Method and device for detecting existence of living body in vehicle, storage medium and electronic device

Abstract

The invention provides an in-vehicle living body existence detection method and device, a storage medium and an electronic device.The method comprises the steps that a star flash communication module of a vehicle is called to collect first static initial information in the vehicle in a basic SLB mode, and collect second static initial information in the vehicle in a low-power-consumption SLE mode; judging whether a target living body exists in the vehicle or not according to the first static initial information and the second static initial information; if the target living body exists in the vehicle, the satellite flash communication module is controlled to locate the living body position of the target living body in an SLE mode; and controlling the satellite flash communication module to identify the living body type of the target living body in an SLB mode. According to the embodiment of the invention, the detection of the position and type of the living body in the vehicle is realized, the detection precision of the living body in the vehicle is improved, and the false alarm when an adult is in the vehicle is avoided.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method and device for detecting the presence of a living body in a vehicle, a storage medium, and an electronic device. Background Art

[0002] In related technologies, the dangers posed to children and pets in enclosed, oxygen-deficient environments in cars are gaining increasing attention, and child presence detection (CPD) is gradually becoming a mandatory requirement for automakers. Early CPD primarily used sensors (such as pressure, infrared, thermal, and visual sensors), but these methods were limited in detection range and interference immunity. In recent years, methods have emerged that utilize wireless sensing to improve CPD accuracy, such as millimeter-wave radar sensing and channel information sensing. However, millimeter-wave radar, due to its hardware cost disadvantage, is not easily deployed in most high-volume vehicles.

[0003] The CPD method, based on channel information perception, primarily utilizes dynamic characteristics of wireless channels (such as channel state information (CSI), received signal strength (RSSI), Doppler shift, and multipath energy entropy) for biometric detection. This information extracts micro-motion characteristics of living organisms (such as heartbeat and breathing) to determine the presence of living organisms. SparkLink, a new communication technology offering SLE (SparkLink Low Energy) and SLB (SparkLink Basic) modes, has attracted widespread attention for its ultra-low latency, ultra-high reliability, precise synchronization, flexible networking, and high-density connectivity. By flexibly utilizing SparkLink's two unique networking modes to transmit and receive wireless communication signals for in-vehicle biometric detection, system power consumption is minimized while ensuring detection accuracy, making it more suitable for electric-powered new energy vehicles.

[0004] For the above-mentioned problems existing in related technologies, no efficient and accurate solutions have been found yet. Summary of the Invention

[0005] The present invention provides a method and device for detecting the presence of a living body in a vehicle, a storage medium, and an electronic device to solve technical problems in related technologies.

[0006] According to one embodiment of the present invention, a method for detecting the presence of a living body in a vehicle is provided, comprising: calling the Star Flash communication module of the vehicle to collect first static initial information of the interior of the vehicle in the basic SLB mode, and collecting second static initial information of the interior of the vehicle in the low power consumption SLE mode; judging whether there is a target living body in the vehicle based on the first static initial information and the second static initial information; if there is a target living body in the vehicle, controlling the Star Flash communication module to locate the living body position of the target living body in the SLE mode; enhancing the signal power of the Star Flash communication module, controlling the Star Flash communication module to start a beamforming focusing signal to the living body position in the SLB mode, and extracting target feature information of the received signal; extracting biological features from the target feature information; and using the biological features to identify the type of the target living body.

[0007] Optionally, determining whether there is a target living body in the vehicle based on the second static initial information includes: extracting the initial received signal strength indication RSSI sequence in the second static initial information; performing sliding average filtering on the initial RSSI sequence to obtain an intermediate RSSI sequence; calculating the mean and standard deviation of the intermediate RSSI sequence; using the mean and the standard deviation to construct a baseline RSSI threshold for a static environment; continuing to collect real-time RSSI values ​​in the vehicle in the SLE mode; and determining whether there is a target living body in the vehicle based on the baseline RSSI threshold and the real-time RSSI value.

[0008] Optionally, determining whether there is a target living body in the vehicle according to the benchmark RSSI threshold and the real-time RSSI value includes: determining whether the benchmark RSSI threshold and the real-time RSSI value satisfy the following relationship: ; is the real-time RSSI value, is the mean, is the standard deviation, and the benchmark RSSI thresholds include 、 If it matches, it is determined that there is a target living person in the car.

[0009] Optionally, determining whether there is a target living body in the vehicle based on the first static initial information includes: extracting the channel state information CSI signal in the first static initial information, and calculating the subcarrier energy of the CSI signal; calculating the multipath energy entropy of all subcarrier energies; and determining whether there is a target living body inside the vehicle based on the multipath energy entropy.

[0010] Optionally, judging whether there is a target living body inside the vehicle based on the multipath energy entropy includes: determining the material type of the vehicle's interior material, wherein the material type is used to characterize the degree of energy reflection of the material surface; searching for a correction coefficient that matches the material type; updating an energy entropy threshold based on the correction coefficient; judging whether the multipath energy entropy is greater than the energy entropy threshold; if the multipath energy entropy is greater than the energy entropy threshold, determining that there is a target living body inside the vehicle.

[0011] Optionally, controlling the Star Flash communication module to locate the target living body's living position in the SLE mode includes: controlling the vehicle to collect the receiving power and CSI signal of the sampling point in the vehicle in the SLE mode; calculating the RSSI variance of the receiving power, and extracting the ultra-low frequency component in the CSI signal, wherein the frequency range of the ultra-low frequency component corresponds to the body movement frequency of the organism; calculating the signal periodicity index of the ultra-low frequency component; using the RSSI variance and the signal periodicity index to determine whether there is a target living body in the vehicle; if there is a target living body in the vehicle, locating the living body position of the target living body.

[0012] Optionally, using the RSSI variance and the signal periodicity index to determine whether there is a moving object in the vehicle includes: determining whether the RSSI variance is an abnormal mutation value based on a benchmark RSSI threshold, and determining whether the signal periodicity index is greater than a preset threshold; if the RSSI variance is an abnormal mutation value, determining that there is a moving living body in the vehicle; if the signal periodicity index is greater than the preset threshold, determining that there is a silent living body in the vehicle.

[0013] Optionally, locating the living body position of the target living body includes: if the target living body is a moving living body, controlling one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtaining a first communication signal set between the transmitter and the receiver, and triangulating the target living body based on the RSSI signal of the first communication signal set to obtain the first living body position of the target living body, wherein the Star Flash communication module includes at least four nodes; if the target living body is a silent living body, controlling one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtaining a second communication signal set between the transmitter and the receiver, and triangulating the target living body based on the CSI signal of the second communication signal set to obtain the second living body position of the target living body.

[0014] Optionally, the target feature information includes a CSI phase difference sequence, a multipath delay spectrum, and Doppler spectrum information, and extracting biometric features from the target feature information includes: extracting first energy information of the CSI phase difference sequence in a first frequency band and second energy information of a second frequency band, calculating a periodic index and a harmonic component of the CSI phase difference sequence, calculating a standard deviation of the multipath delay spectrum and a multipath reflection energy entropy, and calculating a peak power and a spectrum entropy of the Doppler spectrum information; determining the first energy information as a first breathing feature, determining the second energy information as a first heartbeat feature, determining the periodic index as a second breathing feature, determining the harmonic component as a second heartbeat feature, determining the standard deviation and the multipath reflection energy entropy as body shape features, and determining the peak power and the spectrum entropy as motion features, wherein the biometric features include physiological features, body shape features, and motion features, and the physiological features include heartbeat features and breathing features.

