FIG5, the second determining module 303 is further configured to determine the device to be controlled in the target environment based on the action recognition result;

[0198] The device also includes:

[0199] The first generation module 304 is used to generate device control instructions corresponding to the device under control based on the action recognition results and the device under control.

[0200] The control module 305 is used to control the device under control to perform operating operations that match the device control commands;

[0201] The first generation module 304 generates the device control command corresponding to the device under control based on the action recognition result and the device under control in the following ways:

[0202] Based on the action recognition results and the pre-determined action mapping relationships, the target instruction action that matches the action recognition results is determined from the pre-determined action mapping relationships;

[0203] Based on the target instruction action and the device to be controlled, determine the corresponding device operation parameters of the device to be controlled, and generate the corresponding device control instruction based on the device operation parameters.

[0204] As can be seen, the device described in Figure 5 can determine the device to be controlled in the target environment based on the action recognition results. It generates corresponding device control commands based on the action recognition results and the device to be controlled, and controls the device to perform matching operations. Specifically, it determines the matching target command action based on the action recognition results and the action mapping relationship. Based on the target command action and the device to be controlled, it determines the corresponding device operation parameters and generates the corresponding device control command. This allows for intuitive body movements to control smart devices in a Wi-Fi environment. Users can control devices from a certain distance without physical contact, improving the intelligence and efficiency of device control. Furthermore, by using pre-set action mapping relationships, it quickly converts recognized actions into device control commands, reducing operation steps and time, further improving device control efficiency. The device to be controlled can sense user actions via Wi-Fi signals, enabling more intelligent interaction. Precise action recognition and pre-set action mapping relationships reduce the possibility of misoperation, improving the accuracy and reliability of device control and further enhancing its stability. This achieves intelligent, convenient, and personalized device control, ultimately improving the intelligence, efficiency, and accuracy of user control of the device.

[0205] In another optional embodiment, as shown in FIG5, the first determining module 302 determines the target change information in the target environment based on human motion information in the following specific ways:

[0206] Based on human motion information, channel state information within a preset target time period is determined, wherein the channel state information includes one or more of amplitude state information and phase state information.

[0207] Perform data processing operations on the channel state information to obtain the target state information, and generate target change information under the target environment based on the target state information;

[0208] The data processing operations include one or more of the following: filtering and denoising operations, and normalization operations.

[0209] As can be seen, the device described in Figure 5 can determine the channel state information within a preset target time period based on human motion information and perform data processing operations to obtain target state information. Based on the target state information, it generates target change information in the target environment. It can remove noise and interference from CSI data through filtering operations, retaining signal features related to human motion, and normalize the amplitude and phase of CSI data to a fixed range. This eliminates differences under different device or environmental conditions, making the data more stable and consistent, which is beneficial for improving data stability and accuracy. This, in turn, improves the accuracy and reliability of motion recognition, and also improves signal quality and motion recognition accuracy. Furthermore, by using existing Wi-Fi devices and signals, no additional sensors or hardware are required, thus reducing the system's hardware cost. It also improves the accuracy and reliability of recognizing user actions in the target environment, and further enhances the intelligence, efficiency, and convenience of user control of smart devices in the target environment.

[0210] In another optional embodiment, as shown in FIG5, the first determining module 302 generates target change information in the target environment based on the target state information in the following specific ways:

[0211] Extract time-domain features from the target state information, and extract frequency-domain features from the target state information;

[0212] A feature fusion operation is performed on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result;

[0213] Based on the feature fusion results, target change information in the target environment is generated.

[0214] As can be seen, the device described in Figure 5 can extract time-domain and frequency-domain feature information from the target state information, perform feature fusion operations on the time-domain and frequency-domain feature information to obtain feature fusion results, and generate target change information in the target environment based on the feature fusion results. It can provide multi-dimensional information of the signal through time-domain and frequency-domain features, and can more accurately capture the features of human actions. By fusing time-domain and frequency-domain features, it can reduce the misjudgment that may be caused by a single feature. Furthermore, through feature fusion, multiple features can be merged into a comprehensive feature vector, reducing the dimensionality of features, which is conducive to improving the efficiency of data processing. The optimized feature vector can be processed and analyzed faster, thereby improving the real-time performance of human action recognition and control of the controlled equipment in the target environment. This allows the equipment to respond to human actions faster, which in turn improves the accuracy and reliability of the action recognition results, as well as the intelligence and efficiency of the action recognition results. In addition, it also helps to improve the intelligence, efficiency, and convenience of users controlling intelligent devices in the target environment.

