An in-vehicle life detection method, a detection device, and a computer-readable storage medium

Abstract

This invention relates to a method, device, and computer-readable storage medium for in-vehicle life detection. The method comprises the following steps: S1, acquiring an intermediate frequency (IF) signal and performing FFT processing on the IF signal to obtain a set of phase signals; acquiring the corresponding phase matrix signal through slow-time data accumulation; S2, performing FFT processing on each distance unit of the phase matrix signal to establish a phase change frequency spectrum; S3, acquiring the power distribution of the target frequency band based on the phase change frequency spectrum, acquiring the first signal-to-noise ratio (SNR) distribution data for each distance unit based on the power distribution of the target frequency band, and acquiring second SNR distribution data based on multiple sets of first SNR distribution data; S4, screening the SNR distribution data to obtain the number of effective moving distance units and determining whether a corresponding life form exists. This invention proposes an in-vehicle life detection method, device, and computer-readable storage medium, which can effectively improve the accuracy of life detection.

Description

Technical Field

[0001] This invention relates to the field of millimeter radar wave application technology, and in particular to a method, detection device, and computer-readable storage medium for in-vehicle life detection. Background Technology

[0002] With the increasing number of vehicles, safety accidents caused by children and pets being left in cars occur frequently. Therefore, it is necessary to equip vehicles with life detection devices to effectively prevent such accidents from happening.

[0003] Current methods for detecting life inside vehicles mainly rely on infrared sensors and cameras. However, these technologies are greatly affected by factors such as vehicle interior temperature, occupancy, and light intensity.

[0004] Millimeter-wave radar technology has many advantages, such as low power consumption, good penetration, and immunity to factors like temperature and light, making it valuable for detecting life inside vehicles. However, due to limitations in radar placement, millimeter-wave radar may be affected by interference from living beings outside the vehicle, leading to false alarms. Furthermore, the size distribution of living beings inside a vehicle varies widely, ranging from large adults to small infants and pets, making it particularly prone to missed detections when infants or pets are obstructed by seats (including child seats). Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention proposes an in-vehicle life detection method, detection device, and computer-readable storage medium, which effectively improves the accuracy of life detection.

[0006] Specifically, this invention proposes a method for detecting living beings inside a vehicle, which includes a millimeter-wave radar for scanning the vehicle's interior space, comprising the following steps:

[0007] S1, acquire the intermediate frequency signal and perform FFT processing on the intermediate frequency signal to obtain a set of phase signals; acquire multiple sets of phase matrix signals with different distance units and time units through slow time data accumulation;

[0008] S2, Perform FFT processing on each distance unit of the phase matrix signal to establish a phase change frequency spectrum;

[0009] S3, obtain the power distribution of the target frequency band based on the phase change frequency spectrum, and obtain the first signal-to-noise ratio distribution data of each distance unit based on the power distribution of the target frequency band; at the same time, accumulate and obtain multiple sets of first signal-to-noise ratio distribution data, and obtain second signal-to-noise ratio distribution data based on multiple sets of first signal-to-noise ratio distribution data, wherein the target frequency band is used to characterize the respiratory frequency band of a life group containing different kinds of life forms.

[0010] S4, the first signal-to-noise ratio distribution data is screened to obtain the number of effective motion distance units A; the second signal-to-noise ratio distribution data is screened to obtain the number of effective motion distance units B; based on the number of effective motion distance units A and B, it is determined whether there is a corresponding life form, wherein the effective motion distance unit is used to characterize the possible location of a life form in a breathing state.

[0011] According to one embodiment of the present invention, after step S2 and before step S3, fundamental frequency interference in the phase change frequency spectrum is removed based on the target frequency band.

[0012] According to one embodiment of the present invention, removing fundamental frequency interference includes determining the peak decline within the target frequency band. If the peak is in a continuously declining state, the maximum power value within the target frequency band is set to zero, and the signal-to-noise ratio of the corresponding distance cell is set to zero.

[0013] According to one embodiment of the present invention, in step S3, the signal-to-noise ratio corresponding to each distance cell is obtained to construct the first signal-to-noise ratio distribution data.

[0014] According to one embodiment of the present invention, in step S3, the mean or median signal-to-noise ratio corresponding to each distance cell is obtained to construct the second signal-to-noise ratio distribution data.

[0015] According to one embodiment of the present invention, a slow-time sliding window algorithm is used to obtain multiple sets of signal-to-noise ratios based on the target frequency band, and the mean or median of the signal-to-noise ratio corresponding to each distance cell is obtained based on the multiple sets of signal-to-noise ratios to construct the second signal-to-noise ratio distribution data.