[0015] Optionally, using the biometric to identify the type of the target living body includes: for each target living body, inputting the corresponding biometric into a decision tree logic, and using the decision tree logic to identify the type of the target living body, wherein the living body types include: adults, children, and pets; if the decision tree logic fails to identify, inputting the biometric into a pre-trained neural network model, and using the neural network model to output the type of the target living body.

[0016] Optionally, before calling the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collecting the second static initial information inside the vehicle in low power consumption SLE mode, the method also includes: detecting the status information of the vehicle; judging whether the vehicle is in a preset state based on the status information; if the vehicle is in the preset state, determining to call the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collecting the second static initial information inside the vehicle in low power consumption SLE mode.

[0017] Optionally, determining whether the vehicle is in a preset state based on the status information includes: determining whether the vehicle is in a parked state based on a gear status and a handbrake status; if the vehicle is in a parked state, determining whether the vehicle is in an engine-off state based on a power signal; if the vehicle is in an engine-off state, determining whether the vehicle is in a door-closed state based on a door lock status; if the vehicle is in a door-closed state, determining whether the vehicle is in a sealed state based on an air-conditioning status and a window status; if the vehicle is in a sealed state, determining that the vehicle is in a preset state, and the status information includes: gear status, handbrake status, power signal, door lock status, air-conditioning status, and window status.

[0018] Optionally, after controlling the Star Flash communication module to identify the type of the target living body in the SLE mode, the method further includes: if the type of the living body is a child and there is no adult, sending an alarm message to the remote terminal of the vehicle and starting the air conditioning system of the vehicle.

[0019] According to another embodiment of the present invention, a device for detecting the presence of a living body in a vehicle is provided, comprising: an acquisition module, configured to call the Star Flash communication module of the vehicle to acquire first static initial information of the interior of the vehicle in a basic SLB mode, and to acquire second static initial information of the interior of the vehicle in a low-power SLE mode; a first judgment module, configured to determine whether there is a target living body in the vehicle based on the first static initial information and the second static initial information; a positioning module, configured to control the Star Flash communication module to locate the living body position of the target living body in the SLE mode if there is a target living body in the vehicle; an identification module, configured to enhance the signal power of the Star Flash communication module, control the Star Flash communication module to initiate a beamforming focusing signal toward the living body position in the SLB mode, and extract target feature information of the received signal; extract biological features from the target feature information; and identify the type of the target living body using the biological features.

[0020] Optionally, the first judgment module includes: a first extraction unit, used to extract the initial received signal strength indication RSSI sequence from the second static initial information; a filtering unit, used to perform sliding average filtering on the initial RSSI sequence to obtain an intermediate RSSI sequence; a calculation unit, used to calculate the mean and standard deviation of the intermediate RSSI sequence; a construction unit, used to use the mean and the standard deviation to construct a baseline RSSI threshold for a static environment; a collection unit, used to continue to collect real-time RSSI values ​​in the vehicle in the SLE mode; a first judgment unit, used to determine whether there is a target living body in the vehicle based on the baseline RSSI threshold and the real-time RSSI value.

[0021] Optionally, the first judgment unit includes: a judgment subunit, configured to judge whether the reference RSSI threshold and the real-time RSSI value satisfy the following relationship: ; is the real-time RSSI value, is the mean, is the standard deviation, and the benchmark RSSI thresholds include 、 ; If it matches, it is determined that there is a target living person in the car.

[0022] Optionally, the first judgment module includes: a first extraction unit, used to extract the channel state information CSI signal from the first static initial information and calculate the subcarrier energy of the CSI signal; a calculation unit, used to calculate the multipath energy entropy of all subcarrier energies; and a second judgment unit, used to judge whether there is a target living body inside the vehicle based on the multipath energy entropy.

[0023] Optionally, the second judgment unit includes: a determination subunit, used to determine the material type of the vehicle's interior material, wherein the material type is used to characterize the degree of energy reflection of the material surface; a search subunit, used to find a correction coefficient matching the material type; an update subunit, used to update the energy entropy threshold based on the correction coefficient; a judgment subunit, used to judge whether the multipath energy entropy is greater than the energy entropy threshold; a determination subunit, used to determine that there is a target living body inside the vehicle if the multipath energy entropy is greater than the energy entropy threshold.

[0024] Optionally, the positioning module includes: a control unit, used to control the vehicle to collect the receiving power and CSI signal of the sampling points in the vehicle in SLE mode; a first calculation unit, used to calculate the RSSI variance of the receiving power and extract the ultra-low frequency component in the CSI signal, wherein the frequency range of the ultra-low frequency component corresponds to the body movement frequency of the organism; a second calculation unit, used to calculate the signal periodicity index of the ultra-low frequency component; a first judgment unit, used to use the RSSI variance and the signal periodicity index to determine whether there is a target living body in the vehicle; and a positioning unit, used to locate the living position of the target living body if there is a target living body in the vehicle.

[0025] Optionally, the first judgment unit is also used to: judge whether the RSSI variance is an abnormal mutation value based on a benchmark RSSI threshold, and judge whether the signal periodicity index is greater than a preset threshold; if the RSSI variance is an abnormal mutation value, determine that there is a moving living body in the vehicle; if the signal periodicity index is greater than the preset threshold, determine that there is a silent living body in the vehicle.

[0026] Optionally, the positioning unit is also used to: if the target living body is a moving living body, control one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtain a first communication signal set between the transmitter and the receiver, and triangulate the target living body based on the RSSI signal of the first communication signal set to obtain the first living body position of the target living body, wherein the Star Flash communication module includes at least four nodes; if the target living body is a silent living body, control one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtain a second communication signal set between the transmitter and the receiver, and triangulate the target living body based on the CSI signal of the second communication signal set to obtain the second living body position of the target living body.

[0027] Optionally, the target feature information includes a CSI phase difference sequence, a multipath delay spectrum, and Doppler spectrum information, and the extraction unit is further used to: extract the first energy information of the CSI phase difference sequence in the first frequency band and the second energy information of the second frequency band, calculate the periodic index and harmonic component of the CSI phase difference sequence, calculate the standard deviation of the multipath delay spectrum and the multipath reflection energy entropy, and calculate the peak power and spectrum entropy of the Doppler spectrum information; determine the first energy information as a first breathing feature, determine the second energy information as a first heartbeat feature, determine the periodic index as a second breathing feature, determine the harmonic component as a second heartbeat feature, determine the standard deviation and the multipath reflection energy entropy as body shape features, and determine the peak power and the spectrum entropy as motion features, wherein the biological characteristics include physiological characteristics, body shape characteristics, and motion characteristics, and the physiological characteristics include heartbeat characteristics and breathing characteristics.

[0028] Optionally, the recognition unit is also used to: for each target living body, input the corresponding biometric feature into the decision tree logic, and use the decision tree logic to identify the type of the target living body, wherein the living body types include: adults, children, and pets; if the decision tree logic fails to identify, input the biometric feature into a pre-trained neural network model, and use the neural network model to output the type of the target living body.