[0215] In another optional embodiment, as shown in FIG5, the second determining module 303 determines the specific method of the action recognition result corresponding to the target environment based on the target change information, including:

[0216] Based on the target change information, determine the action characteristic information corresponding to the target change information. The action characteristic information includes one or more of the following: action duration information, action amplitude information, and action cycle information corresponding to human action information.

[0217] Extract target characteristic features from action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain cluster analysis results;

[0218] Based on the cluster analysis results, the action recognition results corresponding to the target environment are determined.

[0219] As can be seen, the device described in Figure 5 can determine the corresponding action characteristic information based on the target change information, extract the target characteristic features from the action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain the cluster analysis results. Based on the cluster analysis results, the device determines the action recognition result corresponding to the target environment. By extracting various action characteristic information such as action duration, action amplitude, and action cycle, it can comprehensively describe the characteristics of human actions from multiple perspectives. Through multi-dimensional feature analysis, it can more accurately identify different types of actions, which is beneficial to improving the accuracy and reliability of subsequent user action recognition. Furthermore, the clustering algorithm can separate similar action features. Action classification allows for more accurate identification of specific actions in the target environment. Through cluster analysis, complex and diverse action features can be mapped to predefined action categories, further improving the accuracy and reliability of identification. The process of extracting action characteristic information (such as duration, amplitude, and period) is relatively simple and efficient. These features can directly reflect the key characteristics of the action. Furthermore, clustering algorithms typically have high computational efficiency, enabling them to quickly process large amounts of feature data and generate clustering results. This is beneficial for improving the intelligence and efficiency of subsequent user action identification. In addition, it also helps to improve the intelligence, efficiency, and convenience of users controlling smart devices in the target environment.

[0220] In yet another alternative embodiment, as shown in FIG5, the device further includes:

[0221] The extraction module 306 is used to extract motion feature information from the human motion information before the first determining module 302 determines the target change information in the target environment based on the human motion information. The motion feature information includes one or more of the following: motion cycle feature information, motion duration feature information, motion amplitude feature information, and motion speed feature information corresponding to the human motion information.

[0222] The first judgment module 307 is used to judge whether the action feature information meets the preset action recognition conditions; when it is judged that the action feature information meets the preset action recognition conditions, the first determination module 302 is triggered to perform the operation of determining the target change information in the target environment based on human action information.

[0223] The second generation module 308 is used to generate motion processing parameters in the target environment based on the motion feature information and the preset motion recognition conditions when the first judgment module 307 determines that the motion feature information does not meet the preset motion recognition conditions. It then performs matching information processing operations on the target environment based on the motion processing parameters to update the human motion information and re-triggers the extraction module 306 to extract the motion feature information from the human motion information and triggers the first judgment module 307 to determine whether the motion feature information meets the preset motion recognition conditions.

[0224] As can be seen, the device described in Figure 5 can extract motion feature information from human motion information and determine whether it meets preset motion recognition conditions. If it does, it triggers the operation of determining target change information in the target environment based on human motion information. If it does not meet the conditions, it generates motion processing parameters in the target environment based on the motion feature information and the preset motion recognition conditions. It then performs matching information processing operations on the target environment based on these parameters to update human motion information, extract motion feature information from the human motion information, and determine whether the motion feature information meets the preset motion recognition conditions. By extracting motion feature information (such as motion cycle, duration, amplitude, speed, etc.) and determining whether the preset conditions are met, it can accurately filter out valid motions that meet the motion recognition requirements, thereby improving the accuracy of motion recognition. When the motion feature information does not meet the preset conditions, it can dynamically generate motion processing parameters based on the current motion feature information. It performs corresponding information processing operations and can adapt to different action characteristics and environmental conditions through a dynamic adjustment mechanism, enhancing the flexibility of the device. By dynamically adjusting sampling time, sampling frequency, signal processing parameters, etc., the device can better adapt to different types of action modes. By filtering effective action feature information through preset conditions, it can avoid further processing of action data that does not meet the requirements, thereby reducing unnecessary data processing steps, improving data processing efficiency, and enhancing the intelligence of data processing. By dynamically adjusting action processing parameters and updating human action information in a timely manner, the device can respond to action changes more quickly and achieve more efficient real-time action recognition. This is conducive to improving the intelligence and efficiency of human action recognition in the target environment, as well as improving the accuracy and reliability of the generated recognition results. Furthermore, it also helps to improve the intelligence, efficiency, and convenience of users controlling intelligent devices in the target environment.

