Method and device for acquiring human body behavior recognition signal and electronic equipment

Abstract

The invention relates to a method for obtaining a human body behavior recognition signal, and the method comprises the steps: obtaining an initial phase difference value according to an initial phase value in an initial phase data sequence; according to the calculated initial phase difference value and the initialized step length, determining an area where phase mutation occurs; in the region, a target phase difference value is calculated according to a count value of a dynamic counter, and a target phase mutation value is determined according to the target phase difference value; calculating the target phase abrupt change value according to the target phase difference value to obtain a target phase data sequence; performing normalization processing on the target phase data sequence, the received channel state data and the received signal strength data; obtaining a conversion phase data sequence according to the calculated distance function difference value; obtaining a signal intensity data difference value according to the obtained signal intensity data; and fusing the converted phase data sequence, the signal intensity data difference value and the normalized channel state data.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things technology and human behavior recognition technology, and in particular to a method, device and electronic device for acquiring human behavior recognition signals. Background Art

[0002] With the rapid development of Internet of Things technology, intelligent recognition and monitoring of human behavior has become a research hotspot. Human behavior recognition technology has shown important application value in many fields, including but not limited to security monitoring, smart home, medical monitoring, autonomous driving, and safe production. Existing research on human behavior recognition is mainly divided into vision-based human behavior recognition technology and wearable sensor-based human behavior recognition technology. The specific methods are as follows: 1. Vision-based human behavior recognition technology: This type of technology uses a camera to capture images or videos containing different human behaviors, uses image processing algorithms to extract behavioral features, and then inputs them into a machine learning model for classification. Although this technology has achieved a high recognition accuracy, it has problems such as sensitivity to ambient lighting conditions, high risk of privacy leakage, and strict hardware installation requirements. In addition, the position and angle of the camera will also affect the final recognition effect.

[0003] 2. Human behavior recognition technology based on wearable sensors: This method mainly uses various sensors to collect information such as acceleration and pressure to identify different human behaviors. Although this technology avoids the problem of privacy leakage, wearing sensors for a long time may cause discomfort to users. In addition, most sensors are expensive and require regular charging and maintenance, which increases the cost and inconvenience of users.

[0004] 3. RFID-based human behavior recognition technology: In recent years, researchers have begun to explore the possibility of using RFID technology for human behavior recognition. RFID technology has the advantages of low cost, easy deployment, and strong signal penetration, and has been widely used in supply chain management, inventory control, three-dimensional positioning and other fields. The basic principle of using RFID technology for behavior recognition is to read the phase and RSSI data change patterns generated by RFID tags during human movement, thereby realizing the recognition of human behavior. However, existing RFID-based behavior recognition methods still face some challenges, such as the impact of tag deployment schemes on recognition accuracy and data instability caused by environmental and equipment interference.

[0005] 4. Human behavior recognition technology based on Wi-Fi CSI: Wi-Fi channel state information (CSI) contains rich spatial and temporal characteristics, and can provide more fine-grained information than traditional received signal strength indication (RSSI), which is suitable for contactless behavior recognition. It can be obtained only through the existing Wi-Fi infrastructure without the need for additional hardware, so it has the advantages of low cost and easy deployment. However, Wi-Fi CSI also faces the problem of data stability caused by multipath effects, noise and other wireless communication interference.

[0006] Therefore, no matter which of the above models is used, it will face the problems of low accuracy, unstable data, and limited types of identifiable human behaviors. Summary of the invention