[0029] Optionally, the device also includes: a detection module, used to detect the status information of the vehicle before the acquisition module calls the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collects the second static initial information inside the vehicle in low power consumption SLE mode; a second judgment module, used to judge whether the vehicle is in a preset state based on the status information; and a determination module, used to determine to call the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collect the second static initial information inside the vehicle in low power consumption SLE mode if the vehicle is in the preset state.

[0030] Optionally, the second judgment module is also used to: judge whether the vehicle is in a parked state according to the gear status and the handbrake status; if the vehicle is in a parked state, judge whether the vehicle is in an engine-off state according to the power signal; if the vehicle is in an engine-off state, judge whether the vehicle is in a door-closed state according to the door lock status; if the vehicle is in a door-closed state, judge whether the vehicle is in a sealed state according to the air-conditioning status and the window status; if the vehicle is in a sealed state, determine that the vehicle is in a preset state, and the status information includes: gear status, handbrake status, power signal, door lock status, air-conditioning status, and window status.

[0031] Optionally, the device also includes: a notification module, which is used to send an alarm message to the remote terminal of the vehicle and start the air-conditioning system of the vehicle after the identification module controls the Star Flash communication module to identify the type of the target living body in the SLE mode, if the type of the living body is a child and there is no adult.

[0032] According to another aspect of an embodiment of the present application, a storage medium is further provided, which includes a stored program, and the above steps are executed when the program is run.

[0033] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; wherein: the memory is used to store computer programs; the processor is used to execute the steps in the above method by running the program stored in the memory.

[0034] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the steps in the above method.

[0035] Beneficial effects of the present invention: 1. A method for biological presence detection based on multi-dimensional channel feature fusion was constructed. Specifically, the Star Flash wireless communication system acquires multi-dimensional channel environment information, including characteristic parameters such as received signal strength indicator (RSSI), channel state information (CSI), Doppler spectrum, and multipath delay spectrum. The method systematically extracts the physiological, body shape, and motion characteristics of the organism, thereby achieving comprehensive determination of the organism's presence characteristics and type recognition, avoiding false alarms and false positives. 2. By utilizing the coexistence of the SLE low-power mode and the SLB basic mode in Star Flash technology, an innovative hierarchical detection mechanism is designed. The dual-mode collaborative working mechanism significantly reduces the overall power consumption of the system while ensuring detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a hardware structure block diagram of a car according to an embodiment of the present invention; Figure 2 is a flow chart of a method for detecting the presence of a living body in a vehicle according to an embodiment of the present invention; Figure 3 Schematic diagram of the layout of the Star Flash communication module provided by an embodiment of the present invention; Figure 4 is a flow chart of a system initialization and dual-mode baseline scanning method according to an embodiment of the present invention; Figure 5 This is a flow chart of an embodiment of the present invention in the SLE mode to assist life detection; Figure 6 This is a flow chart of a static life body depth detection in SLB mode according to an embodiment of the present invention; Figure 7 is a schematic diagram of the principle of classifying a target living body according to an embodiment of the present invention; Figure 8 This is a flow chart of in-vehicle biological presence detection based on the collaboration of the star-flash dual-mode according to an embodiment of the present invention; Figure 9 This is a flow chart of in-vehicle biological presence detection and alarm based on Star Flash according to an embodiment of the present invention; Figure 10 4 is a structural block diagram of a device for detecting the presence of a living body in a vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order 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 in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of this application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0038] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] Example 1 The method embodiment provided in the first embodiment of the present application can be executed in a car, a server, a processor, a safety controller, an automatic driving / assisted driving / intelligent driving controller or a similar processing device. Taking running on a car as an example, Figure 1 This is a hardware structure diagram of a car according to an embodiment of the present invention. Figure 1 As shown, a car may include one or more ( Figure 1 Only one is shown) a processor 11 (the processor 11 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 12 for storing data. Optionally, the above-mentioned car may also include a transmission device 13 for communication functions and an input and output device 14. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned automobile. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0040] The memory 12 can be used to store vehicle programs, for example, software programs and modules of application software, such as the vehicle program corresponding to the method for detecting the presence of a living organism in a vehicle according to an embodiment of the present invention. The processor 11 executes the vehicle program stored in the memory 12 to execute various functional applications and data processing, thereby implementing the above-mentioned method. The memory 12 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 12 may further include memory remotely located relative to the processor 11, and these remote memories may be connected to the vehicle via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0041] The transmission device 13 is used to receive or transmit data via a network. A specific example of such a network may include a wireless network provided by the vehicle's telecommunications provider. In one embodiment, the transmission device 13 includes a network interface controller (NIC), which can connect to other network devices via a base station to enable communication with the internet. In another embodiment, the transmission device 13 may be a radio frequency (RF) module, which is used to communicate with the internet wirelessly.

[0042] In this embodiment, a method for detecting the presence of a living body in a vehicle is provided. Figure 2 FIG. 1 is a flow chart of a method for detecting the presence of a living body in a vehicle according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201, calling the Star Flash communication module of the vehicle to collect first static initial information inside the vehicle in the basic SLB mode, and to collect second static initial information inside the vehicle in the low power consumption SLE mode; The Star Flash communication module in this embodiment is the Star Flash device. Figure 3 This is a schematic diagram of the layout of the Starflash communication module provided by an embodiment of the present invention. Two Starflash devices are deployed in front of the windshield and behind the rear seats in the vehicle, forming a 2×2 array to transmit and receive wireless signals. Wireless signals are transmitted through multipath transmission throughout the vehicle interior, ensuring no blind spots. Optionally, the Starflash devices can also be integrated with other sensors, head units, and other equipment within the vehicle to perform specific communication tasks.

[0043] The first static initial information and the second static initial information of this embodiment are communication signals between various nodes of the Star Flash communication module in a static environment inside the vehicle.

[0044] Step S202: determining whether there is a target living body in the vehicle based on the first static initial information and the second static initial information; The target living body in this embodiment can be any living body, such as a pet, a child, an adult, etc.

[0045] Step S203: If there is a target living body in the vehicle, control the Star Flash communication module to locate the target living body in the SLE mode; Step S204: enhance the signal power of the Star Flash communication module, control the Star Flash communication module to initiate a beamforming focusing signal to the living body position in the SLB mode, and extract target feature information of the received signal; extract biological features from the target feature information; and use the biological features to identify the type of the target living body.

[0046] The beamforming focused signal of this embodiment is the focused signal after the Starflash communication module starts beamforming toward the living position in the SLB mode. By adjusting the radiation parameters of the antenna array of the Starflash communication module, the signal energy is concentrated toward the living position to form a focused signal.

[0047] Through the above steps, the Star Flash communication module of the vehicle is called to collect the first static initial information inside the vehicle in the basic SLB mode, and the second static initial information inside the vehicle in the low power consumption SLE mode; according to the first static initial information and the second static initial information, it is judged whether there is a target living body in the vehicle; if there is a target living body in the vehicle, the Star Flash communication module is controlled to locate the living body position of the target living body in the SLE mode; the Star Flash communication module is controlled to identify the living body type of the target living body in the SLB mode, thereby realizing the detection of the position and type of living bodies in the vehicle, improving the detection accuracy of living bodies in the vehicle, and avoiding false alarms when adults are in the vehicle.

