A method, detection device, and computer-readable storage medium for detecting life inside a vehicle.

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 multiple sets of phase matrix signals with different distance and time units 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, obtaining the power distribution of the target frequency band based on the phase change frequency spectrum, and obtaining the signal-to-noise ratio (SNR) distribution data of each distance unit based on the power distribution of the target frequency band; S4, obtaining the number of effective moving distance units through SNR distribution data screening, and determining the presence of a corresponding life form based on the number of effective moving distance units. 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 life inside a vehicle, which includes a millimeter-wave radar installed inside the vehicle for scanning the interior space, comprising 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, obtain the power distribution of the target frequency band based on the phase change frequency spectrum, and obtain the signal-to-noise ratio distribution data of each distance unit based on the power distribution of the target frequency band, wherein the target frequency band is used to characterize the breathing frequency band of a life group containing different kinds of life forms. S4, the number of effective motion distance units is obtained by screening the signal-to-noise ratio distribution data. The effective motion distance units are used to characterize the possible location of a living organism in a breathing state. The existence of a corresponding living organism is determined based on the number of effective motion distance units.

[0007] 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.

[0008] 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.

[0009] According to one embodiment of the present invention, the signal-to-noise ratio distribution data is the average signal-to-noise ratio. In step S3, 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 average signal-to-noise ratio corresponding to each distance cell is calculated sequentially.

[0010] 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 power value of each frequency band as the signal-to-noise ratio of the corresponding distance cell.

[0011] According to one embodiment of the present invention, in step S4, at least two sets of signal-to-noise ratio (SNR) ladders are established, and the SNR distribution data is screened through the SNR ladders to obtain the number of effective motion distance units.

[0012] According to one embodiment of the present invention, in step S4, at least two sets of signal-to-noise ratio (SNR) steps are established, and the SNR of each set of SNR steps is snr. N The signal-to-noise ratio is greater than that of the snr N The distance unit is denoted as the effective motion distance unit of the Nth group of signal-to-noise ratio steps. Let M be the number of effective motion distance units of the Nth group of signal-to-noise ratio steps. N The determination of the existence of a corresponding life form based on the number of effective motion distance units in the signal-to-noise ratio stepwise method includes: if the number of effective motion distance units meets a preset condition, then it is determined that a corresponding life form exists; the preset condition is: PN≤snr N QN≤M N If one or more of the SNRs N M N If the preset conditions are met, it is preliminarily determined that a corresponding life form exists; Wherein, PN is the signal-to-noise ratio threshold for each signal-to-noise ratio step, and QN is the threshold for the number of effective motion distance units for each signal-to-noise ratio step.

[0013] According to an embodiment of the present invention, the in-vehicle life detection method further includes step S5: if the result of step S4 indicates the presence of a living being, then the distance between the living being and the millimeter-wave radar is obtained, and a set threshold for the number of effective motion distance units is determined based on the distance between the living being and the millimeter-wave radar. If the number of effective motion distance units corresponding to the distance between the living being and the millimeter-wave radar is greater than the set threshold, then it is determined that a corresponding living being exists; if the number of effective motion distance units corresponding to the distance between the living being and the millimeter-wave radar is not greater than the set threshold, then it is determined that no corresponding living being exists.

[0014] 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.

[0015] 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.

[0016] 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 signal-to-noise ratio (SNR) distribution data for each distance unit. By screening the SNR distribution data, it obtains the number of effective moving distance units, thereby determining whether a corresponding life form exists. 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.

[0017] 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

[0018] 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. In the drawings: Figure 1 A flowchart of an embodiment of the in-vehicle life detection method of the present invention is shown.

[0019] Figure 2A The phase change frequency spectrum is shown in the absence of strong reflection interference and inanimate targets.

[0020] Figure 2B The phase change frequency spectrum is shown when there is no strong reflection interference and a living target is present.

[0021] Figure 2C The frequency spectrum of phase change is shown when there is raincoat interference and no living target.