[0007] The embodiment of the present invention provides a method, device and electronic device for obtaining human behavior recognition signals, which are used to solve the problems of low accuracy and unstable data in recognizing human behavior in the prior art. The method specifically includes: An initial phase difference value is calculated based on an initial phase value in an acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of a human body; According to the initial phase difference value and the initialization step size, determining the region where the phase mutation occurs from the initial phase data sequence; In the region where the phase mutation occurs, a target phase difference between two adjacent initial phase values ​​is calculated according to the count value of the dynamic counter, and a target phase mutation value is determined according to the target phase difference; the target phase mutation value is calculated according to the target phase difference to obtain a target phase data sequence; Normalizing the target phase data sequence, the received channel state data, and the received signal strength data; Calculating a distance function difference between a transmitting module that transmits the human behavior recognition signal and a receiving module that receives the human behavior recognition signal due to relative motion; Obtaining a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference; Obtaining a signal strength data difference according to the acquired two adjacent normalized signal strength data; The conversion phase data sequence, the signal strength data difference and the channel state data after normalization processing are fused to obtain a fused human behavior recognition signal.

[0008] The step of determining the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size includes: The initialization step length includes a first preset step length and a second preset step length, and the first preset step length is greater than the second preset step length; Find two adjacent initial phase values ​​whose initial phase difference is greater than the first preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are continuous areas, use the second preset step size to find an area where a sudden phase change occurs in the continuous area; Find two adjacent initial phase values ​​whose initial phase difference is less than the second preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are in a continuous area, use the first preset step size to find an area where a phase mutation occurs in the continuous area.

[0009] The step of calculating the target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter includes: Starting from the second initial phase value in the initial phase data sequence, respectively adding the values ​​calculated based on the current counter to obtain a plurality of intermediate phase values; The difference between two adjacent intermediate phase values ​​is used as the target phase difference.

[0010] Wherein, determining a target phase mutation value according to the target phase difference value includes: Using a second preset step size, the initial phase values ​​in the region where the phase mutation occurs are sequentially traversed; Find two adjacent intermediate phase values ​​where the target phase difference value is greater than the first preset target phase difference value, and when the intermediate phase value found is a continuous area, reduce the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value; Find two adjacent intermediate phase values ​​where the target phase difference value is less than the second preset target phase difference value. When the intermediate phase value found is a continuous area, increase the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value.

[0011] The step of calculating the target phase mutation value according to the target phase difference includes: When the target phase difference value is greater than the preset target phase difference value, the preset target value is subtracted from the current target phase mutation value; When the target phase difference value is less than the preset target phase difference value, the preset target value is added to the current target phase mutation value.

[0012] The step of calculating the distance function difference between the transmitting module for transmitting the human behavior recognition signal and the receiving module for receiving the human behavior recognition signal due to relative motion includes: Calculate the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module; The difference between the distance function calculated by the receiving module at the current moment and the distance function calculated at the previous moment is used as the distance function difference.

[0013] The step of calculating the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module includes: The distance function is calculated according to the following formula : ; in, is the distance between the receiving module and the transmitting module; is the wavelength of the receiving module; is the phase offset between the receiving module and the transmitting module.

[0014] The step of obtaining a conversion phase data sequence corresponding to a target phase data sequence after normalization processing according to the distance function difference includes: Calculate the conversion phase data of each data in the target phase data sequence after normalization , obtaining the conversion phase data sequence; ; in, is the time difference generated when the transmitting module and the receiving module move relative to each other; for The distance function difference generated within the time difference.

[0015] The embodiment of the present invention further provides a device for obtaining a human behavior recognition signal, the device comprising: A first calculation module, used to calculate an initial phase difference value according to an initial phase value in an acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of a human body; A first determination module is used to determine the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size; A second calculation module is used to calculate a target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter in the region where the phase mutation occurs, determine a target phase mutation value according to the target phase difference; and calculate the target phase mutation value according to the target phase difference to obtain a target phase data sequence; A first processing module, used for normalizing the target phase data sequence, channel state data, and signal strength data; A third calculation module is used to calculate the distance function difference generated by relative motion between the transmitting module that transmits the human behavior recognition signal and the receiving module that receives the human behavior recognition signal; A second processing module, configured to obtain a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference; A third processing module is used to obtain a signal strength data difference according to the acquired two adjacent normalized signal strength data; The fourth processing module is used to fuse the conversion phase data sequence, the signal strength data difference and the channel state data after normalization processing to obtain a fused human behavior recognition signal.