[0048] In one implementation of this embodiment, determining whether there is a target living body in the vehicle based on the second static initial information includes: extracting the initial received signal strength indication RSSI sequence in the second static initial information; performing a sliding average filter on the initial RSSI sequence to obtain an intermediate RSSI sequence; calculating the mean and standard deviation of the intermediate RSSI sequence; using the mean and the standard deviation to construct a baseline RSSI threshold for a static environment; continuing to collect real-time RSSI values ​​in the vehicle in the SLE mode; and determining whether there is a target living body in the vehicle based on the baseline RSSI threshold and the real-time RSSI value.

[0049] In one example, determining whether there is a target living body in the vehicle according to the baseline RSSI threshold and the real-time RSSI value includes: determining whether the baseline RSSI threshold and the real-time RSSI value satisfy the following relationship: ; is the real-time RSSI value, is the mean, is the standard deviation, and the benchmark RSSI thresholds include 、 ; If it matches, it is determined that there is a target living person in the car.

[0050] In one implementation of this embodiment, determining whether there is a target living body in the vehicle based on the first static initial information includes: extracting the channel state information CSI signal in the first static initial information and calculating the subcarrier energy of the CSI signal; calculating the multipath energy entropy of all subcarrier energies; and determining whether there is a target living body inside the vehicle based on the multipath energy entropy.

[0051] When both the first static initial information and the second static initial information confirm that the target living body exists inside the vehicle, the presence of the target living body inside the vehicle is confirmed. Alternatively, when one of the first static initial information and the second static initial information confirms that the target living body exists inside the vehicle, the presence of the target living body inside the vehicle is confirmed.

[0052] In one example, determining whether a target living body exists inside the vehicle based on the multipath energy entropy includes: determining the material type of the vehicle's interior material, wherein the material type is used to characterize the degree of energy reflection of the material surface; finding a correction coefficient that matches the material type; updating an energy entropy threshold based on the correction coefficient; determining whether the multipath energy entropy is greater than the energy entropy threshold; if the multipath energy entropy is greater than the energy entropy threshold, determining that a target living body exists inside the vehicle.

[0053] Figure 4 Flowchart of the system initialization and dual-mode baseline scanning method according to an embodiment of the present invention, including: The Star Flash detection system starts up and initializes the SLB and SLE modules. It checks the power supply status of the Star Flash RF front-end, antenna impedance matching, baseband processor clock synchronization, etc., loads the Star Flash protocol stack, and configures the physical layer parameters for the SLB and SLE modes, including modulation mode, subcarrier spacing, MIMO beamforming, channel bandwidth, etc., calibrates the radiation pattern, and performs phase synchronization. The Star Flash SLE mode collects RSSI background noise data for the entire vehicle as the second static initial information. Star Flash transceivers are deployed at four points in the vehicle to form a rectangular topology to ensure seamless scanning of the entire vehicle. Optionally, the sampling frequency band is set to the 2.4GHz ISM band, the sampling bandwidth is 1MHz, and the sampling rate is 10 times per second. The original RSSI sequence is subjected to a sliding average filter to suppress sudden interference pulses, and the mean RSSI is calculated using the following formula and variance : ,

[0054] in For the The received power at each sampling point.

[0055] Based on the calculated RSSI mean and variance, a baseline RSSI threshold for a static environment is established. The real-time RSSI value in the car is set as the abnormal judgment basis of RSSI. , which can cover 99.7% of the normal distribution confidence interval and significantly reduce the probability of false alarm; The Star Flash SLB mode performs a fine scan of the omnidirectional CSI signal to obtain the first static initial information. Optionally, the 6GHz unlicensed frequency band is used, with an instantaneous bandwidth of 160MHz and a frequency resolution of 1.25MHz. The receiver uses the least squares method for channel estimation; Calculate the initial CSI multipath energy entropy to preliminarily determine whether there is a target living being. Perform the initial CSI multipath energy entropy calculation and life body prediction, and preliminarily locate the life body through the CSI signal. Multipath energy entropy (E) is an indicator that describes the randomness of the multipath signal energy distribution in the wireless channel. It measures the discreteness or uncertainty of the multipath component energy distribution. Micro-movements of life bodies (such as breathing, heartbeat, etc.) will cause Significantly increased, the calculation formula is as follows:

[0056] in is the normalized subcarrier energy, which is calculated as follows:

[0057] in is the subcarrier channel response.

[0058] When the multipath energy entropy Optionally, dynamic confidence correction can be performed to automatically adjust the threshold based on the vehicle interior material database (leather / fabric / plastic). The adjustment rules are as follows:

[0059] For example, full leather seats (low reflective material) .

[0060] This method performs a preliminary scan of the channel information of the static star flash wireless communication environment inside the vehicle, provides baseline data for subsequent detection, and makes a preliminary prediction of the presence of organisms inside the vehicle.

[0061] In this embodiment, controlling the Star Flash communication module to locate the target living body's living position in the SLE mode includes: controlling the vehicle to collect the received power and CSI signal of the sampling point in the vehicle in the SLE mode; calculating the RSSI variance of the received power, and extracting the ultra-low frequency component in the CSI signal, wherein the frequency range of the ultra-low frequency component corresponds to the body movement frequency of the organism; calculating the signal periodicity index of the ultra-low frequency component; using the RSSI variance and the signal periodicity index to determine whether there is a target living body in the vehicle; if there is a target living body in the vehicle, locating the living body position of the target living body.

[0062] Optionally, using the RSSI variance and the signal periodicity index to determine whether there is a moving object in the vehicle includes: determining whether the RSSI variance is an abnormal mutation value based on a benchmark RSSI threshold, and determining whether the signal periodicity index is greater than a preset threshold; if the RSSI variance is an abnormal mutation value, determining that there is a moving living body in the vehicle; if the signal periodicity index is greater than the preset threshold, determining that there is a silent living body in the vehicle.

[0063] Optionally, locating the living body position of the target living body includes: if the target living body is a moving living body, controlling one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtaining a first communication signal set between the transmitter and the receiver, and triangulating the target living body based on the RSSI signal of the first communication signal set to obtain the first living body position of the target living body, wherein the Star Flash communication module includes at least four nodes; if the target living body is a silent living body, controlling one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtaining a second communication signal set between the transmitter and the receiver, and triangulating the target living body based on the CSI signal of the second communication signal set to obtain the second living body position of the target living body.

[0064] Figure 5 This is a flow chart of assisting life detection in the SLE mode according to an embodiment of the present invention, which can realize the detection of the position of a living body, including the following steps: Star Flash SLE mode provides permanent monitoring, continuously monitoring sudden changes in the RSSI signal inside the vehicle. Star Flash SLE mode has extremely low power consumption, making it suitable for permanent monitoring. When a sudden change in the RSSI signal inside the vehicle is detected (the RSSI variance is continuously calculated and compared with the variance information of the static RSSI inside the vehicle collected by the baseline scan), it preliminarily determines that there is a moving object inside the vehicle.

[0065] The Star Flash SLE mode activates SLE enhanced sampling every 30 seconds and collects 10 seconds of CSI phase information.