[0016] According to one embodiment of the present invention, the acquisition of each signal-to-noise ratio includes the following steps: taking the ratio of the maximum power value in the target frequency band to the average or median power value of each frequency band as the signal-to-noise ratio of the corresponding distance cell.

[0017] According to an embodiment of the present invention, in step S4, at least two sets of first-type signal-to-noise ratio (SNR) steps are set for the first SNR distribution data, and the SNR threshold of each set of first-type SNR steps is denoted as snr. NA The first signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr. NA The distance unit is denoted as an effective motion distance unit of the first type of signal-to-noise ratio step in the Nth group. Let M be the number of effective motion distance units of the first type of signal-to-noise ratio step in the Nth group. NA ;

[0018] For the second signal-to-noise ratio (SNR) distribution data, at least two sets of second-type SNR steps are set, and the SNR threshold for each set of second-type SNR steps is denoted as snr. NB The second signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr.NB The distance unit is denoted as an effective motion distance unit of the second type SNR step in the Nth group. Let M be the number of effective motion distance units of the second type SNR step in the Nth group. NB ;

[0019] S41 performs P single-time life form detections, based on M. NA Judge the result of each single detection of a living being, and count the number of times Q indicates the presence of a living being;

[0020] S42 combined with Q and M NB Conduct a comprehensive assessment of the life form: including

[0021] If Q is large enough, the existence of life can be determined solely based on Q.

[0022] If Q is small, then M should also be considered. NB The size of M NB If the value is greater than the preset value, it is still determined that a living organism exists;

[0023] If M in the higher steps of the second type of signal-to-noise ratio ladder NB If large enough, it is based only on M in the higher order. NB The existence of living organisms is determined.

[0024] The present invention also provides an in-vehicle life detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the aforementioned detection methods.

[0025] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the preceding detection methods.

[0026] This invention provides a method, device, and computer-readable storage medium for in-vehicle life detection. Utilizing millimeter-wave radar technology, it establishes a phase-change frequency spectrum, obtains the power distribution of the target frequency band based on the phase-change frequency spectrum, and then acquires the first signal-to-noise ratio (SNR) distribution data for each distance unit. Based on the accumulated first SNR distribution data, it acquires second SNR distribution data, thereby determining the presence of a corresponding life form. This detection method effectively improves the accuracy of life detection and solves the problems of false alarms and missed alarms caused by signal interference or reduced signal strength during in-vehicle life detection using millimeter-wave radar.

[0027] It should be understood that the above general description and the following detailed description of the invention are exemplary and illustrative, and are intended to provide further explanation of the invention as described in the claims. Attached Figure Description

[0028] The accompanying drawings are included to provide further explanation of the invention. They are incorporated into and constitute a part of this application. The drawings illustrate embodiments of the invention and, together with this specification, serve to explain the principles of the invention.

[0029] In the attached image:

[0030] Figure 1 A flowchart of an embodiment of the in-vehicle life detection method of the present invention is shown.

[0031] Figure 2A The phase change frequency spectrum is shown in the absence of strong reflection interference and inanimate targets.

[0032] Figure 2B The phase change frequency spectrum is shown when there is no strong reflection interference and a living target is present.

[0033] Figure 2C The frequency spectrum of phase change is shown when there is raincoat interference and no living target. Detailed Implementation

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this application or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0037] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0038] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0039] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0040] Figure 1 A flowchart of an embodiment of the in-vehicle life detection method of the present invention is shown. As shown, an in-vehicle life detection method includes a millimeter-wave radar for scanning the in-vehicle space, comprising the following steps:

[0041] S1. The transmitted and received waveform signals acquired by the millimeter-wave radar are mixed and filtered to obtain an intermediate frequency (IF) signal. This IF signal is then processed using an FFT to obtain a set of phase signals. Multiple sets of phase matrix signals with different distance and time units are acquired through slow-time data accumulation. It should be noted that the breathing and heartbeat of a living being inside the vehicle will cause continuous movement of corresponding body parts, resulting in continuous changes in the phase value of the radar signal within the movement distance segment. In other words, the obtained phase matrix signal is closely related to the moving target (living being). In this embodiment, the millimeter-wave radar is installed next to the left armrest of the rear roof. The processing of the millimeter-wave radar signal and subsequent embodiments are described based on the installation location of the millimeter-wave radar. It is easy to understand that this installation location is only illustrative and not limiting. If the millimeter-wave radar is installed next to the right armrest of the rear roof or in other locations, those skilled in the art can use the in-vehicle life detection method provided by this invention to improve the accuracy of life detection.

[0042] S2, perform FFT processing on each distance cell of the acquired phase matrix signal to establish the phase change frequency spectrum.