[0016] An embodiment of the present invention further provides an electronic device, characterized in that the electronic device includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and the program or instruction is executed by the processor to execute the steps of the above method.

[0017] The method provided in the embodiment of the present invention processes the received human body signal and integrates the WiFi signal to form a fusion signal, thereby improving the accuracy of recognition and ensuring data stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for obtaining a human behavior recognition signal provided by an embodiment of the present invention; Figure 2 Schematic diagram of the structure of integrating the ECA mechanism into the residual network model; Figure 3 Schematic diagram of the residual module structure of the ECA mechanism; Figure 4 Schematic diagram of the structure of a deep residual neural network model based on residual blocks; Figure 5 A schematic diagram of a structure for obtaining a human behavior recognition signal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0020] In the description of the present application, it should be understood that if the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. appear, the orientation or position relationship indicated by these terms is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0021] In addition, if the terms "first" or "second" appear, these terms are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, if the term "plurality" appears, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0022] An embodiment of the present invention provides a method for obtaining a human behavior recognition signal. The method is applied to a system for obtaining a human behavior recognition signal, and is mainly used to solve the problems of low accuracy, unstable data, and limited types when obtaining human behavior information in the prior art.

[0023] This system includes a transmitting module for transmitting human behavior recognition signals and a receiving module for receiving human behavior recognition signals, and cooperates with Wi-Fi to transmit signals. This application deploys the transmitting modules in key parts of the human body to maximize the signal capture effect and cooperate with Wi-Fi data for accurate identification. The specific deployment is as follows: place a transmitting module on each arm, a transmitting module on the waist, a transmitting module on the back, and a transmitting module on each thigh. The transmitting module can be an RFID tag. For WIFI, a TP-Link wireless router and a laptop equipped with a Wi-Fi network card are used as experimental equipment. The distance between each transmitting module is greater than 10cm. If Figure 1 As shown, the specific process is as follows: Step 11, calculating an initial phase difference value according to the initial phase value in the acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of the human body.

[0024] The initial phase data sequence is composed of human behavior data acquired by transmitting modules placed at different parts of the human body.

[0025] First, the entire initial phase data sequence is quickly scanned, and the difference between two adjacent initial phase values ​​is calculated, and the difference is used as the initial phase difference value.

[0026] Step 12, according to the initial phase difference value and the initialization step size, determine the region where the phase mutation occurs from the initial phase data sequence. The specific process of this step is as follows: The initialization step length includes a first preset step length and a second preset step length, and the first preset step length is greater than the second preset step length; Find two adjacent initial phase values ​​whose initial phase difference is greater than the first preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are continuous areas, use the second preset step size to find an area where a phase mutation occurs in the continuous area; Two adjacent initial phase values ​​whose initial phase difference is less than the second preset phase difference are found from the initial phase data sequence, and when the found initial phase values ​​are in a continuous area, a first preset step size is used to find an area in the continuous area where a phase mutation occurs.

[0027] The first preset phase difference value is greater than the second preset phase difference value.

[0028] Here are some examples to illustrate the above: In the initial phase data sequence, all initial phase values ​​whose initial phase difference values ​​are greater than the first preset phase difference value and less than the second preset phase difference value are marked. Since the initial phase difference value is calculated from two adjacent initial phase values, the initial phase values ​​marked in this step can be a continuous area or a discontinuous area.