[0066] The 0.1-0.5 Hz ultra-low frequency component is extracted through the compressed sensing algorithm, and the signal periodicity index is calculated.

[0067] The original signal is reconstructed from the sparse CSI signal through the compressed sensing algorithm (which recovers the signal under undersampling, reduces the data volume and computational complexity, and is suitable for low-power SLE mode) and the ultra-low frequency component of 0.1-0.5Hz (corresponding to slight movement of the organism, breathing or heartbeat, etc.) is extracted. The signal periodicity index is calculated according to the following formula .

[0068]

[0069] in Represents the peak of the autocorrelation coefficient of the reconstructed signal, Search for peak values ​​within the range. , and represent The mean and variance of the noise outside the range.

[0070] Based on RSSI signal mutations and CSI phase data, triangulation is used to identify abnormal points within the vehicle. For moving objects (such as active people or pets), the RSSI signal is used as the positioning signal. For silent objects (such as sleeping or unconscious people or pets), the CSI phase data is used as the positioning signal. Using triangulation (when executing this function, one Starflash node acts as a transmitter and the other three nodes act as receivers), the point of abnormality is located and used as the target object's location information.

[0071] Through compressed sensing, the CSI phase signal is restored under undersampling, which reduces the amount of data and computational complexity. It supports the Star Flash SLE low-power mode for permanent detection of life presence, thereby avoiding the power loss caused by frequent use of SLB mode, and provides the trigger conditions (coarse detection results) and location information of potential life forms for deep detection of silent life forms in SLB mode.

[0072] Optionally, the target feature information includes a CSI phase difference sequence, a multipath delay spectrum, and Doppler spectrum information, and extracting biometric features from the target feature information includes: extracting first energy information of the CSI phase difference sequence in a first frequency band and second energy information of a second frequency band, calculating a periodic index and a harmonic component of the CSI phase difference sequence, calculating a standard deviation of the multipath delay spectrum and a multipath reflection energy entropy, and calculating a peak power and a spectrum entropy of the Doppler spectrum information; determining the first energy information as a first breathing feature, determining the second energy information as a first heartbeat feature, determining the periodic index as a second breathing feature, determining the harmonic component as a second heartbeat feature, determining the standard deviation and the multipath reflection energy entropy as body shape features, and determining the peak power and the spectrum entropy as motion features, wherein the biometric features include physiological features, body shape features, and motion features, and the physiological features include heartbeat features and breathing features.

[0073] Optionally, using the biometric to identify the type of the target living body includes: for each target living body, inputting the corresponding biometric into a decision tree logic, and using the decision tree logic to identify the type of the target living body, wherein the living body types include: adults, children, and pets; if the decision tree logic fails to identify, inputting the biometric into a pre-trained neural network model, and using the neural network model to output the type of the target living body.

[0074] Figure 6 This is a flow chart of an embodiment of the present invention for performing depth detection of static living organisms in SLB mode, which can realize the identification of living organism types, including: Start beamforming on the outlier area to focus the signal path and enhance the signal power. Start beamforming on the outlier area located by RSSI or CSI phase information to focus the signal and enhance the signal power. The Star Flash device sends a linear frequency modulated continuous wave and obtains CSI phase difference sequence, multipath delay spectrum and micro-Doppler spectrum information; Multi-dimensional feature extraction of living organisms, including biological features such as physiological features, body shape features, and motion features. The physiological features are hidden in the CSI phase information. The energy corresponding to the 0.1-0.5Hz and 1-2Hz frequency bands are extracted respectively. The periodic index of the CSI phase signal is calculated as a representation of the respiratory information, and the harmonic component of the CSI phase information is calculated as a representation of the heartbeat signal. Body shape information can be obtained from the multipath delay spectrum. The multipath delay standard deviation and the multipath reflection energy entropy are calculated to jointly represent the body shape information. The larger the body size, the larger the multipath delay standard deviation and the smaller the multipath reflection energy entropy, and vice versa. The motion state information is represented by calculating the peak power and spectral entropy of the Doppler frequency shift; Multi-dimensional feature classification of organisms (adults, children, pets). Based on the extracted biological feature information, the organisms are classified into categories (adults, children, pets). First, the organisms are initially classified using decision tree logic. If accurate classification is not possible, a pre-trained neural network model is used for secondary classification. Figure 7 This is a schematic diagram of the principle of classifying target living bodies according to an embodiment of the present invention. The judgment logic of the decision tree and the input of the neural network model can be formulated according to the differences in periodic index, harmonic components, standard deviation, reflected energy entropy, peak power, and spectral entropy among adults, children, and pets. If there are multiple outliers, the above process is executed repeatedly until all outliers are detected.

[0075] This embodiment uses star-flash beamforming to detect multiple life forms at fixed points. It detects life forms inside the vehicle by fusing channel features such as CSI, Doppler spectrum, and multipath delay, and distinguishes the living organisms inside the vehicle into adults, children, and pets, avoiding false alarms or false positives caused by the presence of adults in the vehicle.

[0076] Figure 8 This is a flow chart of in-vehicle biological presence detection based on the collaboration of the star-flash dual-mode according to an embodiment of the present invention, including: System initialization and dual-mode baseline scanning are performed to obtain static initial information and determine whether the CSI multipath energy entropy is greater than the threshold E. After the Star Flash liveness detection system is started, the system is first initialized, and static initial information in the vehicle is collected through SLB mode and SLE mode respectively, and the CSI multipath energy entropy and RSSI in a static environment are calculated. If the CSI multipath energy entropy is greater than the threshold E (for example, 2.5), it means that there may be a silent life form in the vehicle, and the system directly enters the Star Flash SLB silent life form deep detection mode. Otherwise, it enters the Star Flash SLE vital signs auxiliary detection mode (detecting the location of the life form); Starlight SLE vital signs auxiliary detection determines whether the auxiliary detection meets the trigger conditions. Use Starlight's low-power SLE mode to monitor the RSSI signal and CSI phase signal characteristics in the car. When the auxiliary detection meets the trigger conditions, it enters the Starlight SLB silent life deep detection mode, otherwise it continues to perform SLE vital signs auxiliary detection. Trigger conditions include: 1) RSSI variance sudden increase (living body movement trigger); 2) CSI phase signal periodicity index > 0.7 (living body micro-movement, heartbeat or breathing trigger, etc.), any one of the conditions is met; Star Flash SLB mode performs in-depth detection of silent life forms. After switching Star Flash SLB to ultra-fine mode, it transmits radio waves, collects and uses multi-dimensional information such as CSI phase difference sequence, multipath delay spectrum and micro-Doppler spectrum information to determine whether there are silent life forms in the car, and classifies silent life forms into adults, pets and children.

[0077] The solution of this embodiment cleverly utilizes the characteristics of Star Flash dual-mode communication to design a two-step judgment mechanism for SLE mode resident detection and SLB mode deep detection, and combines multi-dimensional information such as RSSI, CSI, multipath delay spectrum, Doppler spectrum, etc. to jointly judge the presence characteristics of multiple organisms in the vehicle and classify them, ensuring detection accuracy while minimizing system power consumption, making this method more universal in electric-powered new energy vehicles.