[0043] S3. The power distribution of the target frequency band is obtained based on the phase change frequency spectrum. The first signal-to-noise ratio (SNR) distribution data for each range unit is then obtained based on the power distribution of the target frequency band. Simultaneously, multiple sets of first SNR distribution data are accumulated, and a second SNR distribution data is obtained based on these multiple sets of first SNR distribution data. The target frequency band is used to characterize the respiratory frequency band of a life group containing different types of life forms. Generally, the respiratory rate of an adult is 12–20 breaths / min, varying with age. Younger children have faster respiratory rates; newborns typically have a respiratory rate of 40–45 breaths / min, and sometimes even up to 60 breaths / min. Dogs have a respiratory rate of 20–30 breaths / min, and cats have a respiratory rate of 30–40 breaths / min. Since the breathing of the life forms that may be present inside the vehicle will cause changes in their body displacement, and this displacement change corresponds one-to-one with the phase change of the detection signal of the millimeter-wave radar, the SNR distribution data for each range unit is obtained through the power distribution of the respiratory frequency band of the life forms.

[0044] S4: Screen the first signal-to-noise ratio distribution data to obtain the number of effective motion distance units A; screen the second signal-to-noise ratio distribution data to obtain the number of effective motion distance units B. Based on the number of effective motion distance units A and B, determine whether a corresponding life form exists. The effective motion distance unit is used to characterize the possible location of a life form in a breathing state. The purpose of this step is to screen out motion targets that match the target life group.

[0045] Preferably, after step S2 and before step S3, fundamental frequency interference in the phase-change frequency spectrum is removed based on the target frequency band. This involves removing interference from highly reflective static targets inside the vehicle to ensure the accuracy of subsequent calculations. Static targets refer to inanimate objects. More preferably, removing fundamental frequency interference includes determining peak descent within the target frequency band. If the peak is in a continuously decreasing state, the maximum power value within that target frequency band is set to zero, and the signal-to-noise ratio of the corresponding range cell is set to zero. Figure 2A The phase change frequency spectrum is shown in the absence of strong reflection interference and inanimate targets. Figure 2B The phase change frequency spectrum is shown when there is no strong reflection interference and a living target is present. Figure 2C The phase change frequency spectrum is shown when there is raincoat interference and no living target. Figures 2A to 2C In the diagram, the X-axis represents frequency, and the Y-axis represents power. Specifically, the method for removing fundamental frequency interference mainly determines whether a rising segment exists in the first four frequency units, referring to... Figure 2A If there is no rising segment in the first four frequency units and the peaks continuously decline, then the maximum power value of that distance unit is set to 0, and the signal-to-noise ratio of the corresponding distance unit is also set to 0. (Reference) Figure 2B If an increase occurs between the first and second frequency units, the maximum power value and the average power value in the third to sixth frequency units can be used as the basis for subsequent signal-to-noise ratio calculations. The third to sixth frequency units represent the effective frequency range for living targets. Typically, fundamental frequency interference manifests as a high power value at 0 GHz with a wide 0 GHz lobe, affecting the subsequent low-frequency region of the target. (Reference) Figure 2C When interference from the raincoat was present, the power values ​​of the 2nd, 3rd, and 4th frequency units were all increased. Based on the continuous decline of the peak, no significant rising peak was observed within the movement frequency range. Therefore, it was determined that fundamental frequency interference existed in this distance unit, and the maximum power value of this distance unit was set to 0, along with the corresponding signal-to-noise ratio (SNR). As an example, and not a limitation, the presence of fundamental frequency interference can also be determined by using the inflection point or the rate of change of the differential value.

[0046] Preferably, in step S3, the signal-to-noise ratio (SNR) corresponding to each distance unit is obtained to construct first SNR distribution data. A set of first SNR distribution data serves as the basis for determining whether a single life form exists. Accumulating multiple sets of first SNR distribution data serves as the basis for determining whether multiple single life forms exist.

[0047] Preferably, in step S3, the mean or median signal-to-noise ratio (SNR) for each distance unit is obtained to construct the second SNR distribution data. More preferably, a slow-time sliding window algorithm is used to obtain multiple sets of SNR based on the target frequency band, and the mean or median SNR for each distance unit is obtained based on these multiple sets of SNR to construct the second SNR distribution data. Since the SNR stability of external and internal interference is poor, while the SNR stability of small or partially obscured targets inside the vehicle is relatively good, taking multiple SNR mean or median values ​​can effectively remove interference signals and retain useful signals from small moving targets, thereby improving the accuracy of the detection results for life inside the vehicle. As an example and not a limitation, the final second SNR distribution data can also be obtained by methods such as taking the weighted sum of multiple SNR values, or taking the mean or median after low-pass filtering. It is easy to understand that the second SNR distribution data is used as the basis for determining the presence of life inside the vehicle after removing interference signals.