[0029] Since the signals used to describe normal human behavior are generally relatively smooth signals, if a large phase difference is generated, it means that the signal has a phase mutation. Therefore, in this step, it is necessary to find the area where the phase difference changes more obviously, which indicates that a phase mutation has occurred. However, in the actual operation, since relying solely on this method may cause errors, in order to more accurately find the phase mutation, it is necessary to dynamically adjust the step size, the specific method is: Set the initialization step size Δ0=1, set the maximum step size, i.e. the first preset step size, to Δmax=5, and set the minimum step size, i.e. the second preset step size, to Δmin=1; When the initial phase difference is less than the second preset phase difference, the first preset step size Δmax=5 is used to search for phase mutations in the continuous area. When a phase mutation is detected, the second preset step size Δmin=1 is used to continue searching to ensure the accuracy of the search. After that, if multiple consecutive initial phase values ​​do not produce phase mutations, the step size can be gradually increased but the maximum value does not exceed Δmax=5.

[0030] When the initial phase difference is greater than the first preset phase difference, a second preset step size Δmin=1 is used to search for phase mutations in the continuous area. If multiple consecutive initial phase values ​​do not produce phase mutations, the step size can be gradually increased but not more than Δmax=5.

[0031] In this step, the initialization step length, the first preset step length and the second preset step length can be set according to the actual detection object.

[0032] Step 13, in the region where the phase mutation occurs, according to the count value of the dynamic counter, calculate the target phase difference between two adjacent initial phase values, calculate the target phase difference between two adjacent initial phase values, and determine the target phase mutation value according to the target phase difference; calculate the target phase mutation value according to the target phase difference to obtain a target phase data sequence; In this step, the dynamic counter is used to record the phase drop and phase increase. Used to record the number of phase drops, is the number of sudden increases in the recorded phase. In the initial case .

[0033] In this step, the method for calculating the target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter is: Starting from the second initial phase value in the initial phase data sequence, the values ​​calculated based on the current counter are added respectively to obtain multiple intermediate phase values; the difference between two adjacent intermediate phase values ​​is used as the target phase difference value. The calculation method of the value calculated based on the current counter is as follows: First calculate the counter difference , starting from the second initial phase value, each initial phase value is added , which is used as the intermediate phase value.

[0034] In this step, the specific method for determining the target phase mutation value according to the target phase difference value is: Using a second preset step size, the initial phase values ​​in the region where the phase mutation occurs are sequentially traversed; Find two adjacent intermediate phase values ​​where the target phase difference value is greater than the first preset target phase difference value, and when the intermediate phase value found is a continuous area, reduce the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value; Find two adjacent intermediate phase values ​​where the target phase difference value is less than the second preset target phase difference value. When the intermediate phase value found is a continuous area, increase the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value.

[0035] Calculating the target phase mutation value according to the target phase difference includes: When the target phase difference value is greater than the preset target phase difference value, subtract the preset target value from the current target phase value; When the target phase difference value is less than the preset target phase difference value, the preset target value is added to the current target phase value.

[0036] For example: If the target phase difference is greater than the first preset target phase difference , it means that the phase has increased suddenly, then the current target phase mutation value minus the preset target value , Add 1; If the target phase difference is less than , it means that the phase has dropped suddenly, and the current target phase mutation value plus the preset target value , Add 1, update the counter machine counter difference .

[0037] So far, the target phase data sequence is obtained.

[0038] In the method provided in the present application, step 13 can be directly executed, that is, step 13 is used as the initial step. When step 13 is the initial step, it is not necessary to first determine the area where the phase mutation occurs, and the process of "calculating the target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter, and determining the target phase mutation value according to the target phase difference" can be directly performed. Considering the premise of saving calculation time and improving calculation efficiency, steps 11 and 12 can be executed first and then step 13. Step 14, normalizing the target phase data sequence, the received channel state data, and the received signal strength data, specifically this step includes: Among them, the signal strength data is used to indicate the received signal strength of the receiving module; the channel state data is used to indicate the Wi-Fi channel state information, which contains rich spatial and temporal characteristics and can provide finer-grained information than the signal strength data of the receiving module, and is suitable for contactless behavior recognition.