[0225] In another optional embodiment, as shown in FIG5, the second determining module 303 is further configured to determine the number of users in the target environment based on all detected human motion information after the detection module detects human motion information in the target environment within a preset target time period.

[0226] The device also includes:

[0227] The second judgment module 309 is used to determine whether the number of users is greater than or equal to a preset number threshold.

[0228] The second determining module 303 is further configured to, when the second judging module 309 determines that the number of users is greater than or equal to a preset number threshold, determine the user information of each user included in the target environment, and determine the identification priority of each user based on the user information of each user; and determine the target user from all users based on the identification priority.

[0229] The update module 310 is used to perform an update operation on the human motion information based on the action information corresponding to the target user, so as to obtain the updated human motion information.

[0230] As can be seen, the device described in Figure 5 can determine the number of users in the target environment based on all detected human motion information, determine whether the number of users is greater than or equal to a preset threshold, and if so, determine the user information of each user in the target environment and then determine the recognition priority of each user. Based on the recognition priority, the target user is identified, and the human motion information is updated according to the motion information corresponding to the target user to obtain the updated human motion information. By detecting all human motion information, the device can accurately determine the number of users in the target environment and identify whether multiple users exist in the environment, thus providing a user command data basis for subsequent device control. By determining the recognition priority of each user, the device can prioritize the processing of motion information of important users, which is beneficial for improving efficiency. This system enhances user convenience and comfort, provides personalized services based on user priorities, and updates human motion information according to the target user's actions, ensuring real-time performance and accuracy. It promptly reflects the target user's latest action status, reducing misjudgments or delays caused by changes in user actions. This improves the intelligence and accuracy of user control over the device. Furthermore, by dynamically updating human motion information, it ensures real-time and accurate device control, enabling smooth real-time interaction. This improves the intelligence and efficiency of human motion recognition within the target environment, as well as the accuracy and reliability of generated recognition results. Ultimately, it enhances the intelligence, efficiency, and convenience of user control of smart devices within the target environment.

[0231] Example 4

[0232] Please refer to Figure 6, which is a schematic diagram of another motion intelligent recognition device in a Wi-Fi environment disclosed in this application. As shown in Figure 6, the motion intelligent recognition device in a Wi-Fi environment may include:

[0233] Memory 401 storing executable program code;

[0234] Processor 402 coupled to memory 401;

[0235] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent action recognition method in the Wi-Fi environment described in Embodiment 1 or Embodiment 2 of this application.

[0236] Example 5

[0237] This application discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the action intelligent recognition method in a Wi-Fi environment described in Embodiment 1 or Embodiment 2 of this application.

[0238] Example 6

[0239] This application discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the action intelligent recognition method in the Wi-Fi environment described in Embodiment 1 or Embodiment 2.

[0240] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0241] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0242] Finally, it should be noted that the action intelligent recognition method and device in a Wi-Fi environment disclosed in the embodiments of this application are only preferred embodiments of this application and are only used to illustrate the technical solutions of this application, not to limit them; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent action recognition in a Wi-Fi environment, the method comprising: Detect human motion information in a target environment within a preset target time period, wherein the target environment is a Wi-Fi environment; Based on the human motion information, target change information in the target environment is determined; wherein, the target change information includes CSI change information of the target environment within the preset target time period; Based on the target change information, determine the action recognition result corresponding to the target environment.