[0078] In a scenario of this embodiment, before calling the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collecting the second static initial information inside the vehicle in low power consumption SLE mode, it also includes: detecting the status information of the vehicle; judging whether the vehicle is in a preset state based on the status information; if the vehicle is in the preset state, determining to call the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collecting the second static initial information inside the vehicle in low power consumption SLE mode.

[0079] Optionally, determining whether the vehicle is in a preset state based on the status information includes: determining whether the vehicle is in a parked state based on a gear status and a handbrake status; if the vehicle is in a parked state, determining whether the vehicle is in an engine-off state based on a power signal; if the vehicle is in an engine-off state, determining whether the vehicle is in a door-closed state based on a door lock status; if the vehicle is in a door-closed state, determining whether the vehicle is in a sealed state based on an air-conditioning status and a window status; if the vehicle is in a sealed state, determining that the vehicle is in a preset state, and the status information includes: gear status, handbrake status, power signal, door lock status, air-conditioning status, and window status.

[0080] First, the vehicle's parking status is detected. When the vehicle is in P gear and the electronic parking brake is activated, the vehicle is currently parked. The gear position signal status of the transmission control module (TCM) can be read via the CAN bus. When the gear position code value range is 0×A1-0×A3 (ISO 11898 standard), it corresponds to P gear. The motor current characteristics of the electronic parking brake (EPB) are detected. When the current characteristics are at normal locking current and there is no current fluctuation for 3 seconds, it indicates that the electronic parking brake is activated. It is also confirmed that the power system is completely shut down. When the drive motor phase current drops to 50mA, the vehicle is judged to be stalled. When the stall signal is detected, a 30-second countdown begins. If any door is detected to be open during this period, the timer is immediately reset. This operation prevents false triggering caused by temporary stalling. It is determined that the door has been continuously closed. The body control module (BCM) obtains the status of each door lock and checks it once per second. If no door is unlocked within two minutes, the vehicle is considered fully closed. If any door lock is detected unlocked, the timer is reset to eliminate interference from brief door openings and closings. The vehicle's interior is primarily detected as a confined space based on the status of the air conditioning and windows (including the sunroof). A barometric pressure sensor can also be used to detect the rate of change of the interior air pressure. A rate of change of less than 0.1 Pa / s indicates the vehicle is enclosed.

[0081] Through information such as the handbrake, motor system, door locks, air conditioning and ventilation, it is possible to accurately determine whether the vehicle is in a preset non-operating state, thereby avoiding power loss caused by turning on the Star Flash Bio-Presence Detection System during temporary parking.

[0082] In an implementation scenario of this embodiment, after controlling the Star Flash communication module to identify the type of the target living body in the SLE mode, it also includes: if the type of the living body is a child and there is no adult, sending an alarm message to the remote terminal of the vehicle and starting the air-conditioning system of the vehicle.

[0083] It can control the Star Flash device or the car computer system in the car to send an alarm message to the owner's remote smart device (such as a mobile phone), and at the same time send an air-conditioning start command to the car control system to start heating / cooling and ventilation. When the air-conditioning system fails to start, it will open the windows or doors.

[0084] Figure 9 The flowchart of the in-vehicle biological presence detection and alarm based on Star Flash according to an embodiment of the present invention includes the following steps: Determine whether the vehicle has completely entered a non-operating state. Determine whether the vehicle has completely entered a non-operating state by factors such as the parking brake, motor system, door locks, and air conditioning ventilation; Activate the in-car Star Flash dual-mode biological detection system to detect the presence of multiple creatures in the car and distinguish between adults, children, and pets. Children and pets are defaulted to vulnerable creatures with no self-rescue ability. Determine whether there is a vulnerable creature alone in the car. If there is both a vulnerable creature and an adult in the car, the adult can perform self-rescue actions and the process ends; if there is only a vulnerable creature in the car, the vulnerable creature cannot perform self-rescue actions and proceed to the next step; The Star Flash device in the car sends an alarm message to the owner's smart device and sends an air conditioning and ventilation start instruction to the car control system, and the process ends; By classifying multiple organisms within the vehicle and classifying those without self-rescue capabilities as vulnerable, the system triggers an alarm only when a vulnerable organism is alone, reducing false alarms. Furthermore, the system further reduces power consumption by reusing the Star Flash device used for organism presence detection to send both alarm information and commands.

[0085] This embodiment provides a method for detecting biological presence in vehicles based on Starflash dual-mode collaboration. This method leverages multi-dimensional channel characteristics (including CSI information, Doppler shift, and multipath delay profiles) within the Starflash wireless communication channel environment to jointly detect the presence of multiple biological entities within the vehicle and distinguish their types. Furthermore, the method cleverly leverages the characteristics of Starflash dual-mode communication to design a two-step determination mechanism for in-vehicle biological presence detection: resident detection in SLE mode and deep detection in SLB mode. This ensures detection accuracy while minimizing system power consumption, making this method more universally applicable in electric-powered new energy vehicles. Starflash equipment is less expensive than millimeter-wave radar, making its use for in-vehicle child detection cost-effective and reducing overall vehicle costs.

[0086] Through the description of the above embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0087] Example 2 This embodiment also provides a device for detecting the presence of living organisms in a vehicle. This device is used to implement the above-mentioned embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0088] Figure 10FIG. 1 is a structural block diagram of a device for detecting the presence of a living body in a vehicle according to an embodiment of the present invention. Figure 10 As shown, the device includes: The acquisition module 101 is configured to call the Star Flash communication module of the vehicle to acquire first static initial information inside the vehicle in the basic SLB mode and to acquire second static initial information inside the vehicle in the low power consumption SLE mode; A first judgment module 102 is configured to judge whether there is a target living body in the vehicle according to the first static initial information and the second static initial information; The positioning module 103 is used to control the Star Flash communication module to locate the target living body in the SLE mode if there is a target living body in the vehicle; The identification module 104 includes: a processing unit for enhancing the signal power of the Star Flash communication module, controlling the Star Flash communication module to initiate a beamforming focusing signal to the living body position in the SLB mode, and extracting target feature information of the received signal; an extraction unit for extracting biological features from the target feature information; and an identification unit for using the biological features to identify the type of the target living body.

[0089] Optionally, the first judgment module includes: a first extraction unit, used to extract the initial received signal strength indication RSSI sequence from the second static initial information; a filtering unit, used to perform sliding average filtering on the initial RSSI sequence to obtain an intermediate RSSI sequence; a calculation unit, used to calculate the mean and standard deviation of the intermediate RSSI sequence; a construction unit, used to use the mean and the standard deviation to construct a baseline RSSI threshold for a static environment; a collection unit, used to continue to collect real-time RSSI values ​​in the vehicle in the SLE mode; a first judgment unit, used to determine whether there is a target living body in the vehicle based on the baseline RSSI threshold and the real-time RSSI value.

[0090] Optionally, the first judgment unit includes: a judgment subunit, configured to judge whether the reference RSSI threshold and the real-time RSSI value satisfy the following relationship: ; is the real-time RSSI value, is the mean, is the standard deviation, and the benchmark RSSI thresholds include 、 ; If it matches, it is determined that there is a target living person in the car.