[0039] In order to ensure that the three parts of the data sequence: the phase data sequence, the signal strength data sequence of the receiving module, and the data sequence of the channel data module that generates the channel status data have the same length in the same sampling period, it is necessary to normalize these three parts of the data sequence respectively, and calculate the current data in each data sequence according to the following formula (1): Formula (1); in is the current sampling time for normalization processing. and It is the sampling time point adjacent to the current sampling time. It is the calculation result of the current data in the data sequence, that is, the time Phase value data or signal strength data value or channel status data at.

[0040] In order to reduce the interference of noise on the experimental results, a Kalman filter can also be used to smooth the data after completing the above processing process.

[0041] Step 15, calculating the distance function difference between the transmitting module that transmits the human behavior recognition signal and the receiving module that receives the human behavior recognition signal due to relative motion; the specific process of this step is as follows: According to the distance d between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module And the wavelength of the receiving module Calculate the distance function ; Calculate the distance function The formula (2) is as follows: Formula (2); According to the distance function calculated by the receiving module at the current moment , the difference between the distance function calculated at the previous moment , as the distance function difference , where i in formula (2) is the data arranged in order.

[0042] Step 16, obtaining a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference, and obtaining the conversion phase data sequence by formula (3): : Formula (3); in, is the sampling time difference.

[0043] Since the reading and writing speed of the transmitter module is very fast, Moment and The moving speed v of the transmitting module during the time can be regarded as constant. Time has come The relative distance change between the transmitting module and the receiving module within a certain time It can be obtained according to formula (4): Formula (4); in, is the angle between the transmitting module and the receiving module; from formula (2) (4) we can get and = The relationship between is formula (5): Formula (5); Formula (6) can be obtained from formula (4) and formula (5): (Formula 6); When the angle between the receiving module and the transmitting module is , and when the receiving module moves at speed v, the phase data sequence is converted Calculated by the following formula (7): (Formula 7); According to formula (6) and formula (7), formula (3) can be obtained.

[0044] Step 17: According to the acquired signal strength data of the two adjacent normalized signals, , get the signal strength data difference , where i in formula (8) represents the time point, formula (8) is as follows: (Formula 8); After completing the above process, the data can also be smoothed to effectively suppress background noise, thereby reducing training time and improving the accuracy of behavior classification.

[0045] Step 18, fusing the conversion phase data sequence, the signal strength data difference and the normalized channel state data to obtain a fused human behavior recognition signal.

[0046] In this step, in order to achieve effective fusion of the data of the transmitting module and the receiving module, these time series data are fused by splicing the conversion phase data sequence, the signal strength data difference and the channel state data after normalization in the channel dimension. The data obtained in this fusion process will be used for subsequent feature extraction and classification analysis to provide more accurate support for human behavior recognition. The fusion method adopted by the present invention belongs to a multi-channel data input fusion strategy, which combines the data source characteristics of different sensors.

[0047] In this application, feature extraction and behavior recognition can be further performed, including: This application integrates the ECA mechanism into the residual network model and assigns different weights to each channel, thereby significantly improving the performance of the CNN model. Unlike many existing methods that often increase network complexity when optimizing performance, the ECA module can enhance cross-channel information interaction and improve model performance while only introducing a small number of additional parameters.

[0048] like Figure 2 As shown in the ECA mechanism flow chart, the workflow of the ECA module first performs global average pooling (GAP) on all channels while keeping the number of channels unchanged. Subsequently, the weight sharing principle and one-dimensional convolution operation of size k are used to generate channel attention. This process not only emphasizes the importance of each channel, but also pays special attention to the interaction between adjacent channels, ensuring the effective transmission and utilization of information between channels.

[0049] In the ECA-ResNet architecture, the residual module structure of the ECA mechanism is as follows Figure 3 As shown in the figure, the performance of the residual network is improved by introducing the ECA mechanism. Each basic unit is composed of a series of cascaded components, including a convolutional layer (Conv), batch normalization (BN), an activation function, and an attention module. In order to deal with the problems of gradient disappearance and network degradation, these units use skip connections (SC).