[0022] Figure 3A This is a diagram showing a person leaning over the left-hand window, using a slow-time sliding window algorithm to obtain multiple signal-to-noise ratios based on the target frequency band.

[0023] Figure 3B Based on Figure 3A A schematic diagram showing the average signal-to-noise ratio for each distance cell.

[0024] Figure 4A This is a schematic diagram showing how a fan is turned on and placed on the back seat, and how a slow-time sliding window algorithm is used to obtain multiple signal-to-noise ratios based on the target frequency band.

[0025] Figure 4B Based on Figure 4A A schematic diagram showing the average signal-to-noise ratio for each distance cell.

[0026] Figure 5A This is a schematic diagram showing how multiple signal-to-noise ratios based on the target frequency band are obtained by using a slow-time sliding window algorithm when an infant is sitting in the left rear child seat.

[0027] Figure 5B Based on Figure 5A A schematic diagram showing the average signal-to-noise ratio for each distance cell. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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: 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 subjected to 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 in this embodiment, the millimeter-wave radar is installed next to the left armrest of the rear roof. The processing of the signals acquired by the millimeter-wave radar 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.

[0035] S2, perform FFT processing on each distance cell of the acquired phase matrix signal to establish the phase change frequency spectrum.

[0036] S3. The power distribution of the target frequency band is obtained based on the phase change frequency spectrum, and the signal-to-noise ratio (SNR) distribution data for each range cell is obtained based on the power distribution of the target frequency band. The target frequency band is used to characterize the respiratory frequency range of life groups containing different types of organisms. 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, sometimes reaching 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 any life group that may be present inside the vehicle will cause changes in their body displacement, and these displacement changes correspond one-to-one with the phase changes of the millimeter-wave radar detection signal, the SNR distribution data for each range cell is obtained through the power distribution of the respiratory frequency band of the life group.

[0037] S4 involves screening the signal-to-noise ratio distribution data to obtain the number of effective motion distance units (AMUs). These AMUs characterize the possible locations of living organisms in a respiratory state. The presence of a corresponding living organism is determined based on the number of AMUs. The screening step involves using the phase change frequency spectrum to identify moving targets matching the target life group and locating their positions.

[0038] 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 2AThe 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.

[0039] Preferably, the signal-to-noise ratio (SNR) distribution data is the mean SNR. In step S3, a slow-time sliding window algorithm is used to obtain multiple sets of SNR based on the target frequency band, and the mean SNR corresponding to each range cell is calculated sequentially. More preferably, obtaining each set of SNR includes the following steps: using the ratio of the maximum power value in the target frequency band to the mean power value of each frequency band as the SNR of the corresponding range cell.

[0040] Figure 3A This is a diagram showing a person leaning over the left-hand window, using a slow-time sliding window algorithm to obtain multiple signal-to-noise ratios based on the target frequency band. Figure 3B Based on Figure 3A A schematic diagram showing the average signal-to-noise ratio for each distance cell. Figure 4A This is a schematic diagram showing how a fan is turned on and placed on the back seat, and how a slow-time sliding window algorithm is used to obtain multiple signal-to-noise ratios based on the target frequency band. Figure 4B Based on Figure 4A A schematic diagram showing the average signal-to-noise ratio for each distance cell. Figure 5A This is a schematic diagram showing how multiple signal-to-noise ratios based on the target frequency band are obtained by using a slow-time sliding window algorithm when an infant is sitting in the left rear child seat. Figure 5B Based on Figure 5A A diagram illustrating the average signal-to-noise ratio (SNR) for each distance cell is provided. Figure 3A , 4A In 5A, the X-axis represents the distance cell, the Y-axis represents the slow-time sliding window sequence, and the Z-axis represents the signal-to-noise ratio.