[0091] Optionally, the first judgment module includes: a first extraction unit, used to extract the channel state information CSI signal from the first static initial information and calculate the subcarrier energy of the CSI signal; a calculation unit, used to calculate the multipath energy entropy of all subcarrier energies; and a second judgment unit, used to judge whether there is a target living body inside the vehicle based on the multipath energy entropy.

[0092] Optionally, the second judgment unit includes: a determination subunit, used to determine the material type of the vehicle's interior material, wherein the material type is used to characterize the degree of energy reflection of the material surface; a search subunit, used to find a correction coefficient matching the material type; an update subunit, used to update the energy entropy threshold based on the correction coefficient; a judgment subunit, used to judge whether the multipath energy entropy is greater than the energy entropy threshold; a determination subunit, used to determine that there is a target living body inside the vehicle if the multipath energy entropy is greater than the energy entropy threshold.

[0093] Optionally, the positioning module includes: a control unit, used to control the vehicle to collect the receiving power and CSI signal of the sampling points in the vehicle in SLE mode; a first calculation unit, used to calculate the RSSI variance of the receiving power and extract the ultra-low frequency component in the CSI signal, wherein the frequency range of the ultra-low frequency component corresponds to the body movement frequency of the organism; a second calculation unit, used to calculate the signal periodicity index of the ultra-low frequency component; a first judgment unit, used to use the RSSI variance and the signal periodicity index to determine whether there is a target living body in the vehicle; and a positioning unit, used to locate the living position of the target living body if there is a target living body in the vehicle.

[0094] Optionally, the first judgment unit is also used to: judge whether the RSSI variance is an abnormal mutation value based on a benchmark RSSI threshold, and judge whether the signal periodicity index is greater than a preset threshold; if the RSSI variance is an abnormal mutation value, determine that there is a moving living body in the vehicle; if the signal periodicity index is greater than the preset threshold, determine that there is a silent living body in the vehicle.

[0095] Optionally, the positioning unit is also used to: if the target living body is a moving living body, control one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtain a first communication signal set between the transmitter and the receiver, and triangulate the target living body based on the RSSI signal of the first communication signal set to obtain the first living body position of the target living body, wherein the Star Flash communication module includes at least four nodes; if the target living body is a silent living body, control one node of the Star Flash communication module as a transmitter, and the other three nodes as receivers, obtain a second communication signal set between the transmitter and the receiver, and triangulate the target living body based on the CSI signal of the second communication signal set to obtain the second living body position of the target living body.

[0096] Optionally, the target feature information includes a CSI phase difference sequence, a multipath delay spectrum, and Doppler spectrum information, and the extraction unit is further used to: extract the first energy information of the CSI phase difference sequence in the first frequency band and the second energy information of the second frequency band, calculate the periodic index and harmonic component of the CSI phase difference sequence, calculate the standard deviation of the multipath delay spectrum and the multipath reflection energy entropy, and calculate the peak power and spectrum entropy of the Doppler spectrum information; determine the first energy information as a first breathing feature, determine the second energy information as a first heartbeat feature, determine the periodic index as a second breathing feature, determine the harmonic component as a second heartbeat feature, determine the standard deviation and the multipath reflection energy entropy as body shape features, and determine the peak power and the spectrum entropy as motion features, wherein the biological characteristics include physiological characteristics, body shape characteristics, and motion characteristics, and the physiological characteristics include heartbeat characteristics and breathing characteristics.

[0097] Optionally, the recognition unit is also used to: for each target living body, input the corresponding biometric feature into the decision tree logic, and use the decision tree logic to identify the type of the target living body, wherein the living body types include: adults, children, and pets; if the decision tree logic fails to identify, input the biometric feature into a pre-trained neural network model, and use the neural network model to output the type of the target living body.

[0098] Optionally, the device also includes: a detection module, used to detect the status information of the vehicle before the acquisition module calls the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collects the second static initial information inside the vehicle in low power consumption SLE mode; a second judgment module, used to judge whether the vehicle is in a preset state based on the status information; and a determination module, used to determine to call the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and collect the second static initial information inside the vehicle in low power consumption SLE mode if the vehicle is in the preset state.

[0099] Optionally, the second judgment module is also used to: judge whether the vehicle is in a parked state according to the gear status and the handbrake status; if the vehicle is in a parked state, judge whether the vehicle is in an engine-off state according to the power signal; if the vehicle is in an engine-off state, judge whether the vehicle is in a door-closed state according to the door lock status; if the vehicle is in a door-closed state, judge whether the vehicle is in a sealed state according to the air-conditioning status and the window status; if the vehicle is in a sealed state, determine that the vehicle is in a preset state, and the status information includes: gear status, handbrake status, power signal, door lock status, air-conditioning status, and window status.

[0100] Optionally, the device also includes: a notification module, which is used to send an alarm message to the remote terminal of the vehicle and start the air-conditioning system of the vehicle after the identification module controls the Star Flash communication module to identify the type of the target living body in the SLE mode, if the type of the living body is a child and there is no adult.

[0101] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0102] Example 3 An embodiment of the present invention further provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0103] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps: S1, calling the Star Flash communication module of the vehicle to collect first static initial information inside the vehicle in the basic SLB mode, and to collect second static initial information inside the vehicle in the low power consumption SLE mode; S2, determining whether there is a target living body in the vehicle based on the first static initial information and the second static initial information; S3, if there is a target living body in the vehicle, controlling the Star Flash communication module to locate the target living body in SLE mode; S4, enhance the signal power of the Star Flash communication module, control the Star Flash communication module to start the beamforming focusing signal to the living body position in the SLB mode, and extract the target feature information of the received signal; extract the biological feature from the target feature information; use the biological feature to identify the type of the target living body.

[0104] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0105] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0106] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0107] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program: S1, calling the Star Flash communication module of the vehicle to collect first static initial information inside the vehicle in the basic SLB mode, and to collect second static initial information inside the vehicle in the low power consumption SLE mode; S2, determining whether there is a target living body in the vehicle based on the first static initial information and the second static initial information; S3, if there is a target living body in the vehicle, controlling the Star Flash communication module to locate the target living body in SLE mode; S4, enhance the signal power of the Star Flash communication module, control the Star Flash communication module to start the beamforming focusing signal to the living body position in the SLB mode, and extract the target feature information of the received signal; extract the biological feature from the target feature information; use the biological feature to identify the type of the target living body.

[0108] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0110] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0111] It should be understood that the terms used herein are for the purpose of describing specific example embodiments only and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "comprise", "include", "contain" and "have" are inclusive and therefore specify the presence of stated features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the specific order described or illustrated, unless the order of execution is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0112] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for detecting the presence of a living body in a vehicle, characterized in that: include: Invoke the vehicle's Star Flash communication module to collect first static initial information inside the vehicle in the basic SLB mode, and to collect second static initial information inside the vehicle in the low power consumption SLE mode; Determining whether there is a target living body in the vehicle according to the first static initial information and the second static initial information; If there is a target living body in the vehicle, control the Star Flash communication module to locate the target living body in the SLE mode; Enhance the signal power of the Star Flash communication module, control the Star Flash communication module to start a beamforming focusing signal to the living body position in SLB mode, and extract target feature information of the received signal; extract biological features from the target feature information; and use the biological features to identify the type of the target living body.