[0050] Specifically, the ECA attention module is embedded in two different types of residual bottleneck structures Block to form the basic unit of ECA-ResNet, marked as Block1 and Block2 respectively: Block 1: When the number of channels of the input and output are inconsistent, additional convolutional layers are added in the skip connection to match the dimensions of the feature map. This ensures that information flows smoothly even when the network depth increases, thus maintaining the effectiveness of the network.

[0051] Block2: When the number of input and output channels is the same, the jump connection can directly add the input to the output, simplifying the structure and reducing the computational burden.

[0052] This design not only enhances the model's attention to important channels, but also improves the gradient propagation problem during training through optimized information flow paths, thereby improving the model's learning ability and final performance. In this way, ECA-ResNet achieves more efficient feature extraction and higher recognition accuracy while maintaining lower complexity.

[0053] In the behavior recognition part, the fused data needs to be input into the residual network structure of the constructed fusion ECA mechanism to identify and distinguish different behavior changes. The three input channels are the phase conversion data sequence after phase conversion, the signal strength data difference, and the channel state data after normalization. Finally, the unified behavior recognition result is output through the fully connected layer. The deep residual neural network model structure based on the residual block constructed in this application is as follows: Figure 4 shown.

[0054] Deep residual neural network model structure based on residual blocks First, the dimension of the feature map is adjusted by Block1, and then three Block2 modules are stacked to form a group of large Block modules for extracting deep features. In order to improve the feature extraction capability, 3×3 convolution kernels and 2×2 average pooling are used to replace the traditional 7×7 convolution kernel, avoiding the limitation of large convolution kernels on key feature extraction. Then, three groups of large Block modules are used for convolution and attention training. Through the effective attention mechanism, the selectivity of the network for important features is enhanced. After the convolution and attention modules, average pooling and maximum pooling are used to extract global features, and behavior classification (standing, sitting, squatting, sleeping, drinking water) is performed through the fully connected layer and Softmax function. The dataset is randomly divided into training set, validation set and test set in a ratio of 6:2:2 to complete the training and evaluation of the model. The improved ECA-ResNet model effectively improves the recognition accuracy of human behavior by optimizing the convolution design and introducing the attention mechanism.

[0055] like Figure 5 As shown, an embodiment of the present invention provides a device for obtaining a human behavior recognition signal, the device comprising: A first calculation module 51 is used to calculate an initial phase difference value according to an initial phase value in an acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of a human body; A first determining module 52 is used to determine the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size; A second calculation module 53 is used to calculate a target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter in the region where the phase mutation occurs, and determine a target phase mutation value according to the target phase difference; and calculate the target phase mutation value according to the target phase difference to obtain a target phase data sequence; A first processing module 54 is used to perform normalization processing on the target phase data sequence, the received channel state data, and the received signal strength data; A third calculation module 55 is used to calculate the distance function difference between the transmitting module that transmits the human behavior recognition signal and the receiving module that receives the human behavior recognition signal due to relative motion; A second processing module 56, configured to obtain a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference; The third processing module 57 is used to obtain a signal strength data difference according to the acquired two adjacent normalized signal strength data; The fourth processing module 58 is used to fuse the conversion phase data sequence, the signal strength data difference and the channel state data after normalization processing to obtain a fused human behavior recognition signal.

[0056] The first determination module 52 is further configured to determine the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size, and includes: The initialization step length includes a first preset step length and a second preset step length, and the first preset step length is greater than the second preset step length; Find two adjacent initial phase values ​​whose initial phase difference is greater than the first preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are continuous areas, use the second preset step size to find an area where a sudden phase change occurs in the continuous area; Find two adjacent initial phase values ​​whose initial phase difference is less than the second preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are in a continuous area, use the first preset step size to find an area where a phase mutation occurs in the continuous area.