[0048] Preferably, the acquisition of each signal-to-noise ratio includes the following steps: taking the ratio of the maximum power value in the target frequency band to the average or median power value of each frequency band as the signal-to-noise ratio of the corresponding range cell.

[0049] Preferably, in step S4, the first signal-to-noise ratio (SNR) distribution data and the second SNR distribution data are screened by setting SNR steps. Specifically, at least two sets of first-type SNR steps are set for the first SNR distribution data, and the SNR threshold of each set of first-type SNR steps is denoted as snr. NA The first signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr. NA The distance unit is denoted as an effective motion distance unit of the first type of signal-to-noise ratio step in the Nth group. Let M be the number of effective motion distance units of the first type of signal-to-noise ratio step in the Nth group. NA ;

[0050] For the second signal-to-noise ratio (SNR) distribution data, at least two sets of second-type SNR steps are set, and the SNR threshold for each set of second-type SNR steps is denoted as snr. NB The second signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr. NB The distance unit is denoted as an effective motion distance unit of the second type SNR step in the Nth group. Let M be the number of effective motion distance units of the second type SNR step in the Nth group. NB ;

[0051] S41 performs P single-time life form detections, based on M. NA Judge the result of each single detection of a living being, and count the number of times Q indicates the presence of a living being;

[0052] S42 combined with Q and M NB Conduct a comprehensive assessment of the life form: including

[0053] First condition: If Q is large enough, then the existence of life is determined solely based on Q;

[0054] Second condition: If Q is small, then M should also be considered. NB The size of M NB If the value is greater than the preset value, it is still determined that a living organism exists;

[0055] Third condition: If M in the higher steps of the second type of signal-to-noise ratio ladder... NB If large enough, it is based only on M in the higher order. NB The presence of a living being is determined. Since the second signal-to-noise ratio distribution data, after removing interference signals, serves as the basis for determining the presence of a living being inside the vehicle, the presence of a living being can be determined as long as the number of effective motion example units corresponding to its higher-order steps is not less than 1.

[0056] For example, based on the first condition, 40 single-life entity detections are performed in step S41, based on M. NA The number of times a life form was detected in each individual detection was counted, Q, which is 37. 37 is relatively large compared to the threshold, indicating a very high probability of life form presence; therefore, life form is detected.

[0057] According to the second condition, if the number of times a living organism is identified (Q) is 30, which is close to the threshold, then the number of effective motion distance units obtained from the second signal-to-noise ratio (SNR) distribution data needs to be used for the determination. Let the SNR threshold snr of the first group of second-type SNR steps be... 1B The signal-to-noise ratio (SNR) is 2.1. In the second type of SNR distribution data, the number of effective motion distance units with an SNR greater than 2.1 is M. 1B Let the signal-to-noise ratio (SNR) threshold snr of the second group, type II SNR ladder be 1. 1B The signal-to-noise ratio (SNR) is 2.8. In the second type of SNR distribution data, the number of effective motion distance units with an SNR greater than 2.8 is M. 2B The value is 0. Based on Q being 30, M... 1B M is 1. 2B A value of 0 indicates the presence of a living organism.

[0058] According to the third condition, if we set the signal-to-noise ratio threshold snr of the second type of signal-to-noise ratio ladder in the second group... 1B The signal-to-noise ratio (SNR) is 2.8. In the second type of SNR distribution data, the number of effective motion distance units with an SNR greater than 2.8 is M. 2B If it is 1, then based on M 2B A value of 1 is sufficient to determine the presence of life, without needing to consider Q and M. 1B The corresponding value.

[0059] It is easy to understand that in step S4, the existence of a living being can be determined as long as any one of the above three conditions is met. If none of the conditions are met, it is determined that no living being exists.

[0060] In summary, the in-vehicle life detection method provided by this invention acquires first signal-to-noise ratio (SNR) distribution data and second SNR distribution data as two complementary sets of judgment data, which can effectively filter out interfering targets inside and outside the vehicle while maintaining the ability to detect small moving targets, thereby improving the accuracy of in-vehicle life detection results.

[0061] The present invention also provides an in-vehicle life detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any of the aforementioned detection methods.

[0062] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the aforementioned detection methods.

[0063] The specific implementation methods and technical effects of the in-vehicle life detection device and the computer-readable storage medium can be found in the embodiments of the detection method provided by the present invention, and will not be repeated here.