[0041] Due to the poor signal-to-noise ratio stability of interference from both outside and inside the vehicle. Figure 3A The display shows that when there is an interfering target outside the left window, 40 sets of signal-to-noise ratios (SNRs) based on the target frequency band are obtained by performing a slow-time sliding window every 10 frames. In a single SNR test, several values ​​greater than 4 are observed near the 15th distance cell, but the stability is poor. (Reference) Figure 3B After averaging, the average signal-to-noise ratio (SNR) of the 15th range cell is much less than 3, therefore it can be eliminated as an unstable interference signal. Specifically, while targets outside the vehicle can usually be illuminated by millimeter-wave radar, the small size of the windows causes significant obstruction. Even at relatively close distances, the detected SNR data is significantly lower than that of living targets at the same distance inside the cabin. Therefore, the correspondence between different range cells and SNR data can be used to eliminate living targets outside the window. This correspondence between range cells and SNR thresholds can be a dynamic inverse linear relationship or a threshold set empirically in segments. It should be noted that a mean SNR greater than 4 and a range cell greater than 2 are generally considered to indicate the possible presence of a living target; if the mean SNR is not greater than 4, it is considered that no corresponding living target exists.

[0042] Figure 4A The display shows that when handheld fan interference is present in the vehicle, 40 sets of signal-to-noise ratios (SNRs) based on the target frequency band are obtained using a slow-time sliding window algorithm. Several SNR values ​​greater than 4 are observed near the 35th distance cell in a single reading, but the stability is poor. (Reference) Figure 4B After averaging, the mean signal-to-noise ratio (SNR) of the 35th distance cell is much less than 4, indicating that fan interference can be eliminated. The SNR of small or partially obscured in-vehicle life targets remains relatively stable. Figure 5A The simulation of a baby (with respiratory movements) sitting in the child seat on the left side shows a relatively stable signal-to-noise ratio (SNR). Forty SNR values ​​based on the target frequency band were obtained using a slow-time sliding window algorithm. Within a single measurement, multiple SNR values ​​greater than 4 were observed between the 20th and 35th distance units. (Reference) Figure 5B After averaging, it was found that there were multiple signal-to-noise ratios greater than 5 between the 20th and 35th distance units, which preserved the useful signals as targets for microorganisms.

[0043] As shown above, the slow-time sliding window algorithm can quickly acquire multiple sets of signal-to-noise ratio (SNR) distribution data and sequentially calculate the SNR mean for each distance cell. Since the SNR stability of external and internal vehicle interference is poor, this unstable interference signal is eliminated after averaging. However, the SNR stability of small or partially obscured internal targets is relatively good; taking multiple SNR averages can effectively remove interference signals while retaining the useful signal of small targets. Therefore, by using a sliding window to quickly acquire multiple sets of SNR data, averaging them to filter out interference signals from inside and outside the vehicle, and retaining the useful signal, the accuracy of detecting internal targets can be improved. As an example, and not a limitation, other methods can be used, such as alternating frames or continuous sliding windows, and methods such as weighted summation, median, low-pass filtering followed by averaging or median averaging can be used to obtain the final SNR distribution data.

[0044] Preferably, in step S4, at least two sets of signal-to-noise ratio (SNR) ladders are established, and the SNR distribution data is screened using these ladders to obtain the number of effective motion distance units. More preferably, in step S4, at least two sets of SNR ladders are established, and the SNR of each set of SNR ladders is snr. N The signal-to-noise ratio is greater than that of SNR. N The distance unit is denoted as the effective motion distance unit of the Nth group of signal-to-noise ratio steps. Let M be the number of effective motion distance units of the Nth group of signal-to-noise ratio steps. N The determination of the existence of a corresponding life form based on the number of effective motion range units (PNs) in the signal-to-noise ratio (SNR) stepwise method includes: if the number of effective motion range units meets a preset condition, then it is determined that a corresponding life form exists; the preset condition is: PN ≤ snr N QN≤M N If one or more of the SNRs N M N If preset conditions are met, a corresponding living being is preliminarily identified. Here, PN is the signal-to-noise ratio (SNR) threshold for each SNR step, and QN is the threshold for the number of effective motion range units (MLUs) for each SNR step. Because the size distribution of living beings inside the vehicle is wide, ranging from large adults to small infants and pets, and infants or pets are often obscured by seats (including child seats), the SNR distribution range of living beings selected through frequency filtering is large, and the number of detected MLUs also varies significantly. Infants may have fewer effective MLUs, but their corresponding effective MLUs may have a higher SNR; adults may have more effective MLUs, but their corresponding SNR may be lower. To ensure the identification effect of various living beings, multiple sets of SNR step filtering thresholds and effective motion unit number filtering thresholds are used for living being screening.