[0057] The second calculation module 53 is further used to add the values ​​calculated based on the current counter starting from the second initial phase value in the initial phase data sequence to obtain multiple intermediate phase values; The difference between two adjacent intermediate phase values ​​is used as the target phase difference.

[0058] Wherein, determining a target phase mutation value according to the target phase difference value includes: Using a second preset step size, the initial phase values ​​in the region where the phase mutation occurs are sequentially traversed; Find two adjacent intermediate phase values ​​where the target phase difference value is greater than the first preset target phase difference value, and when the intermediate phase value found is a continuous area, reduce the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value; Find two adjacent intermediate phase values ​​where the target phase difference value is less than the second preset target phase difference value. When the intermediate phase value found is a continuous area, increase the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value.

[0059] The step of calculating the target phase mutation value according to the target phase difference includes: When the target phase difference value is greater than the preset target phase difference value, the preset target value is subtracted from the current target phase mutation value; When the target phase difference value is less than the preset target phase difference value, the preset target value is added to the current target phase mutation value.

[0060] The third calculation module 55 is further used to calculate the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module; The difference between the distance function calculated by the receiving module at the current moment and the distance function calculated at the previous moment is used as the distance function difference.

[0061] The step of calculating the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module includes: The distance function is calculated according to the following formula :

[0062] in, is the distance between the receiving module and the transmitting module; is the wavelength of the receiving module; is the phase offset between the receiving module and the transmitting module.

[0063] The step of obtaining a conversion phase data sequence corresponding to a target phase data sequence after normalization processing according to the distance function difference includes: Calculate the conversion phase data of each data in the target phase data sequence after normalization , obtaining the conversion phase data sequence;

[0064] in, is the time difference generated when the transmitting module and the receiving module move relative to each other; for The distance function difference generated within the time difference.

[0065] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction is executed by the processor to perform the steps of the above-mentioned method for obtaining a human behavior recognition signal.

[0066] In this application, unless otherwise clearly specified and limited, if the terms "installed", "connected", "connected", "fixed" and the like appear, these terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integrated connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0067] In the present application, unless otherwise clearly specified and limited, if there is a description that a first feature is "above" or "below" a second feature, etc., or similar descriptions appear, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "above" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0068] It should be noted that if an element is referred to as being "fixed on" or "disposed on" another element, it may be directly on the other element or there may also be a centered element. If an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a centered element at the same time. If any, the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used in this application are for illustrative purposes only and do not represent the only implementation method. The technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for obtaining a human behavior recognition signal, characterized in that: The method comprises: An initial phase difference value is calculated based on an initial phase value in an acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of a human body; According to the initial phase difference value and the initialization step size, determining the region where the phase mutation occurs from the initial phase data sequence; In the region where the phase mutation occurs, a target phase difference between two adjacent initial phase values ​​is calculated according to the count value of the dynamic counter, and a target phase mutation value is determined according to the target phase difference; the target phase mutation value is calculated according to the target phase difference to obtain a target phase data sequence; Normalizing the target phase data sequence, the received channel state data, and the received signal strength data; Calculating a distance function difference between a transmitting module that transmits the human behavior recognition signal and a receiving module that receives the human behavior recognition signal due to relative motion; Obtaining a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference; Obtaining a signal strength data difference according to the acquired two adjacent normalized signal strength data; The conversion phase data sequence, the signal strength data difference and the channel state data after normalization processing are fused to obtain a fused human behavior recognition signal.

2. The method according to claim 1, characterized in that The step of determining the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size includes: The initialization step length includes a first preset step length and a second preset step length, and the first preset step length is greater than the second preset step length; Find two adjacent initial phase values ​​whose initial phase difference is greater than the first preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are continuous areas, use the second preset step size to find an area where a sudden phase change occurs in the continuous area; Find two adjacent initial phase values ​​whose initial phase difference is less than the second preset phase difference from the initial phase data sequence, and when the found initial phase values ​​are in a continuous area, use the first preset step size to find an area where a phase mutation occurs in the continuous area.