2. The action intelligent recognition method in a Wi-Fi environment according to claim 1, the method further includes: Based on the action recognition results, the device to be controlled in the target environment is determined; Based on the action recognition result and the device to be controlled, a device control command corresponding to the device to be controlled is generated, and the device to be controlled is controlled to perform an operation that matches the device control command; The step of generating a device control command corresponding to the device under control based on the action recognition result and the device under control includes: Based on the action recognition result and the pre-determined action mapping relationship, the target instruction action that matches the action recognition result is determined from the pre-determined action mapping relationship; Based on the target instruction action and the device to be controlled, determine the device operation parameters corresponding to the device to be controlled, and generate the device control instruction corresponding to the device to be controlled according to the device operation parameters.

3. The intelligent action recognition method in a Wi-Fi environment according to claim 1 or 2, wherein, The step of determining target change information in the target environment based on the human motion information includes: Based on the human motion information, channel state information within the preset target time period is determined, wherein the channel state information includes one or more of amplitude state information and phase state information; Perform data processing operations on the channel state information to obtain target state information, and generate target change information under the target environment based on the target state information; The data processing operations include one or more of the following: filtering and denoising operations, and normalization operations.

4. The intelligent action recognition method in a Wi-Fi environment according to claim 3, wherein, The step of generating target change information in the target environment based on the target state information includes: Extract time-domain feature information from the target state information, and extract frequency-domain feature information from the target state information; A feature fusion operation is performed on the time-domain feature information and the frequency-domain feature information to obtain the feature fusion result; Based on the feature fusion results, target change information in the target environment is generated.

5. The intelligent action recognition method in a Wi-Fi environment according to claim 1 or 2, wherein, The step of determining the action recognition result corresponding to the target environment based on the target change information includes: Based on the target change information, determine the motion characteristic information corresponding to the target change information, wherein the motion characteristic information includes one or more of the motion duration information, motion amplitude information, and motion cycle information corresponding to the human motion information; Extract the target characteristic features from the action characteristic information, and perform cluster analysis on the target characteristic features using a preset clustering algorithm to obtain the cluster analysis results; Based on the clustering analysis results, the action recognition results corresponding to the target environment are determined.

6. The intelligent action recognition method in a Wi-Fi environment according to claim 1, wherein, Before determining the target change information in the target environment based on the human motion information, the method further includes: Extract motion feature information from the human motion information, wherein the motion feature information includes one or more of the following: motion cycle feature information, motion duration feature information, motion amplitude feature information, and motion speed feature information corresponding to the human motion information; Determine whether the action feature information meets the preset action recognition conditions; When it is determined that the action feature information meets the preset action recognition conditions, the operation of determining the target change information in the target environment based on the human action information is triggered. When it is determined that the action feature information does not meet the preset action recognition conditions, action processing parameters in the target environment are generated based on the action feature information and the preset action recognition conditions. Then, a matching information processing operation is performed on the target environment based on the action processing parameters to update the human action information. The operation of extracting action feature information from the human action information and determining whether the action feature information meets the preset action recognition conditions are triggered again.

7. The intelligent action recognition method in a Wi-Fi environment according to claim 2, wherein, After detecting human motion information in the target environment within a preset target time period, the method further includes: Based on all the detected human motion information, determine the number of users in the target environment, and determine whether the number of users is greater than or equal to a preset number threshold; When it is determined that the number of users is greater than or equal to the preset number threshold, the user information of each user included in the target environment is determined, and the identification priority of each user is determined based on the user information of each user. Based on the identification priority, a target user is determined from all the users, and an update operation is performed on the human motion information according to the action information corresponding to the target user to obtain the updated human motion information.

8. A method of action intelligent recognition in a Wi-Fi environment, wherein, The device includes: The detection module is used to detect human motion information in a target environment within a preset target time period, wherein the target environment is a Wi-Fi environment; The first determining module is used to determine target change information in the target environment based on the human motion information; wherein the target change information includes CSI change information of the target environment within a preset target time period; The second determining module is used to determine the action recognition result corresponding to the target environment based on the target change information.

9. A method and apparatus for intelligent action recognition in a Wi-Fi environment, wherein, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent action recognition method in a Wi-Fi environment as described in any one of claims 1-7.

10. A computer storage medium, wherein, The computer storage medium stores computer instructions, which, when invoked, are used to execute the intelligent action recognition method in a Wi-Fi environment as described in any one of claims 1-7.

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