[0045] In step S4 of an embodiment of the present invention, two sets of signal-to-noise ratio steps are established, which are a first signal-to-noise ratio step and a second signal-to-noise ratio step. The signal-to-noise ratio of the first signal-to-noise ratio step is snr1, wherein range units with a signal-to-noise ratio greater than snr1 are recorded as effective moving range units of the first signal-to-noise ratio step. The signal-to-noise ratio of the second signal-to-noise ratio step is snr2, wherein range units with a signal-to-noise ratio greater than snr2 are recorded as effective moving range units of the second signal-to-noise ratio step. Let the counted number of effective moving range units of the first signal-to-noise ratio step be M1, and the counted number of effective moving range units of the second signal-to-noise ratio step be M2, then determining whether a corresponding living being exists based on the numbers of effective moving range units of the two signal-to-noise ratio steps comprises: if the number of effective moving range units satisfies a preset condition, it is determined that a corresponding living being exists; wherein the preset condition is: P1≤snr1, P2≤snr2, P1<P2, Q1≤M1, Q2≤M2; if snr1 and M1 satisfy the preset condition and / or snr2 and M2 satisfy the preset condition, it is preliminarily determined that a corresponding living being exists. Assuming P1=4 and P2=7, then 4≤snr1<7, 7≤snr2, Q1=5, Q2=1, wherein if the counted number of effective moving range units M1 for the first signal-to-noise ratio step snr1 is 8, and the counted number of effective moving range units M2 for the second signal-to-noise ratio step snr2 is 1, then Q1 (5)≤M1 (8), Q2 (1)≤M2 (1), it is preliminarily determined that there is a corresponding living being target in the first signal-to-noise ratio step snr1, and there is also a corresponding living being target in the second signal-to-noise ratio step snr2; if the counted number of effective moving range units M1 for the first signal-to-noise ratio step snr1 is 3, and the counted number of effective moving range units M2 for the second signal-to-noise ratio step snr2 is 3, then Q1 (5)≤M1 (3) is not satisfied, but Q2 (1)≤M2 (3) is satisfied, it is preliminarily determined that there is no corresponding living being target in the first signal-to-noise ratio step snr1, and there is a corresponding living being target in the second signal-to-noise ratio step snr2; if the counted number of effective moving range units M1 for the first signal-to-noise ratio step snr1 is 3, and the counted number of effective moving range units M2 for the second signal-to-noise ratio step snr2 is 0, then Q1 (5)≤M1 (3) is not satisfied, and Q2 (1)≤M2 (0) is also not satisfied, it is preliminarily determined that there is no corresponding living being target in neither the first signal-to-noise ratio step snr1 nor the second signal-to-noise ratio step snr2.

[0046] In step S4 of another embodiment of the present invention, 3 sets of signal-to-noise ratio (SNR) steps are established, which are a first SNR step, a second SNR step, and a third SNR step. The SNR of the first SNR step is snr1, wherein range cells with an SNR greater than snr1 are recorded as effective motion range cells of the first SNR step. The SNR of the second SNR step is snr2, wherein range cells with an SNR greater than snr2 are recorded as effective motion range cells of the second SNR step. The SNR of the third SNR step is snr3, wherein range cells with an SNR greater than snr3 are recorded as effective motion range cells of the third SNR step. Let the counted number of effective motion range cells of the first SNR step be M1, the counted number of effective motion range cells of the second SNR step be M2, and the counted number of effective motion range cells of the third SNR step be M3. Then judging whether a corresponding living organism exists based on the numbers of effective motion range cells of the three SNR steps comprises: if the number of effective motion range cells satisfies a preset condition, judging that the corresponding living organism exists; Wherein, the preset condition is: P1≤snr1, P2≤snr2, P3≤snr3, P1<P2<P3, Q1≤M1, Q2≤M2, Q3≤M3; if snr1 and M1 satisfy the preset condition, or snr2 and M2 satisfy the preset condition, or snr3 and M3 satisfy the preset condition, it is preliminarily judged that the corresponding living organism exists. P1 may be set to 2, P2 set to 3, P3 set to 6, Q1 set to 6, Q2 set to 3, Q3 set to 1, so as to correspondingly determine the SNR range of each SNR step, and predict whether the corresponding living organism exists according to the statistical result of the number of corresponding effective motion range cells of each SNR step. For the specific method, reference is made to the foregoing embodiments, and details are not described herein again.