[0064] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0065] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0066] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0067] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0068] It will be apparent to those skilled in the art that various modifications and variations can be made to the exemplary embodiments described above without departing from the spirit and scope of the invention. Therefore, it is intended that this invention cover modifications and variations falling within the scope of the appended claims and their equivalents.

Claims

1. A method for detecting life inside a vehicle, comprising a millimeter-wave radar for scanning the interior space of the vehicle, including the following steps: S1, acquire the intermediate frequency signal and perform FFT processing on the intermediate frequency signal to obtain a set of phase signals; acquire multiple sets of phase matrix signals with different distance units and time units through slow time data accumulation; S2, perform FFT processing on each distance unit of the phase matrix signal to establish a phase change frequency spectrum; S3, based on the phase change frequency spectrum, obtain the power distribution of the target frequency band, and based on the power distribution of the target frequency band, obtain the first signal-to-noise ratio (SNR) distribution data for each range cell; simultaneously, accumulate and obtain multiple sets of first SNR distribution data, and based on the multiple sets of first SNR distribution data, obtain second SNR distribution data, wherein... The target frequency band is used to characterize the respiratory frequency band of a life group containing different kinds of life forms; S4, the first signal-to-noise ratio distribution data is screened to obtain the number of effective motion distance units A; the second signal-to-noise ratio distribution data is screened to obtain the number of effective motion distance units B; based on the number of effective motion distance units A and B, it is determined whether there is a corresponding life form, wherein the effective motion distance unit is used to characterize the possible location of a life form in a breathing state; For the first signal-to-noise ratio (SNR) distribution data, at least two sets of first-type SNR steps are set, and the SNR threshold of each set of first-type SNR steps is denoted as snr. NA The first signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr. NA The distance unit is denoted as an effective motion distance unit of the first type of signal-to-noise ratio step in the Nth group. Let M be the number of effective motion distance units of the first type of signal-to-noise ratio step in the Nth group. NA ; For the second signal-to-noise ratio (SNR) distribution data, at least two sets of second-type SNR steps are set, and the SNR threshold for each set of second-type SNR steps is denoted as snr. NB The second signal-to-noise ratio distribution data includes those with a signal-to-noise ratio greater than that of the snr. NB The distance unit is denoted as an effective motion distance unit of the second type SNR step in the Nth group. Let M be the number of effective motion distance units of the second type SNR step in the Nth group. NB ; S41 performs P single-time life form detections, based on M. NA Judge the result of each single detection of a living being, and count the number of times Q indicates the presence of a living being; S42 combined with Q and M NB Make a comprehensive assessment of the living organism.

2. The in-vehicle life detection method as described in claim 1, characterized in that, After step S2 and before step S3, fundamental frequency interference in the phase-change frequency spectrum is removed based on the target frequency band.

3. The in-vehicle life detection method as described in claim 2, characterized in that, Removing fundamental frequency interference includes determining the peak decline within the target frequency band. If the peak is in a continuous decline state, the maximum power value within the target frequency band is set to zero, and the signal-to-noise ratio of the corresponding distance cell is set to zero.

4. The in-vehicle life detection method as described in claim 1, characterized in that, In step S3, the signal-to-noise ratio corresponding to each distance cell is obtained to construct the first signal-to-noise ratio distribution data.

5. The in-vehicle life detection method as described in claim 1, characterized in that, In step S3, the mean or median signal-to-noise ratio (SNR) of each distance cell is obtained to construct the second SNR distribution data.

6. The in-vehicle life detection method as described in claim 5, characterized in that, The slow-time sliding window algorithm is used to obtain multiple sets of signal-to-noise ratios based on the target frequency band. Based on the multiple sets of signal-to-noise ratios, the mean or median of the signal-to-noise ratio corresponding to each distance cell is obtained to construct the second signal-to-noise ratio distribution data.

7. The in-vehicle life detection method as described in claim 1, characterized in that, The acquisition of each signal-to-noise ratio includes the following steps: the ratio of the maximum power value in the target frequency band to the average or median power value of each frequency band is taken as the signal-to-noise ratio of the corresponding distance cell.

8. The in-vehicle life detection method as described in claim 1, characterized in that, step S42 includes: If Q is large enough, the existence of life can be determined solely based on Q. If Q is small, then M should also be considered. NB The size of M NB If the value is greater than the preset value, it is still determined that a living organism exists; If M in the higher steps of the second type of signal-to-noise ratio ladder NB If large enough, it is based only on M in the higher order. NB The existence of living organisms is determined.

9. An in-vehicle life detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the in-vehicle life detection method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the in-vehicle life detection method as described in any one of claims 1-8.

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