3. The method according to claim 1, characterized in that The step of calculating the target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter includes: Starting from the second initial phase value in the initial phase data sequence, respectively adding the values ​​calculated based on the current counter to obtain a plurality of intermediate phase values; The difference between two adjacent intermediate phase values ​​is used as the target phase difference.

4. The method according to claim 2, characterized in that Determining a target phase mutation value according to the target phase difference value includes: Using a second preset step size, the initial phase values ​​in the region where the phase mutation occurs are sequentially traversed; Find two adjacent intermediate phase values ​​where the target phase difference value is greater than the first preset target phase difference value, and when the intermediate phase value found is a continuous area, reduce the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value; Find two adjacent intermediate phase values ​​where the target phase difference value is less than the second preset target phase difference value. When the intermediate phase value found is a continuous area, increase the second preset step size by a preset value and then search the continuous area to determine the target phase mutation value.

5. The method according to claim 4, characterized in that Calculating the target phase mutation value according to the target phase difference includes: When the target phase difference value is greater than the preset target phase difference value, the preset target value is subtracted from the current target phase mutation value; When the target phase difference value is less than the preset target phase difference value, the preset target value is added to the current target phase mutation value.

6. The method according to claim 1, characterized in that The calculating of the distance function difference between the transmitting module for transmitting the human behavior recognition signal and the receiving module for receiving the human behavior recognition signal due to the relative motion comprises: Calculate the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module; The difference between the distance function calculated by the receiving module at the current moment and the distance function calculated at the previous moment is used as the distance function difference.

7. The method according to claim 6, characterized in that The calculating the distance function according to the distance between the receiving module and the transmitting module, the phase offset value between the receiving module and the transmitting module, and the wavelength of the receiving module includes: The distance function is calculated according to the following formula : ； in, is the distance between the receiving module and the transmitting module; is the wavelength of the receiving module; is the phase offset between the receiving module and the transmitting module.

8. The method according to claim 7, characterized in that The step of obtaining a conversion phase data sequence corresponding to a target phase data sequence after normalization processing according to the distance function difference comprises: Calculate the conversion phase data of each data in the target phase data sequence after normalization , obtaining the conversion phase data sequence; ； in, is the time difference generated when the transmitting module and the receiving module move relative to each other; for The distance function difference generated within the time difference.

9. A device for obtaining human behavior recognition signals, characterized in that: The device comprises: A first calculation module, used to calculate an initial phase difference value according to an initial phase value in an acquired initial phase data sequence; the initial phase data sequence includes a plurality of initial phase values ​​for identifying behaviors of different parts of a human body; A first determination module is used to determine the region where the phase mutation occurs from the initial phase data sequence according to the initial phase difference value and the initialization step size; A second calculation module is used to calculate a target phase difference between two adjacent initial phase values ​​according to the count value of the dynamic counter in the region where the phase mutation occurs, determine a target phase mutation value according to the target phase difference; and calculate the target phase mutation value according to the target phase difference to obtain a target phase data sequence; A first processing module, used for normalizing the target phase data sequence, the received channel state data, and the received signal strength data; A third calculation module is used to calculate the distance function difference generated by relative motion between the transmitting module that transmits the human behavior recognition signal and the receiving module that receives the human behavior recognition signal; A second processing module, configured to obtain a conversion phase data sequence corresponding to the target phase data sequence after normalization processing according to the distance function difference; A third processing module is used to obtain a signal strength data difference according to the acquired two adjacent normalized signal strength data; The fourth processing module is used to fuse the conversion phase data sequence, the signal strength data difference and the channel state data after normalization processing to obtain a fused human behavior recognition signal.

10. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction is executed by the processor to perform the steps of any one of the methods for obtaining a human behavior recognition signal in claims 1-8.

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