[0047] Preferably, the in-vehicle living organism detection method further comprises step S5: if the judgment result in step S4 is that a living organism exists, acquiring the distance between the living organism and the millimeter-wave radar, determining a corresponding set threshold for the number of effective motion range cells according to the distance between the living organism and the millimeter-wave radar; if the number of effective motion range cells corresponding to the distance between the living organism and the millimeter-wave radar is greater than the set threshold, determining that the corresponding living organism exists; if the number of effective motion range cells corresponding to the distance of the living organism is not greater than the set threshold, determining that the corresponding living organism does not exist. Step S5 is used to conduct in-vehicle inspection on the preliminary judgment result of step S4, confirm whether there is a living organism target, and acquire the in-vehicle position of the living organism target if it exists.

[0048] The present invention further provides an in-vehicle living organism detection device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of any one of the foregoing detection methods when executing the computer program.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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, obtain the power distribution of the target frequency band based on the phase change frequency spectrum, and obtain the signal-to-noise ratio distribution data of each range cell based on the power distribution of the target frequency band, 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 number of effective motion distance units is obtained by screening the signal-to-noise ratio distribution data. The effective motion distance units are used to characterize the possible locations of living beings in a breathing state. The existence of a corresponding living being is determined based on the number of effective motion distance units. In step S4, at least two sets of signal-to-noise ratio (SNR) steps are established, with each set of SNR steps having an SNR of snr. N The signal-to-noise ratio is greater than that of the snr N The distance unit is denoted as the effective motion distance unit of the Nth group of signal-to-noise ratio steps. Let M be the number of effective motion distance units of the Nth group of signal-to-noise ratio steps. N The determination of the existence of a corresponding life form based on the number of effective motion distance units in the signal-to-noise ratio stepwise method includes: if the number of effective motion distance units meets a preset condition, then it is determined that a corresponding life form exists; the preset condition is: PN≤snr N QN≤M N If one or more of the SNRs N M N If the preset conditions are met, it is preliminarily determined that a corresponding life form exists; Wherein, PN is the signal-to-noise ratio threshold for each signal-to-noise ratio step, and QN is the threshold for the number of effective motion distance units for each signal-to-noise ratio step.

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, The signal-to-noise ratio distribution data is the average signal-to-noise ratio. In step S3, 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 average signal-to-noise ratio corresponding to each distance cell is calculated in sequence.

5. The in-vehicle life detection method as described in claim 4, 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 power value of each frequency band is taken as the signal-to-noise ratio of the corresponding distance cell.

6. The in-vehicle life detection method as described in claim 1, characterized in that, The method also includes step S5. If the result of step S4 indicates the presence of a living organism, the distance between the living organism and the millimeter-wave radar is obtained. A set threshold for the number of effective motion range units is determined based on the distance between the living organism and the millimeter-wave radar. If the number of effective motion range units corresponding to the distance between the living organism and the millimeter-wave radar is greater than the set threshold, it is determined that a corresponding living organism exists. If the number of effective motion range units corresponding to the distance between the living organism and the millimeter-wave radar is not greater than the set threshold, it is determined that no corresponding living organism exists.

7. 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 1-6.

8. 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 1-6.

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