2. The method according to claim 1, characterized in that Determining whether there is a target living body in the vehicle according to the second static initial information includes: Extracting an initial received signal strength indication RSSI sequence from the second static initial information; Performing a sliding average filter on the initial RSSI sequence to obtain an intermediate RSSI sequence; Calculating the mean and standard deviation of the intermediate RSSI sequence; Constructing a baseline RSSI threshold for a static environment using the mean and the standard deviation; Continue to collect the real-time RSSI value in the vehicle in the SLE mode; It is determined whether there is a target living body in the vehicle according to the reference RSSI threshold and the real-time RSSI value.

3. The method according to claim 2, characterized in that Determining whether there is a target living body in the vehicle according to the reference RSSI threshold and the real-time RSSI value includes: Determine whether the reference RSSI threshold and the real-time RSSI value meet the following relationship: ; is the real-time RSSI value, is the mean, is the standard deviation, and the benchmark RSSI thresholds include 、 ; If it matches, it is determined that there is a target living person in the car.

4. The method according to claim 1, wherein Determining whether there is a target living body in the vehicle according to the first static initial information includes: Extracting a channel state information CSI signal from the first static initial information, and calculating subcarrier energy of the CSI signal; Calculate the multipath energy entropy of all subcarrier energies; It is determined whether there is a target living body inside the vehicle based on the multipath energy entropy.

5. The method according to claim 4, characterized in that Determining whether there is a target living body inside the vehicle based on the multipath energy entropy includes: Determining a material type of the interior material of the vehicle, wherein the material type is used to characterize the energy reflectivity of the material surface; Find the correction factor that matches the material type; updating the energy entropy threshold based on the correction coefficient; Determining whether the multipath energy entropy is greater than the energy entropy threshold; If the multipath energy entropy is greater than the energy entropy threshold, it is determined that a target living body exists inside the vehicle.

6. The method according to claim 1, wherein Controlling the Star Flash communication module to locate the living body position of the target living body in the SLE mode includes: Controlling the vehicle to collect received power and CSI signals at sampling points within the vehicle in SLE mode; Calculating the RSSI variance of the received power and extracting an ultra-low frequency component from the CSI signal, wherein a frequency range of the ultra-low frequency component corresponds to a body motion frequency of a biological body; Calculating a signal periodicity index of the ultra-low frequency component; Using the RSSI variance and the signal periodicity index to determine whether there is a target living body in the vehicle; If there is a target living body in the car, locate the target living body's living position.

7. The method according to claim 6, characterized in that Using the RSSI variance and the signal periodicity index to determine whether there is a moving object in the vehicle includes: Determining whether the RSSI variance is an abnormal mutation value based on a reference RSSI threshold, and determining whether the signal periodicity index is greater than a preset threshold; If the RSSI variance is an abnormal mutation value, it is determined that there is a moving living body in the car. If the signal periodicity index is greater than a preset threshold, it is determined that there is a silent living body in the car.

8. The method according to claim 6, characterized in that The living body position of the target living body includes: If the target living body is a moving living body, control one node of the Star Flash communication module as a transmitter and the other three nodes as receivers, obtain a first communication signal set between the transmitter and the receiver, and triangulate the target living body based on the RSSI signal of the first communication signal set to obtain a first living body position of the target living body, wherein the Star Flash communication module includes at least four nodes; If the target living body is a silent living body, one node of the Star Flash communication module is controlled as a transmitter, and the other three nodes are receivers. A second communication signal set between the transmitter and the receiver is obtained, and the target living body is triangulated based on the CSI signal of the second communication signal set to obtain the second living body position of the target living body.

9. The method according to claim 1, characterized in that The target feature information includes a CSI phase difference sequence, a multipath delay spectrum, and Doppler spectrum information, and extracting a biometric feature from the target feature information includes: Extracting first energy information of the CSI phase difference sequence in the first frequency band and second energy information of the second frequency band, respectively, calculating the periodic index and harmonic components of the CSI phase difference sequence, calculating the standard deviation of the multipath delay spectrum and the multipath reflection energy entropy, and calculating the peak power and spectrum entropy of the Doppler spectrum information; The first energy information is determined as a first breathing feature, the second energy information is determined as a first heartbeat feature, the periodic index is determined as a second breathing feature, the harmonic component is determined as a second heartbeat feature, the standard deviation and the multipath reflection energy entropy are determined as body shape features, and the peak power and the spectral entropy are determined as motion features, wherein the biological characteristics include physiological characteristics, body shape characteristics and motion characteristics, and the physiological characteristics include heartbeat characteristics and breathing characteristics.

10. The method according to claim 1, characterized in that The types of the target living body identified by using the biometric feature include: For each target living being, input the corresponding biometric feature into a decision tree logic, and use the decision tree logic to identify the type of the target living being, wherein the type of living being includes: adult, child, and pet; If the decision tree logic recognition fails, the biological feature is input into a pre-trained neural network model, and the neural network model is used to output the living body type of the target living body.

11. The method according to claim 1, wherein Before calling the Star Flash communication module of the vehicle to collect first static initial information inside the vehicle in the SLB mode and collecting second static initial information inside the vehicle in the low power consumption SLE mode, the method further includes: Detect vehicle status information; determining whether the vehicle is in a preset state according to the state information; If the vehicle is in a preset state, it is determined to call the Star Flash communication module of the vehicle to collect the first static initial information inside the vehicle in SLB mode, and to collect the second static initial information inside the vehicle in low power consumption SLE mode.

12. The method according to claim 11, characterized in that Determining whether the vehicle is in a preset state according to the state information includes: Determining whether the vehicle is in a parked state based on a gear state and a parking brake state; If the vehicle is in a parked state, determining whether the vehicle is in an engine-off state according to the power signal; If the vehicle is in an ignition-off state, determining whether the vehicle is in a door-closed state according to the door lock state; If the vehicle is in a closed door state, determining whether the vehicle is in a sealed state based on the air conditioning state and the window state; If the vehicle is in a sealed state, it is determined that the vehicle is in a preset state, and the state information includes: gear state, handbrake state, power signal, door lock state, air conditioning state, and window state.

13. The method according to claim 1, wherein After controlling the Star Flash communication module to identify the type of the target living body in the SLE mode, the method further includes: If the type of the living body is a child and there is no adult, an alarm message is sent to a remote terminal of the vehicle and the air conditioning system of the vehicle is started.

14. A device for detecting the presence of living things in a vehicle, characterized in that: include: A collection module, configured to call the vehicle's Star Flash communication module to collect first static initial information inside the vehicle in a basic SLB mode, and to collect second static initial information inside the vehicle in a low power consumption SLE mode; a first judging module, configured to judge whether there is a target living body in the vehicle according to the first static initial information and the second static initial information; A positioning module is used to control the Star Flash communication module to locate the target living body in the SLE mode if there is a target living body in the vehicle; The identification module includes: a processing unit for enhancing the signal power of the Star Flash communication module, controlling the Star Flash communication module to initiate a beamforming focusing signal to the living body position in SLB mode, and extracting target feature information of the received signal; an extraction unit for extracting biometric features from the target feature information; The identification unit is configured to identify the type of the target living body by using the biometric feature.

15. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 13 when executed.

16. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 